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• What Is Quantitative Research? | Definition, Uses & Methods

What Is Quantitative Research? | Definition, Uses & Methods

Published on June 12, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Quantitative research is the process of collecting and analyzing numerical data. It can be used to find patterns and averages, make predictions, test causal relationships, and generalize results to wider populations.

Quantitative research is the opposite of qualitative research , which involves collecting and analyzing non-numerical data (e.g., text, video, or audio).

Quantitative research is widely used in the natural and social sciences: biology, chemistry, psychology, economics, sociology, marketing, etc.

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Table of contents

Quantitative research methods, quantitative data analysis, advantages of quantitative research, disadvantages of quantitative research, other interesting articles, frequently asked questions about quantitative research.

You can use quantitative research methods for descriptive, correlational or experimental research.

• In descriptive research , you simply seek an overall summary of your study variables.
• In correlational research , you investigate relationships between your study variables.
• In experimental research , you systematically examine whether there is a cause-and-effect relationship between variables.

Correlational and experimental research can both be used to formally test hypotheses , or predictions, using statistics. The results may be generalized to broader populations based on the sampling method used.

To collect quantitative data, you will often need to use operational definitions that translate abstract concepts (e.g., mood) into observable and quantifiable measures (e.g., self-ratings of feelings and energy levels).

Note that quantitative research is at risk for certain research biases , including information bias , omitted variable bias , sampling bias , or selection bias . Be sure that you’re aware of potential biases as you collect and analyze your data to prevent them from impacting your work too much.

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Once data is collected, you may need to process it before it can be analyzed. For example, survey and test data may need to be transformed from words to numbers. Then, you can use statistical analysis to answer your research questions .

Descriptive statistics will give you a summary of your data and include measures of averages and variability. You can also use graphs, scatter plots and frequency tables to visualize your data and check for any trends or outliers.

Using inferential statistics , you can make predictions or generalizations based on your data. You can test your hypothesis or use your sample data to estimate the population parameter .

First, you use descriptive statistics to get a summary of the data. You find the mean (average) and the mode (most frequent rating) of procrastination of the two groups, and plot the data to see if there are any outliers.

You can also assess the reliability and validity of your data collection methods to indicate how consistently and accurately your methods actually measured what you wanted them to.

Quantitative research is often used to standardize data collection and generalize findings . Strengths of this approach include:

• Replication

Repeating the study is possible because of standardized data collection protocols and tangible definitions of abstract concepts.

• Direct comparisons of results

The study can be reproduced in other cultural settings, times or with different groups of participants. Results can be compared statistically.

• Large samples

Data from large samples can be processed and analyzed using reliable and consistent procedures through quantitative data analysis.

• Hypothesis testing

Using formalized and established hypothesis testing procedures means that you have to carefully consider and report your research variables, predictions, data collection and testing methods before coming to a conclusion.

Despite the benefits of quantitative research, it is sometimes inadequate in explaining complex research topics. Its limitations include:

• Superficiality

Using precise and restrictive operational definitions may inadequately represent complex concepts. For example, the concept of mood may be represented with just a number in quantitative research, but explained with elaboration in qualitative research.

• Narrow focus

Predetermined variables and measurement procedures can mean that you ignore other relevant observations.

• Structural bias

Despite standardized procedures, structural biases can still affect quantitative research. Missing data , imprecise measurements or inappropriate sampling methods are biases that can lead to the wrong conclusions.

• Lack of context

Quantitative research often uses unnatural settings like laboratories or fails to consider historical and cultural contexts that may affect data collection and results.

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 goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo 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 .

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

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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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Understanding Quantitative Research Methods: A Comprehensive Guide

In social science, the choice of research method can shape the results of your study. That's why understanding the primary differences between quantitative and qualitative research becomes necessary. Unraveling the nuances between these two methods illustrates the core of academic exploration and the diverse complexity of knowledge construction. 

In this article, I will focus on the quantitative research method, but in the next one, I plan to expand the topic of qualitative research. 

Understanding Quantitative Research 

In short, quantitative research is a type of study that collects and analyzes data to uncover patterns, relationships, and trends using numerical measures.

For brands, quantitative research is all about gathering meaningful info from both current and potential customers. How? Well, in contrast to qualitative research, it uses methods like online surveys , polls, case study research and questionnaires. The cool thing about this approach is that it gives you numbers and real, solid data you can work with . Once you've got these numbers all lined up, you can start making some educated guesses about the future of a product or service. You can also figure out cause-and-effect relationships, which is super helpful for making changes that really hit the mark.

What About Quantitative Research Design?

Before you start your research, it’s best to decide whether your study will be exploratory or conclusive. Exploratory research aims to develop conclusions by examining a topic in depth. Conclusive research, on the other hand, seeks to reach a final conclusion about an issue. 

Let me quote the author of “Types of Quantitative Research Methods and Designs” article published by Grand Canyon University: “ Research design refers to your approach for answering your fundamental research questions. If you are writing a quantitatively based dissertation, your research design will center on numerical data collection and analysis .” If you need an entire article, you can find it here .

Key Components of Quantitative Research Methods

Quantitative research brings some standout qualities, making it a perfect fit for certain tasks. Let's check some of them: 

Sample size

Sample size is very important in quantitative research and refers to the number of participants included in the study. Selecting a fitting sample size is crucial for getting accurate and reliable results. Too small sample may lead to underpowered analyses and generalization, while a large sample size may be impractical and costly. By estimating the right sample size, investigators can ensure that their analysis detects meaningful effects.

The Right Tools

As I mentioned earlier in the text, quantitative research is based on surveys, polls, experiments, and questionnaires. It’s essential to pick the right tool for your research, as the results may be determined by it. 

Close-ended questions

They are asked to each participant in a set order and in an unchanging form, so you can assume that the differences are the result of participant differences, not measurements . Closed questions are those a person answers with a simple yes or no. 

Previous research

Before you even start thinking about your poll, focus on finding prior quantitative studies on the research subjects. Firstly, it’s possible you’ll find your answer there. Secondly, it helps you prepare better questions and frames for your research. 

Visual aids

Tables, charts, graphs, or other numerical forms of visualization of collected data may be crucial for your analysis. Make sure to present them understandably. 

Generalization of results

Quantitative research is based on generalizing its results. That means you can easily use data from research conducted on a small group to make smart choices that benefit a broader population.

Primary And Secondary Quantitative Research Methods

The difference between primary and secondary data is one of the basic divisions in social research methodologies. Primary data is gathered directly from respondents during interviews, surveys, etc. Secondary data is the result of previously conducted analyses.

Primary Quantitative Research Methods:

• Survey research: The easiest and the most common research method. It's based on participants completing a survey questionnaire in the presence of interviewers or without them.
• Descriptive research: Its role is to uncover the what, when, and where of the research problem without delving too deeply into the why. This research focuses on describing and observing the characteristics or behaviors without influencing them in any way. The findings from descriptive research may provide insights into the preferences and behaviors of the target group without attempting to set up a causal relationship. 
• Correlational research: It is used to find the relationship between variables and their influence on each other. 
• Causal-comparative research: This type of research is based on studying the cause-effect relationship between two or more variables. 
• Experimental research: This method is purely theoretical. It is based on one or more theories, but the theories must be proven first before the beginning of the investigation. The whole study research is conducted to provide or disprove the statement. It’s a great method if you’re in the lookout for psychological research.
• Try top-notch technology: Have you ever heard of eye tracking ? In the XXI century, you can test how users react to your website or video. I mean, how they REALLY react. This kind of research is often more informative than any poll or survey, and you can conduct it just by using a simple webcam. A great example of this kind of top-notch tech is RealEye.  Sounds good? Check out the main page for more insights.

Secondary Quantitative Methods:

• Google it: The internet has become the perfect resource for conducting quantitative research. Gathering data through your laptop or mobile device is easier, allowing you to reach larger and more diverse groups. Thanks to this, you can better understand your target group.  
• Libraries: Remember those? They are all still an amazing source of knowledge about many subjects. Especially if you're looking for specific or historical data.
• Market research reports: They are another possible source of quantitative information. Those reports can provide you an overview of the industry and sometimes include quantitative data you won't find anywhere else.
• Use your contacts: Maybe you know somebody who is an expert in the field? Perhaps they already have the proper data? Check your contact list and look for someone who can be helpful in your research. 

Best Practices and Advantages of Quantitative Research

Understanding quantitative research methods entails not just grasping its techniques, but also appreciating its strengths, limitations, and ethical implications. So before you start your journey with qualitative research methods, remember those key practices.

1. Qualitative vs. quantitative research

It's necessary to understand the difference between quantitative and qualitative research and to apply the one that best suits your needs. 

2. Thoughtful Study Design and Sampling

Whether it's experimental, correlational, or descriptive, choosing the right design is crucial. Equally important is the sampling strategy. Make sure that your sample has a perfect size. You're looking for something between a too-small and too-large group.

3. Get Your Goals Straight

Know what you're after before you begin data collection. Quantitative study requires clear research goals and hypotheses. Remember: clarity enables precise analysis and guides the direction of the research towards expected effects.

4. Use Primary and Secondary Quantitative Research Methods

Why waste your time inventing new thesis or results when (possibly) you already have them in your pocket? Conduct research on the internet, in books, and by talking to your friends. Who knows? They may already know some answers to your questions.

If you need some profound insights about human behavior or how people see certain stimuli, RealEye could be the right tool for you. Start a free trial version of RealEye today, and let us help you to get to know your audience.

Other Blog Posts:

Understanding Eye-Tracking Technology: A Beginner’s Guide

RealEye vs. iMotions Comparison

Unravel’s eye-tracking study on Heineken Beers

Exploring the Depths of Market Research with Julius Augustine: A Journey from Fieldwork to AI

Research Methodologies

Quantitative research methodologies.

• Qualitative Research Methodologies
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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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Quantitative Research

What is Quantitative Research?

Quantitative research is the methodology which researchers use to test theories about people’s attitudes and behaviors based on numerical and statistical evidence. Researchers sample a large number of users (e.g., through surveys) to indirectly obtain measurable, bias-free data about users in relevant situations.

“Quantification clarifies issues which qualitative analysis leaves fuzzy. It is more readily contestable and likely to be contested. It sharpens scholarly discussion, sparks off rival hypotheses, and contributes to the dynamics of the research process.” — Angus Maddison, Notable scholar of quantitative macro-economic history

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See how quantitative research helps reveal cold, hard facts about users which you can interpret and use to improve your designs.

Use Quantitative Research to Find Mathematical Facts about Users

Quantitative research is a subset of user experience (UX) research . Unlike its softer, more individual-oriented “counterpart”, qualitative research , quantitative research means you collect statistical/numerical data to draw generalized conclusions about users’ attitudes and behaviors . Compare and contrast quantitative with qualitative research, below:

Qualitative Research

You Aim to Determine

The “what”, “where” & “when” of the users’ needs & problems – to help keep your project’s focus on track during development

The “why” – to get behind how users approach their problems in their world

Highly structured (e.g., surveys) – to gather data about what users do & find patterns in large user groups

Loosely structured (e.g., contextual inquiries) – to learn why users behave how they do & explore their opinions

Number of Representative Users

Ideally 30+

Often around 5

Level of Contact with Users

Less direct & more remote (e.g., analytics)

More direct & less remote (e.g., usability testing to examine users’ stress levels when they use your design)

Statistically

Reliable – if you have enough test users

Less reliable, with need for great care with handling non-numerical data (e.g., opinions), as your own opinions might influence findings

Quantitative research is often best done from early on in projects since it helps teams to optimally direct product development and avoid costly design mistakes later. As you typically get user data from a distance—i.e., without close physical contact with users—also applying qualitative research will help you investigate why users think and feel the ways they do. Indeed, in an iterative design process quantitative research helps you test the assumptions you and your design team develop from your qualitative research. Regardless of the method you use, with proper care you can gather objective and unbiased data – information which you can complement with qualitative approaches to build a fuller understanding of your target users. From there, you can work towards firmer conclusions and drive your design process towards a more realistic picture of how target users will ultimately receive your product.

Quantitative analysis helps you test your assumptions and establish clearer views of your users in their various contexts.

Quantitative Research Methods You Can Use to Guide Optimal Designs

There are many quantitative research methods, and they help uncover different types of information on users. Some methods, such as A/B testing, are typically done on finished products, while others such as surveys could be done throughout a project’s design process. Here are some of the most helpful methods:

A/B testing – You test two or more versions of your design on users to find the most effective. Each variation differs by just one feature and may or may not affect how users respond. A/B testing is especially valuable for testing assumptions you’ve drawn from qualitative research. The only potential concerns here are scale—in that you’ll typically need to conduct it on thousands of users—and arguably more complexity in terms of considering the statistical significance involved.

Analytics – With tools such as Google Analytics, you measure metrics (e.g., page views, click-through rates) to build a picture (e.g., “How many users take how long to complete a task?”).

Desirability Studies – You measure an aspect of your product (e.g., aesthetic appeal) by typically showing it to participants and asking them to select from a menu of descriptive words. Their responses can reveal powerful insights (e.g., 78% associate the product/brand with “fashionable”).

Surveys and Questionnaires – When you ask for many users’ opinions, you will gain massive amounts of information. Keep in mind that you’ll have data about what users say they do, as opposed to insights into what they do . You can get more reliable results if you incentivize your participants well and use the right format.

Tree Testing – You remove the user interface so users must navigate the site and complete tasks using links alone. This helps you see if an issue is related to the user interface or information architecture.

Another powerful benefit of conducting quantitative research is that you can keep your stakeholders’ support with hard facts and statistics about your design’s performance—which can show what works well and what needs improvement—and prove a good return on investment. You can also produce reports to check statistics against different versions of your product and your competitors’ products.

Most quantitative research methods are relatively cheap. Since no single research method can help you answer all your questions, it’s vital to judge which method suits your project at the time/stage. Remember, it’s best to spend appropriately on a combination of quantitative and qualitative research from early on in development. Design improvements can be costly, and so you can estimate the value of implementing changes when you get the statistics to suggest that these changes will improve usability. Overall, you want to gather measurements objectively, where your personality, presence and theories won’t create bias.

Learn More about Quantitative Research

Take our User Research course to see how to get the most from quantitative research.

See how quantitative research methods fit into your design research landscape .

This insightful piece shows the value of pairing quantitative with qualitative research .

Find helpful tips on combining quantitative research methods in mixed methods research .

Questions related to Quantitative Research

Qualitative and quantitative research differ primarily in the data they produce. Quantitative research yields numerical data to test hypotheses and quantify patterns. It's precise and generalizable. Qualitative research, on the other hand, generates non-numerical data and explores meanings, interpretations, and deeper insights. Watch our video featuring Professor Alan Dix on different types of research methods.

This video elucidates the nuances and applications of both research types in the design field.

In quantitative research, determining a good sample size is crucial for the reliability of the results. William Hudson, CEO of Syntagm, emphasizes the importance of statistical significance with an example in our video. 

He illustrates that even with varying results between design choices, we need to discern whether the differences are statistically significant or products of chance. This ensures the validity of the results, allowing for more accurate interpretations. Statistical tools like chi-square tests can aid in analyzing the results effectively. To delve deeper into these concepts, take William Hudson’s Data-Driven Design: Quantitative UX Research Course . 

Quantitative research is crucial as it provides precise, numerical data that allows for high levels of statistical inference. Our video from William Hudson, CEO of Syntagm, highlights the importance of analytics in examining existing solutions. 

Quantitative methods, like analytics and A/B testing, are pivotal for identifying areas for improvement, understanding user behaviors, and optimizing user experiences based on solid, empirical evidence. This empirical nature ensures that the insights derived are reliable, allowing for practical improvements and innovations. Perhaps most importantly, numerical data is useful to secure stakeholder buy-in and defend design decisions and proposals. Explore this approach in our Data-Driven Design: Quantitative Research for UX Research course and learn from William Hudson’s detailed explanations of when and why to use analytics in the research process.

After establishing initial requirements, statistical data is crucial for informed decisions through quantitative research. William Hudson, CEO of Syntagm, sheds light on the role of quantitative research throughout a typical project lifecycle in this video:

 During the analysis and design phases, quantitative research helps validate user requirements and understand user behaviors. Surveys and analytics are standard tools, offering insights into user preferences and design efficacy. Quantitative research can also be used in early design testing, allowing for optimal design modifications based on user interactions and feedback, and it’s fundamental for A/B and multivariate testing once live solutions are available.

To write a compelling quantitative research question:

Create clear, concise, and unambiguous questions that address one aspect at a time.

Use common, short terms and provide explanations for unusual words.

Avoid leading, compound, and overlapping queries and ensure that questions are not vague or broad.

According to our video by William Hudson, CEO of Syntagm, quality and respondent understanding are vital in forming good questions. 

He emphasizes the importance of addressing specific aspects and avoiding intimidating and confusing elements, such as extensive question grids or ranking questions, to ensure participant engagement and accurate responses. For more insights, see the article Writing Good Questions for Surveys .

Survey research is typically quantitative, collecting numerical data and statistical analysis to make generalizable conclusions. However, it can also have qualitative elements, mainly when it includes open-ended questions, allowing for expressive responses. Our video featuring the CEO of Syntagm, William Hudson, provides in-depth insights into when and how to effectively utilize surveys in the product or service lifecycle, focusing on user satisfaction and potential improvements.

He emphasizes the importance of surveys in triangulating data to back up qualitative research findings, ensuring we have a complete understanding of the user's requirements and preferences.

Descriptive research focuses on describing the subject being studied and getting answers to questions like what, where, when, and who of the research question. However, it doesn’t include the answers to the underlying reasons, or the “why” behind the answers obtained from the research. We can use both f qualitative and quantitative methods to conduct descriptive research. Descriptive research does not describe the methods, but rather the data gathered through the research (regardless of the methods used).

When we use quantitative research and gather numerical data, we can use statistical analysis to understand relationships between different variables. Here’s William Hudson, CEO of Syntagm with more on correlation and how we can apply tests such as Pearson’s r and Spearman Rank Coefficient to our data.

This helps interpret phenomena such as user experience by analyzing session lengths and conversion values, revealing whether variables like time spent on a page affect checkout values, for example.

Random Sampling: Each individual in the population has an equitable opportunity to be chosen, which minimizes biases and simplifies analysis.

Systematic Sampling: Selecting every k-th item from a list after a random start. It's simpler and faster than random sampling when dealing with large populations.

Stratified Sampling: Segregate the population into subgroups or strata according to comparable characteristics. Then, samples are taken randomly from each stratum.

Cluster Sampling: Divide the population into clusters and choose a random sample.

Multistage Sampling: Various sampling techniques are used at different stages to collect detailed information from diverse populations.

Convenience Sampling: The researcher selects the sample based on availability and willingness to participate, which may only represent part of the population.

Quota Sampling: Segment the population into subgroups, and samples are non-randomly selected to fulfill a predetermined quota from each subset.

These are just a few techniques, and choosing the right one depends on your research question, discipline, resource availability, and the level of accuracy required. In quantitative research, there isn't a one-size-fits-all sampling technique; choosing a method that aligns with your research goals and population is critical. However, a well-planned strategy is essential to avoid wasting resources and time, as highlighted in our video featuring William Hudson, CEO of Syntagm.

He emphasizes the importance of recruiting participants meticulously, ensuring their engagement and the quality of their responses. Accurate and thoughtful participant responses are crucial for obtaining reliable results. William also sheds light on dealing with failing participants and scrutinizing response quality to refine the outcomes.

The 4 types of quantitative research are Descriptive, Correlational, Causal-Comparative/Quasi-Experimental, and Experimental Research. Descriptive research aims to depict ‘what exists’ clearly and precisely. Correlational research examines relationships between variables. Causal-comparative research investigates the cause-effect relationship between variables. Experimental research explores causal relationships by manipulating independent variables. To gain deeper insights into quantitative research methods in UX, consider enrolling in our Data-Driven Design: Quantitative Research for UX course.

The strength of quantitative research is its ability to provide precise numerical data for analyzing target variables.This allows for generalized conclusions and predictions about future occurrences, proving invaluable in various fields, including user experience. William Hudson, CEO of Syntagm, discusses the role of surveys, analytics, and testing in providing objective insights in our video on quantitative research methods, highlighting the significance of structured methodologies in eliciting reliable results.

To master quantitative research methods, enroll in our comprehensive course, Data-Driven Design: Quantitative Research for UX . 

This course empowers you to leverage quantitative data to make informed design decisions, providing a deep dive into methods like surveys and analytics. Whether you’re a novice or a seasoned professional, this course at Interaction Design Foundation offers valuable insights and practical knowledge, ensuring you acquire the skills necessary to excel in user experience research. Explore our diverse topics to elevate your understanding of quantitative research methods.

Answer a Short Quiz to Earn a Gift

What is the primary goal of quantitative research in design?

• To analyze numerical data and identify patterns
• To explore abstract design concepts for implementation
• To understand people's subjective experiences and opinions

Which of the following methods is an example of quantitative research?

• Conduct a focus groups to collect detailed user feedback
• Participate in open-ended interviews to explore user experiences
• Run usability tests and measure task completion times

What is one key advantage of quantitative research?

• It allows participants to express their opinions in a flexible manner.
• It provides researchers with detailed narratives of user experiences and perspectives.
• It produces standardized, comparable data that researchers can statistically analyze.

What is a significant challenge of quantitative research?

• It lacks objectivity which makes its results difficult to reproduce.
• It may oversimplify complex user behaviors into numbers and miss contextual insights.
• It often results in biased or misleading conclusions.

How can designers effectively combine qualitative and quantitative research?

• They can collect quantitative data first, followed by qualitative insights to explain the findings.
• They can completely replace quantitative methods with qualitative approaches.
• They can treat them as interchangeable methods to gather similar data.

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Literature on Quantitative Research

Here’s the entire UX literature on Quantitative Research by the Interaction Design Foundation, collated in one place:

Learn more about Quantitative Research

Take a deep dive into Quantitative Research with our course User Research – Methods and Best Practices .

How do you plan to design a product or service that your users will love , if you don't know what they want in the first place? As a user experience designer, you shouldn't leave it to chance to design something outstanding; you should make the effort to understand your users and build on that knowledge from the outset. User research is the way to do this, and it can therefore be thought of as the largest part of user experience design .

In fact, user research is often the first step of a UX design process—after all, you cannot begin to design a product or service without first understanding what your users want! As you gain the skills required, and learn about the best practices in user research, you’ll get first-hand knowledge of your users and be able to design the optimal product—one that’s truly relevant for your users and, subsequently, outperforms your competitors’ .

This course will give you insights into the most essential qualitative research methods around and will teach you how to put them into practice in your design work. You’ll also have the opportunity to embark on three practical projects where you can apply what you’ve learned to carry out user research in the real world . You’ll learn details about how to plan user research projects and fit them into your own work processes in a way that maximizes the impact your research can have on your designs. On top of that, you’ll gain practice with different methods that will help you analyze the results of your research and communicate your findings to your clients and stakeholders—workshops, user journeys and personas, just to name a few!

By the end of the course, you’ll have not only a Course Certificate but also three case studies to add to your portfolio. And remember, a portfolio with engaging case studies is invaluable if you are looking to break into a career in UX design or user research!

We believe you should learn from the best, so we’ve gathered a team of experts to help teach this course alongside our own course instructors. That means you’ll meet a new instructor in each of the lessons on research methods who is an expert in their field—we hope you enjoy what they have in store for you!

All open-source articles on Quantitative Research

Best practices for qualitative user research.

• 4 years ago

Card Sorting

Understand the User’s Perspective through Research for Mobile UX

7 Simple Ways to Get Better Results From Ethnographic Research

Question Everything

• 3 years ago

Tree Testing

Adding Quality to Your Design Research with an SSQS Checklist

• 8 years ago

Rating Scales in UX Research: The Ultimate Guide

How to Fit Quantitative Research into the Project Lifecycle

Correlation in User Experience

Why and When to Use Surveys

First-Click Testing

What to Test

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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.

Qualitative vs Quantitative Research Methods & Data Analysis

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis .

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Mixed methods research
• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

Reference management. Clean and simple.

Qualitative vs. quantitative research - what’s the difference?

What is quantitative research?

What is quantitative research used for, how to collect data for quantitative research, what is qualitative research, what is qualitative research used for, how to collect data for qualitative research, when to use which approach, how to analyze qualitative and quantitative research, analyzing quantitative data, analyzing qualitative data, differences between qualitative and quantitative research, frequently asked questions about qualitative vs. quantitative research, related articles.

Both qualitative and quantitative research are valid and effective approaches to study a particular subject. However, it is important to know that these research approaches serve different purposes and provide different results. This guide will help illustrate quantitative and qualitative research, what they are used for, and the difference between them.

Quantitative research focuses on collecting numerical data and using it to measure variables. As such, quantitative research and data are typically expressed in numbers and graphs. Moreover, this type of research is structured and statistical and the returned results are objective.

The simplest way to describe quantitative research is that it answers the questions " what " or " how much ".

To illustrate what quantitative research is used for, let’s look at a simple example. Let’s assume you want to research the reading habits of a specific part of a population.

With this research, you would like to establish what they read. In other words, do they read fiction, non-fiction, magazines, blogs, and so on? Also, you want to establish what they read about. For example, if they read fiction, is it thrillers, romance novels, or period dramas?

With quantitative research, you can gather concrete data about these reading habits. Your research will then, for example, show that 40% of the audience reads fiction and, of that 40%, 60% prefer romance novels.

In other studies and research projects, quantitative research will work in much the same way. That is, you use it to quantify variables, opinions, behaviors, and more.

Now that we've seen what quantitative research is and what it's used for, let's look at how you'll collect data for it. Because quantitative research is structured and statistical, its data collection methods focus on collecting numerical data.

Some methods to collect this data include:

• Surveys . Surveys are one of the most popular and easiest ways to collect quantitative data. These can include anything from online surveys to paper surveys. It’s important to remember that, to collect quantitative data, you won’t be able to ask open-ended questions.
• Interviews . As is the case with qualitative data, you’ll be able to use interviews to collect quantitative data with the proviso that the data will not be based on open-ended questions.
• Observations . You’ll also be able to use observations to collect quantitative data. However, here you’ll need to make observations in an environment where variables can’t be controlled.
• Website interceptors . With website interceptors, you’ll be able to get real-time insights into a specific product, service, or subject. In most cases, these interceptors take the form of surveys displayed on websites or invitations on the website to complete the survey.
• Longitudinal studies . With these studies, you’ll gather data on the same variables over specified time periods. Longitudinal studies are often used in medical sciences and include, for instance, diet studies. It’s important to remember that, for the results to be reliable, you’ll have to collect data from the same subjects.
• Online polls . Similar to website interceptors, online polls allow you to gather data from websites or social media platforms. These polls are short with only a few options and can give you valuable insights into a very specific question or topic.
• Experiments . With experiments, you’ll manipulate some variables (your independent variables) and gather data on causal relationships between others (your dependent variables). You’ll then measure what effect the manipulation of the independent variables has on the dependent variables.

Qualitative research focuses on collecting and analyzing non-numerical data. As such, it's typically unstructured and non-statistical. The main aim of qualitative research is to get a better understanding and insights into concepts, topics, and subjects.

The easiest way to describe qualitative research is that it answers the question " why ".

Considering that qualitative research aims to provide more profound insights and understanding into specific subjects, we’ll use our example mentioned earlier to explain what qualitative research is used for.

Based on this example, you’ve now established that 40% of the population reads fiction. You’ve probably also discovered in what proportion the population consumes other reading materials.

Qualitative research will now enable you to learn the reasons for these reading habits. For example, it will show you why 40% of the readers prefer fiction, while, for instance, only 10% prefer thrillers. It thus gives you an understanding of your participants’ behaviors and actions.

We've now recapped what qualitative research is and what it's used for. Let's now consider some methods to collect data for this type of research.

Some of these data collection methods include:

• Interviews . These include one-on-one interviews with respondents where you ask open-ended questions. You’ll then record the answers from every respondent and analyze these answers later.
• Open-ended survey questions . Open-ended survey questions give you insights into why respondents feel the way they do about a particular aspect.
• Focus groups . Focus groups allow you to have conversations with small groups of people and record their opinions and views about a specific topic.
• Observations . Observations like ethnography require that you participate in a specific organization or group in order to record their routines and interactions. This will, for instance, be the case where you want to establish how customers use a product in real-life scenarios.
• Literature reviews . With literature reviews, you’ll analyze the published works of other authors to analyze the prevailing view regarding a specific subject.
• Diary studies . Diary studies allow you to collect data about peoples’ habits, activities, and experiences over time. This will, for example, show you how customers use a product, when they use it, and what motivates them.

Now, the immediate question is: When should you use qualitative research, and when should you use quantitative research? As mentioned earlier, in its simplest form:

• Quantitative research allows you to confirm or test a hypothesis or theory or quantify a specific problem or quality.
• Qualitative research allows you to understand concepts or experiences.

Let's look at how you'll use these approaches in a research project a bit closer:

• Formulating a hypothesis . As mentioned earlier, qualitative research gives you a deeper understanding of a topic. Apart from learning more profound insights about your research findings, you can also use it to formulate a hypothesis when you start your research.
• Confirming a hypothesis . Once you’ve formulated a hypothesis, you can test it with quantitative research. As mentioned, you can also use it to quantify trends and behavior.
• Finding general answers . Quantitative research can help you answer broad questions. This is because it uses a larger sample size and thus makes it easier to gather simple binary or numeric data on a specific subject.
• Getting a deeper understanding . Once you have the broad answers mentioned above, qualitative research will help you find reasons for these answers. In other words, quantitative research shows you the motives behind actions or behaviors.

Considering the above, why not consider a mixed approach ? You certainly can because these approaches are not mutually exclusive. In other words, using one does not necessarily exclude the other. Moreover, both these approaches are useful for different reasons.

This means you could use both approaches in one project to achieve different goals. For example, you could use qualitative to formulate a hypothesis. Once formulated, quantitative research will allow you to confirm the hypothesis.

So, to answer the initial question, the approach you use is up to you.  However, when deciding on the right approach, you should consider the specific research project, the data you'll gather, and what you want to achieve.

No matter what approach you choose, you should design your research in such a way that it delivers results that are objective, reliable, and valid.

Both these research approaches are based on data. Once you have this data, however, you need to analyze it to answer your research questions. The method to do this depends on the research approach you use.

To analyze quantitative data, you'll need to use mathematical or statistical analysis. This can involve anything from calculating simple averages to applying complex and advanced methods to calculate the statistical significance of the results. No matter what analysis methods you use, it will enable you to spot trends and patterns in your data.

Considering the above, you can use tools, applications, and programming languages like R to calculate:

• The average of a set of numbers . This could, for instance, be the case where you calculate the average scores students obtained in a test or the average time people spend on a website.
• The frequency of a specific response . This will be the case where you, for example, use open-ended survey questions during qualitative analysis. You could then calculate the frequency of a specific response for deeper insights.
• Any correlation between different variables . Through mathematical analysis, you can calculate whether two or more variables are directly or indirectly correlated. In turn, this could help you identify trends in the data.
• The statistical significance of your results . By analyzing the data and calculating the statistical significance of the results, you'll be able to see whether certain occurrences happen randomly or because of specific factors.

Analyzing qualitative data is more complex than quantitative data. This is simply because it's not based on numerical values but rather text, images, video, and the like. As such, you won't be able to use mathematical analysis to analyze and interpret your results.

Because of this, it relies on a more interpretive analysis style and a strict analytical framework to analyze data and extract insights from it.

Some of the most common ways to analyze qualitative data include:

• Qualitative content analysis . In a content analysis, you'll analyze the language used in a specific piece of text. This allows you to understand the intentions of the author, who the audience is, and find patterns and correlations in how different concepts are communicated. A major benefit of this approach is that it follows a systematic and transparent process that other researchers will be able to replicate. As such, your research will produce highly reliable results. Keep in mind, however, that content analysis can be time-intensive and difficult to automate. ➡️  Learn how to do a content analysis in the guide.
• Thematic analysis . In a thematic analysis, you'll analyze data with a view of extracting themes, topics, and patterns in the data. Although thematic analysis can encompass a range of diverse approaches, it's usually used to analyze a collection of texts like survey responses, focus group discussions, or transcriptions of interviews. One of the main benefits of thematic analysis is that it's flexible in its approach. However, in some cases, thematic analysis can be highly subjective, which, in turn, impacts the reliability of the results. ➡️  Learn how to do a thematic analysis in this guide.
• Discourse analysis . In a discourse analysis, you'll analyze written or spoken language to understand how language is used in real-life social situations. As such, you'll be able to determine how meaning is given to language in different contexts. This is an especially effective approach if you want to gain a deeper understanding of different social groups and how they communicate with each other. As such, it's commonly used in humanities and social science disciplines.

We’ve now given a broad overview of both qualitative and quantitative research. Based on this, we can summarize the differences between these two approaches as follows:

Qualitative research focuses on collecting and analyzing non-numerical data. As such, it's typically unstructured and non-statistical. The main aim of qualitative research is to get a better understanding and insights into concepts, topics, and subjects. Quantitative research focuses on collecting numerical data and using it to measure variables. As such, quantitative research and data are typically expressed in numbers and graphs. Moreover, this type of research is structured and statistical and the returned results are objective.

3 examples of qualitative research would be:

• Interviews . These include one-on-one interviews with respondents with open-ended questions. You’ll then record the answers and analyze them later.
• Observations . Observations require that you participate in a specific organization or group in order to record their routines and interactions.

3 examples of quantitative research include:

• Surveys . Surveys are one of the most popular and easiest ways to collect quantitative data. To collect quantitative data, you won’t be able to ask open-ended questions.
• Longitudinal studies . With these studies, you’ll gather data on the same variables over specified time periods. Longitudinal studies are often used in medical sciences.

The main purpose of qualitative research is to get a better understanding and insights into concepts, topics, and subjects. The easiest way to describe qualitative research is that it answers the question " why ".

The purpose of quantitative research is to collect numerical data and use it to measure variables. As such, quantitative research and data are typically expressed in numbers and graphs. The simplest way to describe quantitative research is that it answers the questions " what " or " how much ".

Educational resources and simple solutions for your research journey

Qualitative vs Quantitative Research: Differences, Examples, and Methods

There are two broad kinds of research approaches: qualitative and quantitative research that are used to study and analyze phenomena in various fields such as natural sciences, social sciences, and humanities. Whether you have realized it or not, your research must have followed either or both research types. In this article we will discuss what qualitative vs quantitative research is, their applications, pros and cons, and when to use qualitative vs quantitative research . Before we get into the details, it is important to understand the differences between the qualitative and quantitative research.     

Table of Contents

Qualitative v s Quantitative Research  

Quantitative research deals with quantity, hence, this research type is concerned with numbers and statistics to prove or disapprove theories or hypothesis. In contrast, qualitative research is all about quality – characteristics, unquantifiable features, and meanings to seek deeper understanding of behavior and phenomenon. These two methodologies serve complementary roles in the research process, each offering unique insights and methods suited to different research questions and objectives.    

Qualitative and quantitative research approaches have their own unique characteristics, drawbacks, advantages, and uses. Where quantitative research is mostly employed to validate theories or assumptions with the goal of generalizing facts to the larger population, qualitative research is used to study concepts, thoughts, or experiences for the purpose of gaining the underlying reasons, motivations, and meanings behind human behavior .   

What Are the Differences Between Qualitative and Quantitative Research  

Qualitative and quantitative research differs in terms of the methods they employ to conduct, collect, and analyze data. For example, qualitative research usually relies on interviews, observations, and textual analysis to explore subjective experiences and diverse perspectives. While quantitative data collection methods include surveys, experiments, and statistical analysis to gather and analyze numerical data. The differences between the two research approaches across various aspects are listed in the table below.    

Data Collection Methods  

There are differences between qualitative and quantitative research when it comes to data collection as they deal with different types of data. Qualitative research is concerned with personal or descriptive accounts to understand human behavior within society. Quantitative research deals with numerical or measurable data to delineate relations among variables. Hence, the qualitative data collection methods differ significantly from quantitative data collection methods due to the nature of data being collected and the research objectives. Below is the list of data collection methods for each research approach:    

Qualitative Research Data Collection  

• Interviews  
• Focus g roups  
• Content a nalysis  
• Literature review  
• Observation  
• Ethnography  

Qualitative research data collection can involve one-on-one group interviews to capture in-depth perspectives of participants using open-ended questions. These interviews could be structured, semi-structured or unstructured depending upon the nature of the study. Focus groups can be used to explore specific topics and generate rich data through discussions among participants. Another qualitative data collection method is content analysis, which involves systematically analyzing text documents, audio, and video files or visual content to uncover patterns, themes, and meanings. This can be done through coding and categorization of raw data to draw meaningful insights. Data can be collected through observation studies where the goal is to simply observe and document behaviors, interaction, and phenomena in natural settings without interference. Lastly, ethnography allows one to immerse themselves in the culture or environment under study for a prolonged period to gain a deep understanding of the social phenomena.   

Quantitative Research Data Collection  

• Surveys/ q uestionnaires  
• Experiments
• Secondary data analysis  
• Structured o bservations  
• Case studies   
• Tests and a ssessments  

Quantitative research data collection approaches comprise of fundamental methods for generating numerical data that can be analyzed using statistical or mathematical tools. The most common quantitative data collection approach is the usage of structured surveys with close-ended questions to collect quantifiable data from a large sample of participants. These can be conducted online, over the phone, or in person.   

Performing experiments is another important data collection approach, in which variables are manipulated under controlled conditions to observe their effects on dependent variables. This often involves random assignment of participants to different conditions or groups. Such experimental settings are employed to gauge cause-and-effect relationships and understand a complex phenomenon. At times, instead of acquiring original data, researchers may deal with secondary data, which is the dataset curated by others, such as government agencies, research organizations, or academic institute. With structured observations, subjects in a natural environment can be studied by controlling the variables which aids in understanding the relationship among various variables. The secondary data is then analyzed to identify patterns and relationships among variables. Observational studies provide a means to systematically observe and record behaviors or phenomena as they occur in controlled environments. Case studies form an interesting study methodology in which a researcher studies a single entity or a small number of entities (individuals or organizations) in detail to understand complex phenomena within a specific context.   

Qualitative vs Quantitative Research Outcomes  

Qualitative research and quantitative research lead to varied research outcomes, each with its own strengths and limitations. For example, qualitative research outcomes provide deep descriptive accounts of human experiences, motivations, and perspectives that allow us to identify themes or narratives and context in which behavior, attitudes, or phenomena occurs.  Quantitative research outcomes on the other hand produce numerical data that is analyzed statistically to establish patterns and relationships objectively, to form generalizations about the larger population and make predictions. This numerical data can be presented in the form of graphs, tables, or charts. Both approaches offer valuable perspectives on complex phenomena, with qualitative research focusing on depth and interpretation, while quantitative research emphasizes numerical analysis and objectivity.  

When to Use Qualitative vs Quantitative Research Approach  

The decision to choose between qualitative and quantitative research depends on various factors, such as the research question, objectives, whether you are taking an inductive or deductive approach, available resources, practical considerations such as time and money, and the nature of the phenomenon under investigation. To simplify, quantitative research can be used if the aim of the research is to prove or test a hypothesis, while qualitative research should be used if the research question is more exploratory and an in-depth understanding of the concepts, behavior, or experiences is needed.     

Qualitative research approach  

Qualitative research approach is used under following scenarios:   

• To study complex phenomena: When the research requires understanding the depth, complexity, and context of a phenomenon.  
• Collecting participant perspectives: When the goal is to understand the why behind a certain behavior, and a need to capture subjective experiences and perceptions of participants.  
• Generating hypotheses or theories: When generating hypotheses, theories, or conceptual frameworks based on exploratory research.  

Example: If you have a research question “What obstacles do expatriate students encounter when acquiring a new language in their host country?”  

This research question can be addressed using the qualitative research approach by conducting in-depth interviews with 15-25 expatriate university students. Ask open-ended questions such as “What are the major challenges you face while attempting to learn the new language?”, “Do you find it difficult to learn the language as an adult?”, and “Do you feel practicing with a native friend or colleague helps the learning process”?  

Based on the findings of these answers, a follow-up questionnaire can be planned to clarify things. Next step will be to transcribe all interviews using transcription software and identify themes and patterns.   

Quantitative research approach  

Quantitative research approach is used under following scenarios:   

• Testing hypotheses or proving theories: When aiming to test hypotheses, establish relationships, or examine cause-and-effect relationships.   
• Generalizability: When needing findings that can be generalized to broader populations using large, representative samples.  
• Statistical analysis: When requiring rigorous statistical analysis to quantify relationships, patterns, or trends in data.   

Example : Considering the above example, you can conduct a survey of 200-300 expatriate university students and ask them specific questions such as: “On a scale of 1-10 how difficult is it to learn a new language?”  

Next, statistical analysis can be performed on the responses to draw conclusions like, on an average expatriate students rated the difficulty of learning a language 6.5 on the scale of 10.    

Mixed methods approach  

In many cases, researchers may opt for a mixed methods approach , combining qualitative and quantitative methods to leverage the strengths of both approaches. Researchers may use qualitative data to explore phenomena in-depth and generate hypotheses, while quantitative data can be used to test these hypotheses and generalize findings to broader populations.  

Example: Both qualitative and quantitative research methods can be used in combination to address the above research question. Through open-ended questions you can gain insights about different perspectives and experiences while quantitative research allows you to test that knowledge and prove/disprove your hypothesis.   

How to Analyze Qualitative and Quantitative Data  

When it comes to analyzing qualitative and quantitative data, the focus is on identifying patterns in the data to highlight the relationship between elements. The best research method for any given study should be chosen based on the study aim. A few methods to analyze qualitative and quantitative data are listed below.  

Analyzing qualitative data  

Qualitative data analysis is challenging as it is not expressed in numbers and consists majorly of texts, images, or videos. Hence, care must be taken while using any analytical approach. Some common approaches to analyze qualitative data include:  

• Organization: The first step is data (transcripts or notes) organization into different categories with similar concepts, themes, and patterns to find inter-relationships.  
• Coding: Data can be arranged in categories based on themes/concepts using coding.  
• Theme development: Utilize higher-level organization to group related codes into broader themes.  
• Interpretation: Explore the meaning behind different emerging themes to understand connections. Use different perspectives like culture, environment, and status to evaluate emerging themes.  
• Reporting: Present findings with quotes or excerpts to illustrate key themes.   

Analyzing quantitative data  

Quantitative data analysis is more direct compared to qualitative data as it primarily deals with numbers. Data can be evaluated using simple math or advanced statistics (descriptive or inferential). Some common approaches to analyze quantitative data include:  

• Processing raw data: Check missing values, outliers, or inconsistencies in raw data.  
• Descriptive statistics: Summarize data with means, standard deviations, or standard error using programs such as Excel, SPSS, or R language.  
• Exploratory data analysis: Usage of visuals to deduce patterns and trends.  
• Hypothesis testing: Apply statistical tests to find significance and test hypothesis (Student’s t-test or ANOVA).  
• Interpretation: Analyze results considering significance and practical implications.  
• Validation: Data validation through replication or literature review.  
• Reporting: Present findings by means of tables, figures, or graphs.   

Benefits and limitations of qualitative vs quantitative research  

There are significant differences between qualitative and quantitative research; we have listed the benefits and limitations of both methods below:  

Benefits of qualitative research  

• Rich insights: As qualitative research often produces information-rich data, it aids in gaining in-depth insights into complex phenomena, allowing researchers to explore nuances and meanings of the topic of study.  
• Flexibility: One of the most important benefits of qualitative research is flexibility in acquiring and analyzing data that allows researchers to adapt to the context and explore more unconventional aspects.  
• Contextual understanding: With descriptive and comprehensive data, understanding the context in which behaviors or phenomena occur becomes accessible.   
• Capturing different perspectives: Qualitative research allows for capturing different participant perspectives with open-ended question formats that further enrich data.   
• Hypothesis/theory generation: Qualitative research is often the first step in generating theory/hypothesis, which leads to future investigation thereby contributing to the field of research.

Limitations of qualitative research  

• Subjectivity: It is difficult to have objective interpretation with qualitative research, as research findings might be influenced by the expertise of researchers. The risk of researcher bias or interpretations affects the reliability and validity of the results.   
• Limited generalizability: Due to the presence of small, non-representative samples, the qualitative data cannot be used to make generalizations to a broader population.  
• Cost and time intensive: Qualitative data collection can be time-consuming and resource-intensive, therefore, it requires strategic planning and commitment.   
• Complex analysis: Analyzing qualitative data needs specialized skills and techniques, hence, it’s challenging for researchers without sufficient training or experience.   
• Potential misinterpretation: There is a risk of sampling bias and misinterpretation in data collection and analysis if researchers lack cultural or contextual understanding.   

Benefits of quantitative research  

• Objectivity: A key benefit of quantitative research approach, this objectivity reduces researcher bias and subjectivity, enhancing the reliability and validity of findings.   
• Generalizability: For quantitative research, the sample size must be large and representative enough to allow for generalization to broader populations.   
• Statistical analysis: Quantitative research enables rigorous statistical analysis (increasing power of the analysis), aiding hypothesis testing and finding patterns or relationship among variables.   
• Efficiency: Quantitative data collection and analysis is usually more efficient compared to the qualitative methods, especially when dealing with large datasets.   
• Clarity and Precision: The findings are usually clear and precise, making it easier to present them as graphs, tables, and figures to convey them to a larger audience.  

Limitations of quantitative research  

• Lacks depth and details: Due to its objective nature, quantitative research might lack the depth and richness of qualitative approaches, potentially overlooking important contextual factors or nuances.   
• Limited exploration: By not considering the subjective experiences of participants in depth , there’s a limited chance to study complex phenomenon in detail.   
• Potential oversimplification: Quantitative research may oversimplify complex phenomena by boiling them down to numbers, which might ignore key nuances.   
• Inflexibility: Quantitative research deals with predecided varibales and measures , which limits the ability of researchers to explore unexpected findings or adjust the research design as new findings become available .  
• Ethical consideration: Quantitative research may raise ethical concerns especially regarding privacy, informed consent, and the potential for harm, when dealing with sensitive topics or vulnerable populations.   

Frequently asked questions  

• What is the difference between qualitative and quantitative research? 

Quantitative methods use numerical data and statistical analysis for objective measurement and hypothesis testing, emphasizing generalizability. Qualitative methods gather non-numerical data to explore subjective experiences and contexts, providing rich, nuanced insights.  

• What are the types of qualitative research? 

Qualitative research methods include interviews, observations, focus groups, and case studies. They provide rich insights into participants’ perspectives and behaviors within their contexts, enabling exploration of complex phenomena.  

• What are the types of quantitative research? 

Quantitative research methods include surveys, experiments, observations, correlational studies, and longitudinal research. They gather numerical data for statistical analysis, aiming for objectivity and generalizability.  

• Can you give me examples for qualitative and quantitative research? 

Qualitative Research Example: 

Research Question: What are the experiences of parents with autistic children in accessing support services?  

Method: Conducting in-depth interviews with parents to explore their perspectives, challenges, and needs.  

Quantitative Research Example: 

Research Question: What is the correlation between sleep duration and academic performance in college students?  

Method: Distributing surveys to a large sample of college students to collect data on their sleep habits and academic performance, then analyzing the data statistically to determine any correlations.  

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Difference Between Qualitative and Quantitative Research

In the fields of business, science and technology, economics, etc., they use two standard ways of conducting research. One is qualitative research and the other is quantitative research. Quantitative research uses statistical and logical observations to get a conclusion whereas the qualitative search relies on verbal and written data. In short, quantitative research is generally expressed in numbers or represented using graphs, whereas qualitative research is expressed using the words for the given data sets . Now, in this article, we are going to discuss the difference between qualitative and quantitative research of different data sets.

Why Do We Need Quantitative and Qualitative Research?

Quantitative research is useful in order to gain an understanding of the underlying opinions, motivations, and reasons. It gives insights into the problems. Also, quantitative research helps to develop ideas and hypotheses, whereas qualitative research is useful in uncovering trends, ideas and opinions, and gives deeper insights into the problem.

Definition of Qualitative and Quantitative Research

Qualitative Research: Qualitative research is used to gain an understanding of human behaviour, intentions, attitudes, experience, etc., based on the observation and interpretation of people. It is an unstructured and exploratory technique that deals with highly complex phenomena. This kind of research is usually done to understand the topic in-depth. It is carried out by taking interviews with open-ended questions, observations that are described in words, and so on.

Quantitative Research: Quantitative research method relies on the methods of natural sciences, which develops hard facts and numerical data. It establishes the cause-and-effect relationship between two variables using different statistical, computational, and statistical methods. As the results are accurately and precisely measured, this research method is also termed as “Empirical Research”. This type of research is generally used to establish generalised facts about a particular topic. This type of research is usually done using surveys, experiments, and so on.

What are the Differences Between Qualitative and Quantitative Research?

Quantitative research is a more methodical approach to solving problems by generating and using data. This form of research is used in quantifying data and variables into concrete data. The surveys used in Quantitative Research includes online surveys, paper surveys and other forms of survey used to complete the research.

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Frequently Asked Questions on the Difference Between Qualitative and Quantitative Research

Mention the types of quantitative research..

The four different types of quantitative research are descriptive research, experimental research, quasi-experimental research, and correlational research.

Mention the types of qualitative research

The different types of qualitative research are case study, ethnographic method, phenomenological method, narrative model, historical model, grounded theory method

Mention the major difference between qualitative and quantitative data.

The major difference between the qualitative and quantitative data is that quantitative data is about the numbers and the qualitative data is descriptive.

Give the examples for quantitative and qualitative data

The examples of quantitative data are age, salary, height, shoe size, etc. The examples of qualitative data are taste, smell, colour, etc

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REVIEW article

One of the major challenges of masking the bitter taste in medications: an overview of quantitative methods for bitterness.

• 1 Department of Pharmacy, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
• 2 School of Pharmacy, Henan University of Chinese Medicine, Zhengzhou, China
• 3 Henan Province Engineering Research Center for Clinical Application, Evaluation and Transformation of Traditional Chinese Medicine, Henan Province Engineering Laboratory for Clinical Evaluation Technology of Chinese Medicine, Zhengzhou, China
• 4 Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases Co-Constructed by Henan Province, Education Ministry of China, Henan University of Chinese Medicine, Zhengzhou, China
• 5 Zhengzhou Traditional Chinese Medicine Hospital, Zhengzhou, China
• 6 Third Level Laboratory of Traditional Chinese Medicine Preparations of the State Administration of Traditional Chinese Medicine, Zhengzhou, China

Objective: The aim of the present study was to carry out a systematic research on bitterness quantification to provide a reference for scholars and pharmaceutical developers to carry out drug taste masking research. Significance: The bitterness of medications poses a significant concern for clinicians and patients. Scientifically measuring the intensity of drug bitterness is pivotal for enhancing drug palatability and broadening their clinical utility.

Methods: The current study was carried out by conducting a systematic literature review that identified relevant papers from indexed databases. Numerous studies and research are cited and quoted in this article to summarize the features, strengths, and applicability of quantitative bitterness assessment methods.

Results: In our research, we systematically outlined the classification and key advancements in quantitative research methods for assessing drug bitterness, including in vivo quantification techniques such as traditional human taste panel methods, as well as in vitro quantification methods such as electronic tongue analysis. It focused on the quantitative methods and difficulties of bitterness of natural drugs with complex system characteristics and their difficulties in quantification, and proposes possible future research directions.

Conclusion: The quantitative methods of bitterness were summarized, which laid an important foundation for the construction of a comprehensive bitterness quantification standard system and the formulation of accurate, efficient and rich taste masking strategies.

1 Introduction

As widely recognized, the axiom “good medicine tastes bitter” epitomizes a fundamental attribute of pharmaceuticals, with many drugs exhibiting a bitter taste ( Bahia et al., 2018 ). Our investigation revealed that bitter herbs or decoction pieces constituted 49.0% of the 2020 edition of the “Chinese Pharmacopoeia" ( Lin et al., 2016 ), while 66% of compounds cataloged in the Drug Bank library were projected to possess a bitter taste ( Dagan-Wiener et al., 2017 ). The Bitter DB database archives over 1,000 bitter molecules. Humans and animals exhibit heightened sensitivity to bitterness perception, capable of discerning bitterness even at lower concentrations ( Aliani and Eskin, 2017 ). The intrinsic aversion to bitterness among humans significantly impacts patient medication adherence ( Beauchamp, 2016 ; Boesveldt and de Graaf, 2017 ), thereby influencing clinical efficacy ( Amin et al., 2018 ; Zheng et al., 2018 ). The prevalent distaste for medications is frequently cited as a primary reason for patient non-compliance, particularly among children ( Clapham et al., 2023 ). Surveys indicate that over 90% of pediatricians identify drug taste and palatability as major barriers to completing clinical treatments ( Milne and Bruss, 2008 ). In a survey involving nearly 700 European children, 63.7% of respondents attributed difficulty in medication intake to dislike for the drug’s taste ( Nordenmalm et al., 2019 ). Peter Drucker, often regarded as the father of modern management, emphasized the necessity of objective and accurate quantitative evaluation of drug bitterness as a crucial prerequisite for understanding its taste patterns and enhancing palatability.

Bitterness primarily arises from the activation of TAS2R (also referred to as bitter receptors). Upon binding of the bitter compound to the receptor, located prominently on taste receptor cells (TRCs), a signal transduction cascade ensues. This activation prompts TAS2R to catalyze the dissociation and liberation of Gβ3/Gγ13 subunits from the Gβ3/Gγ13 heterotrimeric receptor, thereby activating phospholipase C (PLCβ2). Subsequently, PLCβ2 catalyzes the breakdown of phospholipid PIP2, yielding inositol-1,4,5-phosphate (IP3). IP3, in turn, binds to IP3 receptors on the endoplasmic reticulum, eliciting the release of intracellularly stored calcium ions (Ca 2+ ). The elevated intracellular Ca 2+ concentration ([Ca 2+ ]i) prompts the opening of membrane-associated TRPM5 channels, facilitating the influx of sodium ions (Na + ). This ion exchange initiates receptor cell membrane depolarization, triggering the release of adenosine triphosphate (ATP) through calcium homeostasis regulator one and 3 (CALHM1 and CALHM3) channels. Ultimately, the liberated ATP activates purinergic receptors on afferent nerve fibers, converting the chemical signals of bitter compounds into electrical signals, which are relayed to the taste nucleus of the brainstem. Subsequently, these signals are transmitted to the thalamus and eventually to the taste cortex of the cerebral cortex, culminating in the perception of bitterness ( Finger et al., 2005 ; Roper, 2007 ; Ma et al., 2018 ), as shown in Figure 1 .

Figure 1 . Activation mechanism of human TAS2R and signal transduction pathway of bitterness stimulation.

According to the law of taste and bitter perception, the factors affecting drug bitterness are affected by the difference of genes and receptors. Single nucleotide polymorphisms (SNPs) in genes encoding bitter taste receptors (TAS2R) leading to varied responses to bitter stimuli. Consequently, individuals with one gene form may perceive strong bitterness, while those with another gene form may not perceive bitterness at all ( Hayes et al., 2011 ; Mennella et al., 2011 ; Roudnitzky et al., 2011 ; Roudnitzky et al., 2015 ; Nolden and Feeney, 2020 ). The heterologous functional expression experiments have revealed that 25 TAS2Rs exhibit varying degrees of regulation characteristics as bitter taste receptors ( Meyerhof et al., 2010 ). The ability of bitter compounds to stimulate TAS2Rs varies. Compared to the activation of a single receptor, simultaneous activation of multiple receptors by a compound can elicit a larger cellular or neural response, thereby increasing bitterness. Additionally, the factors that affect the bitterness also include the following: (1) Chemical structure. According to the three-point contact theory of bitterness formation, bitter molecules can be categorized into two groups ( Hans-Dieter et al., 1985 ; Meyerhof, 2005 ): one consisting of hydrophobic lipophilic compounds with limited water solubility, such as olefins and terpenoids; the other comprising highly polar compounds capable of forming robust hydrophobic interactions, exemplified by alkaloids ( Zeng, 1990 ). (2) Substance concentration. Within a specific concentration range of the same drug, bitterness demonstrates a positive correlation with concentration. Studies have shown that a logarithmic relationship between human sensory intensity and stimulus physical quantity under moderate intensity stimula56tion conditions (S = KlgR) ( Omür-Ozbek and Dietrich, 2008 ; Li et al., 2016 ). (3) Interaction between substances. The bitterness between substances can be affected by electrostatic interaction ( Schalk et al., 2018 ), hydrophobic interaction ( Ogi et al., 2015 ), covalent bonding ( Bohin et al., 2013 ), and inclusion interaction ( Shah and Mashru, 2008 ). (4) pH. It is established that certain acidic peptides can mitigate bitterness ( Sakurai et al., 2009 ). Bitterness inhibition of sesquiterpene lactone can be achieved by pH control ( Yanagisawa and Misaka, 2021 ). (5) Solution viscosity. Studies have found that an increase in the viscosity of the resulting aqueous solvent leads to a reduction in taste intensity. Additionally, studies have indicated that emulsions demonstrate bitterness inhibition effects on KCl and/or caffeine compared to aqueous solutions ( Torrico et al., 2015 ).

In the realm of food taste assessment, extensive studies have focused on quantitatively evaluating taste attributes. For instance, the bitterness of beer is commonly assessed based on the concentration of isomerized α-acids, the primary source of beer bitterness. Methods such as European Bitterness Units (EBU) ( Polshin et al., 2010 ; Rudnitskaya et al., 2010 ), International Bitterness Units (IBU) ( Howard, 1968 ; Donley and Anheuser, 1992 ; Christensen et al., 2005 ), E.B.C. Bitterness Units ( Bishop, 2013 ), and Bitterness Units (BU) ( Tomlinson et al., 2013 ) are utilized for this purpose. Caffeine, containing numerous bitter compounds, undergoes bitterness intensity evaluation using Sensory Lexicon ( Shibamoto et al., 1981 ; Ginz and Engelhardt, 2000 ; Aree, 2019 ). These established quantification methods in the field of food taste can serve as valuable references for assessing drug bitterness. While the bitterness of chemical drugs can be accurately measured based on the content of bitter compounds due to their clear and singular composition, natural drugs encompass a multitude of bitter substances, intricate substance interactions, and a diverse array of taste components. These complexities confer inherent bitterness to natural drugs, emphasizing the importance of elucidating the mechanisms underlying their bitter taste and exploring tailored quantification methodologies.

As research into drug palatability continues to evolve, investigators have undertaken studies on the measurement and quantitative assessment of drug bitterness ( Gaudette and Pickering, 2013 ). These studies primarily encompass in vivo and in vitro methods. In vivo evaluation methods include the traditional human taste panel method (THTPM) ( Miyanaga et al., 2003 ; Rudnitskaya et al., 2013 ), taste strips test method ( Liu et al., 2020 ), animal behavior tests ( Lemon et al., 2019 ), and facial expression analysis ( Lemon et al., 2019 ), with THTPM recognized as the gold standard for taste assessment ( Gunaratne et al., 2019 ). In vitro bitterness detection methods mainly consist of electronic tongue methods (ETM) ( Hui et al., 2014 ; Immohr et al., 2017 ), and cell-based evaluations ( Qingjun and Ping, 2009 ), among others. Following an understanding of drug bitterness, researchers have employed taste-masking techniques utilizing flavoring agents, bitterness inhibitors, cyclodextrins, and nanoemulsions ( Hu et al., 2023 ). These methods have contributed positively to advancing the objective measurement and precise control of bitterness, thereby enhancing clinical drug compliance. Nonetheless, despite these advancements, there remains a lack of systematic summarization of research methods for bitterness quantification, as each method possesses unique characteristics and applications. This article seeks to analyze the research progress in drug bitterness quantification, delineate the primary factors influencing drug bitterness, and compile the methodologies for bitterness quantification. The aim is to foster a systematic comprehension of the principles, methodologies, and attributes of bitterness quantification, thereby offering insights for research endeavors in areas such as drug bitterness intensity evaluation, taste masking, and related fields.

2 Quantitative methods for bitterness

In light of the various factors influencing drug bitterness as outlined above, researchers are continuously innovating quantitative methods for assessing drug bitterness. Broadly, these methods can be categorized into two types: one involves quantifying the bitterness of drugs by measuring bitter molecules, while the other quantifies drug bitterness based on the intensity of taste stimulation. The results of both methods are elaborated upon below.

THTPM is a method used to assess the taste of drugs or food, relying on specific technical specifications and processes and utilizing the taste sense of the evaluation group ( Medicine, 2024 ). This method falls under the category of quantifying bitterness based on the intensity of human taste stimulus. The main methods were illustrated in Figure 2 . Currently, a range of internationally recognized standards for quantitative sensory evaluation have been established ( Clapham et al., 2023 ), paving the way for researchers to conduct various explorations into quantitative bitterness assessment.

Figure 2 . Quantitative research method of bitterness based on the THTPM. (A) Schematic representation of the quantitative description analysis (QDA). Based on Huan-Huan Li, 2019 ( Li et al., 2019 ). (B) Schematic representation of the general labeled magnitude Scale (gLMS) ( Deng et al., 2022 ) (C) Schematic representation of Rank bitterness intensity (RB) and standard apparent bitterness (SAB). Representative images were our own work ( Li et al., 2023 ). (D) Schematic representation of Ratio of bitterness (ROB). Representative images were our own work ( Gao et al., 2022 ). (E) chematic representation of Molecular bitterness (MB) and equivalent molecular bitterness (EMB). Representative images were our own work ( Wang et al., 2022 ).

2.1.1 Quantitative description analysis (QDA)

QDA is a method used to characterize the sensory properties and intensity of drugs. Within QDA, standard reference materials provide the bitterness value against which samples are compared for evaluation. For instance, when assessing the bitterness of Huanglian Jiedu Decoction (HLJDD), scholars provide a reference standard of 0.2 g/mL HLJDD with a bitterness value of 10. Evaluators use this reference standard to assess the bitterness of HLJDD after masking with [mPEG2000-PLLA2000, γ-CD, and neotame], revealing a reduction in HLJDD bitterness ( Ke et al., 2022 ).

However, the methods mentioned above lack the ability to provide information on the temporal aspects of bitterness perception. Bitterness exhibits unique temporal characteristics, taking more time to reach peak intensity in the mouth and longer to return to baseline ( Guinard et al., 2010 ). Additionally, with repeated intake, the perceived intensity of bitterness tends to increase ( Guinard et al., 1986 ). The presence of polyphenols (in red wine) and isohumulone (in beer) may amplify the bitterness of these beverages during consumption ( Guinard et al., 1994 ; Noble, 1994 ). Hence, some scholars employ the time-intensity method to dynamically assess bitterness throughout the entire perception period. In a study evaluating the bitterness of berberine hydrochloride orally disintegrating tablets, researchers instructed subjects to record bitterness intensity levels at various intervals (30 s, 1 min, 2 min, 5 min, 10 min) within a span of 10 min. Bitterness intensity was rated on a scale from 0 to 3. The findings indicated that when the ratio of the drug to the pH-dependent polymer Eudragit E100 increased from 1:0 to 1:0.8, the bitterness of the drug microcapsules significantly decreased, reaching zero bitterness by the second minute ( Hu et al., 2013 ).

Quantitative descriptive analysis also encompasses the evaluation of bitterness across different taste categories. Some researchers ( Sook Chung and Lee, 2012 ) categorized bitterness into distinct types such as alcohol bitterness, coffee bitterness, grapefruit pith bitterness, medicinal bitterness, and cocoa bitterness. Each type of bitterness corresponds to unique definitions and references, posing higher demands on the evaluator’s assessment skills.

2.1.2 General labeled magnitude scale (gLMS)

The gLMS is a psychophysical scale used for sensory testing to evaluate the taste and aftertaste of various stimuli. It comprises a 150 mm line spanning from the bottom to the top of the vertical axis. The scale includes descriptors such as “almost undetectable” (2.1 mm; 1.4 units), “weak” (9 mm; 6 units), “medium” (25.5 mm; 17 units), “strong” (52.05 mm; 34.7 units), “very strong” (78.75 mm; 52.5 units), and “the most imaginable sensation” (150 mm; 100 units). The scale presents adjectives to evaluators without numerical values. Experimenters place the adjectives on the scale in a semi-logarithmic manner based on determined intervals to generate data equivalent to magnitude estimation ( Green et al., 1993 ; Green et al., 1996 ). Subsequently, numerical data are calculated based on the scale. Using the gLMS method, the intensity of different bitter substances can be directly compared. For instance, Deng ( Deng et al., 2022 ) employed the gLMS to conduct sensory tests on adults, comparing the bitterness intensity of prednisolone solution with that of quinine. The results indicated that the bitterness level of prednisolone saturated solution (average gLMS score: 46.8) was similar to that of a 1 mM quinine solution (average gLMS score: 40.1). However, substantial individual differences may exist in gLMS assessment. For example, researchers measured the minimum and maximum values of 1 mM quinine as 8.7 and 90.0, respectively ( Deng et al., 2022 ). Furthermore, variations in sensory test data among different researchers also occur. While one study ( Deng et al., 2022 ) reported the average gLMS score of 1 mM quinine as 40.1, another study ( Cruickshanks et al., 2009 ) documented a gLMS score of 50 at the same concentration. Hence, stringent and standardized conditions are necessary for bitterness evaluation using this method.

2.1.3 Rank bitterness intensity (RB) and standard apparent bitterness (SAB)

In bitterness evaluation, descriptions like “unbearable bitterness,” “a little bitterness,” and “almost no bitterness” often arise, necessitating a method for grading bitterness. In a study on chlorphenamine maleate bitterness, researchers categorized bitterness into five levels: (A) 5: very strong bitterness, (B) 4: strong bitterness, (C) 3: medium bitterness, (D) 2: slightly bitter, and (E) 1: tasteless. Using the uncomplexed pure drug as a control with an average bitterness value of 5, subjects were instructed to compare the bitterness of different drug resin complexes (DRC) with the control and express the perceived bitterness level. The findings revealed that Indion-234, Tulsion-343, and Tulsion-344 effectively masked the bitterness of chlorphenamine maleate, with the bitterness of the drug in DRC decreasing as the ratio of drug to resin increased ( Yewale et al., 2013 ).

Inspired by this approach, some scholars introduced the concept of “RB” ( Wang et al., 2021 ), using berberine (BBR) as a reference. After volunteers pre-tested multiple concentrations, different concentrations of BBR corresponding to each bitterness level were determined ( Table 1 ). The practical application of graded bitterness involves evaluating unknown bitterness samples by referencing the bitterness level and value range of the reference sample group. Once graded, the specific bitterness value is assigned according to the corresponding bitterness range of each grade. Bitterness determined for the reference material in the solution state is termed “Standard Reference Bitterness (SRB)" ( Liu et al., 2019 ; Zhang et al., 2021 ). Bitterness of other drugs established based on SRB as a reference in the solution state is referred to as “Standard Apparent Bitterness (SAB)", and the level of standard apparent bitterness is known as “Standard Apparent Rank Bitterness (SARB)". Employing these methods enables us to comprehend the bitterness levels and bitterness profiles of different bitter substances.

Table 1 . Bitterness ranking and concentration of corresponding reference samples.

Shi et al. (2013) utilized a berberine hydrochloride aqueous solution as the reference solution and applied the THTPM method to assess the bitterness grade of six notable bitter Chinese herbal decoction pieces, including Cortex Phellodendri, Radix Gentianae, Herba Andrographis, Radix Ginseng, and Nelumbinis Plumulae. They investigated the taste-masking effect of β-CD at various mass fractions. Results indicated that the taste-masking effect improved with increasing β-CD concentration. With the exception of Cortex Phellodendri, the bitterness of the liquid after adding 3% β-CD was within a low range (0.65 ± 0.05), all falling into the almost no bitterness grade. This suggested that β-CD could effectively mask the bitter taste of traditional Chinese medicine. ( Li et al., 2011 ). employed the THTPM method to assess the water decoction of 35 different single Chinese medicine decoction pieces with varying bitterness, using berberine hydrochloride as a reference. They preliminarily obtained the bitterness value and distribution characteristics of the water decoction of Chinese medicine decoction pieces, providing a crucial foundation for subsequent taste-masking research ( Liu et al., 2012 ; Liu et al., 2013 ). conducted research on matrine at different concentrations, with 20 evaluators assessing its bitterness level and specific standard apparent bitterness value. They evaluated sample bitterness using three methods: the order evaluation method (OEM ranking method), score evaluation method (SEM), and integrated score evaluation method (ISEM). Ultimately, the three methods were comprehensively analyzed based on sorting accuracy, judgment sensitivity, assignment precision, and fitting degree, with the ISEM taste evaluation method proving to be the most effective. In order to further explore the bitterness superposition rules of different bitter substances ( Zhang et al., 2021 ), selected nine types of Chinese medicinal slices as research carriers. On the basis of establishing a predictive model between the quality concentration of the monomer slice carrier and the bitterness tasted by mouth, they explored the relationship between the bitterness tasted by mouth when measuring the superposition of binary and ternary systems and the bitterness and quality concentration of the monomer slice. The research found that the quality concentration of the monomer slice can be well fitted to the predictive equation of the bitterness of the superimposed slices, and the contribution rate of Huanglian to the superimposed bitterness is often greater than that of the other components, fully confirming a Chinese saying, “A mute eats Huanglian, and the bitterness is unspeakable."

The bitterness determination method based on RB and SAB offers a direct approach to determining the bitterness of various substances, including monomeric compounds, decoction pieces, and compound decoctions. However, the measurement process may be influenced by the intrinsic structure, concentration, and temperature of the molecule. Therefore, controlling appropriate external conditions during the measurement process is essential.

2.1.4 Ratio of bitterness (ROB)

In the sweetness evaluation method, there exists a calculation method known as “the relative sweetness value (RS)”, utilized for comparing different sweeteners. Researchers established the sweetness (Sr) of a 5% sucrose solution ( C r) as one and determined the mass concentration of other sweet substances equivalent to their sweetness. The RS of the sweet compound was then calculated using the formula RSs = C r/ C s × Sr ( Park et al., 2017 ). Building upon this concept, researchers proposed a method for determining the “Ratio of Bitterness (ROB)” of bitter substances ( Li et al., 2023 ). Specifically, they determined the specific bitterness (ROBr) of a BBR solution with a mass concentration of 0.05 mg/mL ( C r) as one and obtained the mass concentration of other bitter substances equivalent to their bitterness. The ROB of the bitter compound was then calculated using the formula “ROBs = C r/ C s × ROBr”. Due to the significant variance in their values, their natural logarithm is termed the ROB-index (ROBI). Serving as an absolute quantitative index, ROB reflects a fundamental attribute of bitter substances, facilitating a straightforward comparison of bitterness among different bitter substances. Following these principles, researchers successfully determined the ROB of six bitter drug monomers, offering a new bitterness scale for comparing bitterness across various bitter drug monomers and enhancing the scope of research on drug bitterness comparison scales ( Li et al., 2023 ).

2.1.5 Molecular bitterness (MB) and equivalent molecular bitterness (EMB)

Many drugs exhibit a bitter taste despite having different chemical structures. The emergence of bitterness is linked to factors such as the shape, size, and properties of functional groups within the molecule, as well as their positions. Eitan Margulis et al ( Margulis et al., 2021 ) successfully constructed a machine learning tool, termed “BitterIntense,” based on the chemical structural features of molecules. By calculating molecular descriptors, the tool classifies them into categories of “very bitter” or “not very bitter” with an accuracy rate of over 80%. This is significant for the early stages of drug development, as it allows for the rapid identification of compounds with intense bitterness. However, this method is a simple binary classification of bitterness intensity. How to establish a more precise bitterness intensity prediction algorithm based on molecular structural features remains a question that scholars are currently exploring. Liu et al., 2012 addressed the influence of concentration on the bitterness of bitter substances and introduced the concept of “molecular bitterness,” which pertains solely to the properties of drug molecules. The bitterness threshold concentration (BTC) of both the standard bitterness substance and the compound under examination was determined using the “minimum limit method,” representing the lowest concentration at which bitterness is detected by half of the volunteers. The Molecular Bitterness (MB) under the standard bitterness substance BTC was set as 1 (typically using berberine hydrochloride as the reference bitter substance, with an MB of one under BTC). Calculating the MB of the test compounds involved the formula “MBs = C r/ C s × MBr” (where C r signifies the BTC of the standard bitter molecule; MBr denotes the MB of standard bitter molecules; C s represents the BTC of unknown bitter molecules; MBs represents the MB of unknown bitter molecules ( Heath et al., 2006 ; Bora et al., 2008 ; Li Xuelin et al., 2013 ; Jelvehgari et al., 2014 ; Gao et al., 2022 )). Given the substantial variance in BTC among different bitter molecules, MB values differ significantly across substances. Therefore, the introduction of the “molecular bitterness index” (MB-Index, MBI) involves taking the natural logarithm of MB to normalize the magnitude difference, facilitating a more straightforward comparison of bitterness across different substances. Using this method, ( Gao et al., 2022 ) calculated the MB of 19 bitter monomer components such as quinine (alkaloids), naringin (glycosides), andrographolide (terpenes), and L-arginine (peptides) to be 0.8398, 0.0551, 0.0058, and 0.0002, respectively. The corresponding MBI values were −0.1746, −2.8982, −5.1447, and −8.3669, respectively, effectively illustrating the bitterness characteristics of various bitter components in a simple and intuitive manner.

The introduction and application of the MB concept addressed the comparison of bitterness between compounds. However, for natural medicine decoction pieces, and even compounds composed of multiple natural medicine decoction pieces, the evaluation extends beyond a single compound to encompass the combination of various bitter compounds. The change in bitterness value within such complex systems after combination is intricate. With numerous types of natural medicines, there’s an urgent need to establish an objective and appropriate bitterness evaluation method. Taking bitter natural medicines as an example, the current 2020 edition of the “Chinese Pharmacopoeia” includes a total of 2,711 natural medicines. Among them, 133 natural medicines exhibit a single bitter taste, comprising one very bitter, 14 extremely bitter, 47 bitter, one slightly bitter, and 70 slightly bitter ( Supplementary Table S1 ). Additionally, 180 natural medicines possess not only a bitter taste but also other flavors ( Supplementary Table S2 ). There are also nuanced differences in the taste descriptions of various natural medicines; for instance, Gentiana is described as “very bitter,” Sophorae Tonkinensis as “extremely bitter,” Bletilla as “bitter,” Eucommia as “slightly bitter,” and Lily bulb as “a little bitter” However, the distinction in bitterness between each description remains unknown. Furthermore, natural drugs described as ‘extremely bitter,’ such as Sophora flavescens and Aloe vera, pose the question: is their bitterness identical? Drawing from the MB calculation principle and bitterness measurement, Liu et al. ( Gao et al., 2022 ) introduced the concept of “Equivalent Molecular Bitterness (EMB)” for complex systems. This involves determining the BTC of standard bitter substances and unknown complex systems using the “minimum limit method.” The MBr under the standard bitter substance BTC is defined as 1 (typically using berberine hydrochloride as the reference bitter substance, with an MBr of one under BTC). Subsequently, the EMB calculation formula for other bitter Chinese herbal decoction pieces is “EMBs = C r/ C s × MBr,” where C r represents the BTC of berberine hydrochloride, and C s denotes the BTC of unknown Chinese herbal pieces. The natural logarithm of this ratio is termed the EMB-index (EMBI) ( Wang et al., 2022 ). measured the EMB and EMBI of 23 kinds of bitter Chinese herbal pieces using the aforementioned methods and established a quantitative method for determining the bitterness of bitter Chinese herbal pieces. This comparative analysis of the bitterness characteristics of different types of Chinese herbal pieces offers valuable insights, laying a robust foundation for the accurate masking of natural drugs within complex systems.

The comprehensive review reveals that researchers approach quantitative analysis of bitterness from diverse perspectives and levels using THTPM as a foundation. Each method presents distinct advantages, limitations, and applicability ( Table 2 ). When embarking on quantitative investigations into bitterness, it is crucial to select appropriate methodologies tailored to the study’s objectives and the nature of the research subject.

Table 2 . Analysis of the characteristics of various evaluation indexes of bitterness based on THTPM measurements.

Sensory group evaluation poses significant challenges due to medical ethics considerations, associated health risks, and the substantial costs of personnel training. Moreover, the inherent subjectivity among individuals can lead to fatigue, slow evaluation speeds, and a limited sample size. Throughout the evaluation process, there is a risk of sample perception migration and perception saturation, thereby imposing constraints on the assessment of taste within the general population ( Legin et al., 2004 ). The Gustation Analytical Fingerprint Technique (GFAT) represents a recent development in taste recognition and detection technology, relying on taste sensors and chemical information processing methods. These taste sensors function as intelligent recognition electronic systems, emulating the human taste mechanism to generate signals (optical, electrochemical, electrophysiological). They possess the capability to discern subtle differences in basic tastes, such as lingering or transient tastes. Notably, GFAT offers advantages such as rapid analysis, low cost, minimal sample preparation, and automation of analysis ( Rudnitskaya et al., 2010 ; Podrażka et al., 2017 ). Scholars have conducted a systematic evaluation of the application of ETM and sensory groups in taste assessments of pediatric drugs. The findings reveal that sensory tests for children are infrequent (10.3%), with ETM predominating in pediatric drug taste evaluations (57.5%), highlighting the efficacy of ETM ( Guedes et al., 2021 ). Over the past few decades, leveraging electronic tongue technology, researchers have successfully employed methods such as the conversion of electronic tongue taste information value ( Zeng et al., 2015 ; Li et al., 2016 ), bitter distance calculation, and the establishment of relationships between electronic tongue information and human sensory evaluation ( Ito et al., 1998 ; Uchida et al., 2001 ). These advancements have facilitated the quantitative analysis and prediction of various drug tastes, as shown in Figure 3 .

Figure 3 . Quantitative research method of bitterness based on the ETM. Representative images were our own work ( Rui-xin et al., 2013 ; Li et al., 2016 ; Gao et al., 2022 ).

2.2.1 Electronic tongue converts taste information value

Using the TS-5000Z multi-channel taste sensor as an illustration, it employs an artificial lipid membrane sensor technology akin to the functioning principle of human tongue taste cells. This sensor has the capability to objectively and digitally detect five basic tastes as well as flavor attributes such as “sharpness” and “richness.” In the TS-5000Z taste analysis system, Relative value (R) and Change of Membrane Potential caused by Adsorption (CPA) are commonly utilized ( Li et al., 2020 ). To initiate taste analysis, the taste sensor is immersed in a reference solution comprising a mixture of KCl and tartaric acid at a predetermined concentration, yielding the corresponding membrane potential, denoted as Vr. This reference solution is essentially tasteless. Subsequently, the sensor is submerged in the sample solution to determine the potential difference value of the solution potential (Vs.), which is then subtracted from Vr, termed as the first taste (R). Following a gentle cleanse of the taste sensor with the reference solution, it is re-immersed to detect the potential Vr’. The disparity between Vr’ and Vr is referred to as aftertaste (CPA), indicating the potential change induced by chemical adsorption.

The first taste and aftertaste can be calculated by Equations 1 , 2 :

Based on the initial taste and aftertaste values, a specific mathematical conversion is performed to derive the electronic tongue conversion taste information value (I.e.,), serving as a metric for quantitative bitterness evaluation ( Zeng et al., 2015 ). employed the electronic tongue technology-based taste analysis method to quantify the characteristics of Scutellaria baicalensis from various sources. By establishing a positive correlation between the bitterness, astringency, bitter aftertaste, astringency aftertaste, sour taste information of Scutellaria baicalensis, and the baicalin content, it was possible to infer the baicalin content in Scutellaria baicalensis. In a similar vein ( Jing et al., 2022 ), utilized electronic tongue technology to quantify the taste of 20 batches of Magnolia officinalis, assessing the taste of six monomer compounds. Pearson correlation analysis was employed to ascertain the correlation between eight chemical components and the taste sensor response value. The investigation revealed a significant positive correlation between honokiol, magnolol, and spicy menthol magnolol in Magnolia officinalis, and the bitter taste and bitter aftertaste detected by the electronic tongue.

2.2.2 Distance of bitterness

2.2.2.1 distance of bitterness in multidimensional space.

Using the French ASTREE electronic tongue method as a case study, the quantification of bitterness index relies on data gathered from seven sensors within the electronic tongue apparatus. Through meticulous data processing, a numerical value is derived, providing a quantitative or semi-quantitative representation of the drug’s bitterness. This value is termed the Bitterness Distance (D). Utilizing chemometric techniques such as PCA, the spatial disparity between the sample under examination and a reference solution is computed. This approach mirrors principles found in cluster analysis and other methodologies, where the distance between samples is evaluated within a multidimensional space comprising various variables.

Distance serves as a metric to gauge the spatial separation between two entities. Common distance metrics encompass Euclidean distance, Mahalanobis distance, Ming’s distance, among others. The Euclidean distance (EUCLID), or Euclidean metric, stands out as a prevalent distance measure, delineating the true geometric distance between two points within an m-dimensional space. Its applicability is underscored by its capacity to be expressed in a unified recursive formula, making it the most frequently utilized distance metric.

The Euclid and Standardized Euclid can be calculated by Equations 3 , 4 :

Where k represents the number of variables each sample possesses, with x i indicating the value of the first sample on the i-th variable, and y i representing the value of the second sample on the same variable. In the context of bitter samples, the Euclidean distance between samples exhibiting varying degrees of bitterness serves as a measure of the disparity in bitterness levels.

For instance, consider the compound BBR, which was formulated into samples of varying concentrations. Each sample, along with purified water, underwent analysis using an electronic tongue. The resulting dataset facilitated the direct calculation of its Euclidean distance, effectively quantifying the multidimensional space between them. Notably, a larger Euclidean distance between the sample and purified water signifies a higher bitterness level in the sample, and conversely, a smaller distance indicates lower bitterness.

2.2.2.2 Distance of bitterness in reduced-dimensional space

The data collected by the electronic tongue underwent reduction via Principal Component Analysis (PCA) and similar techniques. Subsequently, based on these findings, the distance between each sample and the reference solution within the principal component space (whether in two-dimensional, three-dimensional, or other dimensions) was computed to determine the relative bitterness of each sample.

In a two-dimensional or three-dimensional space, the Euclidean distance serves as the measure of separation between two points, delineating the extent of spatial disparity, as shown in Equations 5 , 6 :

When employing PCA for dimensionality reduction analysis, we can compare the bitterness differences among samples by assessing the distance from each sample to the reference solution in both two-dimensional and three-dimensional spaces. Nakamura et al. conducted a study to assess the taste of orally disintegrating tablets (ODT) containing famotidine and amlodipine besylate using the Astree electronic tongue and THTPM. The palatability of the tablets was further evaluated using a 100 mm VAS scale. The findings indicated that both physical masking and organoleptic masking could enhance the palatability of famotidine and amlodipine. In the electronic tongue analysis, the Euclidean distance of samples subjected to physical masking, organoleptic masking alone, and in combination, was found to be smaller compared to unmasked drugs ( Nakamura et al., 2015 ). Liu et al. investigated bitter drug carriers employing BBR and Andrographis paniculata decoction, screening taste masking agents by assessing bitterness reduction values in reduced-dimensional or multi-dimensional space ( Liu et al., 2013 ; Rui-xin et al., 2013 ). Li et al. (2011) assessed the masking effect of various agents on berberine hydrochloride using bitterness distance, D, and bitterness reduction distance, ΔD ( Li et al., 2013 ).

While the results derived from PCA analysis slightly underperform compared to multi-dimensional space distance, they offer a more intuitive representation through two-dimensional or three-dimensional maps, overcoming the graphical limitations of multi-dimensional spaces. Moreover, data standardization aids in further reducing system errors. However, it is important to note that this method is only applicable to distinguishing bitterness within the same component.

2.2.3 The relationship between electronic tongue taste information value and THTPM

The electronic tongue taste information is typically expressed through relative response values or bitterness values. Establishing the relationship between electronic tongue taste information and THTPM involves data-driven modeling and prediction, relying on experimental data and mathematical methods. Several studies have demonstrated a strong correlation between taste assessed by electronic tongue and human taste perception ( Ito et al., 2013 ; Wang et al., 2013 ; Maniruzzaman et al., 2014 ; Maniruzzaman and Douroumis, 2015 ). In recent years, there has been a proliferation of applications for quantitatively predicting bitterness using electronic tongue. For instance, Li ( Li et al., 2016 ) utilized berberine hydrochloride as a reference and matrine and oxymatrine as model drugs to establish a bitterness prediction model (BPM) based on THTPM bitterness ratings and data from the TS-5000Z electronic tongue sensor. The results indicated a significant correlation between taste bitterness and electronic tongue bitterness (R 2 matrine = 0.8955, R 2 oxymatrine = 0.9793). The electronic tongue-based bitterness prediction model for matrine and oxymatrine exhibited high accuracy (R 2 matrine = 0.9639, R 2 oxymatrine = 0.9535). ( Liu et al., 2014a ) developed a BPM for berberine hydrochloride using a genetic algorithm-back propagation neural network (GA-BP), incorporating bitterness intensity evaluated by sensory groups and data provided by electronic tongue. The model demonstrated excellent fitting (R 2 = 0.99965) and could effectively predict the bitterness of berberine hydrochloride across different concentrations, serving as a reference for developing BPMs for other drugs. Chen ( Chen et al., 2020 ) presented a biosensor utilizing Drosophila odorant binding protein (OBP) as a biosensitive material. This biosensor was employed to study typical bitter molecules such as denatonium, quinine, and berberine using electrochemical impedance spectroscopy. The findings revealed significant binding properties between the bitter molecules and OBP, displaying a linear response within the concentration range of 10-9-10–6 mg/mL, indicating broad application prospects for the OBP-based biosensor. (Xue, 2022) employed Weibull curve fitting to evaluate the taste of oseltamivir phosphate and ginkgo leaves, along with electronic tongue data, enabling quantitative description of bitterness. The prediction model’s accuracy and superiority were assessed through cross-validation. Additionally, the electronic tongue method’s ability to predict the bitterness of bitter substances was validated against THTPM results.

In general, there exists a certain correlation between the taste information provided by the electronic tongue and the outcomes from THTPM, although this correlation may not always be consistent. Numerous factors contribute to this, including the type of electronic tongue, sensor selection, signal processing methods, data analysis techniques, standardization of tasting methods, and the training of evaluators. Due to variations in perception mechanisms and sensitivity between electronic tongues and human taste, the electronic tongue may not fully capture the nuanced characteristics of individual taste perception ( Uchida et al., 2001 ). Consequently, the relationship between electronic tongue taste information and taste assessment methods requires calibration and validation specific to the samples and conditions at hand and cannot be generalized.

2.3 Taste strips and filter paper disc method

Taste strips (TS) consist of filter paper infused with taste substances. When evaluating, the evaluator places the TS on the tongue’s center, closes the mouth, and gradually moves the tongue, allowing saliva to dissolve the taste enhancer on the strip. After a designated period, the strip is removed for taste assessment, as shown in Figure 4 . Ranmal ( Ranmal et al., 2023 ) examined subjects’ hedonistic responses to bitter stimuli from TS. Findings revealed that as the concentration of quinine hydrochloride (QHCl) on TS increased, both children and adults showed heightened aversion to bitterness. Similarly, Schienle ( Schienle and Schlintl, 2020 ) utilized QHCl TS to gauge taste intensity, ranging from no sensation to “the strongest imaginable sensation of any kind.” Green ( Green et al., 2022 ) employed TS containing high and low concentrations of four tastes (sour, sweet, bitter, and salty) to assess taste function in healthy participants. Results indicated elevated recognition levels among participants exposed to high-concentration taste strips in laboratory settings.

Figure 4 . Quantitative research method of bitterness based on taste strips (TS). Based on ( Ranmal et al., 2023 ).

Another bitterness measurement method akin to the TS method is the filter paper disc method (FPD). KATARINA( Berling et al., 2011 ) employed FPD to assess evaluators’ perception thresholds for various flavors. Each flavor agent comprised five different concentrations. Using a scoring system from one to 6, where one indicates the lowest threshold, five represents the highest measurable threshold, and six signifies an unmeasurable high threshold, evaluators progressed from low to high concentrations until they correctly identified the taste, thus determining the recognition threshold. Results indicated standard thresholds for four flavors: bitter 1.9 ± 1.30, acid 2.3 ± 1.09, salty 2.5 ± 1.53, and sweet 2.6 ± 1.37, respectively, with bitterness identified at a lower concentration than other flavors.

The TS and FPD methods offer a straightforward, rapid, safe, and effective out-of-laboratory (OOL) sensory evaluation approach for assessing bitterness perception. Nonetheless, further research is warranted to establish a stronger correlation between the “local stimulation” method and the “full mouth” method based on the classical population taste evaluation method.

2.4 Facial expression analysis

Facial expressions serve as a rich source of emotional information. When individuals taste different flavors of medications, their facial expressions vary accordingly. For instance, tasting non-bitter Chinese medicine may elicit “neutral” expressions, whereas tasting bitter Chinese medicine may provoke expressions of “disgust,” characterized by tight frowns and clenched teeth. Facial expression recognition technology leverages facial expression data to objectively analyze human emotional responses. Utilizing this technology, we can extract facial expression features of individuals and employ suitable expression classification methods to objectively assess taste perception, as shown in Figure 5 .

Figure 5 . Quantitative research method of bitterness based on facial expressions ( Wang, 2022a ).

Wang (2022a) utilized taste stimulation to perceive potential signals from nerve-related facial muscles and gland-related muscles, converting them into digital signals to acquire taste information, thus enabling the acquisition of taste information from electric potential signals. Furthermore, variations in the intensity of expression responses may occur when evaluators taste natural medicines with differing levels of bitterness ( Zhi et al., 2017 ). observed that facial expression intensity can indicate the degree of taste stimulation across various concentrations and levels. Most participants displayed pronounced aversion to medium and high concentrations of bitterness, manifesting as expressions of disgust. With the rapid advancement of deep learning, facial expression recognition technology has progressed from simple classification to intensity level analysis. Yang et al., 2010 introduced a novel technique for facial expression analysis based on a ranking model. They transformed the task of expression intensity analysis into a ranking problem and employed RankBoost modeling. The resulting ranking score can directly estimate intensity and demonstrated good performance on the Cohn-Kanade dataset. As facial expression recognition technology continues to evolve, researchers have established datasets such as JAFFE, FER 2013, and CK + for facial expression analysis. However, an exclusive dataset for bitterness evaluation is yet to be established. Developing such a dataset is crucial to advancing the intelligent and accurate quantification of bitterness.

2.5 Animal behavior test

When one animal is attracted to a stimulus while another avoids it, it suggests that the compound may possess distinct perceptual characteristics for different tasters, leading to varied evaluations ( Loney et al., 2012 ). The two-bottle preference test (TBP) ( Yoneda et al., 2007 ) is employed to assess the aversive taste of food or beverages, utilizing the preference index (PI) as the evaluation metric ( Loney et al., 2011 ). Rodents are commonly chosen as experimental subjects due to their highly homologous bitter taste receptors to humans, thus exhibiting similar taste perceptions ( Noorjahan et al., 2014 ). Han ( Han et al., 2018 ) established the relationship between quinine concentration and animal PI. Subsequently, the PI of 12 bitter Traditional Chinese herbal (TCH) compounds was determined using TBP, and the bitterness results were standardized into a unified numerical system based on the concentration-PI relationship. This standardization offers a methodological framework for sensory evaluation of natural medicines, as shown in Figure 6 .

Figure 6 . Quantitative research method of bitterness based on animal behavior test. Based on ( Han et al., 2018 ).

Magdalena Münster ( Münster et al., 2017 ) assessed the palatability of the bitter compound praziquantel using the rodent transient contact taste aversion (BATA) model and calculated the IC 50 value, representing the concentration of praziquantel inhibiting 50% of the maximum licking response. The findings revealed a decrease in licking frequency with increasing praziquantel concentration, with an IC 50 value of 0.06 mg/mL (95% CI 0.049-0.082). Comparative analysis indicated that praziquantel elicited a stronger aversive response compared to other bitter compounds such as sildenafil citrate, caffeine citrate, diclofenac, or paracetamol ( Soto, 2016 ).

It is important to note that the outcomes of animal studies are influenced by species-specific expression of bitter taste receptors, resulting in bitter taste responses that may differ from those in humans ( Dong et al., 2009 ). Future research endeavors should focus on refining methodologies to achieve more accurate quantitative assessments of bitter taste.

2.6 Cell-based assessment methods

Bitter substances, serving as flavoring agents, can stimulate certain taste bud cells. By describing the interaction strength between them, it is possible to achieve an objective measurement for the quantification of bitterness ( Narukawa et al., 2011 ), as shown in Figure 7 . ( Hui et al., 2012 ) utilized human intestinal endocrine STC-1 cells expressing G protein-coupled receptors and bitter receptors (type 2 members) as sensing devices to conduct specific detection of bitter substances. The findings demonstrated that the sensor utilizing STC-1 cells selectively responded to bitter agents and mixtures, with the type and concentration of bitter agents determinable via signal-to-noise ratio parameters. This approach offers a valuable avenue for investigating taste mechanisms and evaluating bitterness intensity. Nakamura ( Nakamura et al., 2003 ) investigated the effect of quinine on [[Ca 2 + ]i levels in cultured nerve-2a cells, exploring the potential of [[Ca 2 + ]i levels to predict the bitterness of quinine solutions. Following quinine stimulation, [Ca 2 +]i levels in nerve-2a cells increased in a concentration-dependent manner.

Figure 7 . Quantitative research method of bitterness based on animal behavior test. Based on ( Narukawa et al., 2011 ).

However, the cell-based biosensor evaluation method also has certain limitations, as cells may not be able to detect all bitter substances. For instance, Thomas ( Delompré et al., 2022 ) demonstrated the bitterness of vitamins B2 and B3 in sensory analysis, where cell-based assays failed to yield any information. This may be attributed to the inherent fluorescence characteristics of the two vitamins at high concentrations ( Chen and Chung, 2022 ). Additionally, current taste cell culture methods are susceptible to the influence of pseudo-taste cells, potentially leading to overinterpretation. Therefore, caution must be exercised when employing this method.

In summary, researchers have conducted numerous quantitative studies on bitterness using both in vivo and in vitro methods. Throughout this process, researchers have identified various dimensions of bitterness quantification, including local and overall characteristics, static and dynamic features, and external macro performance and internal micro mechanisms. Each method possesses its own advantages and disadvantages ( Table 3 ). When evaluating drug development, taste masking, and palatability, researchers can select appropriate methods based on research objectives, cost, time constraints, and other factors. However, it is important to note that bitterness research methods are still evolving. In the future, researchers need to continue exploring quantitative evaluation methods for bitterness, standardizing the evaluation process to facilitate the high-quality development of bitterness quantification.

Table 3 . Analysis of the advantages and disadvantages of various bitterness quantitative methods.

3 Future research directions

Due to the influence of ethical reviews, the complexity of the regularity of bitter substance structural characteristics, the significant differences in the activation capacity of bitter taste receptors, and the surrounding environment of bitter substances, the quantitative research methods for bitterness are still actively being explored. The current research method system for bitterness, which is primarily based on THTPM and supplemented by other research methods, still requires further improvement to meet basic research needs. The main aspects which is shown in Figure 8 include: (1) Optimizing the quantification and evaluation methods for bitterness. By strictly selecting the evaluation population, establishing standardized operating procedures, and developing methods for handling outliers, the subjectivity of direct bitterness evaluation methods is reduced ( Medicine, 2024 ); by strengthening the basic research on the structural characteristics of bitter substances, the characteristics of activating ligands, and the mechanisms of bitterness presentation, the relationship between concentration-structure-function-bitterness is explored, as well as the relationship between key chromatographic information/electrical signals/fluorescent signals and bitterness. This provides foundational support for optimizing indirect bitterness evaluation methods and actively utilizes machine learning algorithms to enhance the objectivity, accuracy, speed, and transparency of indirect bitterness evaluation methods. (2) Conduct refined quantification of bitterness and explore new methods for bitterness quantification research. Since there is a subtle relationship between people’s preferences or aversions to bitterness ( Mura et al., 2018 ), research methods from the food field can be referenced to make refined distinctions in bitterness, such as good bitterness and bad bitterness, and to carry out refined quantitative evaluation of different types of bitterness in drugs ( Sook Chung and Lee, 2012 ; Araujo et al., 2021 ). At the same time, closely focus on the taste-affecting factors that influence the bitterness of drugs and construct new methods for bitterness quantification and evaluation. For example, based on methods such as virtual screening, biofishing, and physicochemical detection, establish the relationship between key parameters of the above methods and bitterness, and systematically analyze the comprehensive impact of structural characteristics, concentration, and external environmental factors of bitter substances on bitterness. (3) Construct a quantitative research platform for bitterness. Currently, researchers often reveal the mechanisms of bitterness from a mesoscopic or microscopic perspective, and the bitterness platforms constructed are mostly centered around qualitative identification (determining whether it is bitter or not) ( Chu et al., 2024 ). On this basis, there is an urgent need to build a quantitative research platform and equipment for bitterness, and to integrate different types of data in multiple dimensions, to promote the transformation of basic research on bitterness quantification to applied research. (4) Improve and establish a series of standards for quantitative research on bitterness. In order to achieve scientific measurement and effective evaluation of senses, a series of international documents have been issued for sensory analysis. For the sensory evaluation of bitterness, some scholars have already conducted research on the technical specifications for sensory evaluation based on the characteristics of natural medicines ( Medicine, 2024 ). In the future, it is still necessary to formulate industry, national, and global standards around the research design and plan framework guidelines, statistical analysis plans, methodological validation, data processing, etc., of bitterness quantification, to promote the standardization, scientification, and systematization of quantitative research on drug bitterness. (5) Research Extension. Bitter substances possess a variety of physiological activities ( Zuluaga, 2024 ). In traditional Chinese medicine theory, bitterness is believed to have effects such as " downbearing and discharging, drying dampness, and consolidating Yin " Advancing the research on the functional attributes of bitterness and its extension into the field of bioinformatics, including the relationships between bitterness and efficacy, and bitterness and receptors, can provide support for accelerating the development of target drugs.

Figure 8 . A framework addressing the challenges and future development directions in bitterness quantification.

In the process of exploring the quantification of bitterness, we also face many challenges. On one hand, there are issues such as non-standardized operating procedures and inconsistent technical parameters. These mainly include the lack of uniformity in scales, reference solutions, the volume of samples evaluated at one time, temperature, evaluation time and intervals, and limited instrument stability, which hampers the comparability of results between different studies ( Zuluaga, 2024 ). On the other hand, the complexity of bitter taste presentation makes the quantification of bitterness very difficult, especially for the measurement of the comprehensive bitterness in complex systems, where it is urgent to explore the taste rules in the independent state of substances and under the state of complex systems ( Zhang et al., 2021 ; Gao et al., 2023 ). In addition, the flexibility of virtual screening methods, the specificity and sensitivity of indirect measurement methods, and the computational power of different machine algorithms also affect the accuracy of bitterness quantification. In the future, researchers urgently need to further enrich the database of bitter substances, establish standardized and unified operating standards, and by improving detection technology and optimizing algorithm capabilities, jointly explore and mutually verify the characteristics of bitterness from macroscopic, mesoscopic, or microscopic perspectives ( Li et al., 2024 ).

4 Conclusion and foresight

In nature, various taste substances exist alongside intricate taste mechanisms, and numerous factors influence the quantification of drug bitterness to varying extents. In light of this, different quantitative evaluation methods for bitterness have been established, each possessing its own merits. Presently. Currently, an increasing number of researchers are leveraging column chromatography, HPLC, HPLC/ESI-MS, LC/ESI-MS/MS, UPLC-Q-TOF/MS, and nontargeted LC/MS flavoromics analysis to separate and identify the bitter compounds ( Suryawanshi et al., 2006 ; Mustafa et al., 2015 ; Höhme et al., 2023 ). They also combine methods such as sensory-guided, virtual screening, and chromatography-taste association to improve the efficiency of discovering bitter components ( Yu et al., 2020 ; Yang et al., 2023 ). This signifies that the study of bitterness in natural medicines is steadily advancing. However, the identification of bitter components represents merely the initial phase. A precise, dependable, and straightforward method for evaluating drug bitterness is required to investigate bitterness masking strategies for medications. Similar to the measurement of length using the international unit “meter” and temperature using the “degree Celsius,” bitterness should also be subject to standardized, objective quantitative methods and parameters. This review scrutinizes research on bitterness quantification, delineates factors influencing drug bitterness, and acknowledges the role of material, human, and environmental factors in affecting bitterness perception. Consequently, in the quantitative exploration of drug bitterness, it is imperative to identify and regulate these factors to ensure the reliability of outcomes. Furthermore, this paper consolidates the characteristics of various bitterness quantification methods, systematically categorizes the quantitative approaches for representative drugs, and emphasizes the challenges associated with quantifying bitterness in natural drugs characterized by complex systems. It also elucidates the future research directions that urgently need to be undertaken. This is of significant guiding importance for our continued in-depth focus on the research of quantitative bitterness methods and lays an important foundation for the development of precise, efficient, and rich taste-masking strategies. Such efforts aim to foster research into taste masking optimization and palatability enhancement, thereby laying a crucial groundwork for enhancing the clinical acceptance of natural medications.

Author contributions

PW: Writing–original draft. HL: Writing–original draft. YW: Investigation, Writing–review and editing. FD: Investigation, Writing–review and editing. HL: Writing–review and editing. XG: Investigation, Writing–review and editing. YR: Writing–original draft. XG: Investigation, Writing–review and editing. XL: Funding acquisition, Project administration, Writing–review and editing. RL: Funding acquisition, Project administration, Writing–review and editing.

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. We sincerely thank the National Natural Science Foundation of China (Nos 81001646 and 81774452), Henan Provincial Health Commission National Clinical Research Base of Traditional Chinese Medicine Research Project (2022JDZX110), Henan Province Traditional Chinese Medicine Scientific Research Project (2024ZY3019), Key research projects of universities in Henan Province (24B360005), The National Administration of Traditional Chinese Medicine 2022 Young Qihuang Scholars Training Project (No. [2022] 256), The High-level Talents in Henan Province Special Support “Central Plains Thousand Talents Plan” – “Central Plains Young Top Talents” project (No. ZYQR201912158), Excellent Youth Foundation of Henan Scientific Committee (No. 242300421023).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fchem.2024.1449536/full#supplementary-material

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Keywords: bitterness, quantitative method, traditional human taste panel method, active pharmaceuticals ingredients, traditional Chinese medicine

Citation: Wang P, Li H, Wang Y, Dong F, Li H, Gui X, Ren Y, Gao X, Li X and Liu R (2024) One of the major challenges of masking the bitter taste in medications: an overview of quantitative methods for bitterness. Front. Chem. 12:1449536. doi: 10.3389/fchem.2024.1449536

Received: 15 June 2024; Accepted: 29 July 2024; Published: 14 August 2024.

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Copyright © 2024 Wang, Li, Wang, Dong, Li, Gui, Ren, Gao, Li and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Ruixin Liu, [email protected] ; Xuelin Li, [email protected]

† These authors have contributed equally to this work and share first authorship

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Iterating between data-driven research and generative computational models is a powerful approach for emulating biological systems, testing hypotheses, and gaining a deeper understanding of these systems. We developed a hybrid agent-based model (ABM) that integrates a Cellular Potts Model (CPM) designed to investigate cell shape and colony dynamics in human induced pluripotent stem cell (hiPS cell) colonies. This model aimed to first mimic and then explore the dynamics observed in real-world hiPS cell cultures. Initial outputs showed great potential, seeming to mimic small colony behaviors relatively well. However, longer simulations and quantitative comparisons revealed limitations, particularly with the CPM component, which lacked long-range interactions that might be necessary for accurate simulations. This challenge led us to thoroughly examine the hybrid model's potential and limitations, providing insights and recommendations for systems where cell-wide mechanics play significant roles. The CPM supports 2D and 3D cell shapes using a Monte Carlo algorithm to prevent cell fragmentation. Basic "out of the box" CPM Hamiltonian terms of volume and adhesion were insufficient to match live cell imaging of hiPS cell cultures. Adding substrate adhesion resulted in flatter colonies, highlighting the need to consider environmental context in modeling. High-throughput parameter sweeps identified regimes that produced consistent simulated shapes and demonstrated the impact of specific model decisions on emergent dynamics. Full-scale simulations showed that while certain agent rules could form a hiPS cell monolayer in 3D, they could not maintain it over time. Our study underscores that "out of the box" 3D CPMs, which do not natively incorporate long-range cell mechanics like elasticity, may be insufficient for accurately simulating hiPS cell and colony dynamics. To address this limitation, future work could add mechanical constraints to the CPM Hamiltonian or integrate global agent rules. Alternatively, replacing the CPM with a methodology that directly represents cell mechanics might be necessary. Documenting and sharing our model development process fosters open team science and supports the broader research community in developing computational models of complex biological systems.

Competing Interest Statement

The authors have declared no competing interest.

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Rape-Related Pregnancies in the 14 US States With Total Abortion Bans

• 1 Planned Parenthood of Montana, Billings, Montana
• 2 Resound Research for Reproductive Health, Austin, Texas
• 3 Hunter College, City University of New York, New York
• 4 Department of Medicine, Cambridge Health Alliance, Cambridge, Massachusetts
• 5 Department of Medicine, University of California, San Francisco
• Editor's Note Access to Safe Abortion for Survivors of Rape Deborah Grady, MD, MPH; Sharon K. Inouye, MD, MPH; Mitchell H. Katz, MD JAMA Internal Medicine
• Medical News in Brief 65 000 Rape-Related Pregnancies Took Place in US States With Abortion Bans Emily Harris JAMA
• Correction Error in Methods, Results, and Table 2 JAMA Internal Medicine

Many US women report experiencing sexual violence, and many seek abortion for rape-related pregnancies. 1 Following the US Supreme Court’s 2022 Dobbs v Jackson Women’s Health Organization ( Dobbs ) decision overturning Roe v Wade , 14 states have outlawed abortion at any gestational duration. 2 Although 5 of these states allow exceptions for rape-related pregnancies, stringent gestational duration limits apply, and survivors must report the rape to law enforcement, a requirement likely to disqualify most survivors of rape, of whom only 21% report their rape to police. 3

• Editor's Note Access to Safe Abortion for Survivors of Rape JAMA Internal Medicine

Read More About

Dickman SL , White K , Himmelstein DU , Lupez E , Schrier E , Woolhandler S. Rape-Related Pregnancies in the 14 US States With Total Abortion Bans. JAMA Intern Med. 2024;184(3):330–332. doi:10.1001/jamainternmed.2024.0014

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