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What is quantitative research.

Quantitative methodologies use statistics to analyze numerical data gathered by researchers to answer their research questions. Quantitative methods can be used to answer questions such as:

• What are the relationships between two or more variables? 
• What factors are at play in an environment that might affect the behavior or development of the organisms in that environment?

Quantitative methods can also be used to test hypotheses by conducting quasi-experimental studies or designing experiments.

Independent and Dependent Variables

In quantitative research, a variable is something (an intervention technique, a pharmaceutical, a temperature, etc.) that changes. There are two kinds of variables:  independent variables and dependent variables . In the simplest terms, the independent variable is whatever the researchers are using to attempt to make a change in their dependent variable.

* This is a real, repeatable experiment you can try on your plants.

Correlational

Researchers will compare two sets of numbers to try and identify a relationship (if any) between two things.

• Köse S., & Murat, M. (2021). Examination of the relationship between smartphone addiction and cyberchondria in adolescents. Archives of Psychiatric Nursing, 35(6): 563-570.
• Pilger et al. (2021). Spiritual well-being, religious/spiritual coping and quality of life among the elderly undergoing hemodialysis: a correlational study. Journal of Religion, Spirituality & Aging, 33(1): 2-15.

Descriptive

Researchers will attempt to quantify a variety of factors at play as they study a particular type of phenomenon or action. For example, researchers might use a descriptive methodology to understand the effects of climate change on the life cycle of a plant or animal. 

• Lakshmi, E. (2021). Food consumption pattern and body mass index of adolescents: A descriptive study. International Journal of Nutrition, Pharmacology, Neurological Diseases, 11(4), 293–297.
• Lin, J., Singh, S., Sha, L., Tan, W., Lang, D., Gašević, D., & Chen, G. (2022). Is it a good move? Mining effective tutoring strategies from human–human tutorial dialogues. Future Generation Computer Systems, 127, 194–207.

Experimental

To understand the effects of a variable, researchers will design an experiment where they can control as many factors as possible. This can involve creating control and experimental groups. The experimental group will be exposed to the variable to study its effects. The control group provides data about what happens when the variable is absent. For example, in a study about online teaching, the control group might receive traditional face-to-face instruction while the experimental group would receive their instruction virtually. 

• Jinzhang Jia, Yinuo Chen, Guangbo Che, Jinchao Zhu, Fengxiao Wang, & Peng Jia. (2021). Experimental study on the explosion characteristics of hydrogen-methane premixed gas in complex pipe networks. Scientific Reports, 11(1), 1–11.
• Sasaki, R. et al. (2021). Effects of cryotherapy applied at different temperatures on inflammatory pain during the acute phase of arthritis in rats. Physical Therapy, 101(2), 1–9.

Quasi-Experimental/Quasi-Comparative

Researchers will attempt to determine what (if any) effect a variable can have. These studies may have multiple independent variables (causes) and multiple dependent variables (effects), but this can complicate researchers' efforts to find out if A can cause B or if X, Y,  and  Z are also playing a role.

• Jafari, A., Alami, A., Charoghchian, E., Delshad Noghabi, A., & Nejatian, M. (2021). The impact of effective communication skills training on the status of marital burnout among married women. BMC Women’s Health, 21(1), 1-10.
• Phillips, S. W., Kim, D.-Y., Sobol, J. J., & Gayadeen, S. M. (2021). Total recall?: A quasi-experimental study of officer’s recollection in shoot - don’t shoot simulators. Police Practice and Research, 22(3), 1229–1240.

Surveys can be considered a quantitative methodology if the researchers require their respondents to choose from pre-determined responses. 

• Harries et al. (2021). Effects of the COVID-19 pandemic on medical students: A multicenter quantitative study. BMC Medical Education, 21(14), 1-8.
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What is Quantitative Research? Definition, Methods, Types, and Examples

If you’re wondering what is quantitative research and whether this methodology works for your research study, you’re not alone. If you want a simple quantitative research definition , then it’s enough to say that this is a method undertaken by researchers based on their study requirements. However, to select the most appropriate research for their study type, researchers should know all the methods available. 

Selecting the right research method depends on a few important criteria, such as the research question, study type, time, costs, data availability, and availability of respondents. There are two main types of research methods— quantitative research  and qualitative research. The purpose of quantitative research is to validate or test a theory or hypothesis and that of qualitative research is to understand a subject or event or identify reasons for observed patterns.   

Quantitative research methods  are used to observe events that affect a particular group of individuals, which is the sample population. In this type of research, diverse numerical data are collected through various methods and then statistically analyzed to aggregate the data, compare them, or show relationships among the data. Quantitative research methods broadly include questionnaires, structured observations, and experiments.  

Here are two quantitative research examples:  

• Satisfaction surveys sent out by a company regarding their revamped customer service initiatives. Customers are asked to rate their experience on a rating scale of 1 (poor) to 5 (excellent).  
• A school has introduced a new after-school program for children, and a few months after commencement, the school sends out feedback questionnaires to the parents of the enrolled children. Such questionnaires usually include close-ended questions that require either definite answers or a Yes/No option. This helps in a quick, overall assessment of the program’s outreach and success.  

Table of Contents

What is quantitative research ? 1,2

The steps shown in the figure can be grouped into the following broad steps:  

• Theory : Define the problem area or area of interest and create a research question.  
• Hypothesis : Develop a hypothesis based on the research question. This hypothesis will be tested in the remaining steps.  
• Research design : In this step, the most appropriate quantitative research design will be selected, including deciding on the sample size, selecting respondents, identifying research sites, if any, etc.
• Data collection : This process could be extensive based on your research objective and sample size.  
• Data analysis : Statistical analysis is used to analyze the data collected. The results from the analysis help in either supporting or rejecting your hypothesis.  
• Present results : Based on the data analysis, conclusions are drawn, and results are presented as accurately as possible.  

Quantitative research characteristics 4

• Large sample size : This ensures reliability because this sample represents the target population or market. Due to the large sample size, the outcomes can be generalized to the entire population as well, making this one of the important characteristics of quantitative research .  
• Structured data and measurable variables: The data are numeric and can be analyzed easily. Quantitative research involves the use of measurable variables such as age, salary range, highest education, etc.  
• Easy-to-use data collection methods : The methods include experiments, controlled observations, and questionnaires and surveys with a rating scale or close-ended questions, which require simple and to-the-point answers; are not bound by geographical regions; and are easy to administer.  
• Data analysis : Structured and accurate statistical analysis methods using software applications such as Excel, SPSS, R. The analysis is fast, accurate, and less effort intensive.  
• Reliable : The respondents answer close-ended questions, their responses are direct without ambiguity and yield numeric outcomes, which are therefore highly reliable.  
• Reusable outcomes : This is one of the key characteristics – outcomes of one research can be used and replicated in other research as well and is not exclusive to only one study.  

Quantitative research methods 5

Quantitative research methods are classified into two types—primary and secondary.  

Primary quantitative research method:

In this type of quantitative research , data are directly collected by the researchers using the following methods.

– Survey research : Surveys are the easiest and most commonly used quantitative research method . They are of two types— cross-sectional and longitudinal.   

->Cross-sectional surveys are specifically conducted on a target population for a specified period, that is, these surveys have a specific starting and ending time and researchers study the events during this period to arrive at conclusions. The main purpose of these surveys is to describe and assess the characteristics of a population. There is one independent variable in this study, which is a common factor applicable to all participants in the population, for example, living in a specific city, diagnosed with a specific disease, of a certain age group, etc. An example of a cross-sectional survey is a study to understand why individuals residing in houses built before 1979 in the US are more susceptible to lead contamination.  

->Longitudinal surveys are conducted at different time durations. These surveys involve observing the interactions among different variables in the target population, exposing them to various causal factors, and understanding their effects across a longer period. These studies are helpful to analyze a problem in the long term. An example of a longitudinal study is the study of the relationship between smoking and lung cancer over a long period.  

– Descriptive research : Explains the current status of an identified and measurable variable. Unlike other types of quantitative research , a hypothesis is not needed at the beginning of the study and can be developed even after data collection. This type of quantitative research describes the characteristics of a problem and answers the what, when, where of a problem. However, it doesn’t answer the why of the problem and doesn’t explore cause-and-effect relationships between variables. Data from this research could be used as preliminary data for another study. Example: A researcher undertakes a study to examine the growth strategy of a company. This sample data can be used by other companies to determine their own growth strategy.  

– Correlational research : This quantitative research method is used to establish a relationship between two variables using statistical analysis and analyze how one affects the other. The research is non-experimental because the researcher doesn’t control or manipulate any of the variables. At least two separate sample groups are needed for this research. Example: Researchers studying a correlation between regular exercise and diabetes.  

– Causal-comparative research : This type of quantitative research examines the cause-effect relationships in retrospect between a dependent and independent variable and determines the causes of the already existing differences between groups of people. This is not a true experiment because it doesn’t assign participants to groups randomly. Example: To study the wage differences between men and women in the same role. For this, already existing wage information is analyzed to understand the relationship.  

– Experimental research : This quantitative research method uses true experiments or scientific methods for determining a cause-effect relation between variables. It involves testing a hypothesis through experiments, in which one or more independent variables are manipulated and then their effect on dependent variables are studied. Example: A researcher studies the importance of a drug in treating a disease by administering the drug in few patients and not administering in a few.  

The following data collection methods are commonly used in primary quantitative research :  

• Sampling : The most common type is probability sampling, in which a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are—simple random, systematic, stratified, and cluster sampling.  
• Interviews : These are commonly telephonic or face-to-face.  
• Observations : Structured observations are most commonly used in quantitative research . In this method, researchers make observations about specific behaviors of individuals in a structured setting.  
• Document review : Reviewing existing research or documents to collect evidence for supporting the quantitative research .  
• Surveys and questionnaires : Surveys can be administered both online and offline depending on the requirement and sample size.

The data collected can be analyzed in several ways in quantitative research , as listed below:  

• Cross-tabulation —Uses a tabular format to draw inferences among collected data  
• MaxDiff analysis —Gauges the preferences of the respondents  
• TURF analysis —Total Unduplicated Reach and Frequency Analysis; helps in determining the market strategy for a business  
• Gap analysis —Identify gaps in attaining the desired results  
• SWOT analysis —Helps identify strengths, weaknesses, opportunities, and threats of a product, service, or organization  
• Text analysis —Used for interpreting unstructured data  

Secondary quantitative research methods :

This method involves conducting research using already existing or secondary data. This method is less effort intensive and requires lesser time. However, researchers should verify the authenticity and recency of the sources being used and ensure their accuracy.  

The main sources of secondary data are: 

• The Internet  
• Government and non-government sources  
• Public libraries  
• Educational institutions  
• Commercial information sources such as newspapers, journals, radio, TV  

When to use quantitative research 6  

Here are some simple ways to decide when to use quantitative research . Use quantitative research to:  

• recommend a final course of action  
• find whether a consensus exists regarding a particular subject  
• generalize results to a larger population  
• determine a cause-and-effect relationship between variables  
• describe characteristics of specific groups of people  
• test hypotheses and examine specific relationships  
• identify and establish size of market segments  

A research case study to understand when to use quantitative research 7  

Context: A study was undertaken to evaluate a major innovation in a hospital’s design, in terms of workforce implications and impact on patient and staff experiences of all single-room hospital accommodations. The researchers undertook a mixed methods approach to answer their research questions. Here, we focus on the quantitative research aspect.  

Research questions : What are the advantages and disadvantages for the staff as a result of the hospital’s move to the new design with all single-room accommodations? Did the move affect staff experience and well-being and improve their ability to deliver high-quality care?  

Method: The researchers obtained quantitative data from three sources:  

• Staff activity (task time distribution): Each staff member was shadowed by a researcher who observed each task undertaken by the staff, and logged the time spent on each activity.  
• Staff travel distances : The staff were requested to wear pedometers, which recorded the distances covered.  
• Staff experience surveys : Staff were surveyed before and after the move to the new hospital design.  

Results of quantitative research : The following observations were made based on quantitative data analysis:  

• The move to the new design did not result in a significant change in the proportion of time spent on different activities.  
• Staff activity events observed per session were higher after the move, and direct care and professional communication events per hour decreased significantly, suggesting fewer interruptions and less fragmented care.  
• A significant increase in medication tasks among the recorded events suggests that medication administration was integrated into patient care activities.  
• Travel distances increased for all staff, with highest increases for staff in the older people’s ward and surgical wards.  
• Ratings for staff toilet facilities, locker facilities, and space at staff bases were higher but those for social interaction and natural light were lower.  

Advantages of quantitative research 1,2

When choosing the right research methodology, also consider the advantages of quantitative research and how it can impact your study.  

• Quantitative research methods are more scientific and rational. They use quantifiable data leading to objectivity in the results and avoid any chances of ambiguity.  
• This type of research uses numeric data so analysis is relatively easier .  
• In most cases, a hypothesis is already developed and quantitative research helps in testing and validatin g these constructed theories based on which researchers can make an informed decision about accepting or rejecting their theory.  
• The use of statistical analysis software ensures quick analysis of large volumes of data and is less effort intensive.  
• Higher levels of control can be applied to the research so the chances of bias can be reduced.  
• Quantitative research is based on measured value s, facts, and verifiable information so it can be easily checked or replicated by other researchers leading to continuity in scientific research.  

Disadvantages of quantitative research 1,2

Quantitative research may also be limiting; take a look at the disadvantages of quantitative research. 

• Experiments are conducted in controlled settings instead of natural settings and it is possible for researchers to either intentionally or unintentionally manipulate the experiment settings to suit the results they desire.  
• Participants must necessarily give objective answers (either one- or two-word, or yes or no answers) and the reasons for their selection or the context are not considered.   
• Inadequate knowledge of statistical analysis methods may affect the results and their interpretation.  
• Although statistical analysis indicates the trends or patterns among variables, the reasons for these observed patterns cannot be interpreted and the research may not give a complete picture.  
• Large sample sizes are needed for more accurate and generalizable analysis .  
• Quantitative research cannot be used to address complex issues.  

Frequently asked questions on  quantitative research    

Q:  What is the difference between quantitative research and qualitative research? 1  

A:  The following table lists the key differences between quantitative research and qualitative research, some of which may have been mentioned earlier in the article.  

Q:  What is the difference between reliability and validity? 8,9    

A:  The term reliability refers to the consistency of a research study. For instance, if a food-measuring weighing scale gives different readings every time the same quantity of food is measured then that weighing scale is not reliable. If the findings in a research study are consistent every time a measurement is made, then the study is considered reliable. However, it is usually unlikely to obtain the exact same results every time because some contributing variables may change. In such cases, a correlation coefficient is used to assess the degree of reliability. A strong positive correlation between the results indicates reliability.  

Validity can be defined as the degree to which a tool actually measures what it claims to measure. It helps confirm the credibility of your research and suggests that the results may be generalizable. In other words, it measures the accuracy of the research.  

The following table gives the key differences between reliability and validity.  

Q:  What is mixed methods research? 10

A:  A mixed methods approach combines the characteristics of both quantitative research and qualitative research in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method. A mixed methods research design is useful in case of research questions that cannot be answered by either quantitative research or qualitative research alone. However, this method could be more effort- and cost-intensive because of the requirement of more resources. The figure 3 shows some basic mixed methods research designs that could be used.  

Thus, quantitative research is the appropriate method for testing your hypotheses and can be used either alone or in combination with qualitative research per your study requirements. We hope this article has provided an insight into the various facets of quantitative research , including its different characteristics, advantages, and disadvantages, and a few tips to quickly understand when to use this research method.  

References  

• Qualitative vs quantitative research: Differences, examples, & methods. Simply Psychology. Accessed Feb 28, 2023. https://simplypsychology.org/qualitative-quantitative.html#Quantitative-Research  
• Your ultimate guide to quantitative research. Qualtrics. Accessed February 28, 2023. https://www.qualtrics.com/uk/experience-management/research/quantitative-research/  
• The steps of quantitative research. Revise Sociology. Accessed March 1, 2023. https://revisesociology.com/2017/11/26/the-steps-of-quantitative-research/  
• What are the characteristics of quantitative research? Marketing91. Accessed March 1, 2023. https://www.marketing91.com/characteristics-of-quantitative-research/  
• Quantitative research: Types, characteristics, methods, & examples. ProProfs Survey Maker. Accessed February 28, 2023. https://www.proprofssurvey.com/blog/quantitative-research/#Characteristics_of_Quantitative_Research  
• Qualitative research isn’t as scientific as quantitative methods. Kmusial blog. Accessed March 5, 2023. https://kmusial.wordpress.com/2011/11/25/qualitative-research-isnt-as-scientific-as-quantitative-methods/  
• Maben J, Griffiths P, Penfold C, et al. Evaluating a major innovation in hospital design: workforce implications and impact on patient and staff experiences of all single room hospital accommodation. Southampton (UK): NIHR Journals Library; 2015 Feb. (Health Services and Delivery Research, No. 3.3.) Chapter 5, Case study quantitative data findings. Accessed March 6, 2023. https://www.ncbi.nlm.nih.gov/books/NBK274429/  
• McLeod, S. A. (2007).  What is reliability?  Simply Psychology. www.simplypsychology.org/reliability.html  
• Reliability vs validity: Differences & examples. Accessed March 5, 2023. https://statisticsbyjim.com/basics/reliability-vs-validity/  
• Mixed methods research. Community Engagement Program. Harvard Catalyst. Accessed February 28, 2023. https://catalyst.harvard.edu/community-engagement/mmr  

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• Chapter Four: Quantitative Methods (Part 1)

Once you have chosen a topic to investigate, you need to decide which type of method is best to study it. This is one of the most important choices you will make on your research journey. Understanding the value of each of the methods described in this textbook to answer different questions allows you to be able to plan your own studies with more confidence, critique the studies others have done, and provide advice to your colleagues and friends on what type of research they should do to answer questions they have. After briefly reviewing quantitative research assumptions, this chapter is organized in three parts or sections. These parts can also be used as a checklist when working through the steps of your study. Specifically, part 1 focuses on planning a quantitative study (collecting data), part two explains the steps involved in doing a quantitative study, and part three discusses how to make sense of your results (organizing and analyzing data).

• Chapter One: Introduction
• Chapter Two: Understanding the distinctions among research methods
• Chapter Three: Ethical research, writing, and creative work
• Chapter Four: Quantitative Methods (Part 2 - Doing Your Study)
• Chapter Four: Quantitative Methods (Part 3 - Making Sense of Your Study)
• Chapter Five: Qualitative Methods (Part 1)
• Chapter Five: Qualitative Data (Part 2)
• Chapter Six: Critical / Rhetorical Methods (Part 1)
• Chapter Six: Critical / Rhetorical Methods (Part 2)
• Chapter Seven: Presenting Your Results

Quantitative Worldview Assumptions: A Review

In chapter 2, you were introduced to the unique assumptions quantitative research holds about knowledge and how it is created, or what the authors referred to in chapter one as "epistemology." Understanding these assumptions can help you better determine whether you need to use quantitative methods for a particular research study in which you are interested.

Quantitative researchers believe there is an objective reality, which can be measured. "Objective" here means that the researcher is not relying on their own perceptions of an event. S/he is attempting to gather "facts" which may be separate from people's feeling or perceptions about the facts. These facts are often conceptualized as "causes" and "effects." When you ask research questions or pose hypotheses with words in them such as "cause," "effect," "difference between," and "predicts," you are operating under assumptions consistent with quantitative methods. The overall goal of quantitative research is to develop generalizations that enable the researcher to better predict, explain, and understand some phenomenon.

Because of trying to prove cause-effect relationships that can be generalized to the population at large, the research process and related procedures are very important for quantitative methods. Research should be consistently and objectively conducted, without bias or error, in order to be considered to be valid (accurate) and reliable (consistent). Perhaps this emphasis on accurate and standardized methods is because the roots of quantitative research are in the natural and physical sciences, both of which have at their base the need to prove hypotheses and theories in order to better understand the world in which we live. When a person goes to a doctor and is prescribed some medicine to treat an illness, that person is glad such research has been done to know what the effects of taking this medicine is on others' bodies, so s/he can trust the doctor's judgment and take the medicines.

As covered in chapters 1 and 2, the questions you are asking should lead you to a certain research method choice. Students sometimes want to avoid doing quantitative research because of fear of math/statistics, but if their questions call for that type of research, they should forge ahead and use it anyway. If a student really wants to understand what the causes or effects are for a particular phenomenon, they need to do quantitative research. If a student is interested in what sorts of things might predict a person's behavior, they need to do quantitative research. If they want to confirm the finding of another researcher, most likely they will need to do quantitative research. If a student wishes to generalize beyond their participant sample to a larger population, they need to be conducting quantitative research.

So, ultimately, your choice of methods really depends on what your research goal is. What do you really want to find out? Do you want to compare two or more groups, look for relationships between certain variables, predict how someone will act or react, or confirm some findings from another study? If so, you want to use quantitative methods.

A topic such as self-esteem can be studied in many ways. Listed below are some example RQs about self-esteem. Which of the following research questions should be answered with quantitative methods?

• Is there a difference between men's and women's level of self- esteem?
• How do college-aged women describe their ups and downs with self-esteem?
• How has "self-esteem" been constructed in popular self-help books over time?
• Is there a relationship between self-esteem levels and communication apprehension?

What are the advantages of approaching a topic like self-esteem using quantitative methods? What are the disadvantages?

For more information, see the following website: Analyse This!!! Learning to analyse quantitative data

Answers:  1 & 4

Quantitative Methods Part One: Planning Your Study

Planning your study is one of the most important steps in the research process when doing quantitative research. As seen in the diagram below, it involves choosing a topic, writing research questions/hypotheses, and designing your study. Each of these topics will be covered in detail in this section of the chapter.

Topic Choice

Decide on topic.

How do you go about choosing a topic for a research project? One of the best ways to do this is to research something about which you would like to know more. Your communication professors will probably also want you to select something that is related to communication and things you are learning about in other communication classes.

When the authors of this textbook select research topics to study, they choose things that pique their interest for a variety of reasons, sometimes personal and sometimes because they see a need for more research in a particular area. For example, April Chatham-Carpenter studies adoption return trips to China because she has two adopted daughters from China and because there is very little research on this topic for Chinese adoptees and their families; she studied home vs. public schooling because her sister home schools, and at the time she started the study very few researchers had considered the social network implications for home schoolers (cf.  http://www.uni.edu/chatham/homeschool.html ).

When you are asked in this class and other classes to select a topic to research, think about topics that you have wondered about, that affect you personally, or that know have gaps in the research. Then start writing down questions you would like to know about this topic. These questions will help you decide whether the goal of your study is to understand something better, explain causes and effects of something, gather the perspectives of others on a topic, or look at how language constructs a certain view of reality.

Review Previous Research

In quantitative research, you do not rely on your conclusions to emerge from the data you collect. Rather, you start out looking for certain things based on what the past research has found. This is consistent with what was called in chapter 2 as a deductive approach (Keyton, 2011), which also leads a quantitative researcher to develop a research question or research problem from reviewing a body of literature, with the previous research framing the study that is being done. So, reviewing previous research done on your topic is an important part of the planning of your study. As seen in chapter 3 and the Appendix, to do an adequate literature review, you need to identify portions of your topic that could have been researched in the past. To do that, you select key terms of concepts related to your topic.

Some people use concept maps to help them identify useful search terms for a literature review. For example, see the following website: Concept Mapping: How to Start Your Term Paper Research .

Narrow Topic to Researchable Area

Once you have selected your topic area and reviewed relevant literature related to your topic, you need to narrow your topic to something that can be researched practically and that will take the research on this topic further. You don't want your research topic to be so broad or large that you are unable to research it. Plus, you want to explain some phenomenon better than has been done before, adding to the literature and theory on a topic. You may want to test out what someone else has found, replicating their study, and therefore building to the body of knowledge already created.

To see how a literature review can be helpful in narrowing your topic, see the following sources.  Narrowing or Broadening Your Research Topic  and  How to Conduct a Literature Review in Social Science

Research Questions & Hypotheses

Write Your Research Questions (RQs) and/or Hypotheses (Hs)

Once you have narrowed your topic based on what you learned from doing your review of literature, you need to formalize your topic area into one or more research questions or hypotheses. If the area you are researching is a relatively new area, and no existing literature or theory can lead you to predict what you might find, then you should write a research question. Take a topic related to social media, for example, which is a relatively new area of study. You might write a research question that asks:

"Is there a difference between how 1st year and 4th year college students use Facebook to communicate with their friends?"

If, however, you are testing out something you think you might find based on the findings of a large amount of previous literature or a well-developed theory, you can write a hypothesis. Researchers often distinguish between  null  and  alternative  hypotheses. The alternative hypothesis is what you are trying to test or prove is true, while the null hypothesis assumes that the alternative hypothesis is not true. For example, if the use of Facebook had been studied a great deal, and there were theories that had been developed on the use of it, then you might develop an alternative hypothesis, such as: "First-year students spend more time on using Facebook to communicate with their friends than fourth-year students do." Your null hypothesis, on the other hand, would be: "First-year students do  not  spend any more time using Facebook to communication with their friends than fourth-year students do." Researchers, however, only state the alternative hypothesis in their studies, and actually call it "hypothesis" rather than "alternative hypothesis."

Process of Writing a Research Question/Hypothesis.

Once you have decided to write a research question (RQ) or hypothesis (H) for your topic, you should go through the following steps to create your RQ or H.

Name the concepts from your overall research topic that you are interested in studying.

RQs and Hs have variables, or concepts that you are interested in studying. Variables can take on different values. For example, in the RQ above, there are at least two variables – year in college and use of Facebook (FB) to communicate. Both of them have a variety of levels within them.

When you look at the concepts you identified, are there any concepts which seem to be related to each other? For example, in our RQ, we are interested in knowing if there is a difference between first-year students and fourth-year students in their use of FB, meaning that we believe there is some connection between our two variables.

• Decide what type of a relationship you would like to study between the variables. Do you think one causes the other? Does a difference in one create a difference in the other? As the value of one changes, does the value of the other change?

Identify which one of these concepts is the independent (or predictor) variable, or the concept that is perceived to be the cause of change in the other variable? Which one is the dependent (criterion) variable, or the one that is affected by changes in the independent variable? In the above example RQ, year in school is the independent variable, and amount of time spent on Facebook communicating with friends is the dependent variable. The amount of time spent on Facebook depends on a person's year in school.

If you're still confused about independent and dependent variables, check out the following site: Independent & Dependent Variables .

Express the relationship between the concepts as a single sentence – in either a hypothesis or a research question.

For example, "is there a difference between international and American students on their perceptions of the basic communication course," where cultural background and perceptions of the course are your two variables. Cultural background would be the independent variable, and perceptions of the course would be your dependent variable. More examples of RQs and Hs are provided in the next section.

APPLICATION: Try the above steps with your topic now. Check with your instructor to see if s/he would like you to send your topic and RQ/H to him/her via e-mail.

Types of Research Questions/Hypotheses

Once you have written your RQ/H, you need to determine what type of research question or hypothesis it is. This will help you later decide what types of statistics you will need to run to answer your question or test your hypothesis. There are three possible types of questions you might ask, and two possible types of hypotheses. The first type of question cannot be written as a hypothesis, but the second and third types can.

Descriptive Question.

The first type of question is a descriptive question. If you have only one variable or concept you are studying, OR if you are not interested in how the variables you are studying are connected or related to each other, then your question is most likely a descriptive question.

This type of question is the closest to looking like a qualitative question, and often starts with a "what" or "how" or "why" or "to what extent" type of wording. What makes it different from a qualitative research question is that the question will be answered using numbers rather than qualitative analysis. Some examples of a descriptive question, using the topic of social media, include the following.

"To what extent are college-aged students using Facebook to communicate with their friends?"

"Why do college-aged students use Facebook to communicate with their friends?"

Notice that neither of these questions has a clear independent or dependent variable, as there is no clear cause or effect being assumed by the question. The question is merely descriptive in nature. It can be answered by summarizing the numbers obtained for each category, such as by providing percentages, averages, or just the raw totals for each type of strategy or organization. This is true also of the following research questions found in a study of online public relations strategies:

"What online public relations strategies are organizations implementing to combat phishing" (Baker, Baker, & Tedesco, 2007, p. 330), and

"Which organizations are doing most and least, according to recommendations from anti- phishing advocacy recommendations, to combat phishing" (Baker, Baker, & Tedesco, 2007, p. 330)

The researchers in this study reported statistics in their results or findings section, making it clearly a quantitative study, but without an independent or dependent variable; therefore, these research questions illustrate the first type of RQ, the descriptive question.

Difference Question/Hypothesis.

The second type of question is a question/hypothesis of difference, and will often have the word "difference" as part of the question. The very first research question in this section, asking if there is a difference between 1st year and 4th year college students' use of Facebook, is an example of this type of question. In this type of question, the independent variable is some type of grouping or categories, such as age. Another example of a question of difference is one April asked in her research on home schooling: "Is there a difference between home vs. public schoolers on the size of their social networks?" In this example, the independent variable is home vs. public schooling (a group being compared), and the dependent variable is size of social networks. Hypotheses can also be difference hypotheses, as the following example on the same topic illustrates: "Public schoolers have a larger social network than home schoolers do."

Relationship/Association Question/Hypothesis.

The third type of question is a relationship/association question or hypothesis, and will often have the word "relate" or "relationship" in it, as the following example does: "There is a relationship between number of television ads for a political candidate and how successful that political candidate is in getting elected." Here the independent (or predictor) variable is number of TV ads, and the dependent (or criterion) variable is the success at getting elected. In this type of question, there is no grouping being compared, but rather the independent variable is continuous (ranges from zero to a certain number) in nature. This type of question can be worded as either a hypothesis or as a research question, as stated earlier.

Test out your knowledge of the above information, by answering the following questions about the RQ/H listed below. (Remember, for a descriptive question there are no clear independent & dependent variables.)

• What is the independent variable (IV)?
• What is the dependent variable (DV)?
• What type of research question/hypothesis is it? (descriptive, difference, relationship/association)
• "Is there a difference on relational satisfaction between those who met their current partner through online dating and those who met their current partner face-to-face?"
• "How do Fortune 500 firms use focus groups to market new products?"
• "There is a relationship between age and amount of time spent online using social media."

Answers: RQ1  is a difference question, with type of dating being the IV and relational satisfaction being the DV. RQ2  is a descriptive question with no IV or DV. RQ3  is a relationship hypothesis with age as the IV and amount of time spent online as the DV.

Design Your Study

The third step in planning your research project, after you have decided on your topic/goal and written your research questions/hypotheses, is to design your study which means to decide how to proceed in gathering data to answer your research question or to test your hypothesis. This step includes six things to do. [NOTE: The terms used in this section will be defined as they are used.]

• Decide type of study design: Experimental, quasi-experimental, non-experimental.
• Decide kind of data to collect: Survey/interview, observation, already existing data.
• Operationalize variables into measurable concepts.
• Determine type of sample: Probability or non-probability.
• Decide how you will collect your data: face-to-face, via e-mail, an online survey, library research, etc.
• Pilot test your methods.

Types of Study Designs

With quantitative research being rooted in the scientific method, traditional research is structured in an experimental fashion. This is especially true in the natural sciences, where they try to prove causes and effects on topics such as successful treatments for cancer. For example, the University of Iowa Hospitals and Clinics regularly conduct clinical trials to test for the effectiveness of certain treatments for medical conditions ( University of Iowa Hospitals & Clinics: Clinical Trials ). They use human participants to conduct such research, regularly recruiting volunteers. However, in communication, true experiments with treatments the researcher controls are less necessary and thus less common. It is important for the researcher to understand which type of study s/he wishes to do, in order to accurately communicate his/her methods to the public when describing the study.

There are three possible types of studies you may choose to do, when embarking on quantitative research: (a) True experiments, (b) quasi-experiments, and (c) non-experiments.

For more information to read on these types of designs, take a look at the following website and related links in it: Types of Designs .

The following flowchart should help you distinguish between the three types of study designs described below.

True Experiments.

The first two types of study designs use difference questions/hypotheses, as the independent variable for true and quasi-experiments is  nominal  or categorical (based on categories or groupings), as you have groups that are being compared. As seen in the flowchart above, what distinguishes a true experiment from the other two designs is a concept called "random assignment." Random assignment means that the researcher controls to which group the participants are assigned. April's study of home vs. public schooling was NOT a true experiment, because she could not control which participants were home schooled and which ones were public schooled, and instead relied on already existing groups.

An example of a true experiment reported in a communication journal is a study investigating the effects of using interest-based contemporary examples in a lecture on the history of public relations, in which the researchers had the following two hypotheses: "Lectures utilizing interest- based examples should result in more interested participants" and "Lectures utilizing interest- based examples should result in participants with higher scores on subsequent tests of cognitive recall" (Weber, Corrigan, Fornash, & Neupauer, 2003, p. 118). In this study, the 122 college student participants were randomly assigned by the researchers to one of two lecture video viewing groups: a video lecture with traditional examples and a video with contemporary examples. (To see the results of the study, look it up using your school's library databases).

A second example of a true experiment in communication is a study of the effects of viewing either a dramatic narrative television show vs. a nonnarrative television show about the consequences of an unexpected teen pregnancy. The researchers randomly assigned their 367 undergraduate participants to view one of the two types of shows.

Moyer-Gusé, E., & Nabi, R. L. (2010). Explaining the effects of narrative in an entertainment television program: Overcoming resistance to persuasion.  Human Communication Research, 36 , 26-52.

A third example of a true experiment done in the field of communication can be found in the following study.

Jensen, J. D. (2008). Scientific uncertainty in news coverage of cancer research: Effects of hedging on scientists' and journalists' credibility.  Human Communication Research, 34,  347-369.

In this study, Jakob Jensen had three independent variables. He randomly assigned his 601 participants to 1 of 20 possible conditions, between his three independent variables, which were (a) a hedged vs. not hedged message, (b) the source of the hedging message (research attributed to primary vs. unaffiliated scientists), and (c) specific news story employed (of which he had five randomly selected news stories about cancer research to choose from). Although this study was pretty complex, it does illustrate the true experiment in our field since the participants were randomly assigned to read a particular news story, with certain characteristics.

Quasi-Experiments.

If the researcher is not able to randomly assign participants to one of the treatment groups (or independent variable), but the participants already belong to one of them (e.g., age; home vs. public schooling), then the design is called a quasi-experiment. Here you still have an independent variable with groups, but the participants already belong to a group before the study starts, and the researcher has no control over which group they belong to.

An example of a hypothesis found in a communication study is the following: "Individuals high in trait aggression will enjoy violent content more than nonviolent content, whereas those low in trait aggression will enjoy violent content less than nonviolent content" (Weaver & Wilson, 2009, p. 448). In this study, the researchers could not assign the participants to a high or low trait aggression group since this is a personality characteristic, so this is a quasi-experiment. It does not have any random assignment of participants to the independent variable groups. Read their study, if you would like to, at the following location.

Weaver, A. J., & Wilson, B. J. (2009). The role of graphic and sanitized violence in the enjoyment of television dramas.  Human Communication Research, 35  (3), 442-463.

Benoit and Hansen (2004) did not choose to randomly assign participants to groups either, in their study of a national presidential election survey, in which they were looking at differences between debate and non-debate viewers, in terms of several dependent variables, such as which candidate viewers supported. If you are interested in discovering the results of this study, take a look at the following article.

Benoit, W. L., & Hansen, G. J. (2004). Presidential debate watching, issue knowledge, character evaluation, and vote choice.  Human Communication Research, 30  (1), 121-144.

Non-Experiments.

The third type of design is the non-experiment. Non-experiments are sometimes called survey designs, because their primary way of collecting data is through surveys. This is not enough to distinguish them from true experiments and quasi-experiments, however, as both of those types of designs may use surveys as well.

What makes a study a non-experiment is that the independent variable is not a grouping or categorical variable. Researchers observe or survey participants in order to describe them as they naturally exist without any experimental intervention. Researchers do not give treatments or observe the effects of a potential natural grouping variable such as age. Descriptive and relationship/association questions are most often used in non-experiments.

Some examples of this type of commonly used design for communication researchers include the following studies.

• Serota, Levine, and Boster (2010) used a national survey of 1,000 adults to determine the prevalence of lying in America (see  Human Communication Research, 36 , pp. 2-25).
• Nabi (2009) surveyed 170 young adults on their perceptions of reality television on cosmetic surgery effects, looking at several things: for example, does viewing cosmetic surgery makeover programs relate to body satisfaction (p. 6), finding no significant relationship between those two variables (see  Human Communication Research, 35 , pp. 1-27).
• Derlega, Winstead, Mathews, and Braitman (2008) collected stories from 238 college students on reasons why they would disclose or not disclose personal information within close relationships (see  Communication Research Reports, 25 , pp. 115-130). They coded the participants' answers into categories so they could count how often specific reasons were mentioned, using a method called  content analysis , to answer the following research questions:

RQ1: What are research participants' attributions for the disclosure and nondisclosure of highly personal information?

RQ2: Do attributions reflect concerns about rewards and costs of disclosure or the tension between openness with another and privacy?

RQ3: How often are particular attributions for disclosure/nondisclosure used in various types of relationships? (p. 117)

All of these non-experimental studies have in common no researcher manipulation of an independent variable or even having an independent variable that has natural groups that are being compared.

Identify which design discussed above should be used for each of the following research questions.

• Is there a difference between generations on how much they use MySpace?
• Is there a relationship between age when a person first started using Facebook and the amount of time they currently spend on Facebook daily?
• Is there a difference between potential customers' perceptions of an organization who are shown an organization's Facebook page and those who are not shown an organization's Facebook page?

[HINT: Try to identify the independent and dependent variable in each question above first, before determining what type of design you would use. Also, try to determine what type of question it is – descriptive, difference, or relationship/association.]

Answers: 1. Quasi-experiment 2. Non-experiment 3. True Experiment

Data Collection Methods

Once you decide the type of quantitative research design you will be using, you will need to determine which of the following types of data you will collect: (a) survey data, (b) observational data, and/or (c) already existing data, as in library research.

Using the survey data collection method means you will talk to people or survey them about their behaviors, attitudes, perceptions, and demographic characteristics (e.g., biological sex, socio-economic status, race). This type of data usually consists of a series of questions related to the concepts you want to study (i.e., your independent and dependent variables). Both of April's studies on home schooling and on taking adopted children on a return trip back to China used survey data.

On a survey, you can have both closed-ended and open-ended questions. Closed-ended questions, can be written in a variety of forms. Some of the most common response options include the following.

Likert responses – for example: for the following statement, ______ do you strongly agree agree neutral disagree strongly disagree

Semantic differential – for example: does the following ______ make you Happy ..................................... Sad

Yes-no answers for example: I use social media daily. Yes / No.

One site to check out for possible response options is  http://www.360degreefeedback.net/media/ResponseScales.pdf .

Researchers often follow up some of their closed-ended questions with an "other" category, in which they ask their participants to "please specify," their response if none of the ones provided are applicable. They may also ask open-ended questions on "why" a participant chose a particular answer or ask participants for more information about a particular topic. If the researcher wants to use the open-ended question responses as part of his/her quantitative study, the answers are usually coded into categories and counted, in terms of the frequency of a certain answer, using a method called  content analysis , which will be discussed when we talk about already-existing artifacts as a source of data.

Surveys can be done face-to-face, by telephone, mail, or online. Each of these methods has its own advantages and disadvantages, primarily in the form of the cost in time and money to do the survey. For example, if you want to survey many people, then online survey tools such as surveygizmo.com and surveymonkey.com are very efficient, but not everyone has access to taking a survey on the computer, so you may not get an adequate sample of the population by doing so. Plus you have to decide how you will recruit people to take your online survey, which can be challenging. There are trade-offs with every method.

For more information on things to consider when selecting your survey method, check out the following website:

Selecting the Survey Method .

There are also many good sources for developing a good survey, such as the following websites. Constructing the Survey Survey Methods Designing Surveys

Observation.

A second type of data collection method is  observation . In this data collection method, you make observations of the phenomenon you are studying and then code your observations, so that you can count what you are studying. This type of data collection method is often called interaction analysis, if you collect data by observing people's behavior. For example, if you want to study the phenomenon of mall-walking, you could go to a mall and count characteristics of mall-walkers. A researcher in the area of health communication could study the occurrence of humor in an operating room, for example, by coding and counting the use of humor in such a setting.

One extended research study using observational data collection methods, which is cited often in interpersonal communication classes, is John Gottman's research, which started out in what is now called "The Love Lab." In this lab, researchers observe interactions between couples, including physiological symptoms, using coders who look for certain items found to predict relationship problems and success.

Take a look at the YouTube video about "The Love Lab" at the following site to learn more about the potential of using observation in collecting data for a research study:  The "Love" Lab .

Already-Existing Artifacts.

The third method of quantitative data collection is the use of  already-existing artifacts . With this method, you choose certain artifacts (e.g., newspaper or magazine articles; television programs; webpages) and code their content, resulting in a count of whatever you are studying. With this data collection method, researchers most often use what is called quantitative  content analysis . Basically, the researcher counts frequencies of something that occurs in an artifact of study, such as the frequency of times something is mentioned on a webpage. Content analysis can also be used in qualitative research, where a researcher identifies and creates text-based themes but does not do a count of the occurrences of these themes. Content analysis can also be used to take open-ended questions from a survey method, and identify countable themes within the questions.

Content analysis is a very common method used in media studies, given researchers are interested in studying already-existing media artifacts. There are many good sources to illustrate how to do content analysis such as are seen in the box below.

See the following sources for more information on content analysis. Writing Guide: Content Analysis A Flowchart for the Typical Process of Content Analysis Research What is Content Analysis?

With content analysis and any method that you use to code something into categories, one key concept you need to remember is  inter-coder or inter-rater reliability , in which there are multiple coders (at least two) trained to code the observations into categories. This check on coding is important because you need to check to make sure that the way you are coding your observations on the open-ended answers is the same way that others would code a particular item. To establish this kind of inter-coder or inter-rater reliability, researchers prepare codebooks (to train their coders on how to code the materials) and coding forms for their coders to use.

To see some examples of actual codebooks used in research, see the following website:  Human Coding--Sample Materials .

There are also online inter-coder reliability calculators some researchers use, such as the following:  ReCal: reliability calculation for the masses .

Regardless of which method of data collection you choose, you need to decide even more specifically how you will measure the variables in your study, which leads us to the next planning step in the design of a study.

Operationalization of Variables into Measurable Concepts

When you look at your research question/s and/or hypotheses, you should know already what your independent and dependent variables are. Both of these need to be measured in some way. We call that way of measuring  operationalizing  a variable. One way to think of it is writing a step by step recipe for how you plan to obtain data on this topic. How you choose to operationalize your variable (or write the recipe) is one all-important decision you have to make, which will make or break your study. In quantitative research, you have to measure your variables in a valid (accurate) and reliable (consistent) manner, which we discuss in this section. You also need to determine the level of measurement you will use for your variables, which will help you later decide what statistical tests you need to run to answer your research question/s or test your hypotheses. We will start with the last topic first.

Level of Measurement

Level of measurement has to do with whether you measure your variables using categories or groupings OR whether you measure your variables using a continuous level of measurement (range of numbers). The level of measurement that is considered to be categorical in nature is called nominal, while the levels of measurement considered to be continuous in nature are ordinal, interval, and ratio. The only ones you really need to know are nominal, ordinal, and interval/ratio.

Nominal  variables are categories that do not have meaningful numbers attached to them but are broader categories, such as male and female, home schooled and public schooled, Caucasian and African-American.  Ordinal  variables do have numbers attached to them, in that the numbers are in a certain order, but there are not equal intervals between the numbers (e.g., such as when you rank a group of 5 items from most to least preferred, where 3 might be highly preferred, and 2 hated).  Interval/ratio  variables have equal intervals between the numbers (e.g., weight, age).

For more information about these levels of measurement, check out one of the following websites. Levels of Measurement Measurement Scales in Social Science Research What is the difference between ordinal, interval and ratio variables? Why should I care?

Validity and Reliability

When developing a scale/measure or survey, you need to be concerned about validity and reliability. Readers of quantitative research expect to see researchers justify their research measures using these two terms in the methods section of an article or paper.

Validity.   Validity  is the extent to which your scale/measure or survey adequately reflects the full meaning of the concept you are measuring. Does it measure what you say it measures? For example, if researchers wanted to develop a scale to measure "servant leadership," the researchers would have to determine what dimensions of servant leadership they wanted to measure, and then create items which would be valid or accurate measures of these dimensions. If they included items related to a different type of leadership, those items would not be a valid measure of servant leadership. When doing so, the researchers are trying to prove their measure has internal validity. Researchers may also be interested in external validity, but that has to do with how generalizable their study is to a larger population (a topic related to sampling, which we will consider in the next section), and has less to do with the validity of the instrument itself.

There are several types of validity you may read about, including face validity, content validity, criterion-related validity, and construct validity. To learn more about these types of validity, read the information at the following link: Validity .

To improve the validity of an instrument, researchers need to fully understand the concept they are trying to measure. This means they know the academic literature surrounding that concept well and write several survey questions on each dimension measured, to make sure the full idea of the concept is being measured. For example, Page and Wong (n.d.) identified four dimensions of servant leadership: character, people-orientation, task-orientation, and process-orientation ( A Conceptual Framework for Measuring Servant-Leadership ). All of these dimensions (and any others identified by other researchers) would need multiple survey items developed if a researcher wanted to create a new scale on servant leadership.

Before you create a new survey, it can be useful to see if one already exists with established validity and reliability. Such measures can be found by seeing what other respected studies have used to measure a concept and then doing a library search to find the scale/measure itself (sometimes found in the reference area of a library in books like those listed below).

Reliability .  Reliability  is the second criterion you will need to address if you choose to develop your own scale or measure. Reliability is concerned with whether a measurement is consistent and reproducible. If you have ever wondered why, when taking a survey, that a question is asked more than once or very similar questions are asked multiple times, it is because the researchers one concerned with proving their study has reliability. Are you, for example, answering all of the similar questions similarly? If so, the measure/scale may have good reliability or consistency over time.

Researchers can use a variety of ways to show their measure/scale is reliable. See the following websites for explanations of some of these ways, which include methods such as the test-retest method, the split-half method, and inter-coder/rater reliability. Types of Reliability Reliability

To understand the relationship between validity and reliability, a nice visual provided below is explained at the following website (Trochim, 2006, para. 2). Reliability & Validity

Self-Quiz/Discussion:

Take a look at one of the surveys found at the following poll reporting sites on a topic which interests you. Critique one of these surveys, using what you have learned about creating surveys so far.

http://www.pewinternet.org/ http://pewresearch.org/ http://www.gallup.com/Home.aspx http://www.kff.org/

One of the things you might have critiqued in the previous self-quiz/discussion may have had less to do with the actual survey itself, but rather with how the researchers got their participants or sample. How participants are recruited is just as important to doing a good study as how valid and reliable a survey is.

Imagine that in the article you chose for the last "self-quiz/discussion" you read the following quote from the Pew Research Center's Internet and American Life Project: "One in three teens sends more than 100 text messages a day, or 3000 texts a month" (Lenhart, 2010, para.5). How would you know whether you could trust this finding to be true? Would you compare it to what you know about texting from your own and your friends' experiences? Would you want to know what types of questions people were asked to determine this statistic, or whether the survey the statistic is based on is valid and reliable? Would you want to know what type of people were surveyed for the study? As a critical consumer of research, you should ask all of these types of questions, rather than just accepting such a statement as undisputable fact. For example, if only people shopping at an Apple Store were surveyed, the results might be skewed high.

In particular, related to the topic of this section, you should ask about the sampling method the researchers did. Often, the researchers will provide information related to the sample, stating how many participants were surveyed (in this case 800 teens, aged 12-17, who were a nationally representative sample of the population) and how much the "margin of error" is (in this case +/- 3.8%). Why do they state such things? It is because they know the importance of a sample in making the case for their findings being legitimate and credible.  Margin of error  is how much we are confident that our findings represent the population at large. The larger the margin of error, the less likely it is that the poll or survey is accurate. Margin of error assumes a 95% confidence level that what we found from our study represents the population at large.

For more information on margin of error, see one of the following websites. Answers.com Margin of Error Stats.org Margin of Error Americanresearchgroup.com Margin of Error [this last site is a margin of error calculator, which shows that margin of error is directly tied to the size of your sample, in relationship to the size of the population, two concepts we will talk about in the next few paragraphs]

In particular, this section focused on sampling will talk about the following topics: (a) the difference between a population vs. a sample; (b) concepts of error and bias, or "it's all about significance"; (c) probability vs. non-probability sampling; and (d) sample size issues.

Population vs. Sample

When doing quantitative studies, such as the study of cell phone usage among teens, you are never able to survey the entire population of teenagers, so you survey a portion of the population. If you study every member of a population, then you are conducting a census such as the United States Government does every 10 years. When, however, this is not possible (because you do not have the money the U.S. government has!), you attempt to get as good a sample as possible.

Characteristics of a population are summarized in numerical form, and technically these numbers are called  parameters . However, numbers which summarize the characteristics of a sample are called  statistics .

Error and Bias

If a sample is not done well, then you may not have confidence in how the study's results can be generalized to the population from which the sample was taken. Your confidence level is often stated as the  margin of error  of the survey. As noted earlier, a study's margin of error refers to the degree to which a sample differs from the total population you are studying. In the Pew survey, they had a margin of error of +/- 3.8%. So, for example, when the Pew survey said 33% of teens send more than 100 texts a day, the margin of error means they were 95% sure that 29.2% - 36.8% of teens send this many texts a day.

Margin of error is tied to  sampling error , which is how much difference there is between your sample's results and what would have been obtained if you had surveyed the whole population. Sample error is linked to a very important concept for quantitative researchers, which is the notion of  significance . Here, significance does not refer to whether some finding is morally or practically significant, it refers to whether a finding is statistically significant, meaning the findings are not due to chance but actually represent something that is found in the population.  Statistical significance  is about how much you, as the researcher, are willing to risk saying you found something important and be wrong.

For the difference between statistical significance and practical significance, see the following YouTube video:  Statistical and Practical Significance .

Scientists set certain arbitrary standards based on the probability they could be wrong in reporting their findings. These are called  significance levels  and are commonly reported in the literature as  p <.05  or  p <.01  or some other probability (or  p ) level.

If an article says a statistical test reported that  p < .05 , it simply means that they are most likely correct in what they are saying, but there is a 5% chance they could be wrong and not find the same results in the population. If p < .01, then there would be only a 1% chance they were wrong and would not find the same results in the population. The lower the probability level, the more certain the results.

When researchers are wrong, or make that kind of decision error, it often implies that either (a) their sample was biased and was not representative of the true population in some way, or (b) that something they did in collecting the data biased the results. There are actually two kinds of sampling error talked about in quantitative research: Type I and Type II error.  Type 1 error  is what happens when you think you found something statistically significant and claim there is a significant difference or relationship, when there really is not in the actual population. So there is something about your sample that made you find something that is not in the actual population. (Type I error is the same as the probability level, or .05, if using the traditional p-level accepted by most researchers.)  Type II error  happens when you don't find a statistically significant difference or relationship, yet there actually is one in the population at large, so once again, your sample is not representative of the population.

For more information on these two types of error, check out the following websites. Hypothesis Testing: Type I Error, Type II Error Type I and Type II Errors - Making Mistakes in the Justice System

Researchers want to select a sample that is representative of the population in order to reduce the likelihood of having a sample that is biased. There are two types of bias particularly troublesome for researchers, in terms of sampling error. The first type is  selection bias , in which each person in the population does not have an equal chance to be chosen for the sample, which happens frequently in communication studies, because we often rely on convenience samples (whoever we can get to complete our surveys). The second type of bias is  response bias , in which those who volunteer for a study have different characteristics than those who did not volunteer for the study, another common challenge for communication researchers. Volunteers for a study may very well be different from persons who choose not to volunteer for a study, so that you have a biased sample by relying just on volunteers, which is not representative of the population from which you are trying to sample.

Probability vs. Non-Probability Sampling

One of the best ways to lower your sampling error and reduce the possibility of bias is to do probability or random sampling. This means that every person in the population has an equal chance of being selected to be in your sample. Another way of looking at this is to attempt to get a  representative  sample, so that the characteristics of your sample closely approximate those of the population. A sample needs to contain essentially the same variations that exist in the population, if possible, especially on the variables or elements that are most important to you (e.g., age, biological sex, race, level of education, socio-economic class).

There are many different ways to draw a probability/random sample from the population. Some of the most common are a  simple random sample , where you use a random numbers table or random number generator to select your sample from the population.

There are several examples of random number generators available online. See the following example of an online random number generator:  http://www.randomizer.org/ .

A  systematic random sample  takes every n-th number from the population, depending on how many people you would like to have in your sample. A  stratified random sample  does random sampling within groups, and a  multi-stage  or  cluster sample  is used when there are multiple groups within a large area and a large population, and the researcher does random sampling in stages.

If you are interested in understanding more about these types of probability/random samples, take a look at the following website: Probability Sampling .

However, many times communication researchers use whoever they can find to participate in their study, such as college students in their classes since these people are easily accessible. Many of the studies in interpersonal communication and relationship development, for example, used this type of sample. This is called a convenience sample. In doing so, they are using a non- probability or non-random sample. In these types of samples, each member of the population does not have an equal opportunity to be selected. For example, if you decide to ask your facebook friends to participate in an online survey you created about how college students in the U.S. use cell phones to text, you are using a non-random type of sample. You are unable to randomly sample the whole population in the U.S. of college students who text, so you attempt to find participants more conveniently. Some common non-random or non-probability samples are:

• accidental/convenience samples, such as the facebook example illustrates
• quota samples, in which you do convenience samples within subgroups of the population, such as biological sex, looking for a certain number of participants in each group being compared
• snowball or network sampling, where you ask current participants to send your survey onto their friends.

For more information on non-probability sampling, see the following website: Nonprobability Sampling .

Researchers, such as communication scholars, often use these types of samples because of the nature of their research. Most research designs used in communication are not true experiments, such as would be required in the medical field where they are trying to prove some cause-effect relationship to cure or alleviate symptoms of a disease. Most communication scholars recognize that human behavior in communication situations is much less predictable, so they do not adhere to the strictest possible worldview related to quantitative methods and are less concerned with having to use probability sampling.

They do recognize, however, that with either probability or non-probability sampling, there is still the possibility of bias and error, although much less with probability sampling. That is why all quantitative researchers, regardless of field, will report statistical significance levels if they are interested in generalizing from their sample to the population at large, to let the readers of their work know how confident they are in their results.

Size of Sample

The larger the sample, the more likely the sample is going to be representative of the population. If there is a lot of variability in the population (e.g., lots of different ethnic groups in the population), a researcher will need a larger sample. If you are interested in detecting small possible differences (e.g., in a close political race), you need a larger sample. However, the bigger your population, the less you have to increase the size of your sample in order to have an adequate sample, as is illustrated by an example sample size calculator such as can be found at  http://www.raosoft.com/samplesize.html .

Using the example sample size calculator, see how you might determine how large of a sample you might need in order to study how college students in the U.S. use texting on their cell phones. You would have to first determine approximately how many college students are in the U.S. According to ANEKI, there are a little over 14,000,000 college students in the U.S. ( Countries with the Most University Students ). When inputting that figure into the sample size calculator below (using no commas for the population size), you would need a sample size of approximately 385 students. If the population size was 20,000, you would need a sample of 377 students. If the population was only 2,000, you would need a sample of 323. For a population of 500, you would need a sample of 218.

It is not enough, however, to just have an adequate or large sample. If there is bias in the sampling, you can have a very bad large sample, one that also does not represent the population at large. So, having an unbiased sample is even more important than having a large sample.

So, what do you do, if you cannot reasonably conduct a probability or random sample? You run statistics which report significance levels, and you report the limitations of your sample in the discussion section of your paper/article.

Pilot Testing Methods

Now that we have talked about the different elements of your study design, you should try out your methods by doing a pilot test of some kind. This means that you try out your procedures with someone to try to catch any mistakes in your design before you start collecting data from actual participants in your study. This will save you time and money in the long run, along with unneeded angst over mistakes you made in your design during data collection. There are several ways you might do this.

You might ask an expert who knows about this topic (such as a faculty member) to try out your experiment or survey and provide feedback on what they think of your design. You might ask some participants who are like your potential sample to take your survey or be a part of your pilot test; then you could ask them which parts were confusing or needed revising. You might have potential participants explain to you what they think your questions mean, to see if they are interpreting them like you intended, or if you need to make some questions clearer.

The main thing is that you do not just assume your methods will work or are the best type of methods to use until you try them out with someone. As you write up your study, in your methods section of your paper, you can then talk about what you did to change your study based on the pilot study you did.

Institutional Review Board (IRB) Approval

The last step of your planning takes place when you take the necessary steps to get your study approved by your institution's review board. As you read in chapter 3, this step is important if you are planning on using the data or results from your study beyond just the requirements for your class project. See chapter 3 for more information on the procedures involved in this step.

Conclusion: Study Design Planning

Once you have decided what topic you want to study, you plan your study. Part 1 of this chapter has covered the following steps you need to follow in this planning process:

• decide what type of study you will do (i.e., experimental, quasi-experimental, non- experimental);
• decide on what data collection method you will use (i.e., survey, observation, or already existing data);
• operationalize your variables into measureable concepts;
• determine what type of sample you will use (probability or non-probability);
• pilot test your methods; and
• get IRB approval.

At that point, you are ready to commence collecting your data, which is the topic of the next section in this chapter.

Research Methodology Example

Detailed Walkthrough + Free Methodology Chapter Template

If you’re working on a dissertation or thesis and are looking for an example of a research methodology chapter , you’ve come to the right place.

In this video, we walk you through a research methodology from a dissertation that earned full distinction , step by step. We start off by discussing the core components of a research methodology by unpacking our free methodology chapter template . We then progress to the sample research methodology to show how these concepts are applied in an actual dissertation, thesis or research project.

If you’re currently working on your research methodology chapter, you may also find the following resources useful:

• Research methodology 101 : an introductory video discussing what a methodology is and the role it plays within a dissertation
• Research design 101 : an overview of the most common research designs for both qualitative and quantitative studies
• Variables 101 : an introductory video covering the different types of variables that exist within research.
• Sampling 101 : an overview of the main sampling methods
• Methodology tips : a video discussion covering various tips to help you write a high-quality methodology chapter
• Private coaching : Get hands-on help with your research methodology

PS – If you’re working on a dissertation, be sure to also check out our collection of dissertation and thesis examples here .

FAQ: Research Methodology Example

Research methodology example: frequently asked questions, is the sample research methodology real.

Yes. The chapter example is an extract from a Master’s-level dissertation for an MBA program. A few minor edits have been made to protect the privacy of the sponsoring organisation, but these have no material impact on the research methodology.

Can I replicate this methodology for my dissertation?

As we discuss in the video, every research methodology will be different, depending on the research aims, objectives and research questions. Therefore, you’ll need to tailor your literature review to suit your specific context.

You can learn more about the basics of writing a research methodology chapter here .

Where can I find more examples of research methodologies?

The best place to find more examples of methodology chapters would be within dissertation/thesis databases. These databases include dissertations, theses and research projects that have successfully passed the assessment criteria for the respective university, meaning that you have at least some sort of quality assurance.

The Open Access Thesis Database (OATD) is a good starting point.

How do I get the research methodology chapter template?

You can access our free methodology chapter template here .

Is the methodology template really free?

Yes. There is no cost for the template and you are free to use it as you wish.

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Home Market Research

Quantitative Research: What It Is, Practices & Methods

Quantitative research involves analyzing and gathering numerical data to uncover trends, calculate averages, evaluate relationships, and derive overarching insights. It’s used in various fields, including the natural and social sciences. Quantitative data analysis employs statistical techniques for processing and interpreting numeric data.

Research designs in the quantitative realm outline how data will be collected and analyzed with methods like experiments and surveys. Qualitative methods complement quantitative research by focusing on non-numerical data, adding depth to understanding. Data collection methods can be qualitative or quantitative, depending on research goals. Researchers often use a combination of both approaches to gain a comprehensive understanding of phenomena.

What is Quantitative Research?

Quantitative research is a systematic investigation of phenomena by gathering quantifiable data and performing statistical, mathematical, or computational techniques. Quantitative research collects statistically significant information from existing and potential customers using sampling methods and sending out online surveys , online polls , and questionnaires , for example.

One of the main characteristics of this type of research is that the results can be depicted in numerical form. After carefully collecting structured observations and understanding these numbers, it’s possible to predict the future of a product or service, establish causal relationships or Causal Research , and make changes accordingly. Quantitative research primarily centers on the analysis of numerical data and utilizes inferential statistics to derive conclusions that can be extrapolated to the broader population.

An example of a quantitative research study is the survey conducted to understand how long a doctor takes to tend to a patient when the patient walks into the hospital. A patient satisfaction survey can be administered to ask questions like how long a doctor takes to see a patient, how often a patient walks into a hospital, and other such questions, which are dependent variables in the research. This kind of research method is often employed in the social sciences, and it involves using mathematical frameworks and theories to effectively present data, ensuring that the results are logical, statistically sound, and unbiased.

Data collection in quantitative research uses a structured method and is typically conducted on larger samples representing the entire population. Researchers use quantitative methods to collect numerical data, which is then subjected to statistical analysis to determine statistically significant findings. This approach is valuable in both experimental research and social research, as it helps in making informed decisions and drawing reliable conclusions based on quantitative data.

Quantitative Research Characteristics

Quantitative research has several unique characteristics that make it well-suited for specific projects. Let’s explore the most crucial of these characteristics so that you can consider them when planning your next research project:

• Structured tools: Quantitative research relies on structured tools such as surveys, polls, or questionnaires to gather quantitative data . Using such structured methods helps collect in-depth and actionable numerical data from the survey respondents, making it easier to perform data analysis.
• Sample size: Quantitative research is conducted on a significant sample size  representing the target market . Appropriate Survey Sampling methods, a fundamental aspect of quantitative research methods, must be employed when deriving the sample to fortify the research objective and ensure the reliability of the results.
• Close-ended questions: Closed-ended questions , specifically designed to align with the research objectives, are a cornerstone of quantitative research. These questions facilitate the collection of quantitative data and are extensively used in data collection processes.
• Prior studies: Before collecting feedback from respondents, researchers often delve into previous studies related to the research topic. This preliminary research helps frame the study effectively and ensures the data collection process is well-informed.
• Quantitative data: Typically, quantitative data is represented using tables, charts, graphs, or other numerical forms. This visual representation aids in understanding the collected data and is essential for rigorous data analysis, a key component of quantitative research methods.
• Generalization of results: One of the strengths of quantitative research is its ability to generalize results to the entire population. It means that the findings derived from a sample can be extrapolated to make informed decisions and take appropriate actions for improvement based on numerical data analysis.

Quantitative Research Methods

Quantitative research methods are systematic approaches used to gather and analyze numerical data to understand and draw conclusions about a phenomenon or population. Here are the quantitative research methods:

• Primary quantitative research methods
• Secondary quantitative research methods

Primary Quantitative Research Methods

Primary quantitative research is the most widely used method of conducting market research. The distinct feature of primary research is that the researcher focuses on collecting data directly rather than depending on data collected from previously done research. Primary quantitative research design can be broken down into three further distinctive tracks and the process flow. They are:

A. Techniques and Types of Studies

There are multiple types of primary quantitative research. They can be distinguished into the four following distinctive methods, which are:

01. Survey Research

Survey Research is fundamental for all quantitative outcome research methodologies and studies. Surveys are used to ask questions to a sample of respondents, using various types such as online polls, online surveys, paper questionnaires, web-intercept surveys , etc. Every small and big organization intends to understand what their customers think about their products and services, how well new features are faring in the market, and other such details.

By conducting survey research, an organization can ask multiple survey questions , collect data from a pool of customers, and analyze this collected data to produce numerical results. It is the first step towards collecting data for any research. You can use single ease questions . A single-ease question is a straightforward query that elicits a concise and uncomplicated response.

This type of research can be conducted with a specific target audience group and also can be conducted across multiple groups along with comparative analysis . A prerequisite for this type of research is that the sample of respondents must have randomly selected members. This way, a researcher can easily maintain the accuracy of the obtained results as a huge variety of respondents will be addressed using random selection. 

Traditionally, survey research was conducted face-to-face or via phone calls. Still, with the progress made by online mediums such as email or social media, survey research has also spread to online mediums.There are two types of surveys , either of which can be chosen based on the time in hand and the kind of data required:

Cross-sectional surveys: Cross-sectional surveys are observational surveys conducted in situations where the researcher intends to collect data from a sample of the target population at a given point in time. Researchers can evaluate various variables at a particular time. Data gathered using this type of survey is from people who depict similarity in all variables except the variables which are considered for research . Throughout the survey, this one variable will stay constant.

• Cross-sectional surveys are popular with retail, SMEs, and healthcare industries. Information is garnered without modifying any parameters in the variable ecosystem.
• Multiple samples can be analyzed and compared using a cross-sectional survey research method.
• Multiple variables can be evaluated using this type of survey research.
• The only disadvantage of cross-sectional surveys is that the cause-effect relationship of variables cannot be established as it usually evaluates variables at a particular time and not across a continuous time frame.

Longitudinal surveys: Longitudinal surveys are also observational surveys , but unlike cross-sectional surveys, longitudinal surveys are conducted across various time durations to observe a change in respondent behavior and thought processes. This time can be days, months, years, or even decades. For instance, a researcher planning to analyze the change in buying habits of teenagers over 5 years will conduct longitudinal surveys.

• In cross-sectional surveys, the same variables were evaluated at a given time, and in longitudinal surveys, different variables can be analyzed at different intervals.
• Longitudinal surveys are extensively used in the field of medicine and applied sciences. Apart from these two fields, they are also used to observe a change in the market trend analysis , analyze customer satisfaction, or gain feedback on products/services.
• In situations where the sequence of events is highly essential, longitudinal surveys are used.
• Researchers say that when research subjects need to be thoroughly inspected before concluding, they rely on longitudinal surveys.

02. Correlational Research

A comparison between two entities is invariable. Correlation research is conducted to establish a relationship between two closely-knit entities and how one impacts the other, and what changes are eventually observed. This research method is carried out to give value to naturally occurring relationships, and a minimum of two different groups are required to conduct this quantitative research method successfully. Without assuming various aspects, a relationship between two groups or entities must be established.

Researchers use this quantitative research design to correlate two or more variables using mathematical analysis methods. Patterns, relationships, and trends between variables are concluded as they exist in their original setup. The impact of one of these variables on the other is observed, along with how it changes the relationship between the two variables. Researchers tend to manipulate one of the variables to attain the desired results.

Ideally, it is advised not to make conclusions merely based on correlational research. This is because it is not mandatory that if two variables are in sync that they are interrelated.

Example of Correlational Research Questions :

• The relationship between stress and depression.
• The equation between fame and money.
• The relation between activities in a third-grade class and its students.

03. Causal-comparative Research

This research method mainly depends on the factor of comparison. Also called quasi-experimental research , this quantitative research method is used by researchers to conclude the cause-effect equation between two or more variables, where one variable is dependent on the other independent variable. The independent variable is established but not manipulated, and its impact on the dependent variable is observed. These variables or groups must be formed as they exist in the natural setup. As the dependent and independent variables will always exist in a group, it is advised that the conclusions are carefully established by keeping all the factors in mind.

Causal-comparative research is not restricted to the statistical analysis of two variables but extends to analyzing how various variables or groups change under the influence of the same changes. This research is conducted irrespective of the type of relationship that exists between two or more variables. Statistical analysis plan is used to present the outcome using this quantitative research method.

Example of Causal-Comparative Research Questions:

• The impact of drugs on a teenager. The effect of good education on a freshman. The effect of substantial food provision in the villages of Africa.

04. Experimental Research

Also known as true experimentation, this research method relies on a theory. As the name suggests, experimental research is usually based on one or more theories. This theory has yet to be proven before and is merely a supposition. In experimental research, an analysis is done around proving or disproving the statement. This research method is used in natural sciences. Traditional research methods are more effective than modern techniques.

There can be multiple theories in experimental research. A theory is a statement that can be verified or refuted.

After establishing the statement, efforts are made to understand whether it is valid or invalid. This quantitative research method is mainly used in natural or social sciences as various statements must be proved right or wrong.

• Traditional research methods are more effective than modern techniques.
• Systematic teaching schedules help children who struggle to cope with the course.
• It is a boon to have responsible nursing staff for ailing parents.

B. Data Collection Methodologies

The second major step in primary quantitative research is data collection. Data collection can be divided into sampling methods and data collection using surveys and polls.

01. Data Collection Methodologies: Sampling Methods

There are two main sampling methods for quantitative research: Probability and Non-probability sampling .

Probability sampling: A theory of probability is used to filter individuals from a population and create samples in probability sampling . Participants of a sample are chosen by random selection processes. Each target audience member has an equal opportunity to be selected in the sample.

There are four main types of probability sampling:

• Simple random sampling: As the name indicates, simple random sampling is nothing but a random selection of elements for a sample. This sampling technique is implemented where the target population is considerably large.
• Stratified random sampling: In the stratified random sampling method , a large population is divided into groups (strata), and members of a sample are chosen randomly from these strata. The various segregated strata should ideally not overlap one another.
• Cluster sampling: Cluster sampling is a probability sampling method using which the main segment is divided into clusters, usually using geographic segmentation and demographic segmentation parameters.
• Systematic sampling: Systematic sampling is a technique where the starting point of the sample is chosen randomly, and all the other elements are chosen using a fixed interval. This interval is calculated by dividing the population size by the target sample size.

Non-probability sampling: Non-probability sampling is where the researcher’s knowledge and experience are used to create samples. Because of the researcher’s involvement, not all the target population members have an equal probability of being selected to be a part of a sample.

There are five non-probability sampling models:

• Convenience sampling: In convenience sampling , elements of a sample are chosen only due to one prime reason: their proximity to the researcher. These samples are quick and easy to implement as there is no other parameter of selection involved.
• Consecutive sampling: Consecutive sampling is quite similar to convenience sampling, except for the fact that researchers can choose a single element or a group of samples and conduct research consecutively over a significant period and then perform the same process with other samples.
• Quota sampling: Using quota sampling , researchers can select elements using their knowledge of target traits and personalities to form strata. Members of various strata can then be chosen to be a part of the sample as per the researcher’s understanding.
• Snowball sampling: Snowball sampling is conducted with target audiences who are difficult to contact and get information. It is popular in cases where the target audience for analysis research is rare to put together.
• Judgmental sampling: Judgmental sampling is a non-probability sampling method where samples are created only based on the researcher’s experience and research skill .

02. Data collection methodologies: Using surveys & polls

Once the sample is determined, then either surveys or polls can be distributed to collect the data for quantitative research.

Using surveys for primary quantitative research

A survey is defined as a research method used for collecting data from a pre-defined group of respondents to gain information and insights on various topics of interest. The ease of survey distribution and the wide number of people it can reach depending on the research time and objective makes it one of the most important aspects of conducting quantitative research.

Fundamental levels of measurement – nominal, ordinal, interval, and ratio scales

Four measurement scales are fundamental to creating a multiple-choice question in a survey. They are nominal, ordinal, interval, and ratio measurement scales without the fundamentals of which no multiple-choice questions can be created. Hence, it is crucial to understand these measurement levels to develop a robust survey.

Use of different question types

To conduct quantitative research, close-ended questions must be used in a survey. They can be a mix of multiple question types, including multiple-choice questions like semantic differential scale questions , rating scale questions , etc.

Survey Distribution and Survey Data Collection

In the above, we have seen the process of building a survey along with the research design to conduct primary quantitative research. Survey distribution to collect data is the other important aspect of the survey process. There are different ways of survey distribution. Some of the most commonly used methods are:

• Email: Sending a survey via email is the most widely used and effective survey distribution method. This method’s response rate is high because the respondents know your brand. You can use the QuestionPro email management feature to send out and collect survey responses.
• Buy respondents: Another effective way to distribute a survey and conduct primary quantitative research is to use a sample. Since the respondents are knowledgeable and are on the panel by their own will, responses are much higher.
• Embed survey on a website: Embedding a survey on a website increases a high number of responses as the respondent is already in close proximity to the brand when the survey pops up.
• Social distribution: Using social media to distribute the survey aids in collecting a higher number of responses from the people that are aware of the brand.
• QR code: QuestionPro QR codes store the URL for the survey. You can print/publish this code in magazines, signs, business cards, or on just about any object/medium.
• SMS survey: The SMS survey is a quick and time-effective way to collect a high number of responses.
• Offline Survey App: The QuestionPro App allows users to circulate surveys quickly, and the responses can be collected both online and offline.

Survey example

An example of a survey is a short customer satisfaction (CSAT) survey that can quickly be built and deployed to collect feedback about what the customer thinks about a brand and how satisfied and referenceable the brand is.

Using polls for primary quantitative research

Polls are a method to collect feedback using close-ended questions from a sample. The most commonly used types of polls are election polls and exit polls . Both of these are used to collect data from a large sample size but using basic question types like multiple-choice questions.

C. Data Analysis Techniques

The third aspect of primary quantitative research design is data analysis . After collecting raw data, there must be an analysis of this data to derive statistical inferences from this research. It is important to relate the results to the research objective and establish the statistical relevance of the results.

Remember to consider aspects of research that were not considered for the data collection process and report the difference between what was planned vs. what was actually executed.

It is then required to select precise Statistical Analysis Methods , such as SWOT, Conjoint, Cross-tabulation, etc., to analyze the quantitative data.

• SWOT analysis: SWOT Analysis stands for the acronym of Strengths, Weaknesses, Opportunities, and Threat analysis. Organizations use this statistical analysis technique to evaluate their performance internally and externally to develop effective strategies for improvement.
• Conjoint Analysis: Conjoint Analysis is a market analysis method to learn how individuals make complicated purchasing decisions. Trade-offs are involved in an individual’s daily activities, and these reflect their ability to decide from a complex list of product/service options.
• Cross-tabulation: Cross-tabulation is one of the preliminary statistical market analysis methods which establishes relationships, patterns, and trends within the various parameters of the research study.
• TURF Analysis: TURF Analysis , an acronym for Totally Unduplicated Reach and Frequency Analysis, is executed in situations where the reach of a favorable communication source is to be analyzed along with the frequency of this communication. It is used for understanding the potential of a target market.

Inferential statistics methods such as confidence interval, the margin of error, etc., can then be used to provide results.

Secondary Quantitative Research Methods

Secondary quantitative research or desk research is a research method that involves using already existing data or secondary data. Existing data is summarized and collated to increase the overall effectiveness of the research.

This research method involves collecting quantitative data from existing data sources like the internet, government resources, libraries, research reports, etc. Secondary quantitative research helps to validate the data collected from primary quantitative research and aid in strengthening or proving, or disproving previously collected data.

The following are five popularly used secondary quantitative research methods:

• Data available on the internet: With the high penetration of the internet and mobile devices, it has become increasingly easy to conduct quantitative research using the internet. Information about most research topics is available online, and this aids in boosting the validity of primary quantitative data.
• Government and non-government sources: Secondary quantitative research can also be conducted with the help of government and non-government sources that deal with market research reports. This data is highly reliable and in-depth and hence, can be used to increase the validity of quantitative research design.
• Public libraries: Now a sparingly used method of conducting quantitative research, it is still a reliable source of information, though. Public libraries have copies of important research that was conducted earlier. They are a storehouse of valuable information and documents from which information can be extracted.
• Educational institutions: Educational institutions conduct in-depth research on multiple topics, and hence, the reports that they publish are an important source of validation in quantitative research.
• Commercial information sources: Local newspapers, journals, magazines, radio, and TV stations are great sources to obtain data for secondary quantitative research. These commercial information sources have in-depth, first-hand information on market research, demographic segmentation, and similar subjects.

Quantitative Research Examples

Some examples of quantitative research are:

• A customer satisfaction template can be used if any organization would like to conduct a customer satisfaction (CSAT) survey . Through this kind of survey, an organization can collect quantitative data and metrics on the goodwill of the brand or organization in the customer’s mind based on multiple parameters such as product quality, pricing, customer experience, etc. This data can be collected by asking a net promoter score (NPS) question , matrix table questions, etc. that provide data in the form of numbers that can be analyzed and worked upon.
• Another example of quantitative research is an organization that conducts an event, collecting feedback from attendees about the value they see from the event. By using an event survey , the organization can collect actionable feedback about the satisfaction levels of customers during various phases of the event such as the sales, pre and post-event, the likelihood of recommending the organization to their friends and colleagues, hotel preferences for the future events and other such questions.

What are the Advantages of Quantitative Research?

There are many advantages to quantitative research. Some of the major advantages of why researchers use this method in market research are:

Collect Reliable and Accurate Data:

Quantitative research is a powerful method for collecting reliable and accurate quantitative data. Since data is collected, analyzed, and presented in numbers, the results obtained are incredibly reliable and objective. Numbers do not lie and offer an honest and precise picture of the conducted research without discrepancies. In situations where a researcher aims to eliminate bias and predict potential conflicts, quantitative research is the method of choice.

Quick Data Collection:

Quantitative research involves studying a group of people representing a larger population. Researchers use a survey or another quantitative research method to efficiently gather information from these participants, making the process of analyzing the data and identifying patterns faster and more manageable through the use of statistical analysis. This advantage makes quantitative research an attractive option for projects with time constraints.

Wider Scope of Data Analysis:

Quantitative research, thanks to its utilization of statistical methods, offers an extensive range of data collection and analysis. Researchers can delve into a broader spectrum of variables and relationships within the data, enabling a more thorough comprehension of the subject under investigation. This expanded scope is precious when dealing with complex research questions that require in-depth numerical analysis.

Eliminate Bias:

One of the significant advantages of quantitative research is its ability to eliminate bias. This research method leaves no room for personal comments or the biasing of results, as the findings are presented in numerical form. This objectivity makes the results fair and reliable in most cases, reducing the potential for researcher bias or subjectivity.

In summary, quantitative research involves collecting, analyzing, and presenting quantitative data using statistical analysis. It offers numerous advantages, including the collection of reliable and accurate data, quick data collection, a broader scope of data analysis, and the elimination of bias, making it a valuable approach in the field of research. When considering the benefits of quantitative research, it’s essential to recognize its strengths in contrast to qualitative methods and its role in collecting and analyzing numerical data for a more comprehensive understanding of research topics.

Best Practices to Conduct Quantitative Research

Here are some best practices for conducting quantitative research:

• Differentiate between quantitative and qualitative: Understand the difference between the two methodologies and apply the one that suits your needs best.
• Choose a suitable sample size: Ensure that you have a sample representative of your population and large enough to be statistically weighty.
• Keep your research goals clear and concise: Know your research goals before you begin data collection to ensure you collect the right amount and the right quantity of data.
• Keep the questions simple: Remember that you will be reaching out to a demographically wide audience. Pose simple questions for your respondents to understand easily.

Quantitative Research vs Qualitative Research

Quantitative research and qualitative research are two distinct approaches to conducting research, each with its own set of methods and objectives. Here’s a comparison of the two:

Quantitative Research

• Objective: The primary goal of quantitative research is to quantify and measure phenomena by collecting numerical data. It aims to test hypotheses, establish patterns, and generalize findings to a larger population.
• Data Collection: Quantitative research employs systematic and standardized approaches for data collection, including techniques like surveys, experiments, and observations that involve predefined variables. It is often collected from a large and representative sample.
• Data Analysis: Data is analyzed using statistical techniques, such as descriptive statistics, inferential statistics, and mathematical modeling. Researchers use statistical tests to draw conclusions and make generalizations based on numerical data.
• Sample Size: Quantitative research often involves larger sample sizes to ensure statistical significance and generalizability.
• Results: The results are typically presented in tables, charts, and statistical summaries, making them highly structured and objective.
• Generalizability: Researchers intentionally structure quantitative research to generate outcomes that can be helpful to a larger population, and they frequently seek to establish causative connections.
• Emphasis on Objectivity: Researchers aim to minimize bias and subjectivity, focusing on replicable and objective findings.

Qualitative Research

• Objective: Qualitative research seeks to gain a deeper understanding of the underlying motivations, behaviors, and experiences of individuals or groups. It explores the context and meaning of phenomena.
• Data Collection: Qualitative research employs adaptable and open-ended techniques for data collection, including methods like interviews, focus groups, observations, and content analysis. It allows participants to express their perspectives in their own words.
• Data Analysis: Data is analyzed through thematic analysis, content analysis, or grounded theory. Researchers focus on identifying patterns, themes, and insights in the data.
• Sample Size: Qualitative research typically involves smaller sample sizes due to the in-depth nature of data collection and analysis.
• Results: Findings are presented in narrative form, often in the participants’ own words. Results are subjective, context-dependent, and provide rich, detailed descriptions.
• Generalizability: Qualitative research does not aim for broad generalizability but focuses on in-depth exploration within a specific context. It provides a detailed understanding of a particular group or situation.
• Emphasis on Subjectivity: Researchers acknowledge the role of subjectivity and the researcher’s influence on the Research Process . Participant perspectives and experiences are central to the findings.

Researchers choose between quantitative and qualitative research methods based on their research objectives and the nature of the research question. Each approach has its advantages and drawbacks, and the decision between them hinges on the particular research objectives and the data needed to address research inquiries effectively.

Quantitative research is a structured way of collecting and analyzing data from various sources. Its purpose is to quantify the problem and understand its extent, seeking results that someone can project to a larger population.

Companies that use quantitative rather than qualitative research typically aim to measure magnitudes and seek objectively interpreted statistical results. So if you want to obtain quantitative data that helps you define the structured cause-and-effect relationship between the research problem and the factors, you should opt for this type of research.

At QuestionPro , we have various Best Data Collection Tools and features to conduct investigations of this type. You can create questionnaires and distribute them through our various methods. We also have sample services or various questions to guarantee the success of your study and the quality of the collected data.

Quantitative research is a systematic and structured approach to studying phenomena that involves the collection of measurable data and the application of statistical, mathematical, or computational techniques for analysis.

Quantitative research is characterized by structured tools like surveys, substantial sample sizes, closed-ended questions, reliance on prior studies, data presented numerically, and the ability to generalize findings to the broader population.

The two main methods of quantitative research are Primary quantitative research methods, involving data collection directly from sources, and Secondary quantitative research methods, which utilize existing data for analysis.

1.Surveying to measure employee engagement with numerical rating scales. 2.Analyzing sales data to identify trends in product demand and market share. 4.Examining test scores to assess the impact of a new teaching method on student performance. 4.Using website analytics to track user behavior and conversion rates for an online store.

1.Differentiate between quantitative and qualitative approaches. 2.Choose a representative sample size. 3.Define clear research goals before data collection. 4.Use simple and easily understandable survey questions.

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

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Quantitative Research Methods

What is quantitative research, about this guide, introduction, quantitative research methodologies.

• Key Resources
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 The purpose of this guide is to provide a starting point for learning about quantitative research. In this guide, you'll find:

• Resources on diverse types of quantitative research.
• An overview of resources for data, methods & analysis
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• Information on how to find quantitative studies

Research involving the collection of data in numerical form for quantitative analysis. The numerical data can be durations, scores, counts of incidents, ratings, or scales. Quantitative data can be collected in either controlled or naturalistic environments, in laboratories or field studies, from special populations or from samples of the general population. The defining factor is that numbers result from the process, whether the initial data collection produced numerical values, or whether non-numerical values were subsequently converted to numbers as part of the analysis process, as in content analysis.

Citation: Garwood, J. (2006). Quantitative research. In V. Jupp (Ed.), The SAGE dictionary of social research methods. (pp. 251-252). London, England: SAGE Publications. doi:10.4135/9780857020116

Watch the following video to learn more about Quantitative Research:

(Video best viewed in Edge and Chrome browsers, or click here to view in the Sage Research Methods Database)

Correlational

Researchers will compare two sets of numbers to try and identify a relationship (if any) between two things.

Descriptive

Researchers will attempt to quantify a variety of factors at play as they study a particular type of phenomenon or action. For example, researchers might use a descriptive methodology to understand the effects of climate change on the life cycle of a plant or animal.

Experimental

To understand the effects of a variable, researchers will design an experiment where they can control as many factors as possible. This can involve creating control and experimental groups. The experimental group will be exposed to the variable to study its effects. The control group provides data about what happens when the variable is absent. For example, in a study about online teaching, the control group might receive traditional face-to-face instruction while the experimental group would receive their instruction virtually.

Quasi-Experimental/Quasi-Comparative

Researchers will attempt to determine what (if any) effect a variable can have. These studies may have multiple independent variables (causes) and multiple dependent variables (effects), but this can complicate researchers' efforts to find out if A can cause B or if X, Y, and Z are also playing a role.

Surveys can be considered a quantitative methodology if the researchers require their respondents to choose from pre-determined responses.

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What Is Quantitative Research? Types, Characteristics & Methods

Market Research Specialist

Emma David, a seasoned market research professional, specializes in employee engagement, survey administration, and data management. Her expertise in leveraging data for informed decisions has positively impacted several brands, enhancing their market position.

Step into the fascinating world of quantitative research , where numbers reveal extraordinary insights!

By gathering and studying data in a systematic way, quantitative research empowers us to understand our ever-changing world better. It helps understand a problem or an already-formed hypothesis by generating numerical data. The results don’t end here, as you can process these numbers to get actionable insights that aid decision-making.

You can use quantitative research to quantify opinions, behaviors, attitudes, and other definitive variables related to the market, customers, competitors, etc. The research is conducted on a larger sample population to draw predictive, average, and pattern-based insights.

Here, we delve into the intricacies of this research methodology, exploring various quantitative methods, their advantages, and real-life examples that showcase their impact and relevance.

Ready to embark on a journey of discovery and knowledge? Let’s go!

What Is Quantitative Research?

Quantitative research is a method that uses numbers and statistics to test theories about customer attitudes and behaviors. It helps researchers gather and analyze data systematically to gain valuable insights and draw evidence-based conclusions about customer preferences and trends.

Researchers use online surveys, questionnaires , polls , and quizzes to question a large number of people to obtain measurable and bias-free data.

In technical terms, quantitative research is mainly concerned with discovering facts about social phenomena while assuming a fixed and measurable reality.

Offering numbers and stats-based insights, this research methodology is a crucial part of primary research and helps understand how well an organizational decision is going to work out.

Let’s consider an example.

Suppose your qualitative analysis shows that your customers are looking for social media-based customer support. In that case, quantitative analysis will help you see how many of your customers are looking for this support.

If 10% of your customers are looking for such a service, you might or might not consider offering this feature. But, if 40% of your regular customers are seeking support via social media, then it is something you just cannot overlook.

Characteristics of Quantitative Research

Quantitative research clarifies the fuzziness of research data from qualitative research analysis. With numerical insights, you can formulate a better and more profitable business decision.

Hence, quantitative research is more readily contestable, sharpens intelligent discussion, helps you see the rival hypotheses, and dynamically contributes to the research process.

Let us have a quick look at some of its characteristics.

1. Measurable Variables

The data collection methods in quantitative research are structured and contain items requiring measurable variables, such as age, number of family members, salary range, highest education, etc.

These structured data collection methods comprise polls, surveys, questionnaires, etc., and may have questions like the ones shown in the following image:

As you can see, all the variables are measurable. This ensures that the research is in-depth and provides less erroneous data for reliable, actionable insights.

2. Sample Size

No matter what data analysis methods for quantitative research are being used, the sample size is kept such that it represents the target market.

As the main aim of the research methodology is to get numerical insights, the sample size should be fairly large. Depending on the survey objective and scope, it might span hundreds of thousands of people.

3. Normal Population Distribution

To maintain the reliability of a quantitative research methodology, we assume that the population distribution curve is normal.

This type of population distribution curve is preferred over a non-normal distribution as the sample size is large, and the characteristics of the sample vary with its size.

This requires adhering to the random sampling principle to avoid the researcher’s bias in result interpretation. Any bias can ruin the fairness of the entire process and defeats the purpose of research.

4. Well-Structured Data Representation

Data analysis in quantitative research produces highly structured results and can form well-defined graphical representations. Some common examples include tables, figures, graphs, etc., that combine large blocks of data.

This way, you can discover hidden data trends, relationships, and differences among various measurable variables. This can help researchers understand the survey data and formulate actionable insights for decision-making.

5. Predictive Outcomes

Quantitative analysis of data can also be used for estimations and prediction outcomes. You can construct if-then scenarios and analyze the data for the identification of any upcoming trends or events.

However, this requires advanced analytics and involves complex mathematical computations. So, it is mostly done via quantitative research tools that come with advanced analytics capabilities.

Types of Quantitative Research Methods

Quantitative research is usually conducted using two methods. They are-

• Primary quantitative research methods
• Secondary quantitative research methods

1. Primary quantitative research methods

Primary quantitative research is the most popular way of conducting market research. The differentiating factor of this method is that the researcher relies on collecting data firsthand instead of relying on data collected from previous research.

There are multiple types of primary quantitative research. They can be distinguished based on three distinctive aspects, which are:

1.1. Techniques & Types of Studies:

• Survey Research

Surveys are the easiest, most common, and one of the most sought-after quantitative research techniques. The main aim of a survey is to widely gather and describe the characteristics of a target population or customers. Surveys are the foremost quantitative method preferred by both small and large organizations.

They help them understand their customers, products, and other brand offerings in a proper manner.

Surveys can be conducted using various methods, such as online polls, web-based surveys, paper questionnaires, phone calls, or face-to-face interviews. Survey research allows organizations to understand customer opinions, preferences, and behavior, making it crucial for market research and decision-making.

You can watch this quick video to learn more about creating surveys.

Watch: How to Create a Survey Using ProProfs Survey Maker

Surveys are of two types:

• Cross-Sectional Surveys Cross-sectional surveys are used to collect data from a sample of the target population at a specific point in time. Researchers evaluate various variables simultaneously to understand the relationships and patterns within the data.
• Cross-sectional surveys are popular in retail, small and medium-sized enterprises (SMEs), and healthcare industries, where they assess customer satisfaction, market trends, and product feedback.
• Longitudinal Surveys Longitudinal surveys are conducted over an extended period, observing changes in respondent behavior and thought processes.
• Researchers gather data from the same sample multiple times, enabling them to study trends and developments over time. These surveys are valuable in fields such as medicine, applied sciences, and market trend analysis.

Surveys can be distributed via various channels. Some of the most popular ones are listed below:

• Email: Sending surveys via email is a popular and effective method. People recognize your brand, leading to a higher response rate. With ProProfs Survey Maker’s in-mail survey-filling feature, you can easily send out and collect survey responses.
• Embed on a website: Boost your response rate by embedding the survey on your website. When visitors are already engaged with your brand, they are more likely to take the survey.
• Social media: Take advantage of social media platforms to distribute your survey. People familiar with your brand are likely to respond, increasing your response numbers.
• QR codes: QR codes store your survey’s URL, and you can print or publish these codes in magazines, signs, business cards, or any object to make it easy for people to access your survey.
• SMS survey: Collect a high number of responses quickly with SMS surveys. It’s a time-effective way to reach your target audience.

1.2. Correlational Research:

Correlational research aims to establish relationships between two or more variables.

Researchers use statistical analysis to identify patterns and trends in the data, but it does not determine causality between the variables. This method helps understand how changes in one variable may impact another.

Examples of correlational research questions include studying the relationship between stress and depression, fame and money, or classroom activities and student performance.

1.3. Causal-Comparative Research:

Causal-comparative research, also known as quasi-experimental research, seeks to determine cause-and-effect relationships between variables.

Researchers analyze how an independent variable influences a dependent variable, but they do not manipulate the independent variable. Instead, they observe and compare different groups to draw conclusions.

Causal-comparative research is useful in situations where it’s not ethical or feasible to conduct true experiments.

Examples of questions for this type of research include analyzing the effect of training programs on employee performance, studying the influence of customer support on client retention, investigating the impact of supply chain efficiency on cost reduction, etc.

1.4. Experimental Research:

Experimental research is based on testing theories to validate or disprove them. Researchers conduct experiments and manipulate variables to observe their impact on the outcomes.

This type of research is prevalent in natural and social sciences, and it is a powerful method to establish cause-and-effect relationships. By randomly assigning participants to experimental and control groups, researchers can draw more confident conclusions.

Examples of experimental research include studying the effectiveness of a new drug, the impact of teaching methods on student performance, or the outcomes of a marketing campaign.

2. Data collection methodologies

After defining research objectives, the next significant step in primary quantitative research is data collection. This involves using two main methods: sampling and conducting surveys or polls.

2.1Sampling methods:

In quantitative research, there are two primary sampling methods: Probability and Non-probability sampling.

2.2Probability Sampling

In probability sampling, researchers use the concept of probability to create samples from a population. This method ensures that every individual in the target audience has an equal chance of being selected for the sample.

There are four main types of probability sampling:

• Simple random sampling: Here, the elements or participants of a sample are selected randomly, and this technique is used in studies that are conducted over considerably large audiences. It works well for large target populations.
• Stratified random sampling: In this method, the entire population is divided into strata or groups, and the sample members get chosen randomly from these strata only. It is always ensured that different segregated strata do not overlap with each other.
• Cluster sampling: Here, researchers divide the population into clusters, often based on geography or demographics. Then, random clusters are selected for the sample.
• Systematic sampling: In this method, only the starting point of the sample is randomly chosen. All the other participants are chosen using a fixed interval. Researchers calculate this interval by dividing the size of the study population by the target sample size.

2.3Non-probability Sampling

Non-probability sampling is a method where the researcher’s knowledge and experience guide the selection of samples. This approach doesn’t give all members of the target population an equal chance of being included in the sample.

There are five non-probability sampling models:

• Convenience sampling: The elements or participants are chosen on the basis of their nearness to the researcher. The people in close proximity can be studied and analyzed easily and quickly, as there is no other selection criterion involved. Researchers simply choose samples based on what is most convenient for them.
• Consecutive sampling: Similar to convenience sampling, researchers select samples one after another over a significant period. They can opt for a single participant or a group of samples to conduct quantitative research in a consecutive manner for a significant period of time. Once this is over, they can conduct the research from the start.
• Quota sampling: With quota sampling, researchers use their understanding of target traits and personalities to form groups (strata). They then choose samples from each stratum based on their own judgment.
• Snowball sampling: This method is used where the target audiences are difficult to contact and interviewed for data collection. Researchers start with a few participants and then ask them to refer others, creating a snowball effect.
• Judgmental sampling: In judgmental sampling, researchers rely solely on their experience and research skills to handpick samples that they believe will be most relevant to the study.

3. Data analysis techniques

To analyze the quantitative data accurately, you’ll need to use specific statistical methods such as:

• SWOT Analysis: This stands for Strengths, Weaknesses, Opportunities, and Threats analysis. Organizations use SWOT analysis to evaluate their performance internally and externally. It helps develop effective improvement strategies.
• Conjoint Analysis: This market research method uncovers how individuals make complex purchasing decisions. It involves considering trade-offs in their daily activities when choosing from a list of product/service options.
• Cross-tabulation: A preliminary statistical market analysis method that reveals relationships, patterns, and trends within various research study parameters.
• TURF Analysis: Short for Totally Unduplicated Reach and Frequency Analysis, this method helps analyze the reach and frequency of favorable communication sources. It provides insights into the potential of a target market.
• By using these statistical techniques and inferential statistics methods like confidence intervals and margin of error, you can draw meaningful insights from your primary quantitative research that you can use in making informed decisions.

2. Secondary Quantitative Research Methods

• Secondary quantitative research, also known as desk research, is a valuable method that uses existing data, called secondary data.
• Instead of collecting new data, researchers analyze and combine already available information to enhance their research. This approach involves gathering quantitative data from various sources such as the internet, government databases, libraries, and research reports.
• Secondary quantitative research plays a crucial role in validating data collected through primary quantitative research. It helps reinforce or challenge existing findings.

Here are five commonly used secondary quantitative research methods:

A. Data Available on the Internet:

The Internet has become a vast repository of data, making it easier for researchers to access a wealth of information. Online databases, websites, and research repositories provide valuable quantitative data for researchers to analyze and validate their primary research findings.

B. Government and Non-Government Sources:

Government agencies and non-government organizations often conduct extensive research and publish reports. These reports cover a wide range of topics, providing researchers with reliable and comprehensive data for quantitative analysis.

C. Public Libraries:

While less commonly used in the digital age, public libraries still hold valuable research reports, historical data, and publications that can contribute to quantitative research.

D. Educational Institutions:

Educational institutions frequently conduct research on various subjects. Their research reports and publications can serve as valuable sources of information for researchers, validating and supporting primary quantitative research outcomes.

E. Commercial Information Sources:

Commercial sources such as local newspapers, journals, magazines, and media outlets often publish relevant data on economic trends, market research, and demographic analyses. Researchers can access this data to supplement their own findings and draw better conclusions.

Advantages of Quantitative Research Methods

Quantitative research data is often standardized and can be easily used to generalize findings for making crucial business decisions and uncover insights to supplement the qualitative research findings.

Here are some core benefits this research methodology offers.

Direct Result Comparison

As the studies can be replicated for different cultural settings and different times, even with different groups of participants, they tend to be extremely useful. Researchers can compare the results of different studies in a statistical manner and arrive at comprehensive conclusions for a broader understanding.

Replication

Researchers can repeat the study by using standardized data collection protocols over well-structured data sets. They can also apply tangible definitions of abstract concepts to arrive at different conclusions for similar research objectives with minor variations.

Large Samples

As the research data comes from large samples, the researchers can process and analyze the data via highly reliable and consistent analysis procedures. They can arrive at well-defined conclusions that can be used to make the primary research more thorough and reliable.

Hypothesis Testing

This research methodology follows standardized and established hypothesis testing procedures. So, you have to be careful while reporting and analyzing your research data , and the overall quality of results gets improved.

Proven Examples of Quantitative Research Methods

Below, we discuss two excellent examples of quantitative research methods that were used by highly distinguished business and consulting organizations. Both examples show how different types of analysis can be performed with qualitative approaches and how the analysis is done once the data is collected.

1. STEP Project Global Consortium / KPMG 2019 Global Family Business survey

This research utilized quantitative methods to identify ways that kept the family businesses sustainably profitable with time.

The study also identified the ways in which the family business behavior changed with demographic changes and had “why” and “how” questions. Their qualitative research methods allowed the KPMG team to dig deeper into the mindsets and perspectives of the business owners and uncover unexpected research avenues as well.

It was a joint effort in which STEP Project Global Consortium collected 26 cases, and KPMG collected 11 cases.

The research reached the stage of data analysis in 2020, and the analysis process spanned over 4 stages.

The results, which were also the reasons why family businesses tend to lose their strength with time, were found to be:

• Family governance
• Family business legacy

2. EY Seren Teams Research 2020

This is yet another commendable example of qualitative research where the EY Seren Team digs into the unexplored depths of human behavior and how it affected their brand or service expectations.

The research was done across 200+ sources and involved in-depth virtual interviews with people in their homes, exploring their current needs and wishes. It also involved diary studies across the entire UK customer base to analyze human behavior changes and patterns.

The study also included interviews with professionals and design leaders from a wide range of industries to explore how COVID-19 transformed their industries. Finally, quantitative surveys were conducted to gain insights into the EY community after every 15 days.

The insights and results were:

• A culture of fear, daily resilience, and hopes for a better world and a better life – these were the macro trends.
• People felt massive digitization to be a resourceful yet demanding aspect as they have to adapt every day.
• Some people wished to have a new world with lots of possibilities, and some were looking for a new purpose.

8 Best Practices to Conduct Quantitative Research

Here are some best practices to keep in mind while conducting quantitative research:

1. Define Research Objectives

There can be many ways to collect data via quantitative research methods that are chosen as per the research objective and scope. These methods allow you to build your own observations regarding any hypotheses – unknown, entirely new, or unexplained. 

You can hypothesize a proof and build a prediction of outcomes supporting the same. You can also create a detailed stepwise plan for data collection, analysis, and testing. 

Below, we explore quantitative research methods and discuss some examples to enhance your understanding of them.

2. Keep Your Questions Simple

The surveys are meant to reach people en-masse, and that includes a wide demographic range with recipients from all walks of life. Asking simple questions will ensure that they grasp what’s being asked easily.

3. Develop a Solid Research Design

Choose an appropriate research design that aligns with your objectives, whether it’s experimental, quasi-experimental, or correlational. You also need to pay attention to the sample size and sampling technique such that it represents the target population accurately.

4. Use Reliable & Valid Instruments

It’s crucial to select or develop measurement instruments such as questionnaires, scales, or tests that have been validated and are reliable. Before proceeding with the main study, pilot-test these instruments on a small sample to assess their effectiveness and make any necessary improvements.

5. Ensure Data Quality

Implement data collection protocols to minimize errors and bias during data gathering. Double-check data entries and cleaning procedures to eliminate any inconsistencies or missing values that may affect the accuracy of your results. For instance, you might regularly cross-verify data entries to identify and correct any discrepancies.

6. Employ Appropriate Data Analysis Techniques

Select statistical methods that match the nature of your data and research questions. Whether it’s regression analysis, t-tests, ANOVA, or other techniques, using the right approach is important for drawing meaningful conclusions. Utilize software tools like SPSS or R for data analysis to ensure the accuracy and reproducibility of your findings.

7. Interpret Results Objectively

Present your findings in a clear and unbiased manner. Avoid making unwarranted causal claims, especially in correlational studies. Instead, focus on describing the relationships and patterns observed in your data.

8. Address Ethical Considerations

Prioritize ethical considerations throughout your research process. Obtain informed consent from participants, ensuring their voluntary participation and confidentiality of data. Comply with ethical guidelines and gain approval from a governing body if necessary.

Enhance Your Quantitative Research With Cutting-Edge Software

While no single research methodology can produce 100% reliable results, you can always opt for a hybrid research method by opting for the methods that are most relevant to your objective.

This understanding comes gradually as you learn how to implement the correct combination of qualitative and quantitative research methods for your research projects. For the best results, we recommend investing in smart, efficient, and scalable research tools that come with delightful reporting and advanced analytics to make every research initiative a success.

These software tools, such as ProProfs Survey Maker, come with pre-built survey templates and question libraries and allow you to create a high-converting survey in just a few minutes.

So, choose the best research partner, create the right research plan, and gather insights that drive sustainable growth for your business.

About the author

Emma David is a seasoned market research professional with 8+ years of experience. Having kick-started her journey in research, she has developed rich expertise in employee engagement, survey creation and administration, and data management. Emma believes in the power of data to shape business performance positively. She continues to help brands and businesses make strategic decisions and improve their market standing through her understanding of research methodologies.

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Home » Research Methodology – Types, Examples and writing Guide

Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

• Provide an overview of the research problem and the need for a research methodology section
• Outline the main research questions and objectives

II. Research Design

• Explain the research design chosen and why it is appropriate for the research question(s) and objectives
• Discuss any alternative research designs considered and why they were not chosen
• Describe the research setting and participants (if applicable)

III. Data Collection Methods

• Describe the methods used to collect data (e.g., surveys, interviews, observations)
• Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

• Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
• Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or software used for data analysis

V. Ethical Considerations

• Discuss any ethical issues that may arise from the research and how they were addressed
• Explain how informed consent was obtained (if applicable)
• Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

• Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

• Summarize the key aspects of the research methodology section
• Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

• Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
• Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
• Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
• Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
• Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
• Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
• Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
• Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

• To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
• To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
• To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
• To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
• To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
• To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
• To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
• To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
• To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

• To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
• To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
• To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
• To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
• To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

• Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
• Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
• Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
• Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
• Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
• Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
• Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
• Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

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Researcher, Academic Writer, Web developer

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CHAPTER 3 - RESEARCH METHODOLOGY: Data collection method and Research tools

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Quantitative Research Questionnaire – Types & Examples

Published by Alvin Nicolas at August 20th, 2024 , Revised On August 21, 2024

Research is usually done to provide solutions to an ongoing problem. Wherever the researchers see a gap, they tend to launch research to enhance their knowledge and to provide solutions to the needs of others. If they want to research from a subjective point of view, they consider qualitative research. On the other hand, when they research from an objective point of view, they tend to consider quantitative research.

There’s a fine line between subjectivity and objectivity. Qualitative research, related to subjectivity, assesses individuals’ personal opinions and experiences, while quantitative research, associated with objectivity, collects numerical data to derive results. However, the best medium to collect data in quantitative research is a questionnaire.

Let’s discuss what a quantitative research questionnaire is, its types, methods of writing questions, and types of survey questions. By thoroughly understanding these key essential terms, you can efficiently create a professional and well-organised quantitative research questionnaire.

What is a Quantitative Research Questionnaire?

Quantitative research questionnaires are preferably used during quantitative research. They are a well-structured set of questions designed specifically to gather specific, close-ended participant responses. This allows the researchers to gather numerical data and obtain a deep understanding of a particular event or problem.

As you know, qualitative research questionnaires contain open-ended questions that allow the participants to express themselves freely, while quantitative research questionnaires contain close-ended and specific questions, such as multiple-choice and Likert scales, to assess individuals’ behaviour.

Quantitative research questionnaires are usually used in research in various fields, such as psychology, medicine, chemistry, and economics.

Let’s see how you can write quantitative research questions by going through some examples:

• How much do British people consume fast food per week?
• What is the percentage of students living in hostels in London?

Types of Quantitative Research Questions With Examples

After learning what a quantitative research questionnaire is and what quantitative research questions look like, it’s time to thoroughly discuss the different types of quantitative research questions to explore this topic more.

Dichotomous Questions

Dichotomous questions are those with a margin for only two possible answers. They are usually used when the answers are “Yes/No” or “True/False.” These questions significantly simplify the research process and help collect simple responses.

Example: Have you ever visited Istanbul?

Multiple Choice Questions

Multiple-choice questions have a list of possible answers for the participants to choose from. They help assess people’s general knowledge, and the data gathered by multiple-choice questions can be easily analysed.

Example: Which of the following is the capital of France?

Multiple Answer Questions

Multiple-answer questions are similar to multiple-choice questions. However, there are multiple answers for participants to choose from. They are used when the questions can’t have a single, specific answer.

Example: Which of the following movie genres are your favourite?

Likert Scale Questions

Likert scale questions are used when the preferences and emotions of the participants are measured from one extreme to another. The scales are usually applied to measure likelihood, frequency, satisfaction, and agreement. The Likert scale has only five options to choose from.

Example: How satisfied are you with your job?

Semantic Differential Questions

Similar to Likert scales, semantic differential questions are also used to measure the emotions and attitudes of participants. The only difference is that instead of using extreme options such as strongly agree and strongly disagree, opposites of a particular choice are given to reduce bias.

Example: Please rate the services of our company.

Rank Order Questions

Rank-order questions are usually used to measure the preferences and choices of the participants efficiently. In this, multiple choices are given, and participants are asked to rank them according to their perspective. This helps to create a good participant profile.

Example: Rank the given books according to your interest.

Matrix Questions

Matrix questions are similar to Likert scales. In Likert scales, participants’ responses are measured through separate questions, while in matrix questions, multiple questions are compiled in a single row to simplify the data collection method efficiently.

Example: Rate the following activities that you do in daily life.

How To Write Quantitative Research Questions?

Quantitative research questions allow researchers to gather empirical data to answer their research problems. As we have discussed the different types of quantitative research questions above, it’s time to learn how to write the perfect quantitative research questions for a questionnaire and streamline your research process.

Here are the steps to follow to write quantitative research questions efficiently.

Step 1: Determine the Research Goals

The first step in writing quantitative research questions is to determine your research goals. Determining and confirming your research goals significantly helps you understand what kind of questions you need to create and for what grade. Efficiently determining the research goals also reduces the need for further modifications in the questionnaire.

Step 2: Be Mindful About the Variables

There are two variables in the questions: independent and dependent. It is essential to decide what would be the dependent variable in your questions and what would be the independent. It significantly helps to understand where to emphasise and where not. It also reduces the probability of additional and vague questions.

Step 3: Choose the Right Type of Question

It is also important to determine the right type of questions to add to your questionnaire. Whether you want Likert scales, rank-order questions, or multiple-answer questions, choosing the right type of questions will help you measure individuals’ responses efficiently and accurately.

Step 4: Use Easy and Clear Language

Another thing to keep in mind while writing questions for a quantitative research questionnaire is to use easy and clear language. As you know, quantitative research is done to measure specific and simple responses in empirical form, and using easy and understandable language in questions makes a huge difference.

Step 5: Be Specific About The Topic

Always be mindful and specific about your topic. Avoid writing questions that divert from your topic because they can cause participants to lose interest. Use the basic terms of your selected topic and gradually go deep. Also, remember to align your topic and questions with your research objectives and goals.

Step 6: Appropriately Write Your Questions

When you have considered all the above-discussed things, it’s time to write your questions appropriately. Don’t just haste in writing. Think twice about the result of a question and then consider writing it in the questionnaire. Remember to be precise while writing. Avoid overwriting.

Step 7: Gather Feedback From Peers

When you have finished writing questions, gather feedback from your researcher peers. Write down all the suggestions and feedback given by your peers. Don’t panic over the criticism of your questions. Remember that it’s still time to make necessary changes to the questionnaire before launching your campaign.

Step 8: Refine and Finalise the Questions

After gathering peer feedback, make necessary and appropriate changes to your questions. Be mindful of your research goals and topic. Try to modify your questions according to them. Also, be mindful of the theme and colour scheme of the questionnaire that you decided on. After refining the questions, finalise your questionnaire.

Types of Survey Questionnaires in Quantitative Research

Quantitative research questionnaires have close-ended questions that allow the researchers to measure accurate and specific responses from the participants. They don’t contain open-ended questions like qualitative research, where the response is measured by interviews and focus groups. Good combinations of questions are used in the quantitative research survey .

However, here are the types of surveys in quantitative research:

Descriptive Survey

The descriptive survey is used to obtain information about a particular variable. It is used to associate a quantity and quantify research variables. The questions associated with descriptive surveys mostly start with “What is” and “How much”.

Example: A descriptive survey to measure how much money children spend to buy toys.

Comparative Survey

A comparative survey is used to establish a comparison between one or more dependable variables and two or more comparison groups. This survey aims to form a comparative relation between the variables under study. The structure of the question in a comparative survey is, “What is the difference in [dependable variable] between [two or more groups]?”.

Example: A comparative survey on the difference in political awareness between Eastern and Western citizens.

Relationship-Based Survey

Relationship-based survey is used to understand the relationship or association between two or more independent and dependent variables. Cause and effect between two or more variables is measured in the relationship-based survey. The structure of questions in a relationship-based survey is, “What is the relation [between or among] [independent variable] and [dependable variable]?”.

Example: What is the relationship between education and lifestyle in America?

Advantages & Disadvantages of Questionnaires in Quantitative Research

Quantitative research questionnaires are an excellent tool to collect data and information about the responses of individuals. Quantitative research comes with various advantages, but along with advantages, it also has its disadvantages. Check the table below to learn about the advantages and disadvantages of a quantitative research questionnaire.

Quantitative Research Questionnaire Example

Here is an example of a quantitative research questionnaire to help you get the idea and create an efficient and well-developed questionnaire for your research:

Warm welcome, and thank you for participating in our survey. Please provide your response to the questions below. Your esteemed response will significantly help us to achieve our research goals and provide effective solutions to society.

17-20 21-24 25-28 29-32 ii) What is your gender? Male Female Other Prefer not to say ii) Have you graduated? Yes No iii) Are you employed? iv) Are you married? Yes No   Part 2: Provide your honest response.  Question 1: I have tried online shopping. Strongly Disagree Disagree Neutral  Agree Strongly Agree Question 2: I have good experience with online shopping. Strongly Disagree Disagree Neutral Agree Strongly Agree Question 3: I have a bad experience with online shopping. Question 4: I received my order on time.  Question 5: I like physical shopping more.  Frequently Asked Questions What is a quantitative research questionnaire. A quantitative research questionnaire is a well-structured set of questions designed specifically to gather specific and close-ended participant responses. What is the difference between qualitative and quantitative research? The difference between qualitative and quantitative research is subjectivity and objectivity. Subjectivity is associated with qualitative research, while objectivity is associated with quantitative research.  What are the advantages of a quantitative research questionnaire? It is quick and efficient. There is less risk of research bias and subjectivity. It is particular and simple. You May Also Like A meta-analysis is a formal, epidemiological, quantitative study design that uses statistical methods to generalise the findings of the selected independent studies. Disadvantages of primary research – It can be expensive, time-consuming and take a long time to complete if it involves face-to-face contact with customers. A survey includes questions relevant to the research topic. The participants are selected, and the questionnaire is distributed to collect the data. USEFUL LINKS LEARNING RESOURCES COMPANY DETAILS How It Works MS in the Learning Sciences Tuition & Financial Aid Qualitative vs. quantitative data analysis: How do they differ? Learning analytics have become the cornerstone for personalizing student experiences and enhancing learning outcomes. In this data-informed approach to education there are two distinct methodologies: qualitative and quantitative analytics. These methods, which are typical to data analytics in general, are crucial to the interpretation of learning behaviors and outcomes. This blog will explore the nuances that distinguish qualitative and quantitative research, while uncovering their shared roles in learning analytics, program design and instruction. What is qualitative data? Qualitative data is descriptive and includes information that is non numerical. Qualitative research is used to gather in-depth insights that can't be easily measured on a scale like opinions, anecdotes and emotions. In learning analytics qualitative data could include in depth interviews, text responses to a prompt, or a video of a class period. 1 What is quantitative data? Quantitative data is information that has a numerical value. Quantitative research is conducted to gather measurable data used in statistical analysis. Researchers can use quantitative studies to identify patterns and trends. In learning analytics quantitative data could include test scores, student demographics, or amount of time spent in a lesson. 2 Key difference between qualitative and quantitative data It's important to understand the differences between qualitative and quantitative data to both determine the appropriate research methods for studies and to gain insights that you can be confident in sharing. Data Types and Nature Examples of qualitative data types in learning analytics: Observational data of human behavior from classroom settings such as student engagement, teacher-student interactions, and classroom dynamics Textual data from open-ended survey responses, reflective journals, and written assignments Feedback and discussions from focus groups or interviews Content analysis from various media Examples of quantitative data types: Standardized test, assessment, and quiz scores Grades and grade point averages Attendance records Time spent on learning tasks Data gathered from learning management systems (LMS), including login frequency, online participation, and completion rates of assignments Methods of Collection Qualitative and quantitative research methods for data collection can occasionally seem similar so it's important to note the differences to make sure you're creating a consistent data set and will be able to reliably draw conclusions from your data. Qualitative research methods Because of the nature of qualitative data (complex, detailed information), the research methods used to collect it are more involved. Qualitative researchers might do the following to collect data: Conduct interviews to learn about subjective experiences Host focus groups to gather feedback and personal accounts Observe in-person or use audio or video recordings to record nuances of human behavior in a natural setting Distribute surveys with open-ended questions Quantitative research methods Quantitative data collection methods are more diverse and more likely to be automated because of the objective nature of the data. A quantitative researcher could employ methods such as: Surveys with close-ended questions that gather numerical data like birthdates or preferences Observational research and record measurable information like the number of students in a classroom Automated numerical data collection like information collected on the backend of a computer system like button clicks and page views Analysis techniques Qualitative and quantitative data can both be very informative. However, research studies require critical thinking for productive analysis. Qualitative data analysis methods Analyzing qualitative data takes a number of steps. When you first get all your data in one place you can do a review and take notes of trends you think you're seeing or your initial reactions. Next, you'll want to organize all the qualitative data you've collected by assigning it categories. Your central research question will guide your data categorization whether it's by date, location, type of collection method (interview vs focus group, etc), the specific question asked or something else. Next, you'll code your data. Whereas categorizing data is focused on the method of collection, coding is the process of identifying and labeling themes within the data collected to get closer to answering your research questions. Finally comes data interpretation. To interpret the data you'll take a look at the information gathered including your coding labels and see what results are occurring frequently or what other conclusions you can make. 3 Quantitative analysis techniques The process to analyze quantitative data can be time-consuming due to the large volume of data possible to collect. When approaching a quantitative data set, start by focusing in on the purpose of your evaluation. Without making a conclusion, determine how you will use the information gained from analysis; for example: The answers of this survey about study habits will help determine what type of exam review session will be most useful to a class. 4 Next, you need to decide who is analyzing the data and set parameters for analysis. For example, if two different researchers are evaluating survey responses that rank preferences on a scale from 1 to 5, they need to be operating with the same understanding of the rankings. You wouldn't want one researcher to classify the value of 3 to be a positive preference while the other considers it a negative preference. It's also ideal to have some type of data management system to store and organize your data, such as a spreadsheet or database. Within the database, or via an export to data analysis software, the collected data needs to be cleaned of things like responses left blank, duplicate answers from respondents, and questions that are no longer considered relevant. Finally, you can use statistical software to analyze data (or complete a manual analysis) to find patterns and summarize your findings. 4 Qualitative and quantitative research tools From the nuanced, thematic exploration enabled by tools like NVivo and ATLAS.ti, to the statistical precision of SPSS and R for quantitative analysis, each suite of data analysis tools offers tailored functionalities that cater to the distinct natures of different data types. Qualitative research software: NVivo: NVivo is qualitative data analysis software that can do everything from transcribe recordings to create word clouds and evaluate uploads for different sentiments and themes. NVivo is just one tool from the company Lumivero, which offers whole suites of data processing software. 5 ATLAS.ti: Similar to NVivo, ATLAS.ti allows researchers to upload and import data from a variety of sources to be tagged and refined using machine learning and presented with visualizations and ready for insert into reports. 6 SPSS: SPSS is a statistical analysis tool for quantitative research, appreciated for its user-friendly interface and comprehensive statistical tests, which makes it ideal for educators and researchers. With SPSS researchers can manage and analyze large quantitative data sets, use advanced statistical procedures and modeling techniques, predict customer behaviors, forecast market trends and more. 7 R: R is a versatile and dynamic open-source tool for quantitative analysis. With a vast repository of packages tailored to specific statistical methods, researchers can perform anything from basic descriptive statistics to complex predictive modeling. R is especially useful for its ability to handle large datasets, making it ideal for educational institutions that generate substantial amounts of data. The programming language offers flexibility in customizing analysis and creating publication-quality visualizations to effectively communicate results. 8 Applications in Educational Research Both quantitative and qualitative data can be employed in learning analytics to drive informed decision-making and pedagogical enhancements. In the classroom, quantitative data like standardized test scores and online course analytics create a foundation for assessing and benchmarking student performance and engagement. Qualitative insights gathered from surveys, focus group discussions, and reflective student journals offer a more nuanced understanding of learners' experiences and contextual factors influencing their education. Additionally feedback and practical engagement metrics blend these data types, providing a holistic view that informs curriculum development, instructional strategies, and personalized learning pathways. Through these varied data sets and uses, educators can piece together a more complete narrative of student success and the impacts of educational interventions. Master Data Analysis with an M.S. in Learning Sciences From SMU Whether it is the detailed narratives unearthed through qualitative data or the informative patterns derived from quantitative analysis, both qualitative and quantitative data can provide crucial information for educators and researchers to better understand and improve learning. Dive deeper into the art and science of learning analytics with SMU's online Master of Science in the Learning Sciences program . At SMU, innovation and inquiry converge to empower the next generation of educators and researchers. Choose the Learning Analytics Specialization to learn how to harness the power of data science to illuminate learning trends, devise impactful strategies, and drive educational innovation. You could also find out how advanced technologies like augmented reality (AR), virtual reality (VR), and artificial intelligence (AI) can revolutionize education, and develop the insight to apply embodied cognition principles to enhance learning experiences in the Learning and Technology Design Specialization , or choose your own electives to build a specialization unique to your interests and career goals. For more information on our curriculum and to become part of a community where data drives discovery, visit SMU's MSLS program website or schedule a call with our admissions outreach advisors for any queries or further discussion. Take the first step towards transforming education with data today. Retrieved on August 8, 2024, from nnlm.gov/guides/data-glossary/qualitative-data Retrieved on August 8, 2024, from nnlm.gov/guides/data-glossary/quantitative-data Retrieved on August 8, 2024, from cdc.gov/healthyyouth/evaluation/pdf/brief19.pdf Retrieved on August 8, 2024, from cdc.gov/healthyyouth/evaluation/pdf/brief20.pdf Retrieved on August 8, 2024, from lumivero.com/solutions/ Retrieved on August 8, 2024, from atlasti.com/ Retrieved on August 8, 2024, from ibm.com/products/spss-statistics Retrieved on August 8, 2024, from cran.r-project.org/doc/manuals/r-release/R-intro.html#Introduction-and-preliminaries Return to SMU Online Learning Sciences Blog Southern Methodist University has engaged Everspring , a leading provider of education and technology services, to support select aspects of program delivery. This will only take a moment Have a language expert improve your writing Run a free plagiarism check in 10 minutes, generate accurate citations for free. Knowledge Base Methodology Research Methods | Definitions, Types, Examples Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make. First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question : Qualitative vs. quantitative : Will your data take the form of words or numbers? Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else? Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment? Second, decide how you will analyze the data . For quantitative data, you can use statistical analysis methods to test relationships between variables. For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data. Table of contents Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods. Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research. Qualitative vs. quantitative data Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop. For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data . If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data . Qualitative to broader populations. . Quantitative . You can also take a mixed methods approach , where you use both qualitative and quantitative research methods. Primary vs. secondary research Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies). If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice. Primary . methods. Secondary Descriptive vs. experimental data In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method . In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design . To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect. Descriptive . . Experimental Prevent plagiarism. Run a free check. Research methods for collecting data Research method Primary or secondary? Qualitative or quantitative? When to use Primary Quantitative To test cause-and-effect relationships. Primary Quantitative To understand general characteristics of a population. Interview/focus group Primary Qualitative To gain more in-depth understanding of a topic. Observation Primary Either To understand how something occurs in its natural setting. Secondary Either To situate your research in an existing body of work, or to evaluate trends within a research topic. Either Either To gain an in-depth understanding of a specific group or context, or when you don’t have the resources for a large study. Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis. Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses. Qualitative analysis methods Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected: From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers. Using non-probability sampling methods . Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias . Quantitative analysis methods Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments). You can use quantitative analysis to interpret data that was collected either: During an experiment . Using probability sampling methods . Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers. Research methods for analyzing data Research method Qualitative or quantitative? When to use Quantitative To analyze data collected in a statistically valid manner (e.g. from experiments, surveys, and observations). Meta-analysis Quantitative To statistically analyze the results of a large collection of studies. Can only be applied to studies that collected data in a statistically valid manner. Qualitative To analyze data collected from interviews, , or textual sources. To understand general themes in the data and how they are communicated. Either To analyze large volumes of textual or visual data collected from surveys, literature reviews, or other sources. Can be quantitative (i.e. frequencies of words) or qualitative (i.e. meanings of words). Here's why students love Scribbr's proofreading services Discover proofreading & editing If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples. Chi square test of independence Statistical power Descriptive statistics Degrees of freedom Pearson correlation Null hypothesis Double-blind study Case-control study Research ethics Data collection Hypothesis testing Structured interviews Research bias Hawthorne effect Unconscious bias Recall bias Halo effect Self-serving bias Information bias Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings. Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail. In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question . A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students. In statistics, sampling allows you to test a hypothesis about the characteristics of a population. The research methods you use depend on the type of data you need to answer your research question . If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods . If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data. If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods. Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives. Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ). In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section . In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods. Is this article helpful? Other students also liked, writing strong research questions | criteria & examples. 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Study Protocol Open access Published: 19 August 2024 Evaluating the impact of the global evidence, local adaptation (GELA) project for enhancing evidence-informed guideline recommendations for newborn and young child health in three African countries: a mixed-methods protocol Tamara Kredo   ORCID: orcid.org/0000-0001-7115-9535 1 , 2 , 3 , 4 , Emmanuel Effa 5 , Nyanyiwe Mbeye 6 , Denny Mabetha 1 , Bey-Marrié Schmidt 1 , 7 , Anke Rohwer 2 , Michael McCaul 2 , Idriss Ibrahim Kallon 2 , Susan Munabi-Babigumira 8 , Claire Glenton 8 , Taryn Young 2 , Simon Lewin 1 , 9 , Per Olav Vandvik 10 , 11 & Sara Cooper 2 , 4 , 12   Health Research Policy and Systems volume  22 , Article number:  114 ( 2024 ) Cite this article 115 Accesses Metrics details Poverty-related diseases (PRD) remain amongst the leading causes of death in children under-5 years in sub-Saharan Africa (SSA). Clinical practice guidelines (CPGs) based on the best available evidence are key to strengthening health systems and helping to enhance equitable health access for children under five. However, the CPG development process is complex and resource-intensive, with substantial scope for improving the process in SSA, which is the goal of the Global Evidence, Local Adaptation (GELA) project. The impact of research on PRD will be maximized through enhancing researchers and decision makers’ capacity to use global research to develop locally relevant CPGs in the field of newborn and child health. The project will be implemented in three SSA countries, Malawi, South Africa and Nigeria, over a 3-year period. This research protocol is for the monitoring and evaluation work package of the project. The aim of this work package is to monitor the various GELA project activities and evaluate the influence these may have on evidence-informed decision-making and guideline adaptation capacities and processes. The specific project activities we will monitor include (1) our ongoing engagement with local stakeholders, (2) their capacity needs and development, (3) their understanding and use of evidence from reviews of qualitative research and, (4) their overall views and experiences of the project. We will use a longitudinal, mixed-methods study design, informed by an overarching project Theory of Change. A series of interconnected qualitative and quantitative data collections methods will be used, including knowledge translation tracking sheets and case studies, capacity assessment online surveys, user testing and in-depth interviews, and non-participant observations of project activities. Participants will comprise of project staff, members of the CPG panels and steering committees in Malawi, South Africa and Nigeria, as well as other local stakeholders in these three African countries. Ongoing monitoring and evaluation will help ensure the relationship between researchers and stakeholders is supported from the project start. This can facilitate achievement of common goals and enable researchers in South Africa, Malawi and Nigeria to make adjustments to project activities to maximize stakeholder engagement and research utilization. Ethical approval has been provided by South African Medical Research Council Human Research Ethics Committee (EC015-7/2022); The College of Medicine Research and Ethics Committee, Malawi (P.07/22/3687); National Health Research Ethics Committee of Nigeria (01/01/2007). Peer Review reports Sub-Saharan Africa (SSA) has the highest under-five mortality rate in the world [ 1 ]. Although the global under-five mortality rate declined from 76 to 38 per 1000 live births between 2000 and 2019, more than half of the deaths in children and youth in 2019 were among children under 5 years, approximately 5.2 million deaths [ 1 ]. Poverty-related diseases including pneumonia, diarrhoea and malaria remain amongst the leading causes of death in children under-5 years [ 2 ].Thus, despite progress in the health of young children globally, most countries in SSA fall below the average gains and do not meet maternal and child health targets set by the United Nations Sustainable Development Goal 3 to ‘ensure healthy lives and promote wellbeing’ (1). As of December 2021, under-five mortality rates were reported as 113.8, 38.6 and 32.2 per 1000 live births for Nigeria, Malawi and South Africa, respectively [ 3 ]. Factors accounting for regional disparities in child mortality rates include poverty, socioeconomic inequities, poor health systems and poor nutrition, with coronavirus disease 2019 (COVID-19) adding substantially to the burden [ 4 ]. Addressing healthcare issues such as these requires an evidence-informed approach, where intervention design and implementation are based on the best available evidence, to ensure that scarce resources are used effectively and efficiently, avoid harm, maximize good and improve healthcare delivery and outcomes [ 5 , 6 , 7 ]. Evidence-informed practices have been growing in SSA [ 6 ], and evidence ecosystems are becoming stronger. The evidence ecosystem reflects the formal and informal linkages and interactions between different actors (and their capacities and resources) involved in the production, translation and use of evidence [ 6 , 8 , 9 ]. Guidance that can be developed through this ecosystem includes evidence-based health technology assessments (HTA) and clinical practice guidelines (CPGs). CPGs include recommendations that are actionable statements that are informed by systematic reviews of evidence, and an assessment of the benefits and harms of alternative care options and are intended to optimize patient care [ 10 ]. They can help bridge the gap between research evidence and practice and are recognized as important quality-improvement tools that aim to standardize care, inform funding decisions and improve access to care, among others. CPG method advancements, challenges and research gaps Over the past decade, internationally and in SSA, there has been a rapid growth of CPGs developed for a range of conditions [ 11 ]. In particular, rapid evidence syntheses and guideline development methods has advanced in response to urgent evidence needs, especially during COVID [ 12 , 13 ]. For example, WHO has developed guidelines for all key infectious conditions that cause most deaths. This development has been accompanied by a growing volume of research evidence around CPGs, including the processes for their rapid development, adaptation, contextualization, implementation and evaluation, and further spurred on by COVID. For example, global knowledge leaders, such as the WHO and the GRADE Working Group, have set standards for CPG development, outlining the steps of what is known as ‘de novo’ (from scratch) CPG development [ 14 ]. Another global group, the Guidelines International Network (G-I-N), is a network dedicated to leading, strengthening and supporting collaboration in CPG development, adaptation and implementation. They have published minimum standards and the G-I-N McMaster guideline checklist, which contains a comprehensive list of topics and items outlining the practical steps to consider for developing CPGs [ 15 ]. As CPG standards have evolved, however, so has the complexity of development and adaptation. In the context of poorer settings, such as sub-Saharan Africa (SSA), CPG development is prohibitively human and finance resource intensive. It requires scarce skills, even in the growing evidence-based healthcare (EBHC) community, and financial investments by government where resources are often directed to healthcare services, rather than policymaking processes. Against this backdrop, several studies have found that CPGs in the region often perform poorly on reporting on their rigour of development and editorial independence [ 16 , 17 , 18 ]. Other, more resource-efficient methods for guideline development in SSA are, therefore, essential and urgently needed. Moreover, investment in the overall management of the process is needed, including convening the guideline group and moving stepwise through a rigorous process. Approaches for and challenges of guideline adaptation There is also increased international recognition of the value of taking guidelines developed in one country and applying them to other countries. This can avoid duplication of effort and research waste in de novo guideline development, when useful guidelines may exist elsewhere [ 12 , 19 ]. Against this backdrop, several adaptation methods are emerging for contextualization of recommendations to country needs (e.g. ADAPTE, adolopment and SNAP-it, amongst others) [ 19 , 20 , 21 ]. For example, WHO is developing strategies for adapting and implementing their CPGs at country level. One example is the WHO Antenatal Care Recommendations Adaptation Toolkit lead by the Department of Sexual and Reproductive Health and Research [ 22 ]. Their approach is pragmatic and transparent. Another approach is so-called ‘adolopment’, a GRADE method, in which the original guideline evidence is used, either adopted or adapted, considering contextual evidence such as costs and feasibility and local values [ 20 ]. Adolopment involves convening a guideline panel, reviewing available evidence and local contextual evidence and weighing up the panel’s judgements to make recommendations that are fit for purpose [ 20 ]. Despite these advances in CPG adaptation methods, many countries and professional associations in sub-Saharan Africa still use expert opinion-based approaches or proceed to prepare their own systematic reviews and guidelines, ultimately perpetuating resource wastage and duplication of efforts [ 23 ]. Moreover, when countries do adapt and contextualize other countries’ guidelines, there is frequently a lack of transparency and reporting on changes, without clarity on why or by whom. This in turn casts doubts on the recommendation’s credibility. For example, guidelines for child health in sub-Saharan Africa are usually derived from the WHO and UNICEF. However, adaptation of such guidelines and recommendations to national contexts is not well described [ 24 ]. Transparency in guideline adaptation is critical for creating trustworthy, context-sensitive recommendations. What guideline adaptation methods work best and how these can be transparently implemented in the context of lower resource settings, remain key research questions. Therefore, despite the emergence of several guideline adaptation approaches, we need to explore and understand how best to adapt recommendations from one context to another [ 25 ]. Qualitative evidence to inform guideline panels decisions Another major advancement within guideline research has been growing recognition of the potential contribution of qualitative research evidence [ 26 , 27 ]. Traditionally, guidelines have been informed by systematic reviews of the effectiveness of specific interventions [ 14 ]. Such reviews provide robust evidence about which interventions ‘work’. However, there is appreciation that evidence regarding the potential effectiveness of an intervention is not sufficient for making recommendations or decisions. Policymakers also need to consider other issues, including how different stakeholders’ value different outcomes, the intervention’s acceptability to those affected by it and the feasibility of implementing the intervention [ 28 , 29 , 30 ]. Evidence from qualitative research is particularly well suited to exploring factors that influence an intervention’s acceptability and feasibility [ 31 , 32 ]. The use of qualitative research to inform recommendations by guidelines has become easier in recent years as systematic reviews of qualitative studies have become more common, and the methods for these reviews are now well developed [ 33 ]. The first WHO guideline to systematically incorporate reviews of qualitative studies was published in 2012 in the field of task-shifting for maternal and child health [ 31 ]. The inclusion of this qualitative evidence helped shape the panel’s recommendations [ 32 ], and this approach is now included in the WHO Handbook for Guideline Development and has been applied in many other WHO CPGs [ 34 , 35 ]. However, a key challenge in using findings from systematic reviews of qualitative evidence is communicating often complex findings to users such as guideline panel members to facilitate effective knowledge translation. While there is now considerable research on communicating findings from reviews of intervention effectiveness [ 36 ], there is limited experience on the usefulness of different options for packaging and presenting findings from systematic reviews of qualitative evidence to CPG panels. To make best use of this evidence, we need presentation formats that are accessible to users who may be unfamiliar with qualitative methods, are concise and simple while retaining sufficient detail to inform decisions and clearly present ‘confidence in the evidence from systematic reviews of qualitative evidence’ (GRADE-CERQual) assessments of how much confidence users should place in each finding [ 37 ]. In addition, we need to understand how qualitative evidence included in global guidelines, such as those produced by WHO, is interpreted and used in country-level guideline adaptation processes. Communicating clinical practice guidelines to end-users A final key guideline method advancement has been around the development of multi-layered and digitally structured communication formats for end users [ 38 , 39 ]. Guidelines are not an end in themselves. Recommendations may lack impact if not adequately communicated and disseminated to those who need to implement them, namely healthcare providers, managers and the public. Indeed, in a South African study of primary care guideline national policymakers, subnational health managers and healthcare providers agreed that dissemination is a particular gap [ 40 ]. While guidelines typically are produced as static documents (e.g. PDF formats), information technology is needed to enhance dissemination. The MAGIC authoring and publication Platform (MAGICapp/) was developed for this purpose ( https://magicevidence.org/magicapp/ ). MAGICapp is a web-based tool that enables evidence synthesizers and guideline organizations to create, publish and dynamically update trustworthy and digitally structured evidence summaries, guidelines and decision aids in user-friendly formats on all devices. Such digital multi-layered formats allow different users to rapidly find recommendations, while having the supporting evidence for them one click away [ 41 ]. MAGICapp, used by WHO, NICE and professional societies across the world, holds potential to enhance the impact of evidence-informed guideline recommendations in practice, in an enhanced evidence ecosystem [ 9 ]. However, the usability of the MAGICapp in sub-Saharan Africa, based on local user preferences for different communication formats, are key research questions. Against this backdrop, the Global Evidence, Local Adaptation (GELA) project will maximize the impact of research on poverty-related diseases through enhancing researchers and decision makers’ capacity to use global research to develop locally relevant guidelines for newborn and child health in Malawi, Nigeria and South Africa. These guidelines will build on and add value to the large-scale programme of child health guideline development from agencies such as the WHO, to support adaptation and implementation led by national ministries in collaboration with WHO Afro regional office. Brief overview of the GELA project aim, objectives and approach The overarching aim of GELA is to bridge the gap between current processes and global advances in evidence-informed decision-making and guideline development, adaptation and dissemination by building skills and sharing resources in ways that can be sustained beyond the project period. The project has seven linked and related work packages (WPs) to support delivery of the planned project deliverables. Table 1 provides a brief summary of the activities of each WP. This protocol outlines our approach for the monitoring and overall evaluation of the project activities and impact (WP 6). The project will be implemented in three SSA countries: Malawi, South Africa and Nigeria over a 3-year period. The project adopts a multi-faceted multidisciplinary research and capacity strengthening programme using primary and secondary research, guideline adaptation methodology and digital platforms to support authoring delivery and dynamic adaptation. These processes will offer bespoke capacity strengthening opportunities for policy makers, researchers and civil society. Throughout the project, we plan for innovations in the tools we use, accompanied by comprehensive evaluation of all aspects of the research, research uptake into policy and capacity strengthening. This current proposal is for WP6: monitoring and evaluation Ongoing monitoring and evaluation of project processes and activities will help facilitate ongoing engagement between researchers and stakeholders throughout the research project. This will in turn help ensure that the project is centred on a common goal, with clear understandings of the different research activities and potential impact. This can also promote research uptake and enable researchers to make adjustments to project activities, maximizing stakeholder engagement and research utilization. M&E aims & objectives The overarching aim of the monitoring and evaluation work package is to monitor and evaluate the various GELA project activities and processes, including whether, how and why activities took place or if goals were met. The specific monitoring and evaluation objectives are to: Monitor ongoing engagement with local stakeholders across work packages and explore what worked and didn’t and why; Assess the capacity development needs of guideline panels and steering group committees and explore their views and experiences of the project’s capacity development activities; Explore guideline panelists’ experiences with reading and using evidence from reviews of qualitative research, including their preferences regarding how qualitative review findings are summarized and presented; Evaluate guideline panelists’, steering group committees’ and project team members’ overall views and experiences of the project, including the what works or not, to influence evidence-informed decision-making and guideline adaptation processes Overall approach We will use a longitudinal, mixed-methods study design, informed by an overarching project Theory of Change (Table  2 ). The theoretical underpinning for the GELA project across all work packages is related to the three-layered behaviour change wheel comprising opportunity, capability and motivation [ 42 ]. The design, delivery and implementation of multi-stakeholder integrated activities based on identified priority areas and needs is expected to lead to guideline related improved capacity, practice and policy within each country’s health system. Certain objectives also have specific underpinning theoretical frameworks, in addition to the overarching project Theory of Change, which are explained under the respective objectives below. A series of interconnected qualitative and quantitative data collections methods will be used to address each objective. In what follows, we describe each objective and the methods we will use to achieve it, separately. However, in many cases the qualitative data collection cuts across objectives, with the same interviews and observations being used to explore multiple issues simultaneously (e.g. knowledge translation, capacity, overall views and experiences of the project, etc.). The relationship between the different objectives and associated methods are depicted in Tables 3 and 4 . Table 3 outlines the stakeholder groups included in the monitoring and evaluation work package, including their composition and for which objectives they are targeted. Table 4 provides the timeline for the different data collection methods and how they relate to each across the objectives. 1. Objective 1: monitor ongoing engagement with local stakeholders across work packages and explore what worked and did not work and why Overall approach for this objective. This objective will be guided by an integrated knowledge translation (IKT) approach. IKT focuses on the important role of stakeholder engagement in enhancing evidence-informed decision-making [ 43 ]. As part of work package 4 (‘dissemination and communication’), knowledge translation (KT) champions have been identified in each of the three countries and will work together to develop and implement country-level KT strategies. This will include defining KT objectives, identifying and mapping relevant stakeholders, prioritizing those we will actively engage and developing a strategy for engaging each priority stakeholder. We will monitor these engagements through the development and implementation of a tracking sheet, qualitative case studies and semi-structured interviews. Participants Participants will comprise of knowledge translation (KT) champions and relevant country-level stakeholders. KT champions are GELA project staff who have dedicated time to work on the communication, dissemination and engagement aspects at a country-level. At least one KT champion has been identified for each of Malawi, Nigeria and South Africa. Relevant country-level stakeholders will be identified as part of the KT strategy development (WP4) and will comprise any health decision-makers, e.g. health practitioners, community groups, health system managers, policy-makers, researchers and media. Tracking sheet and qualitative case studies A tracking sheet will be used to capture information for each stakeholder related to the purpose, message, medium or forum, messenger, timing and resources for engagement. KT champions in each country will be responsible for tracking these details on a continuous basis, and the tracking sheet will be monitored bi-monthly at a meeting with KT champions from the three country teams. This will help us monitor whether and how engagement activities are taking place, as well as the strategies for implementation. The tracking sheets will consist of different in-country stakeholders (e.g. government officers, health professional associations, researchers, media, etc.), and there may be several goals for engaging each individual stakeholder. The engagement strategy will be reviewed and updated as priority stakeholders change over the research stages and project period. As such, the sample size will be determined iteratively. We will analyse information with descriptive statistics. For example, we will group and count by categories: number and type of stakeholders, type of engagement activities, type of KT products produced, type of forum or medium used for dissemination, frequency and duration of engagement, follow-ups, intensive engagement period and resources required for engagement. We will also develop case stories (or impact stories) describing engagement activities and processes between project staff and relevant stakeholders. The case studies will help us monitor successful engagement, disseminate best practice scenarios and draw out lessons for future engagements. We will identify case stories through the tracking sheet and at bi-monthly meetings with the KT co-ordinator, where KT champions will be asked to share success stories or learning moments. KT champions will not know which ‘case’ will be selected for the case study in advance. The information will be collected by the KT co-ordinator, who is not involved in any of the country strategy implementation. The information collected from the KT champions (and messenger, if the messenger is not the KT champion) will be via a standard case story template, including aim of engagement, what the engagement was, experiences from both sides (quotes to be included in stories), success of engagement, lessons learnt and any future engagement plans. The number of cases will be determined iteratively. The intention is to develop one case story from each country annually, showcasing different cases, e.g. type of KT goal, type of stakeholder, type of KT medium/forum, etc. Semi-structured interviews At project close (month 30), we will conduct semi-structured interviews to explore if, why and how project KT goals were met and what planned stakeholder engagements worked (and did not work) and why. The interviews will be conducted with KT champions, other messengers (e.g. communication officers), country leads and selected stakeholders. At least two people from each county (KT champion and messenger and/or stakeholder) will be interviewed, and so there will be six to eight interviews in total. Participants will be selected purposively for information-rich cases that can help yield insights and in-depth understanding of the nature and success (or not) of our stakeholder engagements [ 44 ]. These interviews will form part of the interviews conducted with project team members more broadly as part of objective 4, the methods of which are therefore described in more detail below. 2. Objective 2: assess the capacity development needs of guideline panels and steering group committees and explore their views and experiences of the project’s capacity development activities. Overarching theoretical lens. We will draw on the Kirkpatrick model [ 45 ] as the underpinning theoretical framework for this objective. This model evaluates training effectiveness across four levels: (1) reaction, (2) learning, (3) behaviour and (4) results. The ‘reaction level’ assesses the degree of satisfaction of participants with the training event. The ‘learning level’ examines learning among participants both before and after the training event to determine any change in knowledge [ 46 , 47 ]. The ‘behaviour level’ assesses whether the training event has provided any favourable change in behaviour among participants. The final ‘results level’ assesses the use of knowledge gained through the training event within the workplace [ 46 , 47 ]. To assess the potential difference that project capacity development activities make, the outcomes of interest will be those related to training in evidence-based healthcare (EBHC). An overview of systematic reviews by Young and colleagues identified that EBHC training often aims to ‘improve critical appraisal skills and integration of results into decisions, and improved knowledge, skills, attitudes and behaviour among practising health professionals’ [ 48 , 49 ]. We will employ mixed methods to achieve this objective, including three rounds of online surveys (at baseline, mid-line and at the project close) as well as semi-structured interviews (at project close) and non-participant observations of meetings (various). The first online survey at baseline will assess the capacity needs of the guideline panels and steering group committees in South Africa, Malawi and Nigeria, and the two subsequent online surveys will assess the potential difference project capacity development activities make on these groups across all the four levels of the Kirkpatrick model, i.e. reaction, learning, behaviour and results. The capacity needs and progress of these groups will also be explored qualitatively through semi-structured interviews and observations of meetings. Details of the project capacity development activities that will be implemented as part of work package 5 (‘capacity strengthening and sharing’) of the GELA project are outlined in Table  1 (above). All members of the guideline panels and steering group committees in South Africa, Malawi and Nigeria will be invited and encouraged to attend all project capacity development activities. ‘On the job’ capacity building will also take place during the various meetings convened with these groups, as they are supported to identify priority topics, to appraise and discuss the evidence used to inform the recommendations and to formulate the final recommendations. Participants will comprise members of the guideline panels and steering group committees in South Africa, Malawi and Nigeria. Table 3 (above) provides details of the composition of the guideline panels and steering group committees. Online surveys Procedures and data collection tools. At baseline (at approximately 6 months before engagement in any project training activities), at mid-line (month 18) and at the project close (month 30), all members of the guideline panels and steering group committees in South Africa, Malawi and Nigeria will be invited, via email, to participate in a survey. In each of the three countries the guideline development group and steering group committees will include approximately 20 and 10 members, respectively; we will therefore aim to have 90 participants in total complete the survey. The email invitation to all three survey rounds will inform participants about the nature of the study and direct them to an online survey. The landing page of the survey will provide information about the purpose of the research project and what is being requested from the participants, with a consent statement at the end which the participant will be required to agree to before being able to continue with the survey. Data will only be collected from participants who consent to freely participate in the study. The survey will be carried out using a secure online survey platform (such as Microsoft Forms) where all cookies and IP address collectors will be disabled to protect the confidentiality of the participants and to avoid tracking of the participant activities online. Unique identifiers (last six numbers of their ID) will be used to track participants responses over time and link data from baseline to project close. The baseline survey will be a short (10–25 min) form that will ask participants about their capacity needs and knowledge/skills in evidence-based healthcare (EBHC) and decision-making. The survey will capture demographic variables of participants at baseline, mid-term and at the end of the project. It will assess the training needs of participants at baseline, participants’ satisfaction at the end of each training activity, the knowledge and skills at baseline, mid-term and at the end of the project. Participants’ behaviour will also be assessed using open-ended questions and vignettes. The surveys will focus on all four levels (i.e. reaction, learning, behaviour and results) of the Kirkpatrick model. Data management and analysis All data collected on the secure online survey platform will be coded, cleaned and entered into STATA. Data collected for the baseline survey will be analysed using descriptive statistics to determine the frequency of the various training needs and qualitative data gathered using the open-ended questions will be analysed thematically using manual coding (or if available and dataset is large), and NVivo or a similar tool will be used to identify the recurring themes which emerge in the data collected about the key training needs of participants. Data collected for the surveys conducted at midpoint and at project close will be analysed using descriptive statistics to determine if there has been a change in the learning, knowledge gained and behaviours over time, as well as the extent of the potential application of evidence-based practice, while the data collected using the open-ended questions will be analysed using thematic analysis outlining how project capacity development activities informed particular outcomes and results in the participant’s workplace. To determine change in skills (and trends over time such as confidence improvement or decay), the descriptive statistics will be supplemented by appropriate inferential statistics for repeated measures (paired data) such as McNemar or paired t -tests, reporting change in percentages as mean differences (such as self-reported confidence) with 95% confidence intervals or/and frequencies. Descriptive trends over time will also be presented graphically using line graphs or other visual aids as appropriate. However, these will be interpreted with caution as the primary analysis is descriptive. Statistical significance will be set at a p value of 0.05. At project close (month 30), we will conduct semi-structured interviews with a sample of members from the guideline panels and steering group committees in South Africa, Malawi and Nigeria. Sampling will be purposive, with the aim of understanding the broad range of needs, experiences and perspectives and ensuring that the sample reflects a range of socio-demographic characteristics and stakeholder categories. We will begin with a sample size of 10–15 participants in each country; however, sampling will continue if we have not reached saturation of the data through the initial sample size [ 44 ]. Participants will be contacted, either by telephone or via email, and invited to participate in an interview. Interviews will be conducted face-to-face or electronically (e.g. using Microsoft Teams) at a date and time chosen by participants. Face-to-face interviews will take place at a location convenient to participants, which is conducive to a confidential exchange. The interviews will last between 45 and 60 min and will be conducted by researchers trained in qualitative research methodologies and interviewing techniques. The interviews will be guided by a semi-structured topic guide and will include questions informed by the four levels (i.e. reaction, learning, behaviour and results) of the Kirkpatrick model. Specifically, the questions will explore participants’ views and experiences regarding their capacity development needs and expectations of the project; whether and why these expectations were met (or not), the project capacity development activities, what they learned (or not) from these activities and what impact participants believe they have had (or may have) on their practices. Verbal and written information about the study will be provided to all participants taking part in interviews. Written informed consent will be obtained from all participants before proceeding with the interview. With the permission of participants, all interviews will be digitally recorded. Non-participant observations We will conduct non-participant observations of guideline panel and steering group committee meetings. Observational methods can provide useful data on what people do, how they interact with each other and how they engage with particular artefacts in situ (rather than their accounts of these) [ 50 ]. The steering group committees in each country will meet approximately twice over the project duration (with the option for additional meetings): an initial meeting for project orientation (month 2/3) and again to identify priority topics and guideline gaps (month 6). Guideline panels in each country will meet approximately three times over the project duration (with the option for additional meetings): an initial meeting for project orientation and outcome prioritization (month 6/7), another potential meeting if necessary to finalize outcome prioritization and a final meeting to draft recommendations for the guideline (months 17–20). Meetings for both groups will be held virtually or in person, informed by preferences of the committee. With the exception of the initial steering group committee (month 2/3), at least one researcher will be present to observe guideline panel and steering group committee meetings. The observer will aim to identify any capacity-related needs, expectations, gaps, strengths, achievements and challenges and the contexts in which these occur. He or she will also pay particular attention to group dynamics and the interactions between members and different stakeholder groups, and the potential impact of these on capacity-related issues. Observations will be informed by Lofland’s [ 51 ] criteria for organizing analytical observations (acts, activities, meanings, participation, relationships and settings). The observer will take detailed observational notes. With consent of the attendees, all meetings will also be digitally recorded. The recordings will be used to identify further issues not identified and to deepen or clarify issues noted, through the real-time observations of verbal engagements. Data management and analysis: semi-structured interviews and observations Interview and meeting recordings will be transcribed verbatim, and all personal identifying information will be removed from transcripts. The anonymized transcripts, together with observational notes, will be downloaded into Nvivo, a software programme that aids with the management and analysis of qualitative data. Analysis of the qualitative data will proceed in several rounds. First, as with all qualitative data analysis, an ongoing process of iterative analysis of the data will be conducted throughout the data collection period. Second, we will use a thematic analysis approach, using the phases described by Braun and Clarke [ 52 ], to identify key themes pertaining to participants’ capacity development needs and expectations and whether, how and why project capacity development activities met (or not) these needs and expectations. Finally, findings from the surveys (as described above) will also be integrated with the findings from the thematic analysis using a ‘narrative synthesis’ approach, a technique recommended by the Cochrane Collaboration as a way of synthesizing diverse forms of qualitative and quantitative evidence in mixed methods studies [ 53 , 54 ]. This approach will allow for both robust triangulation, and a more comprehensive interpretation of the difference project capacity development activities may have made on the guideline panels and steering group committees. 3. Objective 3: explore guideline panelists’ experiences with reading and using evidence from reviews of qualitative research, including their preferences regarding how qualitative review findings are summarized and presented. Objective 3 of the monitoring and evaluation stakeholder matrix work package explores how guideline panels view and experience evidence from the review(s) of qualitative research, including how it is summarized and presented. Here, we will employ a user testing approach, drawing on the methods and guidance of the SURE user test package 2022 developed by Cochrane Norway ( https://www.cochrane.no/our-user-test-package ) and which has been used to test various evidence-related products [ 55 , 56 , 57 , 58 ]. User testing involves observing people as they engage with a particular product and listening to them ‘think-aloud’. The goal is to gain an understanding of users’ views and experiences, the problems they face and to obtain suggestions for how a product may be improved [ 55 , 56 , 57 , 58 ]. We will begin by identifying or preparing relevant reviews of qualitative research. We will then develop review summary formats and explore guideline panel members’ views and experiences of these formats. We will revise the formats in multiple iterative cycles. Identifying or preparing relevant reviews of qualitative research As part of WP2 of the project (‘evidence synthesis’), we will identify relevant review(s) of qualitative research, including reviews exploring how people affected by the interventions of interest value different outcomes, the acceptability and feasibility of the intervention and potential equity, gender and human rights implications of the intervention. These reviews need to be assessed as sufficiently recent and of a sufficient quality. They also need to have applied GRADE-CERQual assessments to the review findings. Where necessary, we will update existing reviews or prepare reviews ourselves. Developing the review summaries In WP3 of the project (‘decision-making’) the evidence from these reviews will be provided to guideline panels as part of the evidence-to-decision (‘EtD’) frameworks that will inform the recommendations they develop (see Table  1 for further details about project work packages 2 and 3). Our next step will therefore be to prepare summaries of the reviews in a format that can easily be included in the EtD frameworks. Each summary needs to present review findings that are relevant to specific parts of the EtD framework (typically the ‘values’, ‘acceptability’, ‘feasibility’ and ‘equity’ components). It also needs to include information about our confidence in these findings. Finally, the summary needs to indicate where this evidence comes from and to allow guideline panels to move from the summary to more detailed information about the evidence. Most of this information is found in the review’s Summary of Qualitative Findings tables. However, these tables are usually too large for EtD frameworks and are not tailored to each framework component. We will, therefore, start by creating new summaries, using a format that we have previously used in EtD frameworks [ 59 , 60 , 61 ] but that we have not user tested. As opposed to the Summary of Qualitative Findings tables, where each finding and our confidence in the finding, is presented individually in separate rows, this format involves pulling the findings and confidence assessments together in short, narrative paragraphs. User testing the summary format For our first set of user tests, we will observe guideline panels participating in the CPG panel simulation workshops. For our second round of user tests, we will observe how the guideline panels experience and interact with this qualitative evidence during the real guideline processes. Third, we will then test a potentially refined format with a selection of guideline panel members using a semi-structured interview guide. Finally, at the end of the project, we will conduct semi-structured interviews with a selection of guideline panel members to explore their broader views and experiences of interpreting and using evidence from reviews of qualitative studies in their deliberation processes. Figure  1 provides a visual depiction of this iterative process. Iterative approach for user testing evidence from reviews of qualitative research We will draw on the adapted version of Peter Morville’s original honeycomb model of user experience [ 62 ] as the underpinning theoretical framework for this objective [ 63 ] (Fig.  1 ). This adapted version extends and revises the meaning of the facets of user experience depicted in the original model. It includes eight facets: accessibility, findability, usefulness, usability, understandability, credibility, desirability and affiliation. Accessibility involves whether there are physical barriers to gaining access; findability is about whether the person can locate the product or the content that they are looking for; usefulness is about whether the product has practical value for the person; usability comprises how easy and satisfying the product is to use; understandability is about whether the person comprehends correctly both what kind of product it is and the content of the product (and includes both user's subjective perception of her own understanding and an objective measure of actual/correct understanding); credibility comprises whether the product/content is experienced as trustworthy; desirability is about whether the product is something the person wants and has a positive emotional response to it; affiliation involves whether the person identifies with the product, on a personal or a social level, or whether it is alienating and experienced as being not designed for ‘someone like me’. The adapted model also adds to the original model a dimension of user experience over time, capturing the chronological and contingent nature of the different facets. Participants will comprise members of the guideline panels in South Africa, Malawi and Nigeria. Table 3 (above) provides details of the composition of the guideline panels. Non-participant observations: guideline panel simulation workshops and guideline panel meetings We will conduct non-participant observations of the CPG panel simulation workshops and the subsequent guideline panel meetings for developing the recommendations. The CPG panel simulation workshops will run a simulation of a real guideline process and give guideline panels an opportunity to understand how the guideline process works before they participate in real panel meetings. The guideline panels in all three countries will be invited and encouraged to attend these workshops, which will form part of the project capacity development activities of WP5 (Table  1 ). With the participants’ consent, both the simulation workshops and meetings will be digitally recorded and at least two observers will observe and take notes. The observations will focus on how guideline panel members refer to and interact with the summaries of qualitative evidence. Drawing on a user testing approach ( https://www.cochrane.no/our-user-test-package ), we will also look specifically for both problems and facilitators in the way the qualitative evidence is formatted, including ‘show-stoppers’ (the problem is so serious that it hindered participants from correct understanding or from moving forward), ‘big problems/frustrations’ (participants were confused or found something difficult but managed to figure it out or find a way around the problem eventually), ‘minor issues/cosmetic things’ (small irritations, frustrations and small problems that do not have serious consequences, as well as likes/dislikes), ‘positive/negative feedback’, ‘specific suggestions’, ‘preferences’ and any other ‘notable observations’, e.g. feelings of ‘uncertainty’. Structured user testing interviews Based on the insights gained from the non-participant observations (above), we may make changes or refinements to our original summary format (Fig.  1 ). Once the guideline panel meetings have concluded (approximately by month 20), we will then conduct structured user testing interviews to test the potentially refined summary format. These interviews will be conducted with a sample of members from the guideline panels in South Africa, Malawi and Nigeria. Sampling will be purposive, with the aim of understanding the broad range of experiences and perspectives and ensuring the sample reflects a range of socio-demographic characteristics and stakeholder categories. As recommended ( https://www.cochrane.no/our-user-test-package ), we will begin with a sample size of six to eight participants in each country; however, sampling will continue until saturation is achieved [ 44 ]. Participants will be contacted, either telephonically or via email, and invited to participate in an interview. Interviews will be conducted face-to-face or electronically (e.g. using Skype or Teams) at a date and time chosen by participants. Face-to-face interviews will take place at a location convenient to participants, which is conducive to a confidential exchange. In line with the SURE user test package 2022 guidance, the interviews will last approximately 60 min ( https://www.cochrane.no/our-user-test-package ). They will be facilitated by a test leader, who will accompanied by at least one observer who will take notes. Both the test leader and observer(s) will be trained in user testing interviewing methodology and techniques. Verbal and written information about the study will be provided to all participants taking part in interviews. Written informed consent will be obtained from all participants before proceeding with the interview. With the permission of participants, all interviews will be video recorded. For these interviews we will show panel members the latest version of the format, explore immediate first impressions, and then opinions about different elements of the summary. We may also show panel members different formats where we think this may be helpful. We will use a structured interview guide which draws heavily on other interview guides that been developed to user test evidence-related products [ 55 , 56 , 57 , 58 ]. It will include questions related the participant’s background; their immediate first impressions of the summary format(s); in-depth walk-through of the summary format(s), with prompts to think aloud what they are looking at, thinking, doing and feeling; and suggestions for improving the way the summary is formatted and for improving the user testing itself. We may ask follow-up questions to specific issues we observed in the simulation workshops and guideline panel meetings and/or create scenarios that resemble issues we observed in the workshops/meetings. This will be decided upon based on the findings that emerge from these workshops/meetings. The guide will be finalized once the relevant qualitative evidence (from WP2) has been produced and we have gained insights from the workshops and meetings. As with the non-participant observations of meetings and workshops, throughout the interview, the observers will make notes about the participant’s experience as heard, observed and understood. Drawing on a user testing approach, they will look specifically for both problems and facilitators, specific suggestions, preferences and any other notable observations (as described above under ‘non-participant observations’). At project close (month 30), we will also conduct semi-structured interviews with a sample of members from the guideline panels in South Africa, Malawi and Nigeria. These will be the same interviews with guideline panel members as described in objective 2. In addition to exploring participants’ capacity development needs, expectations and achievements, the semi-structured topic guide will also explore their views and experiences of (and specific capacity in) interpreting and using evidence from reviews of qualitative studies in guideline processes. More specifically, questions will investigate participants’ familiarity/experience with qualitative evidence; their perceptions of different types of evidence, what constitutes qualitative evidence and the role of qualitative evidence in guideline processes; and their experiences of using the qualitative evidence in their deliberations as part of the project, including what influenced its use and whether they found it useful. Details pertaining to sampling, data collection procedures and collection tools are described in objective 2. All interview and meeting recordings will be transcribed verbatim, and all personal identifying information will be removed from transcripts. The anonymized transcripts, together with observational notes (from the workshops, meetings and interviews), will be downloaded into a software programme that aids with the management and analysis of qualitative data. Analysis of the data will be guided by the user testing analysis methods described in the SURE user test package 2022 ( https://www.cochrane.no/our-user-test-package ). The analysis will proceed in several, iterative rounds to develop and revise the summary format and to inform the focus of subsequent data collection. After each user test, we will review our notes, first separately and then together. In line with the SURE user test package 2022 guidance, we will look primarily for barriers and facilitators related to correct interpretation of the summary’s contents, ease of use and favourable reception, drawing on the facets of the revised honeycomb model of user experience (Fig.  2 ). We will trace findings back to specific elements or characteristics of the summaries that appeared to facilitate or hinder problems. Before the next set of user tests, we will discuss possible changes that could address any identified barriers and make changes to the summary format. Adapted version of Peter Morville’s honeycomb model of user experience 4. Objective 4: evaluate guideline panelists’, steering group committees’ and project team members’ overall views and experiences of the project, including what works or not, to influence evidence-informed decision-making and guideline adaptation processes. This objective explores overall views and experiences of the project, with a focus on guideline panelists, steering group committees and project team members. Specifically, it seeks to gain an understanding of these three stakeholder groups’ more general views and experiences of the project activities they were involved with and whether, why and how these activities may influence (or not) evidence-informed decision-making and guideline adaptation processes. This will be achieved through semi-structured interviews. Participants will comprise members of the guideline panels and steering group committees in South Africa, Malawi and Nigeria, as well as members of the project team (as described in Table  3 above). At project close (month 30), we will conduct semi-structured interviews with a sample of members from the guideline panels and steering group committees in South Africa, Malawi and Nigeria. These will be the same interviews and participants as described in objective 2. In addition to exploring issues around capacity development and qualitative evidence, the interviews will also investigate participants’ views and experiences of the various project activities they were involved with, and whether, why and how these activities may influence (or not) evidence-informed decision-making and guideline adaptation processes. Details pertaining to sampling, data collection procedures and collection tools are described in objective 2. At project close (month 30), we will also conduct semi-structured interviews with members of the project team (see Table  3 for details of project team composition). We will begin by interviewing all project management team members, WP leads and KT champions. Additional participants will be determined iteratively (depending on what emerges from initial interviews) and purposively, with the aim of understanding the broad range of experiences and perspectives and ensuring the sample reflects the various groups which make up the project team. Interviews will be conducted face-to-face or electronically (e.g. using Skype or Teams) at a date and time chosen by the interviewee. The interviews will last between 45 and 60 min and will be guided by a semi-structured topic guide. The questions will explore participants’ views and experiences of the respective work packages in which they were involved, including what the primary goals of the work package were; if, why and how these goals were met; and what worked and what did not work and why. The same qualitative data analysis procedures and methods will be used as described in objective 2. For this objective, the thematic analysis will identify key themes pertaining to views and experiences of project activities, including what worked (or not) and why, whether, why and how the project may (or not) influence evidence-informed decision-making and guideline development, adaptation and dissemination processes in South Africa, Malawi and Nigeria and potential barriers and facilitators to the sustainability of this influence. Evidence-based guideline development is a multi-stakeholder, multi-perspective, complex set of tasks. There is limited, if any, research that has followed these steps from the perspectives of policymakers or researchers from start to end. The GELA project protocol sets out to monitor and evaluate various key steps in the process, using in-depth qualitative methods alongside appropriate surveys not only to inform the project as it progresses but also to understand the overall impact of all steps on development of transparent and contextually-rich guideline recommendations. Following WHO’s guideline steps, the tasks range from scoping stakeholder-informed priority topics to conducting relevant data gathering and evidence synthesis, followed by guideline panel meetings to reach consensus decisions and finally to produce recommendations that can be useful to end-users and improve health and care outcomes. The GELA project is undertaking a 3-year project to conduct these tasks in the context of newborn and child health priorities. We are doing this in collaboration with national ministries of health, academics, non-governmental partners and civil society groups in Malawi, Nigeria and South Africa. Overall, we aim build capacity across all collaborators for evidence-informed guideline development, while producing fit for context guideline recommendations, in accessible formats that benefit children, caregivers and health care providers. As such, this is a practical research project, in that the products should directly impact care decisions at the national level but with the added benefit of being able to learn about what works or does not work for collaborative guideline development in country. We will also be applying emergent guideline adaptation methods to explore reducing duplication of expensive guideline development efforts in our lower resource settings. Our project addresses newborn and child health, keeping this most vulnerable population in our focus, hoping that producing sound evidence-based recommendations has the potential to impact care. Through some of our formative work, we have completed a landscape analysis identifying and describing all available newborn and child health guidelines in each of the partner countries. In all countries there were similar findings, (1) there is no easy access to guidelines for end-users, thus locating a guideline requires effort and screening through multiple sources; (2) considering national priority conditions in this age group, there were often gaps in available current guidelines for managing children; and (3) when we appraised the guidelines using the global standard, AGREE II tool, we found that the reporting of guideline methods were poor, leaving it uncertain whether the recommendations were credible or whether any influences or interests had determined the direction of a recommendation. Finally, we expected to find many adapted guidelines, based on WHO or UNICEF or similar guidance available globally; however, very few of the identified guidelines stated clearly whether they had been adapted from other sources and, if so, which recommendations were adopted and which adapted. Given progress globally in methods for guideline development, the continued poor reporting on guideline methods at the country level speak to a breakdown in skills-sharing globally, for example, WHO produces guidelines that are recognized as rigorous and follow good practice and reporting, but the same standards are not supported in country. Overall, GELA aims to address these key gaps in national guideline approaches for adaptation, but we need to recognize that this will be a long term process and that we need to learn from each other about what works and what may not serve us. Therefore, this protocol outlines our approach for monitoring several aspects of the project in our efforts to move closer to trustworthy and credible guidelines that all can use and trust for countries like ours. Availability of data and materials Not applicable. Abbreviations Poverty-related diseases Sub-Saharan Africa Clinical practice guidelines Evidence-informed decision-making Evidence-based healthcare Global Evidence Local Adaptation Knowledge translation Levels and trends in child mortality: report 2020. Estimates developed by UNICEF. ISBN: 978-92-806-5147-8. https://www.who.int/publications/m/item/levels-and-trends-in-child-mortality-report-2020 . United Nations Children’s Fund. Accessed Aug 2024. Liu L, Oza S, Hogan D, et al. Global, regional, and national causes of under-5 mortality in 2000–15: an updated systematic analysis with implications for the sustainable development goals. Lancet. 2016;388(10063):3027–35. Article   PubMed   PubMed Central   Google Scholar   Under-five mortality. 2021 https://data.unicef.org/topic/child-survival/under-five-mortality/ . United Nations International Children’s Fund. Accessed Aug 2024. Stewart R, El-Harakeh A, Cherian SA. Evidence synthesis communities in low-income and middle-income countries and the COVID-19 response. Lancet. 2020;396(10262):1539–41. Article   CAS   PubMed   PubMed Central   Google Scholar   Mijumbi RM, Oxman AD, Panisset U, Sewankambo NK. Feasibility of a rapid response mechanism to meet policymakers’ urgent needs for research evidence about health systems in a low income country: a case study. Implementation Sci. 2014;9:114. Article   Google Scholar   Stewart R, Dayal H, Langer L, van Rooyen C. The evidence ecosystem in South Africa: growing resilience and institutionalisation of evidence use. Palgrave Commun. 2019;5(1):90. Uneke CJ, Ezeoha AE, Ndukwe CD, Oyibo PG, Onwe F. Promotion of evidence-informed health policymaking in Nigeria: bridging the gap between researchers and policymakers. Glob Public Health. 2012;7(7):750–65. Article   PubMed   Google Scholar   Young T, Garner P, Clarke M, Volmink J. Series: clinical epidemiology in South Africa Paper 1: evidence-based health care and policy in Africa.: past, present, and future. J Clin Epidemiol. 2017;83:24–30. Vandvik PO, Brandt L. Future of evidence ecosystem series: evidence ecosystems and learning health systems: why bother? J Clin Epidemiol. 2020;123:166–70. Graham R, Mancher M, Wolman D, Greenfield S, Steinberg E. Clinical practice guidelines we can trust. Washington, DC: The National Academy Press; 2011. Book   Google Scholar   Kredo T, Bernhardsson S, Machingaidze S, et al. Guide to clinical practice guidelines: the current state of play. Int J Qual Health Care. 2016;28(1):122–8. McCaul M, Tovey D, Young T, et al. Resources supporting trustworthy, rapid and equitable evidence synthesis and guideline development: results from the COVID-19 evidence network to support decision-making (COVID-END). J Clin Epidemiol. 2022;151:88–95. Tricco AC, Garritty CM, Boulos L, et al. Rapid review methods more challenging during COVID-19: commentary with a focus on 8 knowledge synthesis steps. J Clin Epidemiol. 2020;126:177–83. World Health Organization. WHO handbook for guideline development. Geneva: World Health Organization; 2011. Google Scholar   Schünemann HJ, Wiercioch W, Etxeandia I, et al. Guidelines 2.0: systematic development of a comprehensive checklist for a successful guideline enterprise. CMAJ. 2014;186(3):E123–42. Kredo T, Gerritsen A, van Heerden J, Conway S, Siegfried N. Clinical practice guidelines within the Southern African development community: a descriptive study of the quality of guideline development and concordance with best evidence for five priority diseases. Health Res Policy Syst. 2012;10:1. Malherbe P, Smit P, Sharma K, McCaul M. Guidance we can trust? The status and quality of prehospital clinical guidance in sub-Saharan Africa: a scoping review. African J Emerge Med. 2021;11(1):79–86. McCaul M, Clarke M, Bruijns SR, et al. Global emergency care clinical practice guidelines: a landscape analysis. African J Emerge Med. 2018;8(4):158–63. Dizon JM, Grimmer K, Louw Q, Kredo T, Young T, Machingaidze S. South African guidelines excellence (SAGE): adopt, adapt, or contextualise? South African Med J. 2016;106(12):1177–8. Article   CAS   Google Scholar   Schünemann HJ, Wiercioch W, Brozek J, et al. GRADE evidence to Decision (EtD) frameworks for adoption, adaptation, and de novo development of trustworthy recommendations: GRADE-ADOLOPMENT. J Clin Epidemiol. 2017;81:101–10. ADAPTE Collaboration. 2009 The ADAPTE process: Resource toolkit for guideline adaptation, version 2. http://www.g-i-n.net/ . Accessed Aug 2024. Barreix M, Lawrie TA, Kidula N, et al. Development of the WHO antenatal care recommendations adaptation toolkit: a standardised approach for countries. Health Res Policy Syst. 2020;18(1):70. Ioannidis JP. The mass production of redundant, misleading, and conflicted systematic reviews and meta-analyses. Milbank Q. 2016;94(3):485–514. Opiyo N, Shepperd S, Musila N, English M, Fretheim A. The, “child health evidence week” and grade grid may aid transparency in the deliberative process of guideline development. J Clin Epidemiol. 2012;65(9):962–9. Wang Z, Norris SL, Bero L. The advantages and limitations of guideline adaptation frameworks. Implemen Sci. 2018;13(1):72. Langlois EV, Tunçalp Ö, Norris SL, Askew I, Ghaffar A. Qualitative evidence to improve guidelines and health decision-making. Bull World Health Organ. 2018. https://doi.org/10.2471/BLT.17.206540 . Carmona C, Baxter S, Carroll C. Systematic review of the methodological literature for integrating qualitative evidence syntheses into health guideline development. Res Synth Methods. 2021;12(4):491–505. Guindo LA, Wagner M, Baltussen R, et al. From efficacy to equity: Literature review of decision criteria for resource allocation and healthcare decisionmaking. Cost Eff Res Alloc. 2012;10(1):9. Verkerk K, Van Veenendaal H, Severens JL, Hendriks EJ, Burgers JS. Considered judgement in evidence-based guideline development. Int J Qual Health Care. 2006;18(5):365–9. Alonso-Coello P, Schunemann HJ, Moberg J, et al. GRADE Evidence to Decision (EtD) frameworks: a systematic and transparent approach to making well informed healthcare choices. 1: Introduction. BMJ. 2016. https://doi.org/10.1136/bmj.i2016 . Glenton C, Lewin S, Gülmezoglu AM. Expanding the evidence base for global recommendations on health systems: strengths and challenges of the OptimizeMNH guidance process. Implement Sci. 2016;11:98. Lewin S, Glenton C. Are we entering a new era for qualitative research? using qualitative evidence to support guidance and guideline development by the World Health Organization. Int J Equity Health. 2018;17(1):126. Noyes J, Booth A, Cargo M, Flemming K, Harden A, Harris J, Garside R, Hannes K, Pantoja T, Thomas J. Chapter 21: Qualitative evidence. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, Welch VA (editors). Cochrane Handbook for Systematic Reviews of Interventions version 6.4 (updated August 2023). Cochrane, 2023. Available from www.training.cochrane.org/handbook . Accessed Aug 2024. Glenton C, Lewin S, Norris S. Using evidence from qualitative research to develop WHO guidelines (Chapter 15) World Health Organization Handbook for Guideline Development. 2nd ed. WHO: Geneva; 2016. World Health Organisation. WHO recommendations: intrapartum care for a positive childbirth experience. Geneva: WHO; 2018. Rosenbaum SE, Glenton C, Oxman AD. Summary-of-findings tables in Cochrane reviews improved understanding and rapid retrieval of key information. J Clin Epidemiol. 2010;63(6):620–6. Lewin S, Bohren M, Rashidian A, et al. Applying GRADE-CERQual to qualitative evidence synthesis findings-paper 2: how to make an overall CERQual assessment of confidence and create a summary of qualitative findings table. Implement Sci. 2018;13(Suppl 1):10. Treweek S, Oxman AD, Alderson P, et al. Developing and evaluating communication strategies to support informed decisions and practice based on evidence (DECIDE): protocol and preliminary results. Implement Sci. 2013;8:6. Brandt L, Vandvik PO, Alonso-Coello P, et al. Multilayered and digitally structured presentation formats of trustworthy recommendations: a combined survey and randomised trial. BMJ Open. 2017;7(2): e011569. Kredo T, Cooper S, Abrams AL, et al. ’Building on shaky ground’-challenges to and solutions for primary care guideline implementation in four provinces in South Africa: a qualitative study. BMJ Open. 2020;10(5): e031468. Vandvik PO, Brandt L, Alonso-Coello P, et al. Creating clinical practice guidelines we can trust, use, and share: a new era is imminent. Chest. 2013;144(2):381–9. Michie S, van Stralen MM, West R. The behaviour change wheel: a new method for characterising and designing behaviour change interventions. Implement Sci. 2011;6:42. Jessani NS, Rohwer A, Schmidt B-M, Delobelle P. Integrated knowledge translation to advance noncommunicable disease policy and practice in South Africa: application of the exploration, preparation, implementation, and sustainment (EPIS) framework. Health Res Policy Syst. 2021;19(1):82. Francis JJ, Johnston M, Robertson C, et al. What is an adequate sample size? Operationalising data saturation for theory-based interview studies. Psychol Health. 2010;25(10):1229–45. Roos M, Kadmon M, Kirschfink M, et al. Developing medical educators–a mixed method evaluation of a teaching education program. Med Educ Online. 2014;19:23868. Bijani M, Rostami K, Momennasab M, Yektatalab S. Evaluating the effectiveness of a continuing education program for prevention of occupational exposure to needle stick injuries in nursing staff based on Kirkpatrick’s model. J Natl Med Assoc. 2018;110(5):459–63. PubMed   Google Scholar   Carlfjord S, Roback K, Nilsen P. Five years’ experience of an annual course on implementation science: an evaluation among course participants. Implement Sci. 2017;12(1):101. Young T, Rohwer A, Volmink J, Clarke M. What are the effects of teaching evidence-based health care (EBHC)? Overview of systematic reviews. PLoS ONE. 2014;9(1): e86706. Young T, Dizon J, Kredo T, et al. Enhancing capacity for clinical practice guidelines in South Africa. Pan Afr Med J. 2020;36:18. Green G, Thorogood N. Qualitative methods for health research. London: Sage; 2004. Lofland J. Analyzing social settings. Belmont, CA: Wadsworth Publishing Company; 1971. Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3:77–101. Popay J, Roberts H, Sowden A. Guidance on the conduct of narrative synthesis in systematic reviews a product from the ESRC methods programme. I Health Research: Lancaster; 2006. Pope C, Mays N, Popay J. How can we synthesize qualitative and quantitative evidence for healthcare policy-makers and managers? Healthc Manage Forum. 2006;19(1):27–31. Glenton C, Santesso N, Rosenbaum S, et al. Presenting the results of Cochrane systematic reviews to a consumer audience: a qualitative study. Med Dec Making. 2010;30(5):566–77. Rosenbaum SE, Glenton C, Cracknell J. User experiences of evidence-based online resources for health professionals: user testing of The Cochrane library. BMC Med Inform Decis Mak. 2008;8:34. Rosenbaum SE, Glenton C, Nylund HK, Oxman AD. User testing and stakeholder feedback contributed to the development of understandable and useful Summary of Findings tables for Cochrane reviews. J Clin Epidemiol. 2010;63(6):607–19. Rosenbaum SE, Glenton C, Wiysonge CS, et al. Evidence summaries tailored to health policy-makers in low- and middle-income countries. Bull World Health Organ. 2011;89(1):54–61. Downe S, Finlayson KW, Lawrie TA, et al. Qualitative evidence synthesis (QES) for guidelines: Paper 1 - using qualitative evidence synthesis to inform guideline scope and develop qualitative findings statements. Health Res Policy Syst. 2019;17(1):76. Lewin S, Glenton C, Lawrie TA, et al. Qualitative evidence synthesis (QES) for guidelines: Paper 2 - using qualitative evidence synthesis findings to inform evidence-to-decision frameworks and recommendations. Health Res Policy Syst. 2019;17(1):75. Glenton C, Lewin S, Lawrie TA et al. Qualitative Evidence Synthesis (QES) for Guidelines: Paper 3 – Using qualitative evidence syntheses to develop implementation considerations and inform implementation processes. Health Res Policy Sys. 2019;17:74. https://doi.org/10.1186/s12961-019-0450-1 Morville P. 2004 User Experience Design [honeycomb model]. http://www.semanticstudios.com/publications/semantics/000029.php . Rosenbaum S. 2010 Improving the user experience of evidence: A design approach to evidence-informed health care. PhD thesis. Oslo, Norway: The Oslo School of Architecture and Design. Download references Acknowledgements We gratefully acknowledge the representatives from the National Ministries of Health in Nigeria, Malawi and South Africa for their support and partnership. We would also like to thank the appointed Steering Committees who have been providing input for the research project and guiding the prioritization of topics. We would also like to thank Joy Oliver and Michelle Galloway for their contribution an support of the project. The GELA project is funded by EDCTP2 programme supported by the European Union (grant number RIA2020S-3303-GELA). The funding will cover all the activities for this Monitoring and Evaluation work package, including costs for personnel and publication of papers. Author information Authors and affiliations. Health Systems Research Unit, South African Medical Research Council, Cape Town, South Africa Tamara Kredo, Denny Mabetha, Bey-Marrié Schmidt & Simon Lewin Division of Epidemiology and Biostatistics, Department of Global Health, Stellenbosch University, Cape Town, South Africa Tamara Kredo, Anke Rohwer, Michael McCaul, Idriss Ibrahim Kallon, Taryn Young & Sara Cooper Division of Clinical Pharmacology, Department of Medicine, Stellenbosch University, Cape Town, South Africa Tamara Kredo School of Public Health and Family Medicine, University of Cape Town, Cape Town, South Africa Tamara Kredo & Sara Cooper University of Calabar Teaching Hospital, Calabar, Nigeria Emmanuel Effa Kamuzu University of Health Science, Lilongwe, Malawi Nyanyiwe Mbeye School of Public Health, University of the Western Cape, Cape Town, South Africa Bey-Marrié Schmidt Western Norway University of Applied Sciences, Bergen, Norway Susan Munabi-Babigumira & Claire Glenton Norwegian University of Science and Technology, Trondheim, Norway Simon Lewin MAGIC Evidence Ecosystem Foundation, Oslo, Norway Per Olav Vandvik Department of Health Economics and Health Management, Institute for Health and Society, University of Oslo, Oslo, Norway Cochrane South Africa, South African Medical Research Council, Cape Town, South Africa Sara Cooper You can also search for this author in PubMed   Google Scholar Contributions T.K., S.C., T.Y., S.L., C.G. and P.O.V. conceptualized the protocol idea and S.C. drafted the protocol with input from TK, D.M., A.R., B.M., M.M., I.I., C.G., T.Y., S.L. and P.O.V.; all authors approved the final version for submission for publication. Corresponding author Correspondence to Tamara Kredo . Ethics declarations Ethics approval and consent to participate. Ethics approval has been obtained in each partner country (South Africa, Malawi and Nigeria) from the respective Health Research Ethics Committees or Institutional Review Boards. Information about the project will be provided to, and consent obtained from, all participants completing the online surveys and interviews and all participants taking part in the meetings. The consent forms will make explicit the voluntary nature of participation, that there will be no negative consequences if they decide not to participate and in the case of the interviews and meetings observations will ask explicitly for permission for the interview or meeting to be recorded. The online surveys will ask participants to provide the last six numbers of their ID as a unique identifier to track their capacity development needs and progress throughout the project. To help protect their confidentiality, the information they provide will be private, deidentified and no names will be used. In addition, all cookies and IP address collectors will be disabled to ensure confidentiality. All interview and meeting recordings on the digital recorders will be destroyed following safe storage and transcription, and any identifying information will be redacted from all transcripts. All study data, including recordings, will be stored electronically using password-controlled software only accessible to key project members and project analysts. Reports of study findings will not identify individual participants. We do not anticipate any specific harms or serious risks to participants. However, there is a risk of breaches of confidentiality for participants who take part in guideline panel and steering group committee project meetings. At the start of all meetings, participants will be introduced to each other. The member names of these groups will not be anonymous as they will play an ongoing role in the GELA project. At the start of each meeting, we will discuss the importance of maintaining confidentiality by everyone. As part of guideline development processes, all guideline members will need to declare conflicts of interests and sign a confidentiality agreement. We will explain, however, that while the researchers undertake to maintain confidentiality, we cannot guarantee that other meeting participants will, and there is, thus, a risk of breaches of confidentiality. We will ensure participants are aware of this risk. Participants may also feel anxiety or distress expressing negative views about project activities. Where there is this potential and where participants identify concerns, we will reassure participants of the steps that will be taken to ensure confidentiality. Consent for publication Competing interests. All authors declared no competing interests. 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Health Res Policy Sys 22 , 114 (2024). https://doi.org/10.1186/s12961-024-01189-5 Download citation Received : 02 June 2023 Accepted : 20 July 2024 Published : 19 August 2024 DOI : https://doi.org/10.1186/s12961-024-01189-5 Share this article Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative Related diseases Newborn and child health Saharan Africa (SSA) Informed decision Capacity development Monitoring and evaluation; Research impact Health Research Policy and Systems ISSN: 1478-4505 Submission enquiries: Access here and click Contact Us General enquiries: [email protected] Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. View all journals Explore content About the journal Publish with us Sign up for alerts Review Article Published: 21 August 2024 Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics Gunsagar S. Gulati   ORCID: orcid.org/0000-0003-2798-6220 1   na1 , Jeremy Philip D’Silva   ORCID: orcid.org/0009-0005-0053-7726 2   na1 , Yunhe Liu   ORCID: orcid.org/0000-0002-2120-952X 3 , Linghua Wang 3 , 4 & Aaron M. Newman   ORCID: orcid.org/0000-0002-1857-8172 2 , 5 , 6 , 7   Nature Reviews Molecular Cell Biology ( 2024 ) Cite this article 1618 Accesses 15 Altmetric Metrics details Gene expression analysis Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic. 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The technological landscape and applications of single-cell multi-omics. Nat. Rev. Mol. Cell Biol. 24 , 695–713 (2023). Longo, S. K., Guo, M. G., Ji, A. L. & Khavari, P. A. Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics. Nat. Rev. Genet. 22 , 627–644 (2021). Article   CAS   PubMed   PubMed Central   Google Scholar   Rozenblatt-Rosen, O., Stubbington, M. J. T., Regev, A. & Teichmann, S. A. The Human Cell Atlas: from vision to reality. Nature 550 , 451–453 (2017). Rood, J. E., Maartens, A., Hupalowska, A., Teichmann, S. A. & Regev, A. Impact of the Human Cell Atlas on medicine. Nat. Med. 28 , 2486–2496 (2022). Ferreira, P. G. et al. The effects of death and post-mortem cold ischemia on human tissue transcriptomes. Nat. Commun. 9 , 490 (2018). Article   PubMed   PubMed Central   Google Scholar   Baechler, E. C. et al. Expression levels for many genes in human peripheral blood cells are highly sensitive to ex vivo incubation. Genes Immun. 5 , 347–353 (2004). Massoni-Badosa, R. et al. Sampling time-dependent artifacts in single-cell genomics studies. Genome Biol. 21 , 112 (2020). Zhu, Y., Wang, L., Yin, Y. & Yang, E. Systematic analysis of gene expression patterns associated with postmortem interval in human tissues. Sci. Rep. 7 , 5435 (2017). O’Flanagan, C. H. et al. Dissociation of solid tumor tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses. Genome Biol. 20 , 210 (2019). Liu, Y. et al. Digestion of nucleic acids starts in the stomach. Sci. Rep. 5 , 11936 (2015). Martinez-Diez, M. C., Serrano, M. A., Monte, M. J. & Marin, J. J. Comparison of the effects of bile acids on cell viability and DNA synthesis by rat hepatocytes in primary culture. Biochim. Biophys. Acta 1500 , 153–160 (2000). Sorrentino, S. & Libonati, M. Human pancreatic-type and nonpancreatic-type ribonucleases: a direct side-by-side comparison of their catalytic properties. Arch. Biochem. Biophys. 312 , 340–348 (1994). Denisenko, E. et al. Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows. Genome Biol. 21 , 130 (2020). Cossarizza, A. et al. Guidelines for the use of flow cytometry and cell sorting in immunological studies. Eur. J. Immunol. 47 , 1584–1797 (2017). Lahoz-Beneytez, J. et al. Human neutrophil kinetics: modeling of stable isotope labeling data supports short blood neutrophil half-lives. Blood 127 , 3431–3438 (2016). Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14 , 865–868 (2017). Quan, Y. et al. Impact of cell dissociation on identification of breast cancer stem cells. Cancer Biomark. 12 , 125–133 (2012). Autengruber, A., Gereke, M., Hansen, G., Hennig, C. & Bruder, D. Impact of enzymatic tissue disintegration on the level of surface molecule expression and immune cell function. Eur. J. Microbiol. Immunol. 2 , 112–120 (2012). Article   CAS   Google Scholar   Peterson, V. M. et al. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 35 , 936–939 (2017). Butto, T. et al. Nuclei on the rise: when nuclei-based methods meet next-generation sequencing. Cells 12 , 1051 (2023). Caglayan, E., Liu, Y. & Konopka, G. Neuronal ambient RNA contamination causes misinterpreted and masked cell types in brain single-nuclei datasets. Neuron 110 , 4043–4056.e5 (2022). Thrupp, N. et al. Single-nucleus RNA-Seq is not suitable for detection of microglial activation genes in humans. Cell Rep. 32 , 108189 (2020). Pitchiaya, S. et al. Dynamic recruitment of single RNAs to processing bodies depends on RNA functionality. Mol. Cell 74 , 521–533.e6 (2019). Hicks, S. C., Townes, F. W., Teng, M. & Irizarry, R. A. Missing data and technical variability in single-cell RNA-sequencing experiments. Biostatistics 19 , 562–578 (2018). Article   PubMed   Google Scholar   Koenig, A. L. et al. Single-cell transcriptomics reveals cell-type-specific diversification in human heart failure. Nat. Cardiovasc. Res. 1 , 263–280 (2022). Chervov, A. & Zinovyev, A. Computational challenges of cell cycle analysis using single cell transcriptomics. Preprint at https://doi.org/10.48550/arXiv.2208.05229 (2022). Osorio, D. & Cai, J. J. Systematic determination of the mitochondrial proportion in human and mice tissues for single-cell RNA-sequencing data quality control. Bioinformatics 37 , 963–967 (2021). Tran, H. T. N. et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 21 , 12 (2020). Zhang, Y., Parmigiani, G. & Johnson, W. E. ComBat-seq: batch effect adjustment for RNA-seq count data. NAR Genom. Bioinform. 2 , lqaa078 (2020). Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43 , e47 (2015). Haghverdi, L., Lun, A. T. L., Morgan, M. D. & Marioni, J. C. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36 , 421–427 (2018). Polański, K. et al. BBKNN: fast batch alignment of single cell transcriptomes. Bioinformatics 36 , 964–965 (2020). Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16 , 1289–1296 (2019). Hie, B., Bryson, B. & Berger, B. Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat. Biotechnol. 37 , 685–691 (2019). Welch, J. D. et al. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177 , 1873–1887.e17 (2019). Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177 , 1888–1902.e21 (2019). Nguyen, H. C. T., Baik, B., Yoon, S., Park, T. & Nam, D. Benchmarking integration of single-cell differential expression. Nat. Commun. 14 , 1570 (2023). Junttila, S., Smolander, J. & Elo, L. L. Benchmarking methods for detecting differential states between conditions from multi-subject single-cell RNA-seq data. Brief. Bioinform. 23 , bbac286 (2022). Soneson, C. & Robinson, M. D. Bias, robustness and scalability in single-cell differential expression analysis. Nat. Methods 15 , 255–261 (2018). Dann, E., Henderson, N. C., Teichmann, S. A., Morgan, M. D. & Marioni, J. C. Differential abundance testing on single-cell data using k-nearest neighbor graphs. Nat. Biotechnol. 40 , 245–253 (2022). Lopez, R., Regier, J., Cole, M. B., Jordan, M. I. & Yosef, N. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15 , 1053–1058 (2018). Lakkis, J. et al. A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell transcriptomics. Genome Res. 31 , 1753–1766 (2021). Li, H., McCarthy, D. J., Shim, H. & Wei, S. Trade-off between conservation of biological variation and batch effect removal in deep generative modeling for single-cell transcriptomics. BMC Bioinform. 23 , 460 (2022). Lotfollahi, M., Wolf, F. A. & Theis, F. J. scGen predicts single-cell perturbation responses. Nat. Methods 16 , 715–721 (2019). Bahrami, M. et al. Deep feature extraction of single-cell transcriptomes by generative adversarial network. Bioinformatics 37 , 1345–1351 (2021). Tyler, S. R., Guccione, E. & Schadt, E. E. Erasure of biologically meaningful signal by unsupervised scRNAseq batch-correction methods. Preprint at bioRxiv https://doi.org/10.1101/2021.11.15.468733 (2023). Luecken, M. D. et al. Benchmarking atlas-level data integration in single-cell genomics. Nat. Methods 19 , 41–50 (2022). Zhang, Z. et al. Signal recovery in single cell batch integration. Preprint at bioRxiv https://doi.org/10.1101/2023.05.05.539614 (2023). Dann, E. et al. Precise identification of cell states altered in disease using healthy single-cell references. Nat. Genet. 55 , 1998–2008 (2023). Svensson, V., da Veiga Beltrame, E. & Pachter, L. A curated database reveals trends in single-cell transcriptomics. Database 2020 , baaa073 (2020). Regev, A. et al. The Human Cell Atlas. eLife 6 , e27041 (2017). Eraslan, G. et al. Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function. Science 376 , eabl4290 (2022). Slyper, M. et al. A single-cell and single-nucleus RNA-seq toolbox for fresh and frozen human tumors. Nat. Med. 26 , 792–802 (2020). Zhang, A. W. et al. Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling. Nat. Methods 16 , 1007–1015 (2019). Pliner, H. A., Shendure, J. & Trapnell, C. Supervised classification enables rapid annotation of cell atlases. Nat. Methods 16 , 983–986 (2019). Ianevski, A., Giri, A. K. & Aittokallio, T. Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data. Nat. Commun. 13 , 1246 (2022). Franchini, M., Pellecchia, S., Viscido, G. & Gambardella, G. Single-cell gene set enrichment analysis and transfer learning for functional annotation of scRNA-seq data. NAR Genom. Bioinform. 5 , lqad024 (2023). Aran, D. et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat. Immunol. 20 , 163–172 (2019). Kiselev, V. Y., Yiu, A. & Hemberg, M. scmap: projection of single-cell RNA-seq data across data sets. Nat. Methods 15 , 359–362 (2018). de Kanter, J. K., Lijnzaad, P., Candelli, T., Margaritis, T. & Holstege, F. C. P. CHETAH: a selective, hierarchical cell type identification method for single-cell RNA sequencing. Nucleic Acids Res. 47 , e95 (2019). Lyu, P., Zhai, Y., Li, T. & Qian, J. CellAnn: a comprehensive, super-fast, and user-friendly single-cell annotation web server. Bioinformatics 39 , btad521 (2023). Boufea, K., Seth, S. & Batada, N. N. scID uses discriminant analysis to identify transcriptionally equivalent cell types across single-cell RNA-seq data with batch effect. iScience 23 , 100914 (2020). Lin, Y. et al. scClassify: sample size estimation and multiscale classification of cells using single and multiple reference. Mol. Syst. Biol. 16 , e9389 (2020). Yin, Q. et al. scGraph: a graph neural network-based approach to automatically identify cell types. Bioinformatics 38 , 2996–3003 (2022). Alquicira-Hernandez, J., Sathe, A., Ji, H. P., Nguyen, Q. & Powell, J. E. scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data. Genome Biol. 20 , 264 (2019). Li, C. et al. SciBet as a portable and fast single cell type identifier. Nat. Commun. 11 , 1818 (2020). Hou, W. & Ji, Z. Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis. Nat. Methods https://doi.org/10.1038/s41592-024-02235-4 (2024). Lotfollahi, M., Yuhan, H., Theis, F. J. & Satija, R. The future of rapid and automated single-cell data analysis using reference mapping. Cell 187 , 2343–2358 (2024). Michielsen, L. et al. Single-cell reference mapping to construct and extend cell-type hierarchies. NAR Genom. Bioinform. 5 , lqad070 (2023). Lotfollahi, M. et al. Mapping single-cell data to reference atlases by transfer learning. Nat. Biotechnol. 40 , 121–130 (2022). Osumi-Sutherland, D. et al. Cell type ontologies of the Human Cell Atlas. Nat. Cell Biol. 23 , 1129–1135 (2021). Flanagin, A., Frey, T. & Christiansen, S. L. Updated guidance on the reporting of race and ethnicity in medical and science journals. JAMA 326 , 621–627 (2021). Brbić, M. et al. Annotation of spatially resolved single-cell data with STELLAR. Nat. Methods 19 , 1411–1418 (2022). Wen, L. & Tang, F. Single-cell sequencing in stem cell biology. Genome Biol. 17 , 71 (2016). Chen, H., Ye, F. & Guo, G. Revolutionizing immunology with single-cell RNA sequencing. Cell Mol. Immunol. 16 , 242–249 (2019). Gavish, A. et al. Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours. Nature 618 , 598–606 (2023). Zhang, Y. et al. Single-cell RNA sequencing in cancer research. J. Exp. Clin. Cancer Res. 40 , 81 (2021). Machado, L. et al. Tissue damage induces a conserved stress response that initiates quiescent muscle stem cell activation. Cell Stem Cell 28 , 1125–1135.e7 (2021). Uniken Venema, W. T. C. et al. Gut mucosa dissociation protocols influence cell type proportions and single-cell gene expression levels. Sci. Rep. 12 , 9897 (2022). van den Brink, S. C. et al. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat. Methods 14 , 935–936 (2017). Jones, R. C. et al. The Tabula Sapiens: a multiple-organ, single-cell transcriptomic atlas of humans. Science 376 , eabl4896 (2022). Tabula Muris Consortium. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562 , 367–372 (2018). Article   Google Scholar   Kumar, T. et al. A spatially resolved single-cell genomic atlas of the adult human breast. Nature 620 , 181–191 (2023). Chen, J. Y. et al. Hoxb5 marks long-term haematopoietic stem cells and reveals a homogenous perivascular niche. Nature 530 , 223–227 (2016). Rossi, L., Challen, G. A., Sirin, O., Lin, K. K. & Goodell, M. A. Hematopoietic stem cell characterization and isolation. Methods Mol. Biol. 750 , 47–59 (2011). Ikuta, K. & Weissman, I. L. Evidence that hematopoietic stem cells express mouse c-kit but do not depend on steel factor for their generation. Proc. Natl Acad. Sci. USA 89 , 1502–1506 (1992). Liu, D. D. et al. Purification and characterization of human neural stem and progenitor cells. Cell 186 , 1179–1194.e15 (2023). Chan, C. K. F. et al. Identification of the human skeletal stem cell. Cell 175 , 43–56.e21 (2018). Ziegenhain, C. et al. Comparative analysis of single-cell RNA sequencing methods. Mol. Cell 65 , 631–643.e4 (2017). Al’Khafaji, A. M. et al. High-throughput RNA isoform sequencing using programmed cDNA concatenation. Nat. Biotechnol. 42 , 582–586 (2023). Salmen, F. et al. High-throughput total RNA sequencing in single cells using VASA-seq. Nat. Biotechnol. 40 , 1780–1793 (2022). Herman, J. S., Sagar & Grün, D. FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data. Nat. Methods 15 , 379–386 (2018). Jindal, A., Gupta, P., Jayadeva & Sengupta, D. Discovery of rare cells from voluminous single cell expression data. Nat. Commun. 9 , 4719 (2018). Fa, B. et al. GapClust is a light-weight approach distinguishing rare cells from voluminous single cell expression profiles. Nat. Commun. 12 , 4197 (2021). Dong, R. & Yuan, G. C. GiniClust3: a fast and memory-efficient tool for rare cell type identification. BMC Bioinform. 21 , 158 (2020). Wegmann, R. et al. CellSIUS provides sensitive and specific detection of rare cell populations from complex single-cell RNA-seq data. Genome Biol. 20 , 142 (2019). Song, D., Li, K., Hemminger, Z., Wollman, R. & Li, J. J. scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling. Bioinformatics 37 , i358–i366 (2021). Neufeld, A., Gao, L. L., Popp, J., Battle, A. & Witten, D. Inference after latent variable estimation for single-cell RNA sequencing data. Biostatistics 25 , 270–287 (2023). Song, D., Li, K., Ge, X. & Li, J. J. ClusterDE: a post-clustering differential expression (DE) method robust to false-positive inflation caused by double bioRxiv (2023). Persad, S. et al. SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data. Nat. Biotechnol. 41 , 1746–1757 (2023). Singhal, V. et al. BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis. Nat. Genet. 56 , 431–441 (2024). Cannoodt, R., Saelens, W. & Saeys, Y. Computational methods for trajectory inference from single-cell transcriptomics. Eur. J. Immunol. 46 , 2496–2506 (2016). Saelens, W., Cannoodt, R., Todorov, H. & Saeys, Y. A comparison of single-cell trajectory inference methods. Nat. Biotechnol. 37 , 547–554 (2019). La Manno, G. et al. RNA velocity of single cells. Nature 560 , 494–498 (2018). Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. 38 , 1408–1414 (2020). Gorin, G., Svensson, V. & Pachter, L. Protein velocity and acceleration from single-cell multiomics experiments. Genome Biol. 21 , 39 (2020). Hendriks, G. J. et al. NASC-seq monitors RNA synthesis in single cells. Nat. Commun. 10 , 3138 (2019). Erhard, F. et al. scSLAM-seq reveals core features of transcription dynamics in single cells. Nature 571 , 419–423 (2019). Buenrostro, J. D. et al. Integrated single-cell analysis maps the continuous regulatory landscape of human hematopoietic differentiation. Cell 173 , 1535–1548.e16 (2018). Pei, W. et al. Polylox barcoding reveals haematopoietic stem cell fates realized in vivo. Nature 548 , 456–460 (2017). Sharma, R. et al. The TRACE-seq method tracks recombination alleles and identifies clonal reconstitution dynamics of gene targeted human hematopoietic stem cells. Nat. Commun. 12 , 472 (2021). Yang, D. et al. Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution. Cell 185 , 1905–1923.e25 (2022). Spencer Chapman, M. et al. Lineage tracing of human development through somatic mutations. Nature 595 , 85–90 (2021). Muyas, F. et al. De novo detection of somatic mutations in high-throughput single-cell profiling data sets. Nat. Biotechnol. 42 , 758–767 (2024). Ludwig, L. S. et al. Lineage tracing in humans enabled by mitochondrial mutations and single-cell genomics. Cell 176 , 1325–1339.e22 (2019). Gabbutt, C. et al. Fluctuating methylation clocks for cell lineage tracing at high temporal resolution in human tissues. Nat. Biotechnol. 40 , 720–730 (2022). DuPage, M. & Bluestone, J. A. Harnessing the plasticity of CD4 + T cells to treat immune-mediated disease. Nat. Rev. Immunol. 16 , 149–163 (2016). Huyghe, A., Trajkova, A. & Lavial, F. Cellular plasticity in reprogramming, rejuvenation and tumorigenesis: a pioneer TF perspective. Trends Cell Biol. 34 , 255–267 (2024). Setty, M. et al. Characterization of cell fate probabilities in single-cell data with Palantir. Nat. Biotechnol. 37 , 451–460 (2019). Stassen, S. V., Yip, G. G. K., Wong, K. K. Y., Ho, J. W. K. & Tsia, K. K. Generalized and scalable trajectory inference in single-cell omics data with VIA. Nat. Commun. 12 , 5528 (2021). Pandey, K. & Zafar, H. Inference of cell state transitions and cell fate plasticity from single-cell with MARGARET. Nucleic Acids Res. 50 , e86 (2022). Lönnberg, T. et al. Single-cell RNA-seq and computational analysis using temporal mixture modelling resolves Th1/Tfh fate bifurcation in malaria. Sci. Immunol. 2 , eaal2192 (2017). Velten, L. et al. Human haematopoietic stem cell lineage commitment is a continuous process. Nat. Cell Biol. 19 , 271–281 (2017). Lange, M. et al. CellRank for directed single-cell fate mapping. Nat. Methods 19 , 159–170 (2022). Weiler, P., Lange, M., Klein, M., Pe’er, D. & Theis, F. CellRank 2: unified fate mapping in multiview single-cell data. Nat. Methods 21 , 1196–1205 (2024). Schiebinger, G. et al. Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming. Cell 176 , 928–943.e22 (2019). Tong, A., Huang, J., Wolf, G., van Dijk, D. & Krishnaswamy, S. TrajectoryNet: a dynamic optimal transport network for modeling cellular dynamics. Proc. Mach. Learn. Res. 119 , 9526–9536 (2020). PubMed   PubMed Central   Google Scholar   Kamimoto, K. et al. Dissecting cell identity via network inference and in silico gene perturbation. Nature 614 , 742–751 (2023). Weissman, I. L. Stem cells: units of development, units of regeneration, and units in evolution. Cell 100 , 157–168 (2000). Senra, D., Guisoni, N. & Diambra, L. ORIGINS: a protein network-based approach to quantify cell pluripotency from scRNA-seq data. MethodsX 9 , 101778 (2022). Malta, T. M. et al. Machine learning identifies stemness features associated with oncogenic dedifferentiation. Cell 173 , 338–354.e15 (2018). Müller, F. J. et al. Regulatory networks define phenotypic classes of human stem cell lines. Nature 455 , 401–405 (2008). Zhang, F. et al. FitDevo: accurate inference of single-cell developmental potential using sample-specific gene weight. Brief. Bioinform. 23 , bbac293 (2022). Gulati, G. S. et al. Single-cell transcriptional diversity is a hallmark of developmental potential. Science 367 , 405–411 (2020). Grün, D. et al. De novo prediction of stem cell identity using single-cell transcriptome data. Cell Stem Cell 19 , 266–277 (2016). Kannan, S., Farid, M., Lin, B. L., Miyamoto, M. & Kwon, C. Transcriptomic entropy benchmarks stem cell-derived cardiomyocyte maturation against endogenous tissue at single cell level. PLoS Comput. Biol. 17 , e1009305 (2021). Jin, S., MacLean, A. L., Peng, T. & Nie, Q. scEpath: energy landscape-based inference of transition probabilities and cellular trajectories from single-cell transcriptomic data. Bioinformatics 34 , 2077–2086 (2018). Guo, M., Bao, E. L., Wagner, M., Whitsett, J. A. & Xu, Y. SLICE: determining cell differentiation and lineage based on single cell entropy. Nucleic Acids Res. 45 , e54 (2017). PubMed   Google Scholar   Teschendorff, A. E. & Enver, T. Single-cell entropy for accurate estimation of differentiation potency from a cell’s transcriptome. Nat. Commun. 8 , 15599 (2017). Teschendorff, A. E., Maity, A. K., Hu, X., Weiyan, C. & Lechner, M. Ultra-fast scalable estimation of single-cell differentiation potency from scRNA-seq data. Bioinformatics 37 , 1528–1534 (2021). Ni, X. et al. Accurate estimation of single-cell differentiation potency based on network topology and gene ontology information. IEEE/ACM Trans. Comput. Biol. Bioinform. 19 , 3255–3262 (2022). Kang, M. et al. Mapping single-cell developmental potential in health and disease with interpretable deep learning. Preprint at bioRxiv https://doi.org/10.1101/2024.03.19.585637 (2024). Palla, G., Fischer, D. S., Regev, A. & Theis, F. J. Spatial components of molecular tissue biology. Nat. Biotechnol. 40 , 308–318 (2022). Lohoff, T. et al. Integration of spatial and single-cell transcriptomic data elucidates mouse organogenesis. Nat. Biotechnol. 40 , 74–85 (2022). Hickey, J. W. et al. Organization of the human intestine at single-cell resolution. Nature 619 , 572–584 (2023). Greenwald, A. C. et al. Integrative spatial analysis reveals a multi-layered organization of glioblastoma. Cell 187 , 2485–2501.e26 (2024). Liu, J. et al. Concordance of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing. Life Sci. Alliance 6 , e202201701 (2023). He, S. et al. High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imaging. Nat. Biotechnol. 40 , 1794–1806 (2022). Eng, C. L. et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature 568 , 235–239 (2019). Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361 , eaat5691 (2018). Janesick, A. et al. High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis. Nat. Commun. 14 , 8353 (2023). Liang, S. et al. Single-cell manifold-preserving feature selection for detecting rare cell populations. Nat. Comput. Sci. 1 , 374–384 (2021). Missarova, A. et al. geneBasis: an iterative approach for unsupervised selection of targeted gene panels from scRNA-seq. Genome Biol. 22 , 333 (2021). Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 39 , 313–319 (2021). Wei, X. et al. Single-cell Stereo-seq reveals induced progenitor cells involved in axolotl brain regeneration. Science 377 , eabp9444 (2022). Chen, A. et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185 , 1777–1792.e21 (2022). Nagendran, M. et al. 1457 Visium HD enables spatially resolved, single-cell scale resolution mapping of FFPE human breast cancer tissue. J. Immunother. Cancer 11 , A1620 (2023). Google Scholar   Lee, Y. et al. XYZeq: spatially resolved single-cell RNA sequencing reveals expression heterogeneity in the tumor microenvironment. Sci. Adv . 7 , eabg4755 (2021). Srivatsan, S. R. et al. Embryo-scale, single-cell spatial transcriptomics. Science 373 , 111–117 (2021). Russell, A. J. C. et al. Slide-tags enables single-nucleus barcoding for multimodal spatial genomics. Nature 625 , 101–109 (2024). Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489 , 391–399 (2012). Jung, N. & Kim, T.-K. Spatial transcriptomics in neuroscience. Exp. Mol. Med. 55 , 2105–2115 (2023). Moses, L. & Pachter, L. Museum of spatial transcriptomics. Nat. Methods 19 , 534–546 (2022). Magoulopoulou, A. et al. Padlock probe-based targeted in situ sequencing: overview of methods and applications. Annu. Rev. Genom. Hum. Genet. 24 , 133–150 (2023). Williams, C. G., Lee, H. J., Asatsuma, T., Vento-Tormo, R. & Haque, A. An introduction to spatial transcriptomics for biomedical research. Genome Med. 14 , 68 (2022). Chu, T., Wang, Z., Pe’er, D. & Danko, C. G. Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology. Nat. Cancer 3 , 505–517 (2022). Newman, A. M. et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 37 , 773–782 (2019). Li, B. et al. Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution. Nat. Methods 19 , 662–670 (2022). Kleshchevnikov, V. et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nat. Biotechnol. 40 , 661–671 (2022). Cable, D. M. et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat. Biotechnol. 40 , 517–526 (2022). Vahid, M. R. et al. High-resolution alignment of single-cell and spatial transcriptomes with CytoSPACE. Nat. Biotechnol. 41 , 1543–1548 (2023). Biancalani, T. et al. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nat. Methods 18 , 1352–1362 (2021). Wei, R. et al. Spatial charting of single-cell transcriptomes in tissues. Nat. Biotechnol. 40 , 1190–1199 (2022). Zhao, E. et al. Spatial transcriptomics at subspot resolution with BayesSpace. Nat. Biotechnol. 39 , 1375–1384 (2021). Bergenstråhle, L. et al. Super-resolved spatial transcriptomics by deep data fusion. Nat. Biotechnol. 40 , 476–479 (2022). Hu, J. et al. Deciphering tumor ecosystems at super resolution from spatial transcriptomics with TESLA. Cell Syst. 14 , 404–417.e4 (2023). Zhang, D. et al. Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology. Nat. Biotechnol . https://doi.org/10.1038/s41587-023-02019-9 (2024). Miller, B. F., Huang, F., Atta, L., Sahoo, A. & Fan, J. Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data. Nat. Commun. 13 , 2339 (2022). Garcia-Alonso, L. et al. Single-cell roadmap of human gonadal development. Nature 607 , 540–547 (2022). Fawkner-Corbett, D. et al. Spatiotemporal analysis of human intestinal development at single-cell resolution. Cell 184 , 810–826.e23 (2021). Moor, A. E. et al. Spatial reconstruction of single enterocytes uncovers broad zonation along the intestinal villus axis. Cell 175 , 1156–1167.e15 (2018). Bahar Halpern, K. et al. Lgr5+ telocytes are a signaling source at the intestinal villus tip. Nat. Commun. 11 , 1936 (2020). Valdeolivas, A. et al. Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics. NPJ Precis. Oncol. 8 , 10 (2024). Sibai, M. et al. The spatial landscape of Cancer Hallmarks reveals patterns of tumor ecology. Preprint at bioRxiv https://doi.org/10.1101/2022.06.18.496114 (2023). Heiser, C. N. et al. Molecular cartography uncovers evolutionary and microenvironmental dynamics in sporadic colorectal tumors. Cell 186 , 5620–5637.e16 (2023). Ren, Y. et al. Spatial transcriptomics reveals niche-specific enrichment and vulnerabilities of radial glial stem-like cells in malignant gliomas. Nat. Commun. 14 , 1028 (2023). Arora, R. et al. Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response. Nat. Commun. 14 , 5029 (2023). Meylan, M. et al. Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer. Immunity 55 , 527–541.e5 (2022). Haviv, D. et al. The covariance environment defines cellular niches for spatial inference. Nat. Biotechnol. https://doi.org/10.1038/s41587-024-02193-4 (2024). Abdelaal, T., Mourragui, S., Mahfouz, A. & Reinders, M. J. T. SpaGE: spatial gene enhancement using scRNA-seq. Nucleic Acids Res. 48 , e107 (2020). Sun, E. D., Ma, R., Navarro Negredo, P., Brunet, A. & Zou, J. TISSUE: uncertainty-calibrated prediction of single-cell spatial transcriptomics improves downstream analyses. Nat. Methods 21 , 444–454 (2024). Clifton, K. et al. STalign: alignment of spatial transcriptomics data using diffeomorphic metric mapping. Nat. Commun. 14 , 8123 (2023). Jones, A., Townes, F. W., Li, D. & Engelhardt, B. E. Alignment of spatial genomics data using deep Gaussian processes. Nat. Methods 20 , 1379–1387 (2023). Zhang, M. et al. Molecularly defined and spatially resolved cell atlas of the whole mouse brain. Nature 624 , 343–354 (2023). Preibisch, S., Saalfeld, S. & Tomancak, P. Globally optimal stitching of tiled 3D microscopic image acquisitions. Bioinformatics 25 , 1463–1465 (2009). Varrone, M., Tavernari, D., Santamaria-Martínez, A., Walsh, L. A. & Ciriello, G. CellCharter reveals spatial cell niches associated with tissue remodeling and cell plasticity. Nat. Genet. 56 , 74–84 (2024). Rajachandran, S. et al. Dissecting the spermatogonial stem cell niche using spatial transcriptomics. Cell Rep. 42 , 112737 (2023). Walsh, L. A. & Quail, D. F. Decoding the tumor microenvironment with spatial technologies. Nat. Immunol. 24 , 1982–1993 (2023). Morabito, S., Reese, F., Rahimzadeh, N., Miyoshi, E. & Swarup, V. hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data. Cell Rep. Methods 3 , 100498 (2023). Choi, J. et al. QuadST: a powerful and robust approach for identifying cell-cell interaction-changed genes on spatially resolved transcriptomics. Preprint at bioRxiv https://doi.org/10.1101/2023.12.04.570019 (2023). Pentimalli, T. M. et al. High-resolution molecular atlas of a lung tumor in 3D. Preprint at bioRxiv https://doi.org/10.1101/2023.05.10.539644 (2023). Schürch, C. M. et al. Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front. Cell 182 , 1341–1359.e19 (2020). Qiu, X. et al. Spateo: multidimensional spatiotemporal modeling of single-cell spatial transcriptomics. Preprint at bioRxiv https://doi.org/10.1101/2022.12.07.519417 (2022). Wu, Z. et al. Graph deep learning for the characterization of tumour microenvironments from spatial protein profiles in tissue specimens. Nat. Biomed. Eng. 6 , 1435–1448 (2022). Farah, E. N. et al. Spatially organized cellular communities form the developing human heart. Nature 627 , 854–864 (2024). Bhate, S. S., Barlow, G. L., Schürch, C. M. & Nolan, G. P. Tissue schematics map the specialization of immune tissue motifs and their appropriation by tumors. Cell Syst. 13 , 109–130.e6 (2022). Kim, J. et al. Unsupervised discovery of tissue architecture in multiplexed imaging. Nat. Methods 19 , 1653–1661 (2022). Long, Y. et al. Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST. Nat. Commun. 14 , 1155 (2023). Ren, H., Walker, B. L., Cang, Z. & Nie, Q. Identifying multicellular spatiotemporal organization of cells with SpaceFlow. Nat. Commun. 13 , 4076 (2022). Li, J., Chen, S., Pan, X., Yuan, Y. & Shen, H.-B. Cell clustering for spatial transcriptomics data with graph neural networks. Nat. Comput. Sci. 2 , 399–408 (2022). Pham, D. et al. Robust mapping of spatiotemporal trajectories and cell-cell interactions in healthy and diseased tissues. Nat. Commun. 14 , 7739 (2023). Cang, Z. et al. Screening cell-cell communication in spatial transcriptomics via collective optimal transport. Nat. Methods 20 , 218–228 (2023). Andersson, A. et al. Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions. Nat. Commun. 12 , 6012 (2021). Birk, S. et al. Large-scale characterization of cell niches in spatial atlases using bio-inspired graph learning. Preprint at bioRxiv https://doi.org/10.1101/2024.02.21.581428 (2024). Turesson, G. The genotypical response of the plant species to the habitat. Hereditas 3 , 211–350 (1922). Ortiz, R. Göte Turesson’s research legacy to Hereditas: from the ecotype concept in plants to the analysis of landraces’ diversity in crops. Hereditas 157 , 44 (2020). Luca, B. A. et al. Atlas of clinically distinct cell states and ecosystems across human solid tumors. Cell 184 , 5482–5496.e28 (2021). Steen, C. B. et al. The landscape of tumor cell states and ecosystems in diffuse large B cell lymphoma. Cancer Cell 39 , 1422–1437.e10 (2021). Luca, B. A. et al. Atlas of clinically-distinct cell states and cellular ecosystems across human solid tumors. Cancer Res. 80 , abstr. 3443 (2020). Wu, S. Z. et al. A single-cell and spatially resolved atlas of human breast cancers. Nat. Genet. 53 , 1334–1347 (2021). Jerby-Arnon, L. & Regev, A. DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data. Nat. Biotechnol. 40 , 1467–1477 (2022). Bill, R. et al. CXCL9:SPP1 macrophage polarity identifies a network of cellular programs that control human cancers. Science 381 , 515–524 (2023). Pelka, K. et al. Spatially organized multicellular immune hubs in human colorectal cancer. Cell 184 , 4734–4752.e20 (2021). Ji, A. L. et al. Multimodal analysis of composition and spatial architecture in human squamous cell carcinoma. Cell 182 , 497–514.e22 (2020). He, S. et al. Starfysh integrates spatial transcriptomic and histologic data to reveal heterogeneous tumor-immune hubs. Nat. Biotechnol. https://doi.org/10.1038/s41587-024-02173-8 (2024). Liu, C. et al. Spatiotemporal mapping of gene expression landscapes and developmental trajectories during zebrafish embryogenesis. Dev. Cell 57 , 1284–1298.e5 (2022). Gu, Y., Liu, J., Li, C. & Welch, J. D. Mapping cell fate transition in space and time. Preprint at bioRxiv https://doi.org/10.1101/2024.02.12.579941 (2024). Zhao, T. et al. Spatial genomics enables multi-modal study of clonal heterogeneity in tissues. Nature 601 , 85–91 (2022). Xue, Y. et al. Single-cell mitochondrial variant enrichment resolved clonal tracking and spatial architecture in human embryonic hematopoiesis. Preprint at bioRxiv https://doi.org/10.1101/2023.09.18.558215 (2023). Ratz, M. et al. Clonal relations in the mouse brain revealed by single-cell and spatial transcriptomics. Nat. Neurosci. 25 , 285–294 (2022). Erickson, A. et al. Spatially resolved clonal copy number alterations in benign and malignant tissue. Nature 608 , 360–367 (2022). Lomakin, A. et al. Spatial genomics maps the structure, nature and evolution of cancer clones. Nature 611 , 594–602 (2022). Househam, J. et al. Phenotypic plasticity and genetic control in colorectal cancer evolution. Nature 611 , 744–753 (2022). Lim, J. et al. Transitioning single-cell genomics into the clinic. Nat. Rev. Genet. 24 , 573–584 (2023). Van de Sande, B. et al. Applications of single-cell RNA sequencing in drug discovery and development. Nat. Rev. Drug Discov. 22 , 496–520 (2023). Lozano, A. X. et al. T cell characteristics associated with toxicity to immune checkpoint blockade in patients with melanoma. Nat. Med. 28 , 353–362 (2022). Kwon, M. et al. Determinants of response and intrinsic resistance to PD-1 blockade in microsatellite instability-high gastric cancer. Cancer Discov. 11 , 2168–2185 (2021). Zhou, Y. et al. Single-cell multiomics sequencing reveals prevalent genomic alterations in tumor stromal cells of human colorectal cancer. Cancer Cell 38 , 818–828.e5 (2020). Abe, Y. et al. A single-cell atlas of non-haematopoietic cells in human lymph nodes and lymphoma reveals a landscape of stromal remodelling. Nat. Cell Biol. 24 , 565–578 (2022). Ajani, J. A. et al. YAP1 mediates gastric adenocarcinoma peritoneal metastases that are attenuated by YAP1 inhibition. Gut 70 , 55–66 (2021). Beneyto-Calabuig, S. et al. Clonally resolved single-cell multi-omics identifies routes of cellular differentiation in acute myeloid leukemia. Cell Stem Cell 30 , 706–721.e8 (2023). Miyamoto, D. T., Ting, D. T., Toner, M., Maheswaran, S. & Haber, D. A. Single-cell analysis of circulating tumor cells as a window into tumor heterogeneity. Cold Spring Harb. Symp. Quant. Biol. 81 , 269–274 (2016). Loh, K. M. et al. Mapping the pairwise choices leading from pluripotency to human bone, heart, and other mesoderm cell types. Cell 166 , 451–467 (2016). Karimi, E. et al. Single-cell spatial immune landscapes of primary and metastatic brain tumours. Nature 614 , 555–563 (2023). Wang, X. Q. et al. Spatial predictors of immunotherapy response in triple-negative breast cancer. Nature 621 , 868–876 (2023). Lin, J. R. et al. High-plex immunofluorescence imaging and traditional histology of the same tissue section for discovering image-based biomarkers. Nat. Cancer 4 , 1036–1052 (2023). Sorin, M. et al. Single-cell spatial landscapes of the lung tumour immune microenvironment. Nature 614 , 548–554 (2023). Lin, J. R. et al. Multiplexed 3D atlas of state transitions and immune interaction in colorectal cancer. Cell 186 , 363–381.e19 (2023). Digre, A. & Lindskog, C. The human protein atlas-Integrated omics for single cell mapping of the human proteome. Protein Sci. 32 , e4562 (2023). Cui, H. et al. scGPT: toward building a foundation model for single-cell multi-omics using generative AI. Nat. Methods https://doi.org/10.1038/s41592-024-02201-0 (2024). Carpenter, A. E. & Singh, S. Bringing computation to biology by bridging the last mile. Nat. Cell Biol. 26 , 5–7 (2024). Hu, Y. et al. Unsupervised and supervised discovery of tissue cellular neighborhoods from cell phenotypes. Nat. Methods 21 , 267–278 (2024). Heimberg, G. et al. Scalable querying of human cell atlases via a foundational model reveals commonalities across fibrosis-associated macrophages. Preprint at bioRxiv https://doi.org/10.1101/2023.07.18.549537 (2023). Bommasani, R. et al. On the opportunities and risks of foundation models. Preprint at https://doi.org/10.48550/arXiv.2108.07258 (2022). Yang, F. et al. scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data. Nat. Mach. Intell. 4 , 852–866 (2022). Bian, H. et al. scMulan: a multitask generative pre-trained language model for single-cell analysis. Preprint at bioRxiv https://doi.org/10.1101/2024.01.25.577152 (2024). Hao, M. et al. Large-scale foundation model on single-cell transcriptomics. Nat. Methods https://doi.org/10.1038/s41592-024-02305-7 (2024). Theodoris, C. V. et al. Transfer learning enables predictions in network biology. Nature 618 , 616–624 (2023). Shen, H. et al. Generative pretraining from large-scale transcriptomes for single-cell deciphering. iScience 26 , 106536 (2023). Yang, X. et al. GeneCompass: deciphering universal gene regulatory mechanisms with knowledge-informed cross-species foundation model. Preprint at bioRxiv https://doi.org/10.1101/2023.09.26.559542 (2023). Rosen, Y. et al. Universal cell embeddings: a foundation model for cell biology. Preprint at bioRxiv https://doi.org/10.1101/2023.11.28.568918 (2023). Zhang, R., Luo, Y., Ma, J., Zhang, M. & Wang, S. scPretrain: multi-task self-supervised learning for cell-type classification. Bioinformatics 38 , 1607–1614 (2022). Alsabbagh, A. R. et al. Foundation models meet imbalanced single-cell data when learning cell type annotations. Preprint at bioRxiv https://doi.org/10.1101/2023.10.24.563625 (2023). Boiarsky, R., Singh, N., Buendia, A., Getz, G. & Sontag, D. A deep dive into single-cell RNA sequencing foundation models. Preprint at bioRxiv https://doi.org/10.1101/2023.10.19.563100 (2023). Kedzierska, K. Z., Crawford, L., Amini, A. P. & Lu, A. X. Assessing the limits of zero-shot foundation models in single-cell biology. Preprint at bioRxiv https://doi.org/10.1101/2023.10.16.561085 (2023). Khan, S. A. et al. Reusability report: learning the transcriptional grammar in single-cell RNA-sequencing data using transformers. Nat. Mach. Intell. 5 , 1437–1446 (2023). Schaar, A. C. et al. Nicheformer: a foundation model for single-cell and spatial omics. Preprint at bioRxiv https://doi.org/10.1101/2024.04.15.589472 (2024). Roohani, Y., Huang, K. & Leskovec, J. Predicting transcriptional outcomes of novel multigene perturbations with GEARS. Nat. Biotechnol. 42 , 927–935 (2023). Zhang, Y., Tiňo, P., Leonardis, A. & Tang, K. A survey on neural network interpretability. IEEE Trans. Emerg. Top. Comput. Intell. 5 , 726–742 (2021). Swanson, K., Wu, E., Zhang, A., Alizadeh, A. A. & Zou, J. From patterns to patients: advances in clinical machine learning for cancer diagnosis, prognosis, and treatment. Cell 186 , 1772–1791 (2023). Xu, C. et al. Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models. Mol. Syst. Biol. 17 , e9620 (2021). Zimmerman, K. D., Espeland, M. A. & Langefeld, C. D. A practical solution to pseudoreplication bias in single-cell studies. Nat. Commun. 12 , 738 (2021). Crowell, H. L. et al. muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data. Nat. Commun. 11 , 6077 (2020). He, L. et al. NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data. Commun. Biol. 4 , 629 (2021). Pullin, J. M. & McCarthy, D. J. A comparison of marker gene selection methods for single-cell RNA sequencing data. Genome Biol. 25 , 56 (2024). Wang, X. et al. An R package suite for microarray meta-analysis in quality control, differentially expressed gene analysis and pathway enrichment detection. Bioinformatics 28 , 2534–2536 (2012). Hao, Y. et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat. Biotechnol. 42 , 293–304 (2024). Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566 , 496–502 (2019). Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genom. 19 , 477 (2018). Ellwanger, D. C., Scheibinger, M., Dumont, R. A., Barr-Gillespie, P. G. & Heller, S. Transcriptional dynamics of hair-bundle morphogenesis revealed with CellTrails. Cell Rep. 23 , 2901–2914.e13 (2018). Cannoodt, R. et al. SCORPIUS improves trajectory inference and identifies novel modules in dendritic cell development. Preprint at bioRxiv https://doi.org/10.1101/079509 (2016). Dong, R. & Yuan, G. C. SpatialDWLS: accurate deconvolution of spatial transcriptomic data. Genome Biol. 22 , 145 (2021). Wan, X. et al. Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope. Nat. Commun. 14 , 7848 (2023). Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12 , 453–457 (2015). Wang, X., Park, J., Susztak, K., Zhang, N. R. & Li, M. Bulk tissue cell type deconvolution with multi-subject single-cell expression reference. Nat. Commun. 10 , 380 (2019). Menden, K. et al. Deep learning-based cell composition analysis from tissue expression profiles. Sci. Adv. 6 , eaba2619 (2020). Garmire, L. X. et al. Challenges and perspectives in computational deconvolution of genomics data. Nat. Methods 21 , 391–400 (2024). Li, H. et al. DeconPeaker, a deconvolution model to identify cell types based on chromatin accessibility in ATAC-seq data of mixture samples. Front. Genet. 11 , 392 (2020). Hutson, M. Hunting for the best bioscience software tool? Check this database. Nature https://doi.org/10.1038/d41586-023-00053-w (2023). Decamps, C. et al. Guidelines for cell-type heterogeneity quantification based on a comparative analysis of reference-free DNA methylation deconvolution software. BMC Bioinform. 21 , 16 (2020). Gohil, S. H., Iorgulescu, J. B., Braun, D. A., Keskin, D. B. & Livak, K. J. Applying high-dimensional single-cell technologies to the analysis of cancer immunotherapy. Nat. Rev. Clin. Oncol. 18 , 244–256 (2021). Download references Acknowledgements The authors are grateful to the members of the Newman and Wang laboratories for the valuable discussions and feedback. The original figures were created with Biorender.com. This work was supported by the National Science Foundation (J.P.D., Graduate Research Fellowship DGE-1656518), the National Cancer Institute (L.W., R01CA266280 and U24CA274274; A.M.N., R01CA255450), the Cancer Prevention and Research Institute of Texas (L.W., RP200385), the Break Through Cancer Foundation (L.W.), the University Cancer Foundation via the Institutional Research Grant Program (L.W.), the Melanoma Research Alliance (A.M.N., grant number 926521), and the Virginia and D. K. Ludwig Fund for Cancer Research (A.M.N.). L.W. is an Andrew Sabin Family Foundation Fellow. A.M.N. is a Chan Zuckerberg Biohub – San Francisco Investigator. Author information These authors contributed equally: Gunsagar S. Gulati, Jeremy Philip D’Silva. Authors and Affiliations Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA Gunsagar S. Gulati Department of Biomedical Data Science, Stanford University, Stanford, CA, USA Jeremy Philip D’Silva & Aaron M. Newman Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA Yunhe Liu & Linghua Wang The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA Linghua Wang Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA Aaron M. Newman Stanford Cancer Institute, Stanford University, Stanford, CA, USA Chan Zuckerberg Biohub – San Francisco, San Francisco, CA, USA You can also search for this author in PubMed   Google Scholar Contributions All authors discussed the content of the article, contributed to writing or editing, and reviewed the manuscript before submission. L.W. and A.M.N. jointly supervised the work. Corresponding author Correspondence to Aaron M. Newman . Ethics declarations Competing interests. A.M.N. holds patents related to digital cytometry and cancer biomarkers and has ownership interests in CiberMed, Inc., LiquidCell Dx, Inc. and CytoTrace Biosciences, Inc. The other authors declare no competing interests. Peer review Peer review information. Nature Reviews Molecular Cell Biology thanks Tallulah Andrews and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Related links Gene Ontology: https://geneontology.org/ Human Cell Atlas: https://www.humancellatlas.org/ Human Protein Atlas: http://www.proteinatlas.org Human Tumor Atlas Network: https://humantumoratlas.org/ Extraneous RNA molecules arising from lysed cells during sample processing that can contaminate gene expression measurements. Comprehensive references of cell types and states, typically generated using single-cell omics technologies. A technique for grouping elements of a dataset (for example, cells) by a similarity measure. Quantitative phenotypes (for example, the abundance of a particular cell state or type within a tissue) that are statistically associated with a health outcome (known as ‘prognostic’ biomarkers) or the likelihood of responding to a given treatment (‘predictive’ biomarker). A computational technique typically applied to bulk RNA admixtures to infer the proportions and characteristics of specific cell types within a complex tissue sample using gene expression signatures. A dataset containing information on the expression levels of numerous genes across multiple samples, which in single-cell RNA-seq data are single cells. Statistical methods to correct data by accounting for the effect of one or more linear relationships between variables. A representation of high-dimensional data (for example, expression matrix with thousands of genes) that reduces the number of variables, while preserving important structures and relationships in the data for simplified analysis and visualization. A local multicellular microenvironment, with an exact spatial resolution defined based on the assay and the studied tissue, for example, cells within a radius of 50 µm, or the 200 nearest neighbours of a cell. A problem solved by determining a mapping between two distributions that minimizes a cost function; in the context of single-cell time series data, the distributions can be cell populations at different time points, and the solution finds a mapping that relates cells at a later time point to their inferred antecedents at one or more earlier time points. Recurrent sets of multicellular neighbourhoods characterized by a co-occurring set of phenotypic states (for example, transcriptional programmes) in one or more cell types. A cellular phenotype characterized by small or nuanced differences in gene expression compared to other states of the same cell type. Rights and permissions Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Reprints and permissions About this article Cite this article. Gulati, G.S., D’Silva, J.P., Liu, Y. et al. Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics. Nat Rev Mol Cell Biol (2024). https://doi.org/10.1038/s41580-024-00768-2 Download citation Accepted : 16 July 2024 Published : 21 August 2024 DOI : https://doi.org/10.1038/s41580-024-00768-2 Share this article Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative Quick links Explore articles by subject Guide to authors Editorial policies Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly. Integrations What's new? 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Armin Tanovic Behind every what, there’s a why . Qualitative research is how you uncover that why. It enables you to connect with users and understand their thoughts, feelings, wants, needs, and pain points. There’s many methods for conducting qualitative research, and many objectives it can help you pursue—you might want to explore ways to improve NPS scores, combat reduced customer retention, or understand (and recreate) the success behind a well-received product. The common thread? All these metrics impact your business, and qualitative research can help investigate and improve that impact. In this article, we’ll take you through seven methods and examples of qualitative research, including when and how to use them. Qualitative UX research made easy Conduct qualitative research with Maze, analyze data instantly, and get rich, descriptive insights that drive decision-making. 7 Qualitative research methods: An overview There are various qualitative UX research methods that can help you get in-depth, descriptive insights. Some are suited to specific phases of the design and development process, while others are more task-oriented. Here’s our overview of the most common qualitative research methods. Keep reading for their use cases, and detailed examples of how to conduct them. Method User interviews Focus groups Ethnographic research Qualitative observation Case study research Secondary research Open-ended surveys to extract descriptive insights. 1. User interviews A user interview is a one-on-one conversation between a UX researcher, designer or Product Manager and a target user to understand their thoughts, perspectives, and feelings on a product or service. User interviews are a great way to get non-numerical data on individual experiences with your product, to gain a deeper understanding of user perspectives. Interviews can be structured, semi-structured, or unstructured . Structured interviews follow a strict interview script and can help you get answers to your planned questions, while semi and unstructured interviews are less rigid in their approach and typically lead to more spontaneous, user-centered insights. When to use user interviews Interviews are ideal when you want to gain an in-depth understanding of your users’ perspectives on your product or service, and why they feel a certain way. Interviews can be used at any stage in the product design and development process, being particularly helpful during: The discovery phase: To better understand user needs, problems, and the context in which they use your product—revealing the best potential solutions The design phase: To get contextual feedback on mockups, wireframes, and prototypes, helping you pinpoint issues and the reasons behind them Post-launch: To assess if your product continues to meet users’ shifting expectations and understand why or why not How to conduct user interviews: The basics Draft questions based on your research objectives Recruit relevant research participants and schedule interviews Conduct the interview and transcribe responses Analyze the interview responses to extract insights Use your findings to inform design, product, and business decisions 💡 A specialized user interview tool makes interviewing easier. With Maze Interview Studies , you can recruit, host, and analyze interviews all on one platform. User interviews: A qualitative research example Let’s say you’ve designed a recruitment platform, called Tech2Talent , that connects employers with tech talent. Before starting the design process, you want to clearly understand the pain points employers experience with existing recruitment tools'. You draft a list of ten questions for a semi-structured interview for 15 different one-on-one interviews. As it’s semi-structured, you don’t expect to ask all the questions—the script serves as more of a guide. One key question in your script is: “Have tech recruitment platforms helped you find the talent you need in the past?” Most respondents answer with a resounding and passionate ‘no’ with one of them expanding: “For our company, it’s been pretty hit or miss honestly. They let just about anyone make a profile and call themselves tech talent. It’s so hard sifting through serious candidates. I can’t see any of their achievements until I invest time setting up an interview.” You begin to notice a pattern in your responses: recruitment tools often lack easily accessible details on talent profiles. You’ve gained contextual feedback on why other recruitment platforms fail to solve user needs. 2. Focus groups A focus group is a research method that involves gathering a small group of people—around five to ten users—to discuss a specific topic, such as their’ experience with your new product feature. Unlike user interviews, focus groups aim to capture the collective opinion of a wider market segment and encourage discussion among the group. When to use focus groups You should use focus groups when you need a deeper understanding of your users’ collective opinions. The dynamic discussion among participants can spark in-depth insights that might not emerge from regular interviews. Focus groups can be used before, during, and after a product launch. They’re ideal: Throughout the problem discovery phase: To understand your user segment’s pain points and expectations, and generate product ideas Post-launch: To evaluate and understand the collective opinion of your product’s user experience When conducting market research: To grasp usage patterns, consumer perceptions, and market opportunities for your product How to conduct focus group studies: The basics Draft prompts to spark conversation, or a series of questions based on your UX research objectives Find a group of five to ten users who are representative of your target audience (or a specific user segment) and schedule your focus group session Conduct the focus group by talking and listening to users, then transcribe responses Analyze focus group responses and extract insights Use your findings to inform design decisions The number of participants can make it difficult to take notes or do manual transcriptions. We recommend using a transcription or a specialized UX research tool , such as Maze, that can automatically create ready-to-share reports and highlight key user insights. Focus groups: A qualitative research example You’re a UX researcher at FitMe , a fitness app that creates customized daily workouts for gym-goers. Unlike many other apps, FitMe takes into account the previous day’s workout and aims to create one that allows users to effectively rest different muscles. However, FitMe has an issue. Users are generating workouts but not completing them. They’re accessing the app, taking the necessary steps to get a workout for the day, but quitting at the last hurdle. Time to talk to users. You organize a focus group to get to the root of the drop-off issue. You invite five existing users, all of whom have dropped off at the exact point you’re investigating, and ask them questions to uncover why. A dialog develops: Participant 1: “Sometimes I’ll get a workout that I just don’t want to do. Sure, it’s a good workout—but I just don’t want to physically do it. I just do my own thing when that happens.” Participant 2: “Same here, some of them are so boring. I go to the gym because I love it. It’s an escape.” Participant 3: “Right?! I get that the app generates the best one for me on that specific day, but I wish I could get a couple of options.” Participant 4: “I’m the same, there are some exercises I just refuse to do. I’m not coming to the gym to do things I dislike.” Conducting the focus groups and reviewing the transcripts, you realize that users want options. A workout that works for one gym-goer doesn’t necessarily work for the next. A possible solution? Adding the option to generate a new workout (that still considers previous workouts)and the ability to blacklist certain exercises, like burpees. 3. Ethnographic research Ethnographic research is a research method that involves observing and interacting with users in a real-life environment. By studying users in their natural habitat, you can understand how your product fits into their daily lives. Ethnographic research can be active or passive. Active ethnographic research entails engaging with users in their natural environment and then following up with methods like interviews. Passive ethnographic research involves letting the user interact with the product while you note your observations. When to use ethnographic research Ethnographic research is best suited when you want rich insights into the context and environment in which users interact with your product. Keep in mind that you can conduct ethnographic research throughout the entire product design and development process —from problem discovery to post-launch. However, it’s mostly done early in the process: Early concept development: To gain an understanding of your user's day-to-day environment. Observe how they complete tasks and the pain points they encounter. The unique demands of their everyday lives will inform how to design your product. Initial design phase: Even if you have a firm grasp of the user’s environment, you still need to put your solution to the test. Conducting ethnographic research with your users interacting with your prototype puts theory into practice. How to conduct ethnographic research: Recruit users who are reflective of your audience Meet with them in their natural environment, and tell them to behave as they usually would Take down field notes as they interact with your product Engage with your users, ask questions, or host an in-depth interview if you’re doing an active ethnographic study Collect all your data and analyze it for insights While ethnographic studies provide a comprehensive view of what potential users actually do, they are resource-intensive and logistically difficult. A common alternative is diary studies. Like ethnographic research, diary studies examine how users interact with your product in their day-to-day, but the data is self-reported by participants. ⚙️ Recruiting participants proving tough and time-consuming? Maze Panel makes it easy, with 400+ filters to find your ideal participants from a pool of 3 million participants. Ethnographic research: A qualitative research example You're a UX researcher for a project management platform called ProFlow , and you’re conducting an ethnographic study of the project creation process with key users, including a startup’s COO. The first thing you notice is that the COO is rushing while navigating the platform. You also take note of the 46 tabs and Zoom calls opened on their monitor. Their attention is divided, and they let out an exasperated sigh as they repeatedly hit “refresh” on your website’s onboarding interface. You conclude the session with an interview and ask, “How easy or difficult did you find using ProFlow to coordinate a project?” The COO answers: “Look, the whole reason we turn to project platforms is because we need to be quick on our feet. I’m doing a million things so I need the process to be fast and simple. The actual project management is good, but creating projects and setting up tables is way too complicated.” You realize that ProFlow ’s project creation process takes way too much time for professionals working in fast-paced, dynamic environments. To solve the issue, propose a quick-create option that enables them to move ahead with the basics instead of requiring in-depth project details. 4. Qualitative observation Qualitative observation is a similar method to ethnographic research, though not as deep. It involves observing your users in a natural or controlled environment and taking notes as they interact with a product. However, be sure not to interrupt them, as this compromises the integrity of the study and turns it into active ethnographic research. When to qualitative observation Qualitative observation is best when you want to record how users interact with your product without anyone interfering. Much like ethnographic research, observation is best done during: Early concept development: To help you understand your users' daily lives, how they complete tasks, and the problems they deal with. The observations you collect in these instances will help you define a concept for your product. Initial design phase: Observing how users deal with your prototype helps you test if they can easily interact with it in their daily environments How to conduct qualitative observation: Recruit users who regularly use your product Meet with users in either their natural environment, such as their office, or within a controlled environment, such as a lab Observe them and take down field notes based on what you notice Qualitative observation: An qualitative research example You’re conducting UX research for Stackbuilder , an app that connects businesses with tools ideal for their needs and budgets. To determine if your app is easy to use for industry professionals, you decide to conduct an observation study. Sitting in with the participant, you notice they breeze past the onboarding process, quickly creating an account for their company. Yet, after specifying their company’s budget, they suddenly slow down. They open links to each tool’s individual page, confusingly switching from one tab to another. They let out a sigh as they read through each website. Conducting your observation study, you realize that users find it difficult to extract information from each tool’s website. Based on your field notes, you suggest including a bullet-point summary of each tool directly on your platform. 5. Case study research Case studies are a UX research method that provides comprehensive and contextual insights into a real-world case over a long period of time. They typically include a range of other qualitative research methods, like interviews, observations, and ethnographic research. A case study allows you to form an in-depth analysis of how people use your product, helping you uncover nuanced differences between your users. When to use case studies Case studies are best when your product involves complex interactions that need to be tracked over a longer period or through in-depth analysis. You can also use case studies when your product is innovative, and there’s little existing data on how users interact with it. As for specific phases in the product design and development process: Initial design phase: Case studies can help you rigorously test for product issues and the reasons behind them, giving you in-depth feedback on everything between user motivations, friction points, and usability issues Post-launch phase: Continuing with case studies after launch can give you ongoing feedback on how users interact with the product in their day-to-day lives. These insights ensure you can meet shifting user expectations with product updates and future iterations How to conduct case studies: Outline an objective for your case study such as examining specific user tasks or the overall user journey Select qualitative research methods such as interviews, ethnographic studies, or observations Collect and analyze your data for comprehensive insights Include your findings in a report with proposed solutions Case study research: A qualitative research example Your team has recently launched Pulse , a platform that analyzes social media posts to identify rising digital marketing trends. Pulse has been on the market for a year, and you want to better understand how it helps small businesses create successful campaigns. To conduct your case study, you begin with a series of interviews to understand user expectations, ethnographic research sessions, and focus groups. After sorting responses and observations into common themes you notice a main recurring pattern. Users have trouble interpreting the data from their dashboards, making it difficult to identify which trends to follow. With your synthesized insights, you create a report with detailed narratives of individual user experiences, common themes and issues, and recommendations for addressing user friction points. Some of your proposed solutions include creating intuitive graphs and summaries for each trend study. This makes it easier for users to understand trends and implement strategic changes in their campaigns. 6. Secondary research Secondary research is a research method that involves collecting and analyzing documents, records, and reviews that provide you with contextual data on your topic. You’re not connecting with participants directly, but rather accessing pre-existing available data. For example, you can pull out insights from your UX research repository to reexamine how they apply to your new UX research objective. Strictly speaking, it can be both qualitative and quantitative—but today we focus on its qualitative application. When to use secondary research Record keeping is particularly useful when you need supplemental insights to complement, validate, or compare current research findings. It helps you analyze shifting trends amongst your users across a specific period. Some other scenarios where you need record keeping include: Initial discovery or exploration phase: Secondary research can help you quickly gather background information and data to understand the broader context of a market Design and development phase: See what solutions are working in other contexts for an idea of how to build yours Secondary research is especially valuable when your team faces budget constraints, tight deadlines, or limited resources. Through review mining and collecting older findings, you can uncover useful insights that drive decision-making throughout the product design and development process. How to conduct secondary research: Outline your UX research objective Identify potential data sources for information on your product, market, or target audience. Some of these sources can include: a. Review websites like Capterra and G2 b. Social media channels c. Customer service logs and disputes d. Website reviews e. Reports and insights from previous research studies f. Industry trends g. Information on competitors Analyze your data by identifying recurring patterns and themes for insights Secondary research: A qualitative research example SafeSurf is a cybersecurity platform that offers threat detection, security audits, and real-time reports. After conducting multiple rounds of testing, you need a quick and easy way to identify remaining usability issues. Instead of conducting another resource-intensive method, you opt for social listening and data mining for your secondary research. Browsing through your company’s X, you identify a recurring theme: many users without a background in tech find SafeSurf ’s reports too technical and difficult to read. Users struggle with understanding what to do if their networks are breached. After checking your other social media channels and review sites, the issue pops up again. With your gathered insights, your team settles on introducing a simplified version of reports, including clear summaries, takeaways, and step-by-step protocols for ensuring security. By conducting secondary research, you’ve uncovered a major usability issue—all without spending large amounts of time and resources to connect with your users. 7. Open-ended surveys Open-ended surveys are a type of unmoderated UX research method that involves asking users to answer a list of qualitative research questions designed to uncover their attitudes, expectations, and needs regarding your service or product. Open-ended surveys allow users to give in-depth, nuanced, and contextual responses. When to use open-ended surveys User surveys are an effective qualitative research method for reaching a large number of users. You can use them at any stage of the design and product development process, but they’re particularly useful: When you’re conducting generative research : Open-ended surveys allow you to reach a wide range of users, making them especially useful during initial research phases when you need broad insights into user experiences When you need to understand customer satisfaction: Open-ended customer satisfaction surveys help you uncover why your users might be dissatisfied with your product, helping you find the root cause of their negative experiences In combination with close-ended surveys: Get a combination of numerical, statistical insights and rich descriptive feedback. You’ll know what a specific percentage of your users think and why they think it. How to conduct open-ended surveys: Design your survey and draft out a list of survey questions Distribute your surveys to respondents Analyze survey participant responses for key themes and patterns Use your findings to inform your design process Open-ended surveys: A qualitative research example You're a UX researcher for RouteReader , a comprehensive logistics platform that allows users to conduct shipment tracking and route planning. Recently, you’ve launched a new predictive analytics feature that allows users to quickly identify and prepare for supply chain disruptions. To better understand if users find the new feature helpful, you create an open-ended, in-app survey. The questions you ask your users: “What has been your experience with our new predictive analytics feature?" “Do you find it easy or difficult to rework your routes based on our predictive suggestions?” “Does the predictive analytics feature make planning routes easier? Why or why not?” Most of the responses are positive. Users report using the predictive analytics feature to make last-minute adjustments to their route plans, and some even rely on it regularly. However, a few users find the feature hard to notice, making it difficult to adjust their routes on time. To ensure users have supply chain insights on time, you integrate the new feature into each interface so users can easily spot important information and adjust their routes accordingly. 💡 Surveys are a lot easier with a quality survey tool. Maze’s Feedback Surveys solution has all you need to ensure your surveys get the insights you need—including AI-powered follow-up and automated reports. Qualitative research vs. quantitative research: What’s the difference? Alongside qualitative research approaches, UX teams also use quantitative research methods. Despite the similar names, the two are very different. Here are some of the key differences between qualitative research and quantitative research . Research type Qualitative research . Quantitative research Before selecting either qualitative or quantitative methods, first identify what you want to achieve with your UX research project. As a general rule of thumb, think qualitative data collection for in-depth understanding and quantitative studies for measurement and validation. Conduct qualitative research with Maze You’ll often find that knowing the what is pointless without understanding the accompanying why . Qualitative research helps you uncover your why. So, what about how —how do you identify your 'what' and your 'why'? The answer is with a user research tool like Maze. Maze is the leading user research platform that lets you organize, conduct, and analyze both qualitative and quantitative research studies—all from one place. Its wide variety of UX research methods and advanced AI capabilities help you get the insights you need to build the right products and experiences faster. Frequently asked questions about qualitative research examples What is qualitative research? Qualitative research is a research method that aims to provide contextual, descriptive, and non-numerical insights on a specific issue. Qualitative research methods like interviews, case studies, and ethnographic studies allow you to uncover the reasoning behind your user’s attitudes and opinions. Can a study be both qualitative and quantitative? Absolutely! You can use mixed methods in your research design, which combines qualitative and quantitative approaches to gain both descriptive and statistical insights. For example, user surveys can have both close-ended and open-ended questions, providing comprehensive data like percentages of user views and descriptive reasoning behind their answers. Is qualitative or quantitative research better? The choice between qualitative and quantitative research depends upon your research goals and objectives. Qualitative research methods are better suited when you want to understand the complexities of your user’s problems and uncover the underlying motives beneath their thoughts, feelings, and behaviors. Quantitative research excels in giving you numerical data, helping you gain a statistical view of your user's attitudes, identifying trends, and making predictions. What are some approaches to qualitative research? There are many approaches to qualitative studies. An approach is the underlying theory behind a method, and a method is a way of implementing the approach. Here are some approaches to qualitative research: Grounded theory: Researchers study a topic and develop theories inductively Phenomenological research: Researchers study a phenomenon through the lived experiences of those involved Ethnography: Researchers immerse themselves in organizations to understand how they operate vPro-MS enables identification of human-pathogenic viruses from patient samples by untargeted proteomics Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: [email protected] Info/History Preview PDF Viral infections are commonly diagnosed by the detection of viral genome fragments or proteins using targeted methods such as PCR and immunoassays. In contrast, metagenomics enables the untargeted identification of viral genomes, expanding its applicability across a broader spectrum. In this study, we introduce proteomics as a complementary approach for the untargeted identification of human-pathogenic viruses from patient samples. The viral proteomics workflow (vPro-MS) is based on an in-silico derived peptide library covering the human virome in UniProtKB (331 viruses, 20,386 genomes, 121,977 peptides), which was especially designed for diagnostic purposes. A scoring algorithm (vProID score) was developed to assess the confidence of virus identification from proteomics data. In combination with high-throughput diaPASEF-based data acquisition, this workflow enables the analysis of up to 60 samples per day. The specificity was determined to be > 99,9 % in an analysis of 221 plasma, swab and cell culture samples covering 18 different viruses (e.g. SARS, MERS, EBOV, MPXV). The sensitivity of this approach for the detection of SARS-CoV-2 in nasopharyngeal swabs corresponds to a PCR cycle threshold of 27 with comparable quantitative accuracy to metagenomics. vPro-MS enables the integration of untargeted virus identification in large-scale proteomic studies of biofluids such as human plasma to detect previously undiscovered virus infections in patient specimens. Competing Interest Statement The authors have declared no competing interest. Funding Statement This study did not receive any funding Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The ethics committee of the Berlin Chamber of Physicians (Berliner Aerztekammer, [email protected]) gave ethical approval for this work (Eth 20/40). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Data Availability All data produced in the present study are available upon reasonable request to the authors View the discussion thread. Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. 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What Is Quantitative Research? Quantitative research is the opposite of qualitative research, which involves collecting and analyzing non-numerical data (e.g., text, video, or audio). Quantitative Research Quantitative Research Quantitative research is a type of research that collects and analyzes numerical data to test hypotheses and answer research questions. This research typically involves a large sample size and uses statistical analysis to make inferences about a population based on the data collected. It often involves the use of surveys, experiments, or other structured data collection ... What is Quantitative Research? Definition, Methods, Types, and Examples Before adopting quantitative research for your study, you need to understand what is quantitative research. Read this article to learn the quantitative research definition, key characteristics, types of quantitative research, methods and examples, and pros and cons of quantitative research. Sampling Methods Probability sampling methods Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research. If you want to produce results that are representative of the whole population, probability sampling techniques are the most valid choice. There are four main types of probability sample. 1. Simple random sampling In a simple ... What Is Research Methodology? Definition + Examples Learn exactly what a research methodology is with Grad Coach's plain language, easy-to-understand explanation, including examples and videos. What Is a Research Methodology? Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation, or research paper, the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research and your dissertation topic. PDF Introduction to Quantitative Research This pdf provides an overview of quantitative research methods, including their advantages, limitations, and ethical issues. It also introduces some common statistical techniques and tools for data analysis. A tutorial on methodological studies: the what, when, how and why Even though methodological studies can be conducted on qualitative or mixed methods research, this paper focuses on and draws examples exclusively from quantitative research. Chapter Four: Quantitative Methods (Part 1) Learn how to choose and apply quantitative methods for your research topic. Explore the value, types, and examples of quantitative methods in this chapter. Research Methodology Example (PDF + Template) Research Methodology Example. Detailed Walkthrough + Free Methodology Chapter Template. If you're working on a dissertation or thesis and are looking for an example of a research methodology chapter, you've come to the right place. In this video, we walk you through a research methodology from a dissertation that earned full distinction ... Quantitative Research: What It Is, Practices & Methods What is Quantitative Research? Quantitative research is a systematic investigation of phenomena by gathering quantifiable data and performing statistical, mathematical, or computational techniques. Quantitative research collects statistically significant information from existing and potential customers using sampling methods and sending out online surveys, online polls, and questionnaires ... PDF Methodology The methodology section of your paper describes how your research was conducted. This information allows readers to check whether your approach is accurate and dependable. A good methodology can help increase the reader's trust in your findings. First, we will define and differentiate quantitative and qualitative research. What is Quantitative Research? Definition, Examples, Key Advantages Quantitative research stands as a powerful research methodology dedicated to the systematic collection and analysis of measurable data. Learn more about quantitative research Examples, key advantages, methods and best practices. A Practical Guide to Writing Quantitative and Qualitative Research Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed ... What is Quantitative Research? Research involving the collection of data in numerical form for quantitative analysis. The numerical data can be durations, scores, counts of incidents, ratings, or scales. Quantitative data can be collected in either controlled or naturalistic environments, in laboratories or field studies, from special populations or from samples of the ... What Is Quantitative Research? Types, Characteristics & Methods Learn what quantitative research is, its types, and the different methodologies it uses for researching data sets with examples. What Is a Research Design A research design is a strategy for answering your research question using empirical data. Creating a research design means making decisions about: Your overall research objectives and approach. Whether you'll rely on primary research or secondary research. Your sampling methods or criteria for selecting subjects. Your data collection methods. Research Methodology Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect, analyze, and interpret data to answer research questions or solve research problems. (PDF) An Overview of Quantitative Research Methods To test the developed hypotheses statements and analyse the large sample data acquired through the survey, the quantitative method opts as the study methodology (Ghanad, 2023). CHAPTER 3 Learn how to conduct a dissertation research with various methods and tools in this PDF chapter from ResearchGate. Chapter 3 Sample RESEARCH METHODOLOGY This chapter presents the methods and techniques of the study, the population and sample of the study, the research instrument, the data gathering procedure, and the data processing and the statistical treatment. Quantitative Research Questionnaire Types of Survey Questionnaires in Quantitative Research. Quantitative research questionnaires have close-ended questions that allow the researchers to measure accurate and specific responses from the participants. They don't contain open-ended questions like qualitative research, where the response is measured by interviews and focus groups. The effects of singing interventions on quality of life, mood and We assessed the risk of bias using the Mixed Methods Appraisal Tool (MMAT) which has criteria for 5 study designs: qualitative, quantitative randomized controlled, quantitative nonrandomized, quantitative descriptive, and mixed methods (Hong et al., 2018). Each study category contained 5 criteria with a point given for each criterion met ... Qualitative vs. Quantitative Data Analysis in Education Examples of quantitative data types: Standardized test, assessment, and quiz scores; Grades and grade point averages; ... Quantitative research methods. Quantitative data collection methods are more diverse and more likely to be automated because of the objective nature of the data. A quantitative researcher could employ methods such as: Research Methods Research Methods | Definitions, Types, Examples Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design. When planning your methods, there are two key decisions you will make. Evaluating the impact of the global evidence, local adaptation (GELA Overall approach. We will use a longitudinal, mixed-methods study design, informed by an overarching project Theory of Change (Table 2).The theoretical underpinning for the GELA project across all work packages is related to the three-layered behaviour change wheel comprising opportunity, capability and motivation [].The design, delivery and implementation of multi-stakeholder integrated ... Profiling cell identity and tissue architecture with single-cell and a, Example workflow of sample processing.Tissue is dissected from an organism and subjected to mechanical and then chemical digestion before single-cell capture and RNA sequencing. Time after ... Qualitative Research: 7 Methods and Examples Maze is the leading user research platform that lets you organize, conduct, and analyze both qualitative and quantitative research studies—all from one place. Its wide variety of UX research methods and advanced AI capabilities help you get the insights you need to build the right products and experiences faster. vPro-MS enables identification of human-pathogenic viruses from patient Viral infections are commonly diagnosed by the detection of viral genome fragments or proteins using targeted methods such as PCR and immunoassays. In contrast, metagenomics enables the untargeted identification of viral genomes, expanding its applicability across a broader spectrum. In this study, we introduce proteomics as a complementary approach for the untargeted identification of human ... assignment essay homework paper phd report resume review speech thesis