qualitative and quantitative research paper

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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 ".

'Qualitative' and 'quantitative' methods and approaches across subject fields: implications for research values, assumptions, and practices

• Open access
• Published: 30 September 2023
• Volume 58 , pages 2357–2387, ( 2024 )

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• Nick Pilcher   ORCID: orcid.org/0000-0002-5093-9345 1 &
• Martin Cortazzi 2  

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There is considerable literature showing the complexity, connectivity and blurring of 'qualitative' and 'quantitative' methods in research. Yet these concepts are often represented in a binary way as independent dichotomous categories. This is evident in many key textbooks which are used in research methods courses to guide students and newer researchers in their research training. This paper analyses such textbook representations of 'qualitative' and 'quantitative' in 25 key resources published in English (supported by an outline survey of 23 textbooks written in German, Spanish and French). We then compare these with the perceptions, gathered through semi-structured interviews, of university researchers (n = 31) who work in a wide range of arts and science disciplines. The analysis of what the textbooks say compared to what the participants report they do in their practice shows some common features, as might be assumed, but there are significant contrasts and contradictions. The differences tend to align with some other recent literature to underline the complexity and connectivity associated with the terms. We suggest ways in which future research methods courses and newer researchers could question and positively deconstruct such binary representations in order to free up directions for research in practice, so that investigations can use both quantitative or qualitative approaches in more nuanced practices that are appropriate to the specific field and given context of investigations.

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1 Introduction: qualitative and quantitative methods, presentations, and practices

Teaching in research methods courses for undergraduates, postgraduates and newer researchers is commonly supported or guided through textbooks with explanations of 'qualitative' and 'quantitative' methods and cases of how these methods are employed. Student dissertations and theses commonly include methodology chapters closely aligned with these textbook representations. Unexceptionally, dissertations and theses we supervise and examine internationally have methodology chapters and frequently these consider rationales and methods associated with positivist or interpretivist paradigms. Within such positivist or interpretivist frameworks, research approaches are amplified with elaborations of the rationale, the methods, and reasons for their choice over likely alternatives. In an apparent convention, related data are assigned as quantitative or qualitative in nature, with associated labelling as ‘numerical’ or ‘textual'. The different types of data yield different values and interpretive directions, and are clustered conceptually with particular research traditions, approaches, and fields or disciplines. Frequently, these clusters are oriented around 'quantitative' and 'qualitative' conceptualizations.

This paper seeks to show how ‘qualitative’ and ‘quantitative’, whether stereotyped or more nuanced, as binary divisions as presented in textbooks and published resources describing research methods may not always accord with the perceptions and day-to-day practices of university researchers. Such common binary representations of quantitative and qualitative and their associated concepts may hide complexities, some of which are outlined below. Any binary divide between ‘qualitative’ and ‘quantitative’ needs caution to show complexity and awareness of disparities with some researchers’ practices.

To date, as far as the present authors are aware, no study has first identified a range of binary representations of ‘quantitative’ and ‘qualitative’ methods and approaches in a literature review study of the many research methods textbooks and sources which guide students and then, secondly, undertaken an interview study with a range of established participant researchers in widely divergent fields to seek their understandings of ‘quantitative’ and ‘qualitative’ in their own fields. The findings related here complement and extend the complexities and convergences of understanding the concepts in different disciplines. Arguably, this paper demonstrates how students and novice researchers should not be constrained in their studies by any binary representations of ‘quantitative’ and ‘qualitative’ the terms. They should feel free to use either (or neither) or both in strategic combinations, as appropriate to their fields.

1.1 Presentations

Characteristically, presentations in research methods textbooks distinguish postivist and interpretivist approaches or paradigms (e.g. Guba and Lincoln 1994 ; Howe 1988 ; Denzin and Lincoln 2011 ) or ‘two cultures’ (Goertz and Mahoney 2012 ) with associated debates or ‘wars’ (e.g. Creswell 1995 ; Morse 1991 ). Quantitative data are shown as ‘numbers’ gathered through experiments (Moore 2006 ) or mathematical models (Denzin and Lincoln 1998 ), whereas qualitative data are usually words or texts (Punch 2005 ; Goertz and Mahoney 2012 ), characteristically gathered through interviews or life stories (Denzin and Lincoln 2011 ). Regarding analysis, some sources claim that establishing objective causal relationships is key in quantitative analysis (e.g. Goertz and Mahoney 2012 ) whereas qualitative analysis uses more discursive and interpretative procedures.

Thus, much literature presents research in terms of two generally distinct methods—quantitative and qualitative—which many students are taught in research methods courses. The binary divide may seem to be legitimated in the titles of many academic journals. This division prevails as designated strands of separated research methods in courses which apparently handle both (cf. Onwuegbuzie and Leech 2005 ). Consequently, students may follow this seemingly stereotyped binary view or feel uncomfortable to deviate from it. Arguably, PhD candidates need to demonstrate understanding of such concepts and procedures in a viva—or risk failure (cf. Trafford and Leshem 2002 ). The Cambridge Dictionary defines ‘quality’ as “how good or bad something is”; while ‘quantity' is “the amount or number of something, especially that can be measured” (Cambridge 2022 ). But definitions of ‘Qualitative' can be elusive, since “a precise definition of qualitative research, and specifically… its distinctive feature of being “qualitative”, the literature is meager” (Aspers and Corte 2019 , p.139). Some observe a “paradox… that researchers act as if they know what it is, but they cannot formulate a definition” and that “there is no consensus about specific qualitative methods nor… data” (Aspers and Corte 2019 , p40). In general, ‘qualitative research’ is an iterative process to discover more about a phenomenon (ibid.). Elsewhere, 'qualitative’ is defined negatively: "It is research that does not use numbers” (Seale 1999b , p.119). But this oversimplifies and hides possible disciplinary variation. For example, when investigating criminal action, numeric information (quantity) always follows an interpretation (De Gregorio 2014 ), and consequently this is a quantity of a quality (cf. Uher 2022 ).

Indeed, many authorities note the presence of elements of one in the other. For example, in analysis specifically, that what are considered to be quantitative elements such as statistics are used in qualitative analysis (Miles and Huberman 1994 ). More generically, that “a qualitative dimension is present in quantitative work as well” (Aspers and Corte 2019 , p.139). In ‘mixed methods’ research (cf. Tashakkori et al. 1998 ; Johnson et al. 2007 ; Teddlie and Tashakkori 2011 ) many researchers ‘mix’ the two approaches (Seale 1999a ; Mason 2006 ; Dawson 2019 ), either using multiple methods concurrently, or doing so sequentially. Mixed method research logically depends on prior understandings of quantitative and qualitative concepts but this is not always obvious (e.g. De Gregorio 2014 ); for instance Heyvaert et al. ( 2013 ) define mixed methods as combining quantitative and qualitative items, but these key terms are left undefined. Some commentators characterize such mixing as a skin, not a sweater to be changed every day (Marsh and Furlong 2002 , cited in Grix 2004 ). In some disciplines, these terms are often blurred, interchanged or conjoined. In sociology, for instance, “any quality can be quantified. Any quantity is a quality of a social context, quantity versus quality is therefore not a separation” (Hanson 2008 , p.102) and characterizing quantitative as ‘objective’ and qualitative as ‘subjective’ is held to be false when seeking triangulation (Hanson 2008 ). Additionally, approaches to measuring and generating quantitative numerical information can differ in social sciences compared to physics (Uher 2022 ). Indeed, quantity may consist of ‘a multitude’ of divisible aspects and a ‘magnitude’ for indivisible aspects (Uher 2022 ). Notably, “the terms ‘measurement’ and ‘quantification’ have different meanings and are therefore prone to jingle-jangle fallacies” (Uher 2022 ) where individuals use the same words to denote different understandings (cf. Bakhtin 1986 ). Comparatively, the words ‘unit’ and ‘scale’ are multitudinous in different sciences, and the key principles of numerical traceability and data generation traceability arguably need to be applied more to social sciences and psychology (Uher 2022 ). The interdependence of the terms means any quantity is grounded in a quality of something, even if the inverse does not always apply (Uher 2022 ).

1.2 Practices

The present paper compares representations found in research methods textbooks with the reported practices of established researchers given in semi-structured interviews. The differences revealed between what the literature review of methods texts showed and what the interview study showed both underlines and extends this complexity, with implications for how such methodologies are approached and taught. The interview study data (analysed below) show that many participant researchers in disciplines commonly located within an ostensibly ‘positivist’ scientific tradition (e.g. chemistry) are, in fact, using qualitative methods as scientific procedures (contra Tashakkori et al 1998 ; Guba and Lincoln 1994 ; Howe 1988 ; Lincoln and Guba 1985 ; Teddlie and Tashakkori 2011 ; Creswell 1995 ; Morse 1991 ). These interview study data also show that many participant researchers use what they describe as qualitative approaches to provide initial measurements (geotechnics; chemistry) of phenomena before later using quantitative procedures to measure the quantity of a quality (cf. Uher 2022 ). Some participant researchers also say they use quantitative procedures to reveal data for which they subsequently use qualitative approaches to interpret and understand (biology; dendrology) through their creative imaginations or experience (contra e.g. Hammersley, 2013 ). Participant researchers in ostensibly ‘positivist’ areas describe themselves as doubting ‘facts’ measured by machines programmed by humans (thus showing they feel researchers are not outside the world looking in (contra. e.g. Punch 2005 )) or doubting the certainty of quantitative data over time (contra e.g. Punch 2005 ). Critically, the interview study data show that these participant researchers often engage in debate over what a ‘number’ is and the extent to which ‘numbers’ can be considered ‘quantitative’. For example the data show how a mathematician considers that many individuals do not know what they mean by the word ‘quantitative’, and an engineer interprets any numbers involving human judgements as ‘qualitative’. Further, both a chemist and a geotechnician routinely define and use ‘qualitative’ methods and analysis to arrive at numerical values (contra. Davies and Hughes 2014 ; Denzin and Lincoln 2011 ).

Such data refute many textbook and key source representations of quantitative and qualitative as being binary and separately ringfenced entities as shown in the literature review study below (contra e.g. Punch 2005 ; Goertz and Mahoney 2012 ). Nevertheless, they resonate with much recent and current literature in the field (e.g. Uher 2022 ; De Gregorio 2014 ). They also arguably extend the complexities of the terms and approaches. In some disciplines, these participant researchers only do a particular type of research and never need anything other than clear ‘quantitative’ definitions (Mathematics), and some only ever conduct research involving text and never numbers (Literature). Moreover, some participant researchers consider certain aspects lie outside the ‘qualitative’ or ‘quantitative’ (the theoretical in German Literature), or do research which they maintain does not contain ‘knowledge’ (Fine-Art Sculpture), while others outline how they feel they do foundational conceptual research which they believe comes at a stage before any quantity or quality can be assessed (Philosophy). Indeed, of the 31 participant researchers we spoke to, nine of them considered the terms ‘quantitative’ and ‘qualitative’ to be of little relevance for their subject.

1.3 Outline of the two studies

This paper reports and discusses findings from a constructivist grounded approach interview study that interviewed experienced participant researchers (N = 31) in various disciplines (see Table 1 below) about their understandings of ‘qualitative’ and ‘quantitative’ in their subject areas. Findings from this interview study were compared with findings from a research methods literature review study that revealed many disparities with received and often binary presentations of the concepts in much key literature that informs student research methods courses. In this section we outline the review criteria, the method of analysis, and our findings. The findings are grouped according to how the sources reviewed consider ‘quantitative’ and ‘qualitative’ approaches the aspects of positivism and constructivism; the nature of research questions; research methods; analysis; issues of reliability, validity and generalizability; and the value and worth of the different approaches. Following this. We outline the approach, method, and procedure adopted for the interviews with research participants; sampling and saturation; and analysis; beside details of the participant researchers. Subsequently, Theme 2 focuses on contrasts of the interview data with ‘binary’ textbook and key source representations. Theme 3 focuses on what the interview data show about participant researcher perceptions of the value of ‘quantitative’ and ‘qualitative’ methods and approaches. This section outlines where, how, and sometimes why, participant researchers considered ‘quantitative’ and ‘qualitative’ methods approaches to be (or to not be) useful to them. These interview study findings show a surprising range of understandings, usage, and often perceived irrelevance of the terms. In the Discussion section, these findings form the focus of comparison with the literature as well as a consideration of possible implications for approaching and teaching research methods. In the conclusion we summarise the implications for research methods courses, for researchers in different disciplines and interdisciplinary contexts and discuss limitations and suggest future research. Besides adding to the debate on how ‘quantitative’ and ‘qualitative’ are conceptualized and how they are related, the paper appeals to those delivering research methods courses and to novice researchers to consider the concepts as highly complex and overlapping, to loosen constraints, and elaborate nuances of the commonplace binary representations of the terms.

2 Literature review study: some key textbooks and sources for teaching Research Methods.

2.1 review criteria.

To identify how concepts are presented in key materials we undertook a literature review study by consulting research methods course reading lists, library search engines, physically available shelves in institutional libraries, and Google Scholar. We wanted to encompass textbooks and some key texts which are recommended to UG, PG Masters and PhD students., for example, ‘textbooks’ like ‘Doing Your Research Project: A Guide for first-time researchers’ (Bell and Waters 2014 ) and ‘Introduction to Research Methods: A Practical Guide for Anyone Undertaking a Research project (5th Edition)’ (Dawson 2019 ). Such sources were frequently mentioned on reading lists and are freely available in many institutional libraries. We consulted seminal thinkers who have published widely on research methods, such as Denzin and Lincoln, or Cresswell, but we also considered texts which are likely less known such as ‘A tale of two cultures’ (Goertz and Mahoney 2012 ) and key articles such as ‘Five misunderstandings about case-study research’ (Flyvbjerg 2006 ). Students can freely find such sources, and are easily directed to them by supervisors. Although a more comprehensively robust search is possible, we nevertheless followed procedures and standard criteria for literature reviews (Atkinson et al. 2015 ).

3 Method of analysis

We assembled a total of 25 sources to look for a number of key tenets. We examined the sources for occurrence of the following: whether quantitative was described as positivist and qualitative was described as constructivist; whether quantitative was said to be science-based and qualitative was more reflective and non-science based; whether the research questions were presented as predetermined in quantitative methods and initially less focused in qualitative methods; whether quantitative methods were structured and qualitative methods were discussed as less structured; whether quantitative analysis focused on cause-effect type relationships and qualitative analysis was more exploratory; whether reliability, validity and generalizability were achieved through large numbers in quantitative research and through in-depth study in qualitative research; whether for particular subjects such as the sciences quantitative approaches were perceived to be of value (and qualitative was implied to have less value) and whether the converse was the case for other subjects such as history and anthropology; and whether mixed methods were considered possible or not possible. The 25 sources are detailed in Appendix 1 . As a confirmatory but less detailed exercise, and also detailed in Appendix 1 , we checked a further 23 research methods textbooks in German, Spanish and French, authored in those languages (rather than translations from English).

3.1 Findings

Overall, related to what quantitative and qualitative approaches, methods and analysis are, we found many key, often binary representations in this literature review. We outline these here below.

3.2 Positivism and constructivism

Firstly, 20 of the sources we reviewed stated that quantitative is considered positivist, and qualitative constructivist (e.g. Tashakkori et al 1998 ; Guba and Lincoln 1994 ; Howe 1988 ; Lincoln and Guba 1985 ; Teddlie and Tashakkori 2011 ; Creswell 1995 ; Morse 1991 ). Even if not everyone doing quantitative research (e.g. in sociology) consider themselves positivists (Marsh 1979 ), it is generally held quantitative research is positivist. Here, 12 of the sources noted that quantitative is considered ‘scientific’, situating observers outside the world looking in, e.g. through gathering numerical data (Punch 2005 ; Davis and Hughes 2014 ) whereas qualitative “locates the observer in the world” (Denzin and Lincoln 2011 , p.3). Quantitative researchers “collect facts and study the relationship of one set of facts to another”, whereas qualitative researchers “doubt whether social ‘facts’ exist and question whether a ‘scientific’ approach can be used when dealing with human beings” (Bell and Waters 2014 , p. 9).

3.3 The nature of research questions

Secondly, regarding research questions, “qualitative research… typically has… questions and methods… more general at the start, and… more focused as the study progresses” (Punch 2005 , p.28). In contrast, quantitative research uses “numerical data and typically… structured and predetermined research questions, conceptual frameworks and designs” (Punch 2005 , p.28). Of the sources we reviewed, 16 made such assertions. This understanding relates to type, and nature, of data, which is in turn anchored to particular worldviews. Punch ( 2005 , p 3–4) writes of how “in teaching about research, I find it useful to approach the qualitative-quantitative distinction primarily through…. the nature of the data. Later, the distinction can be broadened to include …. ways of conceptualising the reality being studied, and methods.” Here, the nature of data influences approach: numbers are for quantitative, and not-numbers (commonly words) for qualitative. Similarly, for Miles et al. ( 2018 ) “the nature of qualitative data” is “primarily on data in the form of words, that is, language in the form of extended text” (Miles et al. 2018 , no page). These understandings in turn relate to methods used.

Commonly, specific types of methods are said to be related to the type of approach adopted, and 18 of the sources we reviewed presented quantitative methods as being structured, and qualitative methods as less structured. For example, Davies and Hughes ( 2014 , p.23) claim “there are two principal options open to you: 1… quantitative research methods, using the traditions of science. 2… qualitative research, employing a more reflective or exploratory approach.” Here, quantitative methods are “questionnaires or structured interviews” whereas qualitative methods are “such as interviews or focus groups” (Dawson 2019 , no page given). Quantitative methods are more scientific, involve controlling a set of variables, and may involve experiments, something which, “qualitative researchers are agreed in their opposition to this definition of scientific research, or at least its application to social inquiry” (Hammersley 2013 , p. ix). As Punch notes ( 2005 , p.208), “the experiment was seen as the basis for establishing cause-effect relationships between variables, and its outcome (and control) variables had to be measured.”

4.1 Analysis

Such understandings often relate to analysis, and 16 of the sources we reviewed presented quantitative analysis as being statistical and number related, and qualitative analysis as being text based. With quantitative methods, “the data is subjected to statistical analysis, using techniques… likely to produce quantified, and, if possible, generalizable conclusions” (Bell and Waters 2014 , p.281). With qualitative research, however, this “calls for advanced skills in data management and text-driven creativity during the analysis and write-up” (Davies and Hughes 2014 ). Again, the data’s nature is key, and whilst qualitative analysis may condense data, it does not seek numbers. Indeed, “by data condensation, we do not necessarily mean quantification”, however, “occasionally, it may be helpful to convert the data into magnitudes… but this is not always necessary” (Miles et al. 2018 , npg). Qualitative analysis may involve stages such as assigning codes, subsequently sorting and sifting them, isolating patterns, then gradually refining any assertions made and comparing them to other literature (Miles et al. 2018 ). This could involve condensing, displaying, then drawing conclusions from the data (Miles et al. 2018 ). In this respect, some sources consider qualitative and quantitative analysis broadly similar in overall goals, yet different because quantitative analyses use “well-defined, familiar methods; are guided by canons; and are usually more sequential than iterative or cyclical” (Miles et al. 2018 , npg). In contrast, “qualitative researchers are… more fluid and… humanistic” in meaning making (Miles et al. 2018 , npg). Here, both approaches seek causation and may attempt to reveal ‘cause and effect’ but qualitative approaches often seek multiple and interacting influences, and effects and are less rigid (Miles et al. 2018 ). In quantitative inquiry search for causation relates to “causal mechanisms (i.e. how did X cause Y)” whereas in “the human sciences, this distinction relates to causal effects (i.e. whether X causes Y)” (Teddlie and Tashakkori 2011 , p.286). Similarly, that “scientific research in any area… seeks to trace out cause-effect relationships” (Punch 2005 , p.78). In contrast, qualitative research seeks interpretative understandings of human behaviour, “not ‘caused’ in any mechanical way, but… continually constructed and reconstructed” (Punch 2005 , p.126).

4.2 Issues of reliability, validity and generalizability

Regarding reliability, validity and generalizability, 19 of the sources we reviewed presented ideas along the lines that quantitative research is understood to seek large numbers, so quantitative researchers, “use techniques… likely to produce quantified and, if possible, generalizable conclusions (Bell and Waters 2014 , p.9). This means quantitative “research researches many more people” (Dawson 2019 , npg). Given quantitative researchers aim, “to discover answers to questions through the application of scientific procedures” it is anticipated these procedures will “increase the likelihood that the information… will be reliable and unbiased” (Davies and Hughes 2014 , p.9). Conversely, qualitative researchers are considered “more concerned to understand individuals’ perceptions of the world” (Bell and Waters 2014 , p.281) and consequently aim for in-depth data with smaller numbers, “as it is attitudes, behaviour and experiences that are important” (Dawson 2019 , npg). Consequently, generalizability of data is not key, as qualitative research has its “emphasis on a specific case, a focused and bounded phenomenon embedded in its context” (Miles et al. 2018 , npg). Yet, such research is considered generalizable in theoretical insight if not actual data (Flyvbjerg 2006 ).

4.3 The value and worth of the different approaches

Regarding ‘value’ and ‘worth’, many see this related with appropriacy to the question being researched. Thus, if questions involve more quantitative approaches, then these are of value, and if more qualitative, then these are of value, and 6 of the sources we reviewed presented these views (e.g. Bell and Waters 2014 ; Punch 2005 ; Dawson 2019 ). This resonates with disciplinary orientations where choices between given approaches are valued more in specific disciplines. History and Anthropology are seen more qualitative, whereas Economics and Epidemiology may be more quantitative (Kumar 1996 ). Qualitative approaches are valuable to study human behaviour and reveal in-depth pictures of peoples’ lived experience (e.g. Denzin and Lincoln 2011 ; Miles et al. 2018 ). Many consider there to be no real inherent superiority for one approach over another, and “asking whether quantitative or qualitative research is superior to the other is not a useful question” (Goertz and Mahoney 2012 , p.2).

Nevertheless, some give higher pragmatic value to quantitative research for studying individuals and people; neoliberal governments consistently value quantitative over qualitative research (Barone 2007 ; Bloch 2004 ; St Pierre 2004 ). Concomitantly, data produced by qualitative research is criticised by quantitative proponents “because of their problematic generalizability” (Bloor and Wood 2006 , p.179). However, other studies find quantitative researchers see qualitative methods and approaches positively (Pilcher and Cortazzi 2016 ). Some even question the qualitative/quantitative divide, and suggest “a more subtle and realistic set of distinctions that capture variation in research practice better” (Hammersley 2013 , p.99).

The above literature review study of key texts is hardly exhaustive, but shows a general outline of the binary divisions and categorizations that exist in many sources students and newer researchers encounter. Thus, despite the complex and blurred picture as outlined in the introduction above, many key texts students consult and that inform research methods courses often present a binary understanding that quantitative is positivist, focused on determining cause and effect, numerical or magnitude focused, uses experiments, and is grounded in an understanding the world can be observed from the outside in. Conversely, qualitative tends to be constructivist, focused on determining why events occur, is word or textual based (even if these elements are measured by their magnitude in a number or numerical format) and grounded in understanding the researcher is part of the world. The sciences and areas such as economics are said to tend towards the quantitative, and areas such as history and anthropology towards the qualitative.

We also note that in our literature review study we focused on English language textbooks, but we also looked at outline details, descriptions, and contents lists of texts in the languages of German, Spanish and French. We find that these broadly confirm the perception of a division between quantitative and qualitative research, and we detail a number of these in Appendix 1 . These examples are all research methods handbooks and student guides intended for under and post-graduates in social sciences and humanities; many are inter-disciplinary but some are more specifically books devoted to psychology, health care, education, politics, and management. Among the textbooks and handbooks examined in other languages, more recent books pay attention to online research and uses of the internet, social media and sometimes to big data and software for data analysis.

In these sources in languages other than English we find massive predominance of two (quantitative/qualitative) or three approaches (mixed). These are invariably introduced and examined with related theories, examples and cases in exactly that order: quantitative; qualitative; mixed. Here there is perhaps the unexamined implication that this is a historical order of research method development and also of acceptability of use (depending on research purposes). Notably, Molina Marin (2020) is oriented to Latin America and makes the point that most European writing about research methods is in English or German, while there are far fewer publications in Spanish and few with Latin American contextual relevance, which may limit epistemological perspectives. This point is evident in French and Spanish publications (much less the case in German) where bibliographic details seem dominated by English language publications (or translations from them). We now turn to outline our interview study.

5 Interview study

5.1 approach and choice of method.

We approached our interview study from a constructivist standpoint of exploring and investigating different subject specialists’ understandings of quantitative and qualitative. Critically, we were guided by the key constructivist tenet that knowledge is not independent of subjects seeking it (Olssen 1996 ), nor of subjects using it. Extending from this we considered interviews more appropriate than narratives or focus groups. Given the exploratory nature of our study, we considered interviews most suited as we wanted to have a free dialogue (cf. Bakhtin 1981 ) regarding how the terms are understood in their subject contexts as opposed to their neutral dictionary definitions (Bakhtin 1986 ), and not to focus on a specific point with many individuals. Specifically, we used ‘semi’-structured interviews. ‘Semi’ can mean both ‘half in quantity or value’ but also ‘to some extent: partly: incompletely’ (e.g. Merriam Webster 2022 ). Our interviews, following our constructionist and exploratory approach, aligned with the latter definition (see Appendix 2 for the Interview study schedule). This loose ‘semi’ structure was deliberately designed to (and did) lead to interviews directed by the participants, who themselves often specifically asked what was meant by the questions. This created a highly technical dialogue (Buber, 1947) focused on the subject.

5.2 Sampling and saturation

Our sampling combined purposive and snowball sampling (Sharma 2017 ; Levitt et al. 2018 ). Initially, participants were purposively identified by subject given the project sought to understand different subject perspectives of ‘qualitative’ and ‘quantitative.’ Later, a combined purposive and snowball sampling technique was used whereby participants interviewed were asked if they knew others teaching particular subjects. Regarding priorities for participant eligibility, this was done according to subject, although generally participants also had extensive experience (see Table 1 ). For most, English was their first language, where it was not, participants were proficient in English. The language of interview choice was English as it was most familiar to both participants and interviewer (Cortazzi et al. 2011 ).

Regarding saturation, some argue saturation occurs within 12 interviews (Guest et al. 2006 ), others within 17 (Francis et al. 2010 ). Arguably, however, saturation cannot be determined in advance of analysis and is “inescapably situated and subjective” (Braun and Clarke 2021 , p.201). This critical role of subjectivity and context guided how we approached saturation, whereby it was “operationalized in a way consistent with the research question(s) and the theoretical position and analytic framework adopted” (Saunders et al. 2018 , p.1893). We recognise that more could always be found but are satisfied that 31 participants provided sufficient data for our investigation. Indeed, our original intention was to recruit 20 participants, feeling this would provide sufficient saturation (Francis et al. 2010 ; Guest et al. 2006 ) but when we reached 20, and as we had already started analysis (cf. Braun and Clarke 2021 ) as we ourselves transcribed the interviews (Bird 2005 ) we wanted to explore understandings of ‘qualitative’ and ‘quantitative’ with other subject fields. As Table 1 shows, ‘English Literature’, ‘Philosophy, and ‘Sculpture’ were only explored after interview 20. These additional subject fields added significantly (see below) to our data.

5.3 Analysis and participant researcher details

Our analysis followed Braun and Clarke’s ( 2006 ) thematic analysis. Given the study’s exploratory constructionist nature, we combined ‘top down’ deductive type analysis for anticipated themes, and ‘bottom up’ inductive type analysis for any unexpected themes. The latter was similar to a constructivist grounded theory analysis (Charmaz 2010 ) whereby the transcripts were explored through close repeated reading for themes to emerge from the bottom up. We deliberately did not use any CAQDAS software such as NVivo as we wanted to manually read the scripts in one lengthy word document. We recognise that such software could allow us to do this but we were familiar with the approach we used and have found it effective for a number of years. We thus continued to use it here as well. We counted instances of themes through cross-checking after reading transcripts and discussing them, thereby heightening reliability and validity (Golafshani 2003 ). All interviews were undertaken with informed consent and participants were assured all representation was anonymous (Christians 2011 ). The study was approved by relevant ethics committees. Table 1 above shows the subject area, years of experience, and first language of the participant researchers. We also bracket after each subject area whether we consider it to be ‘Science’ or ‘Arts’ or whether we consider them as ‘Arts/Science’ or ‘Science/Arts’. This is of course subjective and in many ways not possible to do, but we were guided in how we categorised these subjects by doing so according to how we feel the methodology sources form the literature review study would categorize them.

5.4 Presentation of the interview study data compared with data from the literature review study

We present our interview study data in the three broad areas that emerged through analysis. Our approach to thematic analysis was to deductively code the interview transcripts manually under the three broad areas of: where data aligns with textbook and key source ‘binary’ representations; where the data contrasts with such representations; and where the data relates to interviewee perceptions of the value of ‘qualitative’ and ‘quantitative’. The latter relates to whether participant researchers expressed views that suggested they considered each approach to be useful, valuable, or not. We also read through the transcripts inductively with a view to being open to emerging and unanticipated themes. For each data citation, we note the subject field to show the range of subject areas. We later discuss these data in terms of their implications for research values, assumptions and practices and for their use when teaching about different methods. We provide illustrative citations and numbers of participant researchers who commented in relation to the key points below, but first provide an overview in Table 2 .

5.4.1 Theme 1: Alignments with ‘binary’ textbook and key source representations

The data often aligned with textbook representations. Seven participant researchers explicitly said, or alluded to the representation that ‘quantitative’ is positivist and seeks objectivity whereas ‘qualitative’ is more constructivist and subjective. For example: “the main distinction… is that qualitative is associated with subjectivity and quantitative being objective.” This was because “traditionally quantitative methods they’ve been associated with the positivist scientific model of research whereas qualitative methods are rooted in the constructivist and interpretivist model” (Psychology). Similarly, “quantitative methods… I see that as more… logical to a scientific mode of generating knowledge so… largely depends on numbers to establish causal relations… qualitative, I want to more broadly summarize that as anything other than numbers” (Communication Studies). One Statistics researcher had “always associated quantitative research more with statistics and numbers… you measure something… I think qualitative… you make a statement… without saying to what extent so… so you run fast but it’s not clear how fast you actually run…. that doesn’t tell you much because it doesn’t tell you how fast.” One mathematics participant researcher said mathematics was “ super quantitative… more beyond quantitative in the sense that not only is there a measurement of size in everything but everything is defined in… really careful terms… in how that quantity kind of interacts with other quantities that are defined so in that sense it’s kind of beyond quantitative.” Further, this applied at pre-data and data integration stages. Conversely, ‘qualitative’ “would be more a kind of verbalistic form of reasoning or… logic.”

Another representation four participant researchers noted was that ‘quantitative ‘ has structured predetermined questions whereas ‘qualitative’ has initially general questions that became more focused as research proceeded. For example, in Tourism, “with qualitative research I would go with open ended questions whereas with quantitative research I would go with closed questions.” This was because ‘qualitative’ was more exploratory: “quantitative methods… I would use when the parameters… are well understood, qualitative research is when I’m dealing with topics where I’m not entirely sure about… the answers.” As one Psychology participant researcher commented: “the main assumption in quantitative… is one single answer… whereas qualitative approaches embrace… multiplicity.”

Nineteen participant researchers considered ‘quantitative’ numbers whereas ‘qualitative’ was anything except numbers. For example, “quantitative research… you’re generating numbers and the analysis is involving numbers… qualitative is… usually… text-based looking for something else… not condensing it down to numbers” (Psychology). Similarly, ‘quantitative’ was “largely… numeric… the arrangement of larger scale patterns” whereas, “in design field, the idea of qualitative…is about the measure… people put against something… not [a] numerical measure” (Design). One participant researcher elaborated about Biology and Ecology, noting that “quantitative it’s a number it’s an amount of something… associated with a numerical dimension… whereas… qualitative data and… observations… in biology…. you’re looking at electron micrographs… you may want to describe those things… purely in… QUALitative terms… and you can do the same in… Ecology” (Human Computer Interaction). One participant researcher also commented on the magnitude of ‘quantitative’ data often involving more than numbers, or having a complex involvement with numbers: “I was thinking… quantitative… just involves numbers…. but it’s not… if… NVivo… counts the occurrence of a word… it’s done in a very structured way…. to the point that you can even… then do statistical analysis” (Logistics).

Regarding mixed methods, data aligned with the textbook representations that there are two distinct ‘camps’ but also that these could be crossed. Six participants felt opposing camps and paradigms existed. For example, in Nursing, that “it does feel quite divided in Nursing I think you’re either a qualitative or a quantitative researcher there’s two different schools… yeah some people in our school would be very anti-qualitative.” Similarly, in Music one participant researcher felt “it is very split and you’ll find… some people position themselves in one or the other of those camps and are reluctant to consider the other side. In Psychology, “yes… they’re quite… territorial and passionately defensive about the rightness of their own approaches so there’s this… narrative that these two paradigms… of positivistic and interpretivist type… cannot be crossed… you need to belong to one camp.” Also, in Communication Studies, “I do think they are kind of mutually exclusive although I accept… they can be combined… but I don’t think they, they fundamentally… speak to each other.” One Linguistics participant researcher felt some Linguists were highly qualitative and never used numbers, but “then you have… the corpus analysts who quantify everything and always under the headline ‘Corpus linguistics finally gets to the point… where we get rid of researcher bias; it objectifies the analysis’ because you have big numbers and you have statistical values and therefore… it’s led by the data not by the researcher.” This participant researcher found such striving for objectivity a “very strange thing” as any choice was based on previously argued ideas, which themselves could not be objective: “because all the decisions that you need to put into which software am I using, which algorithm am I using, which text do I put in…. this is all driven by ideas.”

Nevertheless, three participant researchers felt the approaches not diametrically opposed. For example, the same Psychology participant researcher cited immediately above felt people’s views could change: “some people although highly defensive over time… may soften their view as mixed method approaches become more prominent.” Comparatively flexibly, a Historian commented “I don’t feel very concerned by the division between qualitative and quantitative; I think they’re just two that are separate sometimes complementary approaches to study history.” In Translation and Interpreting, one participant researcher said methods could be quantitative, but have qualitative analysis, saying one project had: “an excellent use of quantitative tools… followed by not a qualitative method but qualitative analysis of what that implied.” Thus, much of the data did align with the binary representations of the key textbooks reviewed above and also the representation that approaches could be combined.

5.4.2 Theme 2: Contrasts with ‘binary’ textbook and key source representations

One recurrent contrast with common textbook representations was where both qualitative and quantitative were used in some sciences; nine participant researchers felt this. For example, in Geotechnics, when ascertaining soil behaviour: “the first check, the Qualitative check is to look whether those [the traditional and new paths of soil direction] bear resemblance, [be] coz if that doesn’t have that shape how can I expect there to be a quantitative comparison or… fit.” Both qualitative and quantitative approaches combined helped “rule out coincidence” and using both represented “a check which moves through qualitative… to quantitative.” Quantitative was a “capital Q for want of a better expression” and consisted of ‘bigger numbers’, which constituted “the quantitative or calculated strength.” However, this ‘capital Q’ quantitative data aimed to quantify a qualitatively measured numerically estimated phenomenon. So both were numerical. Nevertheless, over the long-term, even the quantitative became less certain because: “when you introduce that time element… you create… circumstances in which you need to be careful with the way you define the strength… different people have come up with different values… so the quantitative match has to be done with an element of uncertainty.”

Similarly, in Chemistry, both qualitative and quantitative methods and analysis were used, where “ the qualitative is the first one, and after you have the other ones [I—Right to kind of verify] if… if you need that.” Both were used because, “we need to know what is there and how much of each component is there… and a knowledge of what is there is a qualitative one, how much of each one is a quantitative one.” Moreover, “they are analysed sometimes by the same technique ” which could be quantitative or qualitative: “[I—and chromatography, again… would that be qualitative or quantitative or both?] Both, both… the quantitative is the area of the peak, the qualitative is the position in which this characteristic appears.” Here, both were key, and depending on the research goal: “we… use them according to what we need… sometimes it’s enough to detect [qualitative] … other times you need to know how much [quantitative] ”.

For Biology also, both were key: “quantitative is the facts and… qualitative is the theory you’re trying to make fit to the facts you can’t do it the other way around… the quantitative data… just doesn’t tell you anything without the qualitative imagination of what does it mean?” Inversely, in an area commonly understood as quantitative, Statistics, the qualitative was an initial, hypothetical stage requiring later quantitative testing. For example: “very often the hypothesis is a qualitative hypothesis” and then, “you would test it by putting in all sorts of data and then the test result would give you a p-value… and the p-value of course is quantitative because that’s a number.”

In Engineering, both helped research sound frequencies: “we need to measure the spectrum of the different frequencies… created… all those things were quantifiable, but then we need to get participants to listen and tell us… which one do you prefer?… this is a qualitative answer.” Mathematical Biology also used both: qualitative for change in nature of a state, and quantitative for the magnitude of that change. Here: “quantitative changes the numerical value of the steady state but it doesn’t change its stability… but qualitative change is when you… change the parameters and you either change its stability or you change whether it exists or not… and that point over which you cross to change it from being stable to unstable is called a bifurcation point… that’s where I use quantitative and qualitative the most in my research.”

The idea of ‘quantitative’ involving large data sets was expressed; however, the ‘qualitative’ could help represent these. In Computing Mathematics one participant researcher commented that: “quantitative… I do almost 90% of the time…. calculating metrics… and using significance testing to determine whether the numbers mean anything.” Yet, this participant researcher also used qualitative representations for simplified visual representation of large number sets: “I think for me QUALitative work is almost always about visualizing things in a way that tries to illustrate the trends… so I’m not actually calculating numbers but I’m just saying if I somehow present it in in this way.” Concomitantly, ‘quantitative’ could be smaller scale. For example, in Architecture: “my expectation is it wouldn’t be valid until you have a certain quantity of response but that said [I] have had students use… quantitative analysis on a small sample.” Similarly, in History: “you could have a quantitative study of a small data set or a small… number of statistics I really think it’s determined by the questions… you’re asking.”

Interestingly, two participant researchers questioned their colleagues’ understandings of ‘quantitative’ and of ‘numbers’. For example, one Mathematician considered some researchers did not know what ‘quantitative’ meant, because “when they say quantitative… I think what they mean is the same as qualitative except it’s got numbers in it somewhere.” For example, “I’m talking to a guy who does research in pain and, so I do know now what he means by quantitative research, and what he means is that he doesn’t know what he means [both laugh] and he wants me to define what it means… I think he means he wants some form of modelling with data and… he’s not quite sure how to go about doing that.” For this Mathematician, engineers would, “Mean that purposefully when they talk about quantitative modelling” whereas, “generically you know when politicians [consider these things] quantitative just means there’s a number in it somewhere.”

Three participant researchers felt that when ‘quantitative’ involved human elements or decisions, subjectivity was inevitable. One Logistics participant researcher felt someone doing materials research was “Doing these highly quantitative analyses still there is a degree of subjectivity because… this involves human assessment… they’re using different photometric equipment… taking photos… what is the angle.” Another researcher in Sciences similarly noted, “I don’t know why people believe in machines so much because they’re built by humans and there’s so many errors.” An Engineer commented: “To me, just the involvement of humans… gives it a qualitative element no matter what.” For this researcher, with people’s ‘quantitative’ reaction times and memory recall, “I would call that again qualitative you know… yes we did quantify the reaction time… the correct number of answers, but… it’s a person… I could get somebody else now doing it and not get exactly the same answer, so that uncertainty of human participants to me make it a qualitative approach.” For this participant researcher, anything involving human participants was ‘qualitative’: “I would say anything that is measurable, but by measurable I mean physically measurable… or predictable through numbers is quantitative [and] anything that involves a judgment, therefore human participants… is qualitative.”

‘Qualitative’ was often highly subject-specific. For example, in Film Studies and Media—English, ‘qualitative’ was: “about… the qualities of particular texts…. I’ve read a lot about silence as a texture and a technique in cinema… so silence is a quality, and also what are the qualities of that silence.” One Sciences researcher felt ‘qualitative’ involved experience applied to interpreting data: “Qualitative I would define as using your own experience to see if the data makes sense… and… something that… cannot be measured so far by machine… like the shape of a tree.” One Historian also highlighted the importance of subject-sub-branches, saying, “I’d situate myself in history but I guess you’d probably get a different response depending on… whether that historian saw themselves as a cultural historian or as a social and economic historian or… an intellectual historian.”

A fluidity regarding ‘quantitative’ and ‘qualitative’ was characterized. One Human Computer Interaction participant researcher commented, “I think sometimes people can use both terms quite loosely without really sort of thinking about [them] .” Comparatively, one Psychology participant researcher commented that “even within the Qual[itative] people they disagree about how to do things [laughs] … so you have people talking about doing IPA [Interpretative Phenomenological Analysis] and they’re doing… and presenting it in completely different ways.” Another Psychologist felt using ‘quantitative’ and ‘qualitative’ as an ‘either/or’ binary division erroneously suggested all questions were answerable, whereas: “no method… can… answer this question… and this is something… many people I don’t think are getting is that those different methodologies come with huge limitations… and as a researcher you need… to appreciate… how far your work can go.” One Communication Studies participant researcher even perceived the terms were becoming less used in all disciplines, and that, “we’re certainly in a phase where even these labels now are becoming so arbitrary almost… that they’re not, not carrying a lot of meaning.” However, the terms were considered very context dependent: “I think I’d be very hesitant about… pigeonholing any particular method I’d want to look very closely at the specific context in which that particular method or methodology is being used.” Further, some concepts were considered challenging to align with textbook representations. One German Literature participant researcher, reflecting on how the ‘theoretical’ worked, concluded, “… the theoretical… I’m not sure whether… that is actually within the terms quantitative or qualitative or whether that’s a term… on a different level altogether .” Indeed, many participant researchers (nine in total across many subject areas e.g. Design, Film and Media, Philosophy, Mathematical Biology) confirmed they were fully aware of the commonplace representations, but felt they did not apply to their own research, only using them to communicate with particular audiences (see below).

5.4.3 Theme 3: Perceptions on the value of ‘Quantitative’ and ‘Qualitative’ methods and approaches

As the data above show, many participant researchers valued both ‘quantitative’ and ‘qualitative’, including many scientists (in Geotechnics; Biology, Chemistry, Engineering). Many considered the specific research question key. For example: “I certainly don’t think quantitative bad, qualitative good: it’s horses for courses, yeah” (Tourism). Participant researchers in History and Music Education felt similarly; the latter commenting how “I do feel it’s about using the right tools which is why I wouldn’t want to… enter into this kind of vitriolic negative mud-slinging thing that does happen within the fields because I think people… get too entrenched in one or the other and forget about the fact that these are just various ways to approach inquiry.” Similarly, one Psychologist observed, “I’m always slightly irritated [laughs] when I hear people you know say ‘Oh I’m only doing… qualitative research’ or ‘I’m only doing quantitative research’… I think it’s the research question that should drive the methodological choices.” This participant researcher had “seen good quality in both quantitative and qualitative research.”

Five participant researchers considered quantitative approaches to be of little value if they were applied inappropriately. For example, a Translation and Interpreting participant researcher felt quantitative data-generating eye tracking technology was useful “for marketing,… product placement,… [or] surgeons.” However, for Translation and Interpreting, “I don’t think… it is a method that would yield results… you could find better in a more nuanced manner through other methods, interviews or focus groups, or even ethnographic observation.” One Chemist questioned the value of quantitative methods when the sample was too small. For example, when students were asked about their feedback on classes, and one student in 16 evaluated the classes badly, “4% it was one person [laughs] in 16, one person, but I received that evaluation and I think this is not correct… because sometimes…. I think that one person probably he or she didn’t like me… well, it’s life, so I think these aspects… may happen also but it’s with the precision of the system… the capacity of the system to detect and to measure.” Meaningfulness was held to be key: “When we do the analysis the sample has meaning” . Similarly, a Theoretical Physicist felt quantitative approaches unsuited to education: “in the context of education… we all produce data all the time… we grade students… we assess creativity… people will say… ‘you measure somebody's IQ using this made-up test and you get this kind of statis[tic]..’ and then you realize that all of those things are just bogus… or at least… doesn't measure anything of any real serious significance.” Comparatively, one participant researcher in Design felt ‘quantitative’ had a danger to “lead to stereotypes”; for example, when modern search engines use quantitative data to direct people to particular choices, “There’s potential there to constrain kind of broader behaviours and thinking… and therefore it can become a programmer in its own right.” One Mathematical Biologist commented how statistics can be misused, and how a popular Maths book related “How statistics are a light shone on a particular story from a particular angle to paint a picture that people want you to see but… it’s almost never the whole picture, it’s a half-truth, if you like, at best.”

Seven participant researchers considered that their disciplines valued quantitative over qualitative. This could be non-judgmental, and perhaps inherent in major areas of a discipline, as in Theoretical Physics, where precision is crucial, although this was said not to be ‘disparaging’: “theoretical physics… or physics in general… we… tend to think of ourselves as being very, very quantitative and very precise, and we think of qualitative, I guess… as being a bit vague, right?… which is not disparaging, because sometimes… we have to be a bit vague… and we're working things out.” In Psychology, however, despite “a call to advocate for more qualitative methods”, there, “definitely… is a bias toward quantitative… measures in psychology; all the high impact factor journals advocate for quantitative measures.” In Nursing, quantitative was also deemed paramount, with “the randomized control trial seen as being… you know the apex and… some researchers in our school would absolutely say it’s the only reliable thing… would be very anti-qualitative.”

Yet, four participant researchers were positively oriented towards anything qualitative. For example, one Tourism researcher felt that, “in an uncertain world, such as the one we’re living in today, qualitative research is the way forward.” Also, an Architect highlighted that in one of their studies, “I think the most important finding of my questionnaires was in the subjective comments.” One Music education participant researcher personally favoured qualitative approaches but regretted how their field was biased toward quantitative data, saying they had been informed: “ ‘what journals really care about is that p-value…’ and I remember… thinking… that’s a whole area of humanity… you’re failing to acknowledge.”

Nevertheless, side-stepping this debate, nine researchers considered the terms of little value, and simply irrelevant for their own research. One Film and Media—English participant researcher commented: “I have to say… these are terms I’m obviously familiar with, but… not terms… I… tend to really use in my own research… to describe what I do … mainly because everything that I do is qualitative.” As an English Literature participant researcher noted in email correspondence: “they are not terms we use in literary research, probably because most of what we do is interpretation of texts and substantiating arguments through examples. I have really only encountered these terms in the context of teaching and have never used them myself.” In the interview, this participant researcher commented that “I can imagine… they would be terms… quite common in the sciences and mathematics, but not Social Sciences and Arts.” A German Literature participant researcher felt similarly, commenting that in “German Literature… the term quantitative hadn’t even entered my vocabulary all the way through the PhD [laughs] … because… you could argue the methods in literary research are always qualitative.”

Complementing such perspectives, in Theoretical Physics ‘qualitative’ and ‘quantitative’ was: “not something that ever comes up… I don’t think I read a paper ever that will say we do qualitative research in any way, but I never… or hardly ever handle any data… I just have a bunch of principles that are sort of either taken to be true or are… a model… we’re exploring.” In Mathematics, ‘quantitative’ was simply never used as all mathematics research was quantitative: “I never use the word in the company of my colleagues, never, it’s a non-vocabulary word, for the simple reason that when everything is so well defined why do you need a generic term when you’ve got very specific reference points in the language that you’re using?”.

One Philosopher felt the terms did not fit conceptual analysis in philosophy, given that the object of consideration was uncertain: “I guess… I thought it didn’t fit conceptual analysis… you need to know what you’re dealing with in order to then do the quantitative or qualitative whereas in philosophy it feels like… you don’t quite know what you’re dealing with you’re trying to work out… what are rights?… What is knowledge? What is love?… and then look at its qualities.” For this researcher, Philosophy was tentatively pre-quantitative or pre-qualitative, because philosophy “feels like it’s before then.” The terms were not considered valuable for Philosophy or for the humanities generally: “in philosophy we wouldn’t use the term qualitative or quantitative research… you just use the tools… you need… to develop your argument and so you don’t see the distinction… I would say in the humanities that’s relatively similar.” Further, a Fine Art—Sculpture participant researcher said: “they’re not words I would use… partly because… I’m engaged with… through… research and… teaching… what I’d call practice research… and… my background’s in fine art, predominantly in making sculpture and that doesn’t contain knowledge.” Here, the participant researcher related how they may consider a student’s work hideous but if the student had learned a lot through creating the work, they should be rewarded. This participant researcher spoke of a famous sound artist, concluding, “if you asked him about qualitative and quantitative… it just wouldn’t come into his thing at all…. He doesn’t need to say well there were a thousand visitors plus you know it’s just ‘bang’… he wouldn’t think about those things… not as an artist.”

Six participant researchers said they only ever used the terms for particular audiences. For example, for ‘quantitative’ in Film and Media: “the only time is when it’s been related to public engagement that we’ve ever sort of produced anything that is more along quantitative lines,” and that “it was not complex data we were giving them.” In Fine-Art Sculpture, too, the terms were solely used with a funder, for example, to measure attendance at an exhibition for impact, but “that’s not the type of research that I’m involved with necessarily.” One Logistics participant researcher commented that “it really depends on the audience how you define qualitative or quantitative.” For this researcher, if communicating with “statisticians econometricians or a bunch of people who are number crunchers” then “they will be very precise on what quantitative is and what qualitative is” and would only recognise mathematical techniques as quantitative. Indeed, “they wouldn’t even recognize Excel as quantitative because it’s not that hard.” In contrast, for social scientists, Excel would be quantitative, as would “anything to do with numbers… I suppose you know a questionnaire where you have to analyse responses would be probably classed as quantitative.”

Conversely, a Mathematical Biology participant researcher commented they had been doing far more public outreach work, “using quantitative data so numbers… even with things that might often be treated in a qualitative way… so stuff which… is often treated I think qualitatively we try to quantify… I think partly because it’s easier to make those comparisons when you quantify something.” One researcher in Communication Studies said they advised a student that “it depends on your research objectives; if you are focusing on individual experiences… I think naturally that’s going towards qualitative, but if you’re … doing this research oriented to a leader of … [a] big number of people… for informing policy… then you need some sort of insights that can be standardized… so it’s a choice.”

Another Communication participant researcher felt political shifts in the 1990s and 2000s meant that a ‘third way’ now dominated with a move towards hybridity and a breakdown in ‘qualitative’ and ‘quantitative’ with everything now tied to neoliberalism. Therefore, since “the late 90s and early noughties I’ve seen this kind of hybridity in research methods almost as being in parallel with the third way there seems to be… no longer opposition between left and right everything… just happens to buy into neoliberalism so likewise… with research methods… there’s a breakdown of qual and quant.” Comparatively, a Historian felt underpinning power structures informed approaches, commenting that “the problem is not the terminology it’s the way in which power is working in the society in which we live in that’s the root problem it seems to me and what’s valued and what’s not.” A Philosopher felt numbers appealed to management even when qualitative data were more suitable: “I think management partly… are always more willing to listen to numbers… finding the right number can persuade people of things that actually… you think really a better persuasion would do something more qualitative and in context.” One Fine Art participant researcher felt ‘quantitative’ and ‘qualitative’ only became important when they focused on processes related to the Research Excellence Framework but not for their research as such: “I guess we are using qualitative and quantitative things in the sense of moving ourselves through the process as academics but that’s not what I’d call research.”

6 Discussion: implications for teaching research methods

Research Methods teaching for undergraduate, postgraduate and newer researchers is commonly guided by textbook and seminal text understandings of what constitutes ‘qualitative’ and ‘quantitative’. Often, the two are treated in parallel, or interlinked, and used in combination or sequentially in research. But the relations between these are complex. The above analysis of the interview study with established participant researchers underlines and often extends this complexity, with implications for how such methodologies are approached and taught. Many of these participant researchers in disciplines commonly located within an ostensibly ‘positivist’ scientific tradition are, in fact, using qualitative methods as scientific procedures. They do so to provide initial measurements of phenomena before later using quantitative procedures to measure the quantity of a quality. They also use quantitative procedures to reveal data for which they subsequently use qualitative approaches to interpret and understand through their creative imaginations or experience. Participant researchers in ostensibly positivist disciplines describe themselves as doubting ‘facts’ measured by machines programmed by humans or doubting the certainty of quantitative data over time. Critically, these participant researchers engage in debate over what a ‘number’ is and the extent to which ‘numbers’ can be considered ‘quantitative’. One mathematician spoke of how many individuals do not know what they mean by the word ‘quantitative’, and an engineer interpreted any numbers involving human judgements as ‘qualitative’. Both a chemist and a geotechnician routinely defined and use ‘qualitative’ methods and analysis to arrive at numerical values.

Although this analysis of participant researchers’ reported practices refutes many textbook and key research methods source representations of quantitative and qualitative as being binary and separately ringfenced entities (contra e.g. Punch 2005 ; Goertz and Mahoney 2012 ), they resonate with much recent and current literature in the field (e.g. Uher 2022 ; De Gregorio 2014 ). In some disciplines, participant researchers only do a particular type of research and never need anything other than clear ‘quantitative’ definitions (Mathematics); others only ever conduct research involving text and never numbers (Literature). Further, other participant researchers considered how certain aspects lie outside the ‘qualitative’ or ‘quantitative’ (the ‘theoretical’ in German Literature), or they did research which they maintain does not contain ‘knowledge’ (Fine-Art Sculpture), while others do foundational ‘conceptual’ research which they claim comes at a stage before any quantity or quality can be assessed (Philosophy). Nine researchers considered the terms of little relevance at all to their subject areas.

This leads to subsequent questions. Firstly, do the apparently emerging tensions and contradictions between commonplace textbook and key source presentations and on-the-ground participant researcher practices matter? Secondly, what kind of discourse might reframe the more conventional one?

Regarding whether tensions and contradictions matter: in one practical way, perhaps not, since participant researchers in all these areas continue to be productive in their current research practices. Nevertheless, the foundations of the binary quantitative and qualitative divide are discourse expressions common to research methods courses. These expressions frame how the two terms are understood as the guide for novices to do research. This guiding discourse is evident in specifically designated chapters in research handbooks, in session titles in university research methods modules, and in entries for explanations of research terms within glossaries. The literature review study detailed above illustrates this. ‘Quantitative’ means numbers, ‘qualitative’ means words. ‘Quantitative’ connotes positivist, objective, scientific; ‘qualitative’ implies constructivist, subjective, non-science-based. Arguably, any acceptance of the commonplace research method understanding gives an apparent solidity which can sometimes be a false basis that masks the complexities or inadequacies involved. Such masking can, in turn, allow certain agencies or individuals to claim their policies and practices are based on ‘objective’ numerical data when they are merely framing something as ‘quantitative’ when, as a cited Mathematician participant researcher observed above, it is simply something with a number in it somewhere. Conventionally, limitations are mentioned in research studies, but often they seem ritualized remarks which refer to insufficient numbers, or restricted types of participants, or a constrained focus on a particular area. Rarely do research studies (let alone handbooks and guides for postgraduates) question a taken-for-granted understanding, such as whether the very idea of using numbers with human participants may mean the number is not objective. Ironically, it is the field of Qualitative Inquiry itself in which occasionally some of these issues are mentioned. Concurrently, while the quantitative is promoted as ‘scientific’ and ‘objective evidence’, we find some scientists researching in sciences often question the terms, or consciously set them aside in their practices.

Concerning what could replace the commonplace terms and reframe the research discourse environment: arguably, any discussion of ‘quantitative’/‘qualitative’ should be preceded by key questions of how they are understood by researchers. Hammersley ( 2013 ) has suggested the value of a more nuanced approach. As the Communication Studies participant researcher here commented, the two terms seem to be breaking down somewhat. Nevertheless, alongside the data and arguments here, we see some value in considering things as being ‘quantitative’ or ‘qualitative’, and other value in viewing them as separate. The terms can still be simply outlined, not just as methodological listings of characteristics, but as a critical point, Outlines of methods should include insider practitioner views—illustrations of how they are used and understood by practising researchers in different disciplines (as in Table 2 above). This simple suggestion has benefits. When outlining approaches as qualitative or quantitative, we suggest space is devoted to how this is understood in disciplines, together with the opportunity to question the issues raised by these understandings. This would help to position the understandings of qualitative and quantitative within specific disciplinary contexts, especially in inter-disciplinary fields and, implicitly, it encourages reflection on the objectivity and subjectivity evoked by the terms. Such discussion can be included in research methods texts and in research methods courses, dissertations and frameworks for viva examinations (Cortazzi and Jin 2021 ). Here, rather than start with outlining what the terms mean by using concrete definitions such as ‘Quantitative means X’ the terms should be outlined using subject contextualised phrases such as ‘In the field of X quantitative is understood to mean Y’. In this way, quantitative and qualitative methods and approaches can be seen, understood and contextualised within their subject areas, rather than prescriptively outlined in a generic or common form. Furthermore, if the field is one that has no use for such terms, this can also be stated, to prevent any unnecessary need for their use. Discourse around the terms can be extended if they are seen in line with much current literature and the data above that shows their complexities and overlaps, and goes beyond the binary choices and representations of many textbooks.

7 Conclusion

This paper has presented and discussed data from an interview study with experienced participant researchers (n = 31) regarding their perceptions of ‘qualitative’ and ‘quantitative’ in their research areas. This interview study data was compared with findings from a literature review study of common textbooks and research methods publications (n = 25) that showed often binary and reified representations of the terms and related concepts. The interview study data show many participant researcher understandings do in some ways align with the binary and commonplace representations of ‘qualitative ‘and ‘quantitative’ as shown to be presented in many research methods textbooks and sources from the literature review study. However, the interview study data more often illustrate how such representations are somewhat inaccurate regarding how research is undertaken in the different areas researched by the participant researchers. Rather, they corroborate much of the current literature that shows the blurring and complexity of the terms. Often, they extend this complexity. Sometimes they bypass complexity when these terms are considered irrelevant to their research fields by many researcher participants. For some researchers, the terms are simply valueless. We propose that future research methods courses could present and discuss the data above, perhaps using something akin to Table 2 as a starting point, so that students and novice researchers are able to loosen or break free of the chains of any stereotypical representations of such terms or use them reflectively with awareness of disciplinary specific usage. This could help them to advance their research, recognizing complex caveats related to the boundaries of what they do, what methods they use, and how to conduct research using both quantitative and qualitative approaches, as interpreted and used in their own fields. In multi- or inter-disciplinary research, such reflective awareness seems essential. Future research could also study the impact of the use of the data here in research methods courses so that such courses encompass both qualitative and quantitative methods (cf. Onwuegbuzie and Leech 2005 ) yet also question and contextualise such terms in specific subject areas order to free research from any constraints created by binary representations of the terms.

Whilst we interviewed 31 participant researchers to approach what seems a reasonable level of saturation, clearly future research could add to what we have found here by speaking to a wider range and larger number of researchers. The 25 research methods sources in English (supplemented by 23 sources in German, Spanish and French) examined here can clearly be expanded for a wider analysis of ‘quantitative’ and ‘qualitative’ in other languages for a more comprehensive European perspective. This strategy might ascertain likely asymmetries between the numerous English language texts (and their translations) and relatively smaller numbers of texts written by national or local experts in other languages. As a world-wide consideration, given the relative paucity of published research guidance in many languages, this point is especially significant related to fitting research methods to local contexts and cultures without imposition. Translating and discussing the terms ‘qualitative’ and ‘quantitative’, in and beyond European languages, will need care to avoid binary stereotyped or formulaic expression and to maintain some of the insight, resonances and complexities shown here.

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Appendix 1: Literature review study

The table below contains details of the binary representations and possibilities in the two columns on the left and in the right it contains the numbers of the key sources that conveyed or adhered to these binary representations. The details of these sources and their respective numbers are listed below.

Table: Textbook and key source binary representations

Bell, J., & Waters, S. (2014). Doing your research Project: A Guide for first-time researchers. McGraw-Hill Education (UK). 6 th edn

Bloor, M., & Wood, F. (2006). Keywords in qualitative methods: A vocabulary of research concepts. London, UK: Sage Publications.

Bryman, A. (2008). Social research methods. Oxford, UK: Oxford University Press. [with caveats for many but still using the divide as ‘useful’]

Bryman, A., & Cramer, D. (2009). Quantitative data analysis with SPSS 14, 15 and 16: A guide for social scientists. London, UK: Routledge.

Ceglowski, D., Bacigalupa, C., & Peck, E. (2011). Aced out: Censorship of qualitative research in the age of "scientifically based research." Qualitative Inquiry, 17(8), 679–686.

Daly, K. J. (2007). Qualitative Methods for Family Studies and Human Development. London, UK: Sage.

Davies, M. B., & Hughes, N. (2014).  Doing a successful research project: Using qualitative or quantitative methods . Bloomsbury Publishing.

Dawson, C. (2019).  Introduction to Research Methods 5th Edition: A Practical Guide for Anyone Undertaking a Research Project . Robinson.

Denzin, N. K., & Lincoln, Y. S. (Eds.). (1998). The landscape of qualitative research: Theories and issues. Thousand Oaks, CA: Sage Publications. [with caveat that original qual was positivist in root but not now]

Denzin and Lincoln (2011) Introduction: The Discipline and Practice of Qualitative Research. In Denzin, N. K., & Lincoln, Y. S. (2011). The Sage handbook of qualitative research . Thousand Oaks, Calif: Sage. Pp1-20

Goertz, G., & Mahoney, J. (2012).  A tale of two cultures . Princeton University Press.

Grix, J. (2004). The foundations of research. New York, NY: Palgrave Macmillan.

Hammersley, M. (2007). The issue of quality in qualitative research. International Journal of Research & Method in Education, 30(3), 287–305.

Hammersley, M. (2013). What is qualitative research? London, UK: Bloomsbury Academic. [caveat that some qual do use causal analysis – and if you mix you abandon key assumptions associated with qualitative work]

Harman, W. W. (1996). The shortcomings of western science. Qualitative Inquiry, 2(1), 30–38.

Howe, K. R. (2011). Mixed methods, mixed causes? Qualitative Inquiry, 17(2), 166–171.

Mason, J. (2006). Mixing methods in a qualitatively driven way. Qualitative Research, 6(1), 9–25.

Miles, M. B., Huberman, A. M., & Saldaña, J. (2018).  Qualitative data analysis: A methods sourcebook . Sage publications.

Punch, K. (2005). Introduction to Social Research Quantitative and Qualitative Approaches. Sage.

Sandelowski, M. (1997). "To be of use": Enhancing the utility of qualitative research. Nursing Outlook, 45(3), 125–132 [caveat – does rebut many of the ideas but nevertheless outlines them as how the two are seen – e.g. of generalizability]

Seale, C. (1999). Quality in qualitative research. Qualitative Inquiry, 5, 465–478.

Silverman, D. (2016). Introducing qualitative research.  Qualitative research ,  3 (3), 14–25.

Tashakkori, A., Teddlie, C., & Teddlie, C. B. (1998).  Mixed methodology: Combining qualitative and quantitative approaches  (Vol. 46). sage. [with the caveat that they talk about the differences as existing even though say they are not that wide]

Teddlie, C., & Tashakkori, A. (2011). Mixed methods research. Contemporary Issues in an emerging Field. in The Sage handbook of qualitative research ,  4 , 285–300.

Torrance, H. (2008). Building confidence in qualitative research: Engaging the demands of policy. Qualitative Inquiry, 14(4), 507–527.

1.1 Sources in languages other than English, and brief notes regarding their focus and content

Whilst not part of the literature review study, we also consulted the outline details, abstracts and contents lists of a number of sources in languages other than English. We put brief notes about after each source. Each source, unless specifically noted, adhered to similar binary treatment of quantitative and qualitative methods and approaches as the English language sources outlined above.

1.1.1 German

Blandz, M. (2021) Forschungsmethoden und Statistik für die Soziale Arbeit : Grundlage und Anwendingen. 2 nd . edit. Stuttgart: Kohlhammer Verlag. – this is a multidisciplinary source that focuses mostly on quantitative and mixed methods. It follows the suggestion that a qualitative study can be a preliminary study for the main quantitative study.

Caspari, D; Klippel, F; Legutke, M. & Schram, K. (2022) Forschungsmethoden: in der Fremdsprachendidaktik; Ein Handbuch. Tübingen: Narr Franke Altempo Verlag. [Focused on foreign language teaching, details quantitative, then qualitative and then mixed; all separately]

Dōring, N. (2023) Forschungsmethoden und Evaluation in den Sozial- und Humanwissenschaften. 6. th edit. Berlin: Springer. [Focused on the Social Sciences and humanities; as with the previous source it has separate chapters on quantitative and qualitative and a section on mixed, and contains some critical commentary]

Frankenberger, N. (Ed.) (2022) Grundlagen der Politikwissenschaft : Forschungsmethoden und Forschendes Lernen. Stuttgart: Kohlhammer Verlag. [Political science focused and based around distinctions between quantitative and qualitative approaches, each of which is elaborated with different methods; there is no obvious section on mixed methods]

Hussy, W; Schiener, M; Echterhoff, G. (2013) Forschungsmethoden in Psychologie und Sozialwissenschaften für Bachelor. Berlin: Springer. [This book is focused on psychology and social sciences for undergraduates. It has separate parts to focus on quantitative and on qualitative and then a chapter on mixed, identifying mixed methods as an emerging trend]

Niederberger, M. & Finne, E. (Eds.) (2021) Forschungsmethoden in der Gesundsheitsfōrderung und Prävention. Berlin: Springer. [Focused on Health and wellbeing; develops the roles of quantitative, qualitative and mixed (in combinations) in multidisciplinary, interdisciplinary and transdisciplinary research. Notes much research is exclusively quantitative and that social sciences are more qualitative or mixed. Makes the argument that the quantitative versus qualitative divide was surpassed by ‘post-positivist’ versus ‘combined’ thinking and that integrated approaches are now widely accepted]

1.1.2 Spanish

Campos-Arenas, A. (2014) Métodos mixtos de investigación. Bogota: Magisterio Editorial. [Social science focused; devoted to mixed or combined approaches in Latin American contexts]

Hernandez-Sampieri, R. & Mendoza Torres, C. P. (2018) Metodología de investigación: Las rutas cuantitativa , cualitativa y mixta. Mexico: McGrw-Hill. [Social science focused with an introduction and conclusion focused on ‘three routes to research’ that are exceptionally and thoroughly elaborated; quantitative given 8 chapters; qualitative 3 and mixed just one]

Léon-García, O. G. & Carda-Celay, I. M. (2020) Méthodos de investigación en psicología y educación: Las tradiciones cuantitativas y qualitativas. 5. th edit. Barcelona : McGraw-Hill, España. [Psychology and education focused; based on relatively clearly cut distinctinos between ‘the two traditions’ of quantitative and qualitative]

Molina Marin, G. (Ed.) (2020) Integración de métodos de investigación : Estrategias metodológicas u experiencias en salud pública. Bogotá: Universidad de Antioquia. [Public health focused; gives most attention to multi-method combinations and asks questions about the epistemological integrity of integrating different approaches]

Ortega-Sanchez, D, (Ed.) (2023) ¿Como investigar en didáctica de las ciencias sociales? Fundamentos metodológicos , técnicas e instrumentos de investigación. Barcelona: Octaedro. [Education, research, pedagogy of teaching social sciences; focused on quantitative, qualitative and mixed methods in Spanish contexts]

Páramo-Reales, D. (2020) Métodos de investigación caulitativa : Fundamentos y aplicaciones . Bogota: Editorial Unimagdalena. [Social sciences: basic applications of qualitative approaches in Latin America]

Ponce, O. A. (2014) Investigación de métodos mixtos en educación, 2. nd edit. San Jaun: Publicaciones Puertoriqueñas. [Education and Pedagogy; Puerto Rican context and entirely about mixed methods]

Vasilachis de Giradino, I. (Ed.) (2009) Estrategias de investigación cauitativa. Barcelona: Editorial Gedisa. [Social sciences; much detail on research design; focus exclusively on qualitative methods in Spanish contexts]

1.1.3 French

Bouchard, S. & Cyr, C. (Eds.) (2005) Reserche psycosocial pour harmoniser reserche st pratique. Quebec: Prese de la Université de Quebec. [Focused on psychology and sociology. Despite its title about ‘harmonizing’ research it is mainly focused on quantitative approaches, with a small section on qualitative and nothing on mixed approaches]

Corbière, M. & Lamviere, N. (2021) Méthodes quantitatives , qualitatives et mixtes , dans la reserche en sciences humaines et de la santé. 2. nd edit. Quebec : PU Quebec. [Focused on Humanities and health care; highlights the division between quantitative, qualitative and mixed methods]

Devin, G. (Ed.) (2016) Méthodes de recherche en relations internationals. Paris: Sciences Po. [Focused on politics and international relations; mostly wholly focused on quantitative; only a little on qualitative]

Gavard-Perret, M.L; Gotteland, D; Haon, C. & Jolibert, A. (2018) Methodologie de la recherche en sciences de gestion : Réussir son mémoire ou sa these. Paris: Pearson. [Business and management focused and geared towards thesis research; notes clear distinctions between quantitative and qualitative approaches with nothing on mixed]

Komu, S. C. S. (2020) Le receuil des méthodes en sciences sociales : Mèthodo;ogies en reserche. Manitoba: Sciences Script. [Social sciences focused; mostly quantitative methods with some attention to focus groups and participatory research]

Lepillier, O; Fournier, T; Bricas, N. & Figuié, M. (2011 ) Méthodes d’investigation de l’alimentation et des mangeurs. Versailles: Editions Quae. [Focused on nutrition, health studies and diet; details quantitative and qualitative methods and has very little on mixed]

Millette, M; Millerand, F; Myles, D. & Latako-Toth, T. (2021) Méthodes de reserches en contexte humanique , une orientation qualiificative. Montreal: PU Montreal. [Humanities focused; outlines quantitative and qualitative methods and, unusually, attends to ‘qualitative investigations in numerical contexts’ in Canada]

Moscarda, J. (2018) Faire parler les donées: Méthodologies quantitatives et qualitatives. Caen: Editions EMS. [Has a multidisciplinary focus on ‘let the data talk’; deals with quantitative methods and then qualitative methods and also mixed]

Vallerand, R. J. (2000) Méthodes de recherche en psychologie. Quebec: Gaetan Morin. [Focused on psychology; emphasis on quantitative research; brief section on qualitative; Canadian contexts]

Appendix 2: Interview study schedule

2.1 understandings of ‘qualitative’ and ‘quantitative’.

This research project is exploratory and intends to delve into understandings of the specific terms ‘quantitative’ and ‘qualitative’ as they are perceived, used, and interpreted by researchers in very different fields. Such research is intended to shed light on the fields of quantitative and qualitative research. The idea for the research arises from a previous project where the researcher interviewed quantitative focused researchers and saw the use of qualitative and quantitative being used and interpreted very differently to how the terms are presented and understood in the research methods literature. It is expected that exploring these understandings further will add to the field by shedding light on the subtleties of how they are used and also in turn help researchers make informed decisions about the optimum approaches and methods to use in their own research.

2.2 Interview questions

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Pilcher, N., Cortazzi, M. 'Qualitative' and 'quantitative' methods and approaches across subject fields: implications for research values, assumptions, and practices. Qual Quant 58 , 2357–2387 (2024). https://doi.org/10.1007/s11135-023-01734-4

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Published : 30 September 2023

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Qualitative vs Quantitative Research | Examples & Methods

Published on 4 April 2022 by Raimo Streefkerk . Revised on 8 May 2023.

When collecting and analysing data, quantitative research deals with numbers and statistics, while qualitative research  deals with words and meanings. Both are important for gaining different kinds of knowledge.

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions. Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs quantitative research, how to analyse qualitative and quantitative data, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyse data, and they allow you to answer different kinds of research questions.

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Quantitative and qualitative data can be collected using various methods. It is important to use a data collection method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observations or case studies , your data can be represented as numbers (e.g. using rating scales or counting frequencies) or as words (e.g. with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a sample (online, in person, or over the phone).
• Experiments : Situation in which variables are controlled and manipulated to establish cause-and-effect relationships.
• Observations: Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups: Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organisation for an extended period of time to closely observe culture and behavior.
• Literature review : Survey of published works by other authors.

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to confirm or test something (a theory or hypothesis)
• Use qualitative research if you want to understand something (concepts, thoughts, experiences)

For most research topics you can choose a qualitative, quantitative or mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an inductive vs deductive research approach ; your research question(s) ; whether you’re doing experimental , correlational , or descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Quantitative research approach

You survey 300 students at your university and ask them questions such as: ‘on a scale from 1-5, how satisfied are your with your professors?’

You can perform statistical analysis on the data and draw conclusions such as: ‘on average students rated their professors 4.4’.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: ‘How satisfied are you with your studies?’, ‘What is the most positive aspect of your study program?’ and ‘What can be done to improve the study program?’

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analysed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analysing quantitative data

Quantitative data is based on numbers. Simple maths or more advanced statistical analysis is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores
• The number of times a particular answer was given
• The correlation or causation between two or more variables
• The reliability and validity of the results

Analysing qualitative data

Qualitative data is more difficult to analyse than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analysing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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 organisations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organise your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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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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• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

• How does social media shape body image in teenagers?
• How do children and adults interpret healthy eating in the UK?
• What factors influence employee retention in a large organization?
• How is anxiety experienced around the world?
• How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

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Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

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
• Quantitative research
• 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.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

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.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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Qualitative Vs Quantitative Research – A Comprehensive Guide

Published by Carmen Troy at August 13th, 2021 , Revised On September 20, 2023

What is Quantitative Research?

Quantitative research is associated with numerical data or data that can be measured. It is used to study a large group of population. The information is gathered by performing statistical, mathematical, or computational techniques.

Quantitative research isn’t simply based on  statistical analysis or quantitative techniques but rather uses a certain approach to theory to address research hypotheses or questions, establish an appropriate research methodology, and draw findings & conclusions .

Characteristics of Quantitative Research

Some most commonly employed quantitative research strategies include data-driven dissertations, theory-driven studies, and reflection-driven research. Regardless of the chosen approach, there are some common quantitative research features as listed below.

• Quantitative research tests or builds on other researchers’ existing theories whilst taking a reflective or extensive route.
• Quantitative research aims to test the research hypothesis or answer established research questions.
• It is primarily justified by positivist or post-positivist research paradigms.
• The  research design can be relationship-based, quasi-experimental, experimental, or descriptive.
• It draws on a small sample to make generalisations to a wider population using probability sampling techniques.
• Quantitative data is gathered according to the established research questions using research vehicles such as structured observation, structured interviews, surveys, questionnaires, and laboratory results.
• The researcher uses  statistical analysis tools and techniques to measure variables and gather inferential or descriptive data. In some cases, your tutor or dissertation committee members might find it easier to verify your study results with numbers and statistical analysis.
• The study results’ accuracy is based on external and internal validity and authenticity of the data used.
• Quantitative research answers research questions or tests the hypothesis using charts, graphs, tables, data, and statements.
• It underpins  research questions or hypotheses and findings to make conclusions.
• The researcher can provide recommendations for future research and expand or test existing theories.

What is Qualitative Research?

Qualitative research is a type of scientific research where a researcher collects evidence to seek answers to a  question . It is associated with studying human behavior from an informative perspective. It aims at obtaining in-depth details of the problem.

As the term suggests,  qualitative research  is based on qualitative research methods, including participants’ observations, focus groups, and unstructured interviews.

Qualitative research is very different in nature when compared to quantitative research. It takes an established path towards the  research process , how  research questions  are set up, how existing theories are built upon, what research methods are employed, and how the  findings  are unveiled to the readers.

You may adopt conventional methods, including phenomenological research, narrative-based research, grounded theory research, ethnographies, case studies, and auto-ethnographies.

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Characteristics of Qualitative Research

Again, regardless of the chosen approach to qualitative research, your dissertation will have unique key features as listed below.

• The research questions that you aim to answer will expand or even change as the  dissertation writing process continues . This aspect of the research is typically known as an emergent design where the research objectives evolve with time.
• Qualitative research may use existing theories to cultivate new theoretical understandings or fall back on existing theories to support the research process. However, the original goal of testing a certain theoretical understanding remains the same.
• It can be based on various research models, such as critical theory, constructivism, and interpretivism.
• The chosen research design largely influences the analysis and discussion of results and the choices you make . Research design depends on the adopted research path: phenomenological research, narrative-based research, grounded theory-based research, ethnography, case study-based research, or auto-ethnography.
• Qualitative research answers research questions with theoretical sampling, where data gathered from the organisation or people are studied.
• It involves various research methods to gather qualitative data from participants belonging to the field of study. As indicated previously, some of the most notable qualitative research methods include participant observation, focus groups, and unstructured interviews.
• It incorporates an  inductive process where the researcher analyses and understands the data through his own eyes and judgments to identify concepts and themes that comprehensively depict the researched material.
• The key quality characteristics of qualitative research are transferability, conformity, confirmability, and reliability.
• Results and discussions are largely based on narratives, case study and personal experiences, which help detect inconsistencies, observations, processes, and ideas.
• Qualitative research discusses theoretical concepts obtained from the results whilst taking research questions and/or hypotheses to  draw general  conclusions .

Confused between qualitative and quantitative methods of data analysis? No idea what discourse and content analysis are?

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• Whether you want a full dissertation written or need help forming a dissertation proposal, we can help you with both.
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When to Use Qualitative and Quantitative Research Model?

• The research  title, research questions,  hypothesis , objectives, and study area generally determine the dissertation’s best research method.
• If the primary aim of your research is to test a hypothesis, validate an existing theory or perhaps measure some variables, then the quantitative research model will be the more appropriate choice because it might be easier for you to convince your supervisor or members of the dissertation committee with the use of statistics and numbers.
• On the other hand, oftentimes, statistics and a collection of numbers are not the answer, especially where there is a need to understand meanings, experiences, and beliefs.
• If your research questions or hypothesis can be better addressed through people’s observations and experiences, you should consider qualitative data.
• If you select an inappropriate research method, you will not prove your findings’ accuracy, and your dissertation will be pretty much meaningless. To prove that your research is authentic and reliable, choose a research method that best suits your study’s requirements.
• In the sections that follow, we explain the most commonly employed research methods for the dissertation, including quantitative, qualitative, and mixed research methods.

Now that you know the unique differences between quantitative and qualitative research methods, you may want to learn a bit about primary and secondary research methods.

Here is an article that will help you  distinguish between primary and secondary research  and decide whether you need to use quantitative and/or qualitative methods of primary research in your dissertation.

Alternatively, you can base your dissertation on secondary research, which is descriptive and explanatory.

Limitations of Quantitative and Qualitative Research

What is quantitative research?

What is qualitative research.

Qualitative research is a type of scientific research where a researcher collects evidence to seek answers to a question . It is associated with studying human behavior from an informative perspective. It aims at obtaining in-depth details of the problem.

Qualitative or quantitative, which research type should I use?

The research title, research questions, hypothesis , objectives, and study area generally determine the dissertation’s best research method.

You May Also Like

This article provides the key advantages of primary research over secondary research so you can make an informed decision.

Descriptive research is carried out to describe current issues, programs, and provides information about the issue through surveys and various fact-finding methods.

Ethnography is a type of research where a researcher observes the people in their natural environment. Here is all you need to know about ethnography.

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Qualitative and Quantitative Research: Differences and Similarities

Qualitative research and quantitative research are two complementary approaches for understanding the world around us.

Qualitative research collects non-numerical data , and the results are typically presented as written descriptions, photographs, videos, and/or sound recordings.

In contrast, quantitative research collects numerical data , and the results are typically presented in tables, graphs, and charts.

Debates about whether to use qualitative or quantitative research methods are common in the social sciences (i.e. anthropology, archaeology, economics, geography, history, law, linguistics, politics, psychology, sociology), which aim to understand a broad range of human conditions. Qualitative observations may be used to gain an understanding of unique situations, which may lead to quantitative research that aims to find commonalities.

Within the natural and physical sciences (i.e. physics, chemistry, geology, biology), qualitative observations often lead to a plethora of quantitative studies. For example, unusual observations through a microscope or telescope can immediately lead to counting and measuring. In other situations, meaningful numbers cannot immediately be obtained, and the qualitative research must stand on its own (e.g. The patient presented with an abnormally enlarged spleen (Figure 1), and complained of pain in the left shoulder.)

For both qualitative and quantitative research, the researcher's assumptions shape the direction of the study and thereby influence the results that can be obtained. Let's consider some prominent examples of qualitative and quantitative research, and how these two methods can complement each other.

Qualitative research example

In 1960, Jane Goodall started her decades-long study of chimpanzees in the wild at Gombe Stream National Park in Tanzania. Her work is an example of qualitative research that has fundamentally changed our understanding of non-human primates, and has influenced our understanding of other animals, their abilities, and their social interactions.

Dr. Goodall was by no means the first person to study non-human primates, but she took a highly unusual approach in her research. For example, she named individual chimpanzees instead of numbering them, and used terms such as "childhood", "adolescence", "motivation", "excitement", and "mood". She also described the distinct "personalities" of individual chimpanzees. Dr. Goodall was heavily criticized for describing chimpanzees in ways that are regularly used to describe humans, which perfectly illustrates how the assumptions of the researcher can heavily influence their work.

The quality of qualitative research is largely determined by the researcher's ability, knowledge, creativity, and interpretation of the results. One of the hallmarks of good qualitative research is that nothing is predefined or taken for granted, and that the study subjects teach the researcher about their lives. As a result, qualitative research studies evolve over time, and the focus or techniques used can shift as the study progresses.

Qualitative research methods

Dr. Goodall immersed herself in the chimpanzees' natural surroundings, and used direct observation to learn about their daily life. She used photographs, videos, sound recordings, and written descriptions to present her data. These are all well-established methods of qualitative research, with direct observation within the natural setting considered a gold standard. These methods are time-intensive for the researcher (and therefore monetarily expensive) and limit the number of individuals that can be studied at one time.

When studying humans, a wider variety of research methods are available to understand how people perceive and navigate their world—past or present. These techniques include: in-depth interviews (e.g. Can you discuss your experience of growing up in the Deep South in the 1950s?), open-ended survey questions (e.g. What do you enjoy most about being part of the Church of Latter Day Saints?), focus group discussions, researcher participation (e.g. in military training), review of written documents (e.g. social media accounts, diaries, school records, etc), and analysis of cultural records (e.g. anything left behind including trash, clothing, buildings, etc).

Qualitative research can lead to quantitative research

Qualitative research is largely exploratory. The goal is to gain a better understanding of an unknown situation. Qualitative research in humans may lead to a better understanding of underlying reasons, opinions, motivations, experiences, etc. The information generated through qualitative research can provide new hypotheses to test through quantitative research. Quantitative research studies are typically more focused and less exploratory, involve a larger sample size, and by definition produce numerical data.

Dr. Goodall's qualitative research clearly established periods of childhood and adolescence in chimpanzees. Quantitative studies could better characterize these time periods, for example by recording the amount of time individual chimpanzees spend with their mothers, with peers, or alone each day during childhood compared to adolescence.

For studies involving humans, quantitative data might be collected through a questionnaire with a limited number of answers (e.g. If you were being bullied, what is the likelihood that you would tell at least one parent? A) Very likely, B) Somewhat likely, C) Somewhat unlikely, D) Unlikely).

Quantitative research example

One of the most influential examples of quantitative research began with a simple qualitative observation: Some peas are round, and other peas are wrinkled. Gregor Mendel was not the first to make this observation, but he was the first to carry out rigorous quantitative experiments to better understand this characteristic of garden peas.

As described in his 1865 research paper, Mendel carried out carefully controlled genetic crosses and counted thousands of resulting peas. He discovered that the ratio of round peas to wrinkled peas matched the ratio expected if pea shape were determined by two copies of a gene for pea shape, one inherited from each parent. These experiments and calculations became the foundation of modern genetics, and Mendel's ratios became the default hypothesis for experiments involving thousands of different genes in hundreds of different organisms.

The quality of quantitative research is largely determined by the researcher's ability to design a feasible experiment, that will provide clear evidence to support or refute the working hypothesis. The hallmarks of good quantitative research include: a study that can be replicated by an independent group and produce similar results, a sample population that is representative of the population under study, a sample size that is large enough to reveal any expected statistical significance.

Quantitative research methods

The basic methods of quantitative research involve measuring or counting things (size, weight, distance, offspring, light intensity, participants, number of times a specific phrase is used, etc). In the social sciences especially, responses are often be split into somewhat arbitrary categories (e.g. How much time do you spend on social media during a typical weekday? A) 0-15 min, B) 15-30 min, C) 30-60 min, D) 1-2 hrs, E) more than 2 hrs).

These quantitative data can be displayed in a table, graph, or chart, and grouped in ways that highlight patterns and relationships. The quantitative data should also be subjected to mathematical and statistical analysis. To reveal overall trends, the average (or most common survey answer) and standard deviation can be determined for different groups (e.g. with treatment A and without treatment B).

Typically, the most important result from a quantitative experiment is the test of statistical significance. There are many different methods for determining statistical significance (e.g. t-test, chi square test, ANOVA, etc.), and the appropriate method will depend on the specific experiment.

Statistical significance provides an answer to the question: What is the probably that the difference observed between two groups is due to chance alone, and the two groups are actually the same? For example, your initial results might show that 32% of Friday grocery shoppers buy alcohol, while only 16% of Monday grocery shoppers buy alcohol. If this result reflects a true difference between Friday shoppers and Monday shoppers, grocery store managers might want to offer Friday specials to increase sales.

After the appropriate statistical test is conducted (which incorporates sample size and other variables), the probability that the observed difference is due to chance alone might be more than 5%, or less than 5%. If the probability is less than 5%, the convention is that the result is considered statistically significant. (The researcher is also likely to cheer and have at least a small celebration.) Otherwise, the result is considered statistically insignificant. (If the value is close to 5%, the researcher may try to group the data in different ways to achieve statistical significance. For example, by comparing alcohol sales after 5pm on Friday and Monday.) While it is important to reveal differences that may not be immediately obvious, the desire to manipulate information until it becomes statistically significant can also contribute to bias in research.

So how often do results from two groups that are actually the same give a probability of less than 5%? A bit less than 5% of the time (by definition). This is one of the reasons why it is so important that quantitative research can be replicated by different groups.

Which research method should I choose?

Choose the research methods that will allow you to produce the best results for a meaningful question, while acknowledging any unknowns and controlling for any bias. In many situations, this will involve a mixed methods approach. Qualitative research may allow you to learn about a poorly understood topic, and then quantitative research may allow you to obtain results that can be subjected to rigorous statistical tests to find true and meaningful patterns. Many different approaches are required to understand the complex world around us.

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Qualitative and Quantitative Research

In general, quantitative research seeks to understand the causal or correlational relationship between variables through testing hypotheses, whereas qualitative research seeks to understand a phenomenon within a real-world context through the use of interviews and observation. Both types of research are valid, and certain research topics are better suited to one approach or the other. However, it is important to understand the differences between qualitative and quantitative research so that you will be able to conduct an informed critique and analysis of any articles that you read, because you will understand the different advantages, disadvantages, and influencing factors for each approach. 

The table below illustrates the main differences between qualitative and quantitative research. Be aware that these are generalizations, and that not every research study or article will fit neatly into these categories. 

Systematic reviews, meta-analyses, and integrative reviews are not exactly designs, but they synthesize, analyze, and compare the results from many research studies and are somewhat quantitative in nature. However, they are not truly quantitative or qualitative studies.

References:

LoBiondo-Wood, G., & Haber, J. (2010). Nursing research: Methods and critical appraisal for evidence-based practice (7 th ed.). St. Louis, MO: Mosby Elsevier

Mertens, D. M. (2010). Research and evaluation in education and psychology (3 rd ed.). Los Angeles: SAGE

Quick Overview

This 2-minute video provides a simplified overview of the primary distinctions between quantitative and qualitative research.

It's Not Always One or the Other!

It's important to keep in mind that research studies and articles are not always 100% qualitative or 100% quantitative. A mixed methods study involves both qualitative and quantitative approaches. If you need to find articles that are purely qualitative or purely quanititative, be sure to look carefully at the methodology sections to make sure the studies did not utilize both methods. 

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• v.4(Suppl 1); 2019

Synthesising quantitative and qualitative evidence to inform guidelines on complex interventions: clarifying the purposes, designs and outlining some methods

1 School of Social Sciences, Bangor University, Wales, UK

Andrew Booth

2 School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK

Graham Moore

3 School of Social Sciences, Cardiff University, Wales, UK

Kate Flemming

4 Department of Health Sciences, The University of York, York, UK

Özge Tunçalp

5 Department of Reproductive Health and Research including UNDP/UNFPA/UNICEF/WHO/World Bank Special Programme of Research, Development and Research Training in Human Reproduction (HRP), World Health Organization, Geneva, Switzerland

Elham Shakibazadeh

6 Department of Health Education and Promotion, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran

Associated Data

bmjgh-2018-000893supp001.pdf

bmjgh-2018-000893supp002.pdf

bmjgh-2018-000893supp003.pdf

bmjgh-2018-000893supp005.pdf

bmjgh-2018-000893supp004.pdf

Guideline developers are increasingly dealing with more difficult decisions concerning whether to recommend complex interventions in complex and highly variable health systems. There is greater recognition that both quantitative and qualitative evidence can be combined in a mixed-method synthesis and that this can be helpful in understanding how complexity impacts on interventions in specific contexts. This paper aims to clarify the different purposes, review designs, questions, synthesis methods and opportunities to combine quantitative and qualitative evidence to explore the complexity of complex interventions and health systems. Three case studies of guidelines developed by WHO, which incorporated quantitative and qualitative evidence, are used to illustrate possible uses of mixed-method reviews and evidence. Additional examples of methods that can be used or may have potential for use in a guideline process are outlined. Consideration is given to the opportunities for potential integration of quantitative and qualitative evidence at different stages of the review and guideline process. Encouragement is given to guideline commissioners and developers and review authors to consider including quantitative and qualitative evidence. Recommendations are made concerning the future development of methods to better address questions in systematic reviews and guidelines that adopt a complexity perspective.

Summary box

• When combined in a mixed-method synthesis, quantitative and qualitative evidence can potentially contribute to understanding how complex interventions work and for whom, and how the complex health systems into which they are implemented respond and adapt.
• The different purposes and designs for combining quantitative and qualitative evidence in a mixed-method synthesis for a guideline process are described.
• Questions relevant to gaining an understanding of the complexity of complex interventions and the wider health systems within which they are implemented that can be addressed by mixed-method syntheses are presented.
• The practical methodological guidance in this paper is intended to help guideline producers and review authors commission and conduct mixed-method syntheses where appropriate.
• If more mixed-method syntheses are conducted, guideline developers will have greater opportunities to access this evidence to inform decision-making.

Introduction

Recognition has grown that while quantitative methods remain vital, they are usually insufficient to address complex health systems related research questions. 1 Quantitative methods rely on an ability to anticipate what must be measured in advance. Introducing change into a complex health system gives rise to emergent reactions, which cannot be fully predicted in advance. Emergent reactions can often only be understood through combining quantitative methods with a more flexible qualitative lens. 2 Adopting a more pluralist position enables a diverse range of research options to the researcher depending on the research question being investigated. 3–5 As a consequence, where a research study sits within the multitude of methods available is driven by the question being asked, rather than any particular methodological or philosophical stance. 6

Publication of guidance on designing complex intervention process evaluations and other works advocating mixed-methods approaches to intervention research have stimulated better quality evidence for synthesis. 1 7–13 Methods for synthesising qualitative 14 and mixed-method evidence have been developed or are in development. Mixed-method research and review definitions are outlined in box 1 .

Defining mixed-method research and reviews

Pluye and Hong 52 define mixed-methods research as “a research approach in which a researcher integrates (a) qualitative and quantitative research questions, (b) qualitative research methods* and quantitative research designs, (c) techniques for collecting and analyzing qualitative and quantitative evidence, and (d) qualitative findings and quantitative results”.A mixed-method synthesis can integrate quantitative, qualitative and mixed-method evidence or data from primary studies.† Mixed-method primary studies are usually disaggregated into quantitative and qualitative evidence and data for the purposes of synthesis. Thomas and Harden further define three ways in which reviews are mixed. 53

• The types of studies included and hence the type of findings to be synthesised (ie, qualitative/textual and quantitative/numerical).
• The types of synthesis method used (eg, statistical meta-analysis and qualitative synthesis).
• The mode of analysis: theory testing AND theory building.

*A qualitative study is one that uses qualitative methods of data collection and analysis to produce a narrative understanding of the phenomena of interest. Qualitative methods of data collection may include, for example, interviews, focus groups, observations and analysis of documents.

†The Cochrane Qualitative and Implementation Methods group coined the term ‘qualitative evidence synthesis’ to mean that the synthesis could also include qualitative data. For example, qualitative data from case studies, grey literature reports and open-ended questions from surveys. ‘Evidence’ and ‘data’ are used interchangeably in this paper.

This paper is one of a series that aims to explore the implications of complexity for systematic reviews and guideline development, commissioned by WHO. This paper is concerned with the methodological implications of including quantitative and qualitative evidence in mixed-method systematic reviews and guideline development for complex interventions. The guidance was developed through a process of bringing together experts in the field, literature searching and consensus building with end users (guideline developers, clinicians and reviewers). We clarify the different purposes, review designs, questions and synthesis methods that may be applicable to combine quantitative and qualitative evidence to explore the complexity of complex interventions and health systems. Three case studies of WHO guidelines that incorporated quantitative and qualitative evidence are used to illustrate possible uses of mixed-method reviews and mechanisms of integration ( table 1 , online supplementary files 1–3 ). Additional examples of methods that can be used or may have potential for use in a guideline process are outlined. Opportunities for potential integration of quantitative and qualitative evidence at different stages of the review and guideline process are presented. Specific considerations when using an evidence to decision framework such as the Developing and Evaluating Communication strategies to support Informed Decisions and practice based on Evidence (DECIDE) framework 15 or the new WHO-INTEGRATE evidence to decision framework 16 at the review design and evidence to decision stage are outlined. See online supplementary file 4 for an example of a health systems DECIDE framework and Rehfuess et al 16 for the new WHO-INTEGRATE framework. Encouragement is given to guideline commissioners and developers and review authors to consider including quantitative and qualitative evidence in guidelines of complex interventions that take a complexity perspective and health systems focus.

Designs and methods and their use or applicability in guidelines and systematic reviews taking a complexity perspective

Supplementary data

Taking a complexity perspective.

The first paper in this series 17 outlines aspects of complexity associated with complex interventions and health systems that can potentially be explored by different types of evidence, including synthesis of quantitative and qualitative evidence. Petticrew et al 17 distinguish between a complex interventions perspective and a complex systems perspective. A complex interventions perspective defines interventions as having “implicit conceptual boundaries, representing a flexible, but common set of practices, often linked by an explicit or implicit theory about how they work”. A complex systems perspective differs in that “ complexity arises from the relationships and interactions between a system’s agents (eg, people, or groups that interact with each other and their environment), and its context. A system perspective conceives the intervention as being part of the system, and emphasises changes and interconnections within the system itself”. Aspects of complexity associated with implementation of complex interventions in health systems that could potentially be addressed with a synthesis of quantitative and qualitative evidence are summarised in table 2 . Another paper in the series outlines criteria used in a new evidence to decision framework for making decisions about complex interventions implemented in complex systems, against which the need for quantitative and qualitative evidence can be mapped. 16 A further paper 18 that explores how context is dealt with in guidelines and reviews taking a complexity perspective also recommends using both quantitative and qualitative evidence to better understand context as a source of complexity. Mixed-method syntheses of quantitative and qualitative evidence can also help with understanding of whether there has been theory failure and or implementation failure. The Cochrane Qualitative and Implementation Methods Group provide additional guidance on exploring implementation and theory failure that can be adapted to address aspects of complexity of complex interventions when implemented in health systems. 19

Health-system complexity-related questions that a synthesis of quantitative and qualitative evidence could address (derived from Petticrew et al 17 )

It may not be apparent which aspects of complexity or which elements of the complex intervention or health system can be explored in a guideline process, or whether combining qualitative and quantitative evidence in a mixed-method synthesis will be useful, until the available evidence is scoped and mapped. 17 20 A more extensive lead in phase is typically required to scope the available evidence, engage with stakeholders and to refine the review parameters and questions that can then be mapped against potential review designs and methods of synthesis. 20 At the scoping stage, it is also common to decide on a theoretical perspective 21 or undertake further work to refine a theoretical perspective. 22 This is also the stage to begin articulating the programme theory of the complex intervention that may be further developed to refine an understanding of complexity and show how the intervention is implemented in and impacts on the wider health system. 17 23 24 In practice, this process can be lengthy, iterative and fluid with multiple revisions to the review scope, often developing and adapting a logic model 17 as the available evidence becomes known and the potential to incorporate different types of review designs and syntheses of quantitative and qualitative evidence becomes better understood. 25 Further questions, propositions or hypotheses may emerge as the reviews progress and therefore the protocols generally need to be developed iteratively over time rather than a priori.

Following a scoping exercise and definition of key questions, the next step in the guideline development process is to identify existing or commission new systematic reviews to locate and summarise the best available evidence in relation to each question. For example, case study 2, ‘Optimising health worker roles for maternal and newborn health through task shifting’, included quantitative reviews that did and did not take an additional complexity perspective, and qualitative evidence syntheses that were able to explain how specific elements of complexity impacted on intervention outcomes within the wider health system. Further understanding of health system complexity was facilitated through the conduct of additional country-level case studies that contributed to an overall understanding of what worked and what happened when lay health worker interventions were implemented. See table 1 online supplementary file 2 .

There are a few existing examples, which we draw on in this paper, but integrating quantitative and qualitative evidence in a mixed-method synthesis is relatively uncommon in a guideline process. Box 2 includes a set of key questions that guideline developers and review authors contemplating combining quantitative and qualitative evidence in mixed-methods design might ask. Subsequent sections provide more information and signposting to further reading to help address these key questions.

Key questions that guideline developers and review authors contemplating combining quantitative and qualitative evidence in a mixed-methods design might ask

Compound questions requiring both quantitative and qualitative evidence?

Questions requiring mixed-methods studies?

Separate quantitative and qualitative questions?

Separate quantitative and qualitative research studies?

Related quantitative and qualitative research studies?

Mixed-methods studies?

Quantitative unpublished data and/or qualitative unpublished data, eg, narrative survey data?

Throughout the review?

Following separate reviews?

At the question point?

At the synthesis point?

At the evidence to recommendations stage?

Or a combination?

Narrative synthesis or summary?

Quantitising approach, eg, frequency analysis?

Qualitising approach, eg, thematic synthesis?

Tabulation?

Logic model?

Conceptual model/framework?

Graphical approach?

• WHICH: Which mixed-method designs, methodologies and methods best fit into a guideline process to inform recommendations?

Complexity-related questions that a synthesis of quantitative and qualitative evidence can potentially address

Petticrew et al 17 define the different aspects of complexity and examples of complexity-related questions that can potentially be explored in guidelines and systematic reviews taking a complexity perspective. Relevant aspects of complexity outlined by Petticrew et al 17 are summarised in table 2 below, together with the corresponding questions that could be addressed in a synthesis combining qualitative and quantitative evidence. Importantly, the aspects of complexity and their associated concepts of interest have however yet to be translated fully in primary health research or systematic reviews. There are few known examples where selected complexity concepts have been used to analyse or reanalyse a primary intervention study. Most notable is Chandler et al 26 who specifically set out to identify and translate a set of relevant complexity theory concepts for application in health systems research. Chandler then reanalysed a trial process evaluation using selected complexity theory concepts to better understand the complex causal pathway in the health system that explains some aspects of complexity in table 2 .

Rehfeuss et al 16 also recommends upfront consideration of the WHO-INTEGRATE evidence to decision criteria when planning a guideline and formulating questions. The criteria reflect WHO norms and values and take account of a complexity perspective. The framework can be used by guideline development groups as a menu to decide which criteria to prioritise, and which study types and synthesis methods can be used to collect evidence for each criterion. Many of the criteria and their related questions can be addressed using a synthesis of quantitative and qualitative evidence: the balance of benefits and harms, human rights and sociocultural acceptability, health equity, societal implications and feasibility (see table 3 ). Similar aspects in the DECIDE framework 15 could also be addressed using synthesis of qualitative and quantitative evidence.

Integrate evidence to decision framework criteria, example questions and types of studies to potentially address these questions (derived from Rehfeuss et al 16 )

GIS, Geographical Information System; RCT, randomised controlled trial.

Questions as anchors or compasses

Questions can serve as an ‘anchor’ by articulating the specific aspects of complexity to be explored (eg, Is successful implementation of the intervention context dependent?). 27 Anchor questions such as “How does intervention x impact on socioeconomic inequalities in health behaviour/outcome x” are the kind of health system question that requires a synthesis of both quantitative and qualitative evidence and hence a mixed-method synthesis. Quantitative evidence can quantify the difference in effect, but does not answer the question of how . The ‘how’ question can be partly answered with quantitative and qualitative evidence. For example, quantitative evidence may reveal where socioeconomic status and inequality emerges in the health system (an emergent property) by exploring questions such as “ Does patterning emerge during uptake because fewer people from certain groups come into contact with an intervention in the first place? ” or “ are people from certain backgrounds more likely to drop out, or to maintain effects beyond an intervention differently? ” Qualitative evidence may help understand the reasons behind all of these mechanisms. Alternatively, questions can act as ‘compasses’ where a question sets out a starting point from which to explore further and to potentially ask further questions or develop propositions or hypotheses to explore through a complexity perspective (eg, What factors enhance or hinder implementation?). 27 Other papers in this series provide further guidance on developing questions for qualitative evidence syntheses and guidance on question formulation. 14 28

For anchor and compass questions, additional application of a theory (eg, complexity theory) can help focus evidence synthesis and presentation to explore and explain complexity issues. 17 21 Development of a review specific logic model(s) can help to further refine an initial understanding of any complexity-related issues of interest associated with a specific intervention, and if appropriate the health system or section of the health system within which to contextualise the review question and analyse data. 17 23–25 Specific tools are available to help clarify context and complex interventions. 17 18

If a complexity perspective, and certain criteria within evidence to decision frameworks, is deemed relevant and desirable by guideline developers, it is only possible to pursue a complexity perspective if the evidence is available. Careful scoping using knowledge maps or scoping reviews will help inform development of questions that are answerable with available evidence. 20 If evidence of effect is not available, then a different approach to develop questions leading to a more general narrative understanding of what happened when complex interventions were implemented in a health system will be required (such as in case study 3—risk communication guideline). This should not mean that the original questions developed for which no evidence was found when scoping the literature were not important. An important function of creating a knowledge map is also to identify gaps to inform a future research agenda.

Table 2 and online supplementary files 1–3 outline examples of questions in the three case studies, which were all ‘COMPASS’ questions for the qualitative evidence syntheses.

Types of integration and synthesis designs in mixed-method reviews

The shift towards integration of qualitative and quantitative evidence in primary research has, in recent years, begun to be mirrored within research synthesis. 29–31 The natural extension to undertaking quantitative or qualitative reviews has been the development of methods for integrating qualitative and quantitative evidence within reviews, and within the guideline process using evidence to decision-frameworks. Advocating the integration of quantitative and qualitative evidence assumes a complementarity between research methodologies, and a need for both types of evidence to inform policy and practice. Below, we briefly outline the current designs for integrating qualitative and quantitative evidence within a mixed-method review or synthesis.

One of the early approaches to integrating qualitative and quantitative evidence detailed by Sandelowski et al 32 advocated three basic review designs: segregated, integrated and contingent designs, which have been further developed by Heyvaert et al 33 ( box 3 ).

Segregated, integrated and contingent designs 32 33

Segregated design.

Conventional separate distinction between quantitative and qualitative approaches based on the assumption they are different entities and should be treated separately; can be distinguished from each other; their findings warrant separate analyses and syntheses. Ultimately, the separate synthesis results can themselves be synthesised.

Integrated design

The methodological differences between qualitative and quantitative studies are minimised as both are viewed as producing findings that can be readily synthesised into one another because they address the same research purposed and questions. Transformation involves either turning qualitative data into quantitative (quantitising) or quantitative findings are turned into qualitative (qualitising) to facilitate their integration.

Contingent design

Takes a cyclical approach to synthesis, with the findings from one synthesis informing the focus of the next synthesis, until all the research objectives have been addressed. Studies are not necessarily grouped and categorised as qualitative or quantitative.

A recent review of more than 400 systematic reviews 34 combining quantitative and qualitative evidence identified two main synthesis designs—convergent and sequential. In a convergent design, qualitative and quantitative evidence is collated and analysed in a parallel or complementary manner, whereas in a sequential synthesis, the collation and analysis of quantitative and qualitative evidence takes place in a sequence with one synthesis informing the other ( box 4 ). 6 These designs can be seen to build on the work of Sandelowski et al , 32 35 particularly in relation to the transformation of data from qualitative to quantitative (and vice versa) and the sequential synthesis design, with a cyclical approach to reviewing that evokes Sandelowski’s contingent design.

Convergent and sequential synthesis designs 34

Convergent synthesis design.

Qualitative and quantitative research is collected and analysed at the same time in a parallel or complementary manner. Integration can occur at three points:

a. Data-based convergent synthesis design

All included studies are analysed using the same methods and results presented together. As only one synthesis method is used, data transformation occurs (qualitised or quantised). Usually addressed one review question.

b. Results-based convergent synthesis design

Qualitative and quantitative data are analysed and presented separately but integrated using a further synthesis method; eg, narratively, tables, matrices or reanalysing evidence. The results of both syntheses are combined in a third synthesis. Usually addresses an overall review question with subquestions.

c. Parallel-results convergent synthesis design

Qualitative and quantitative data are analysed and presented separately with integration occurring in the interpretation of results in the discussion section. Usually addresses two or more complimentary review questions.

Sequential synthesis design

A two-phase approach, data collection and analysis of one type of evidence (eg, qualitative), occurs after and is informed by the collection and analysis of the other type (eg, quantitative). Usually addresses an overall question with subquestions with both syntheses complementing each other.

The three case studies ( table 1 , online supplementary files 1–3 ) illustrate the diverse combination of review designs and synthesis methods that were considered the most appropriate for specific guidelines.

Methods for conducting mixed-method reviews in the context of guidelines for complex interventions

In this section, we draw on examples where specific review designs and methods have been or can be used to explore selected aspects of complexity in guidelines or systematic reviews. We also identify other review methods that could potentially be used to explore aspects of complexity. Of particular note, we could not find any specific examples of systematic methods to synthesise highly diverse research designs as advocated by Petticrew et al 17 and summarised in tables 2 and 3 . For example, we could not find examples of methods to synthesise qualitative studies, case studies, quantitative longitudinal data, possibly historical data, effectiveness studies providing evidence of differential effects across different contexts, and system modelling studies (eg, agent-based modelling) to explore system adaptivity.

There are different ways that quantitative and qualitative evidence can be integrated into a review and then into a guideline development process. In practice, some methods enable integration of different types of evidence in a single synthesis, while in other methods, the single systematic review may include a series of stand-alone reviews or syntheses that are then combined in a cross-study synthesis. Table 1 provides an overview of the characteristics of different review designs and methods and guidance on their applicability for a guideline process. Designs and methods that have already been used in WHO guideline development are described in part A of the table. Part B outlines a design and method that can be used in a guideline process, and part C covers those that have the potential to integrate quantitative, qualitative and mixed-method evidence in a single review design (such as meta-narrative reviews and Bayesian syntheses), but their application in a guideline context has yet to be demonstrated.

Points of integration when integrating quantitative and qualitative evidence in guideline development

Depending on the review design (see boxes 3 and 4 ), integration can potentially take place at a review team and design level, and more commonly at several key points of the review or guideline process. The following sections outline potential points of integration and associated practical considerations when integrating quantitative and qualitative evidence in guideline development.

Review team level

In a guideline process, it is common for syntheses of quantitative and qualitative evidence to be done separately by different teams and then to integrate the evidence. A practical consideration relates to the organisation, composition and expertise of the review teams and ways of working. If the quantitative and qualitative reviews are being conducted separately and then brought together by the same team members, who are equally comfortable operating within both paradigms, then a consistent approach across both paradigms becomes possible. If, however, a team is being split between the quantitative and qualitative reviews, then the strengths of specialisation can be harnessed, for example, in quality assessment or synthesis. Optimally, at least one, if not more, of the team members should be involved in both quantitative and qualitative reviews to offer the possibility of making connexions throughout the review and not simply at re-agreed junctures. This mirrors O’Cathain’s conclusion that mixed-methods primary research tends to work only when there is a principal investigator who values and is able to oversee integration. 9 10 While the above decisions have been articulated in the context of two types of evidence, variously quantitative and qualitative, they equally apply when considering how to handle studies reporting a mixed-method study design, where data are usually disaggregated into quantitative and qualitative for the purposes of synthesis (see case study 3—risk communication in humanitarian disasters).

Question formulation

Clearly specified key question(s), derived from a scoping or consultation exercise, will make it clear if quantitative and qualitative evidence is required in a guideline development process and which aspects will be addressed by which types of evidence. For the remaining stages of the process, as documented below, a review team faces challenges as to whether to handle each type of evidence separately, regardless of whether sequentially or in parallel, with a view to joining the two products on completion or to attempt integration throughout the review process. In each case, the underlying choice is of efficiencies and potential comparability vs sensitivity to the underlying paradigm.

Once key questions are clearly defined, the guideline development group typically needs to consider whether to conduct a single sensitive search to address all potential subtopics (lumping) or whether to conduct specific searches for each subtopic (splitting). 36 A related consideration is whether to search separately for qualitative, quantitative and mixed-method evidence ‘streams’ or whether to conduct a single search and then identify specific study types at the subsequent sifting stage. These two considerations often mean a trade-off between a single search process involving very large numbers of records or a more protracted search process retrieving smaller numbers of records. Both approaches have advantages and choice may depend on the respective availability of resources for searching and sifting.

Screening and selecting studies

Closely related to decisions around searching are considerations relating to screening and selecting studies for inclusion in a systematic review. An important consideration here is whether the review team will screen records for all review types, regardless of their subsequent involvement (‘altruistic sifting’), or specialise in screening for the study type with which they are most familiar. The risk of missing relevant reports might be minimised by whole team screening for empirical reports in the first instance and then coding them for a specific quantitative, qualitative or mixed-methods report at a subsequent stage.

Assessment of methodological limitations in primary studies

Within a guideline process, review teams may be more limited in their choice of instruments to assess methodological limitations of primary studies as there are mandatory requirements to use the Cochrane risk of bias tool 37 to feed into Grading of Recommendations Assessment, Development and Evaluation (GRADE) 38 or to select from a small pool of qualitative appraisal instruments in order to apply GRADE; Confidence in the Evidence from Reviews of Qualitative Research (GRADE-CERQual) 39 to assess the overall certainty or confidence in findings. The Cochrane Qualitative and Implementation Methods Group has recently issued guidance on the selection of appraisal instruments and core assessment criteria. 40 The Mixed-Methods Appraisal Tool, which is currently undergoing further development, offers a single quality assessment instrument for quantitative, qualitative and mixed-methods studies. 41 Other options include using corresponding instruments from within the same ‘stable’, for example, using different Critical Appraisal Skills Programme instruments. 42 While using instruments developed by the same team or organisation may achieve a degree of epistemological consonance, benefits may come more from consistency of approach and reporting rather than from a shared view of quality. Alternatively, a more paradigm-sensitive approach would involve selecting the best instrument for each respective review while deferring challenges from later heterogeneity of reporting.

Data extraction

The way in which data and evidence are extracted from primary research studies for review will be influenced by the type of integrated synthesis being undertaken and the review purpose. Initially, decisions need to be made regarding the nature and type of data and evidence that are to be extracted from the included studies. Method-specific reporting guidelines 43 44 provide a good template as to what quantitative and qualitative data it is potentially possible to extract from different types of method-specific study reports, although in practice reporting quality varies. Online supplementary file 5 provides a hypothetical example of the different types of studies from which quantitative and qualitative evidence could potentially be extracted for synthesis.

The decisions around what data or evidence to extract will be guided by how ‘integrated’ the mixed-method review will be. For those reviews where the quantitative and qualitative findings of studies are synthesised separately and integrated at the point of findings (eg, segregated or contingent approaches or sequential synthesis design), separate data extraction approaches will likely be used.

Where integration occurs during the process of the review (eg, integrated approach or convergent synthesis design), an integrated approach to data extraction may be considered, depending on the purpose of the review. This may involve the use of a data extraction framework, the choice of which needs to be congruent with the approach to synthesis chosen for the review. 40 45 The integrative or theoretical framework may be decided on a priori if a pre-developed theoretical or conceptual framework is available in the literature. 27 The development of a framework may alternatively arise from the reading of the included studies, in relation to the purpose of the review, early in the process. The Cochrane Qualitative and Implementation Methods Group provide further guidance on extraction of qualitative data, including use of software. 40

Synthesis and integration

Relatively few synthesis methods start off being integrated from the beginning, and these methods have generally been subject to less testing and evaluation particularly in a guideline context (see table 1 ). A review design that started off being integrated from the beginning may be suitable for some guideline contexts (such as in case study 3—risk communication in humanitarian disasters—where there was little evidence of effect), but in general if there are sufficient trials then a separate systematic review and meta-analysis will be required for a guideline. Other papers in this series offer guidance on methods for synthesising quantitative 46 and qualitative evidence 14 in reviews that take a complexity perspective. Further guidance on integrating quantitative and qualitative evidence in a systematic review is provided by the Cochrane Qualitative and Implementation Methods Group. 19 27 29 40 47

Types of findings produced by specific methods

It is highly likely (unless there are well-designed process evaluations) that the primary studies may not themselves seek to address the complexity-related questions required for a guideline process. In which case, review authors will need to configure the available evidence and transform the evidence through the synthesis process to produce explanations, propositions and hypotheses (ie, findings) that were not obvious at primary study level. It is important that guideline commissioners, developers and review authors are aware that specific methods are intended to produce a type of finding with a specific purpose (such as developing new theory in the case of meta-ethnography). 48 Case study 1 (antenatal care guideline) provides an example of how a meta-ethnography was used to develop a new theory as an end product, 48 49 as well as framework synthesis which produced descriptive and explanatory findings that were more easily incorporated into the guideline process. 27 The definitions ( box 5 ) may be helpful when defining the different types of findings.

Different levels of findings

Descriptive findings —qualitative evidence-driven translated descriptive themes that do not move beyond the primary studies.

Explanatory findings —may either be at a descriptive or theoretical level. At the descriptive level, qualitative evidence is used to explain phenomena observed in quantitative results, such as why implementation failed in specific circumstances. At the theoretical level, the transformed and interpreted findings that go beyond the primary studies can be used to explain the descriptive findings. The latter description is generally the accepted definition in the wider qualitative community.

Hypothetical or theoretical finding —qualitative evidence-driven transformed themes (or lines of argument) that go beyond the primary studies. Although similar, Thomas and Harden 56 make a distinction in the purposes between two types of theoretical findings: analytical themes and the product of meta-ethnographies, third-order interpretations. 48

Analytical themes are a product of interrogating descriptive themes by placing the synthesis within an external theoretical framework (such as the review question and subquestions) and are considered more appropriate when a specific review question is being addressed (eg, in a guideline or to inform policy). 56

Third-order interpretations come from translating studies into one another while preserving the original context and are more appropriate when a body of literature is being explored in and of itself with broader or emergent review questions. 48

Bringing mixed-method evidence together in evidence to decision (EtD) frameworks

A critical element of guideline development is the formulation of recommendations by the Guideline Development Group, and EtD frameworks help to facilitate this process. 16 The EtD framework can also be used as a mechanism to integrate and display quantitative and qualitative evidence and findings mapped against the EtD framework domains with hyperlinks to more detailed evidence summaries from contributing reviews (see table 1 ). It is commonly the EtD framework that enables the findings of the separate quantitative and qualitative reviews to be brought together in a guideline process. Specific challenges when populating the DECIDE evidence to decision framework 15 were noted in case study 3 (risk communication in humanitarian disasters) as there was an absence of intervention effect data and the interventions to communicate public health risks were context specific and varied. These problems would not, however, have been addressed by substitution of the DECIDE framework with the new INTEGRATE 16 evidence to decision framework. A d ifferent type of EtD framework needs to be developed for reviews that do not include sufficient evidence of intervention effect.

Mixed-method review and synthesis methods are generally the least developed of all systematic review methods. It is acknowledged that methods for combining quantitative and qualitative evidence are generally poorly articulated. 29 50 There are however some fairly well-established methods for using qualitative evidence to explore aspects of complexity (such as contextual, implementation and outcome complexity), which can be combined with evidence of effect (see sections A and B of table 1 ). 14 There are good examples of systematic reviews that use these methods to combine quantitative and qualitative evidence, and examples of guideline recommendations that were informed by evidence from both quantitative and qualitative reviews (eg, case studies 1–3). With the exception of case study 3 (risk communication), the quantitative and qualitative reviews for these specific guidelines have been conducted separately, and the findings subsequently brought together in an EtD framework to inform recommendations.

Other mixed-method review designs have potential to contribute to understanding of complex interventions and to explore aspects of wider health systems complexity but have not been sufficiently developed and tested for this specific purpose, or used in a guideline process (section C of table 1 ). Some methods such as meta-narrative reviews also explore different questions to those usually asked in a guideline process. Methods for processing (eg, quality appraisal) and synthesising the highly diverse evidence suggested in tables 2 and 3 that are required to explore specific aspects of health systems complexity (such as system adaptivity) and to populate some sections of the INTEGRATE EtD framework remain underdeveloped or in need of development.

In addition to the required methodological development mentioned above, there is no GRADE approach 38 for assessing confidence in findings developed from combined quantitative and qualitative evidence. Another paper in this series outlines how to deal with complexity and grading different types of quantitative evidence, 51 and the GRADE CERQual approach for qualitative findings is described elsewhere, 39 but both these approaches are applied to method-specific and not mixed-method findings. An unofficial adaptation of GRADE was used in the risk communication guideline that reported mixed-method findings. Nor is there a reporting guideline for mixed-method reviews, 47 and for now reports will need to conform to the relevant reporting requirements of the respective method-specific guideline. There is a need to further adapt and test DECIDE, 15 WHO-INTEGRATE 16 and other types of evidence to decision frameworks to accommodate evidence from mixed-method syntheses which do not set out to determine the statistical effects of interventions and in circumstances where there are no trials.

When conducting quantitative and qualitative reviews that will subsequently be combined, there are specific considerations for managing and integrating the different types of evidence throughout the review process. We have summarised different options for combining qualitative and quantitative evidence in mixed-method syntheses that guideline developers and systematic reviewers can choose from, as well as outlining the opportunities to integrate evidence at different stages of the review and guideline development process.

Review commissioners, authors and guideline developers generally have less experience of combining qualitative and evidence in mixed-methods reviews. In particular, there is a relatively small group of reviewers who are skilled at undertaking fully integrated mixed-method reviews. Commissioning additional qualitative and mixed-method reviews creates an additional cost. Large complex mixed-method reviews generally take more time to complete. Careful consideration needs to be given as to which guidelines would benefit most from additional qualitative and mixed-method syntheses. More training is required to develop capacity and there is a need to develop processes for preparing the guideline panel to consider and use mixed-method evidence in their decision-making.

This paper has presented how qualitative and quantitative evidence, combined in mixed-method reviews, can help understand aspects of complex interventions and the systems within which they are implemented. There are further opportunities to use these methods, and to further develop the methods, to look more widely at additional aspects of complexity. There is a range of review designs and synthesis methods to choose from depending on the question being asked or the questions that may emerge during the conduct of the synthesis. Additional methods need to be developed (or existing methods further adapted) in order to synthesise the full range of diverse evidence that is desirable to explore the complexity-related questions when complex interventions are implemented into health systems. We encourage review commissioners and authors, and guideline developers to consider using mixed-methods reviews and synthesis in guidelines and to report on their usefulness in the guideline development process.

Handling editor: Soumyadeep Bhaumik

Contributors: JN, AB, GM, KF, ÖT and ES drafted the manuscript. All authors contributed to paper development and writing and agreed the final manuscript. Anayda Portela and Susan Norris from WHO managed the series. Helen Smith was series Editor. We thank all those who provided feedback on various iterations.

Funding: Funding provided by the World Health Organization Department of Maternal, Newborn, Child and Adolescent Health through grants received from the United States Agency for International Development and the Norwegian Agency for Development Cooperation.

Disclaimer: ÖT is a staff member of WHO. The author alone is responsible for the views expressed in this publication and they do not necessarily represent the decisions or policies of WHO.

Competing interests: No financial interests declared. JN, AB and ÖT have an intellectual interest in GRADE CERQual; and JN has an intellectual interest in the iCAT_SR tool.

Patient consent: Not required.

Provenance and peer review: Not commissioned; externally peer reviewed.

Data sharing statement: No additional data are available.

Supplemental material: This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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Understanding Qualitative and Quantitative Data

• 7 minute read
• August 22, 2024

Written by:

Smith Alex is a committed data enthusiast and an aspiring leader in the domain of data analytics. With a foundation in engineering and practical experience in the field of data science

Summary: This article delves into qualitative and quantitative data, defining each type and highlighting their key differences. It discusses when to use each data type, the benefits of integrating both, and the challenges researchers face. Understanding these concepts is crucial for effective research design and achieving comprehensive insights.

Introduction

In the realm of research and Data Analysis , two fundamental types of data play pivotal roles: qualitative and quantitative data. Understanding the distinctions between these two categories is essential for researchers, analysts, and decision-makers alike, as each type serves different purposes and is suited to various contexts.

This article will explore the definitions, characteristics, uses, and challenges associated with both qualitative and quantitative data, providing a comprehensive overview for anyone looking to enhance their understanding of data collection and analysis.

Read More:   Exploring 5 Statistical Data Analysis Techniques with Real-World Examples

Defining Qualitative Data

Qualitative data is non-numerical in nature and is primarily concerned with understanding the qualities, characteristics, and attributes of a subject.

This type of data is descriptive and often involves collecting information through methods such as interviews, focus groups, observations, and open-ended survey questions. The goal of qualitative data is to gain insights into the underlying motivations, opinions, and experiences of individuals or groups.

Characteristics of Qualitative Data

• Descriptive : Qualitative data provides rich, detailed descriptions of phenomena, allowing researchers to capture the complexity of human experiences.
• Subjective : The interpretation of qualitative data can vary based on the researcher’s perspective, making it inherently subjective.
• Contextual : This type of data is often context-dependent, meaning that the insights gained can be influenced by the environment or situation in which the data was collected.
• Exploratory : Qualitative data is typically used in exploratory research to generate hypotheses or to understand phenomena that are not well understood.

Examples of Qualitative Data

• Interview transcripts that capture participants’ thoughts and feelings.
• Observational notes from field studies.
• Responses to open-ended questions in surveys.
• Personal narratives or case studies that illustrate individual experiences.

Defining Quantitative Data

Quantitative data, in contrast, is numerical and can be measured or counted. This type of data is often used to quantify variables and analyse relationships between them. Quantitative research typically employs statistical methods to test hypotheses, identify patterns, and make predictions based on numerical data.

Characteristics of Quantitative Data

• Objective : Quantitative data is generally considered more objective than qualitative data, as it relies on measurable values that can be statistically analysed.
• Structured : This type of data is often collected using structured methods such as surveys with closed-ended questions, experiments, or observational checklists.
• Generalizable : Because quantitative data is based on numerical values, findings can often be generalised to larger populations if the sample is representative.
• Statistical Analysis : Quantitative data lends itself to various statistical analyses , allowing researchers to draw conclusions based on numerical evidence.

Examples of Quantitative Data

• Age, height, and weight measurements.
• Survey results with numerical ratings (e.g., satisfaction scores).
• Test scores or academic performance metrics.
• Financial data such as income, expenses, and profit margins.

Key Differences Between Qualitative and Quantitative Data

Understanding the differences between qualitative and quantitative data is crucial for selecting the appropriate research methods and analysis techniques. Here are some key distinctions:

When to Use Qualitative Data

Qualitative data is particularly useful in situations where the research aims to explore complex phenomena, understand human behaviour, or generate new theories. Here are some scenarios where qualitative data is the preferred choice:

Exploratory Research

When investigating a new area of study where little is known, qualitative methods can help uncover insights and generate hypotheses.

Understanding Context

Qualitative data is valuable for capturing the context surrounding a particular phenomenon, providing depth to the analysis.

Gaining Insights into Attitudes and Behaviours

When the goal is to understand why individuals think or behave in a certain way, qualitative methods such as interviews can provide rich, nuanced insights.

Developing Theories

Qualitative research can help in the development of theories by exploring relationships and patterns that quantitative methods may overlook.

When to Use Quantitative Data

Quantitative data is best suited for research that requires measurement, comparison, and statistical analysis. Here are some situations where quantitative data is the preferred choice:

Testing Hypotheses

When researchers have specific hypotheses to test , quantitative methods allow for rigorous statistical analysis to confirm or reject these hypotheses.

Measuring Variables

Quantitative data is ideal for measuring variables and establishing relationships between them, making it useful for experiments and surveys.

Generalising Findings

When the goal is to generalise findings to a larger population, quantitative research provides the necessary data to support such conclusions.

Identifying Patterns and Trends

Quantitative analysis can reveal patterns and trends in data that can inform decision-making and policy development.

Integrating Qualitative and Quantitative Data

While qualitative and quantitative data are distinct, they can be effectively integrated to provide a more comprehensive understanding of a research question. This mixed-methods approach combines the strengths of both types of data, allowing researchers to triangulate findings and gain deeper insights.

Benefits of Integration

Integrating qualitative and quantitative data enhances research by combining numerical analysis with rich, descriptive insights. This mixed-methods approach allows for a comprehensive understanding of complex phenomena, validating findings and providing a more nuanced perspective on research questions.

• Enhanced Validity: By using both qualitative and quantitative data, researchers can validate their findings through multiple sources of evidence.
• Rich Insights : Qualitative data can provide context and depth to quantitative findings, helping to explain the “why” behind numerical trends.
• Comprehensive Understanding: Integrating both types of data allows for a more holistic understanding of complex phenomena, leading to more informed conclusions and recommendations.

Examples of Integration

• Surveys with Open-Ended Questions: Combining closed-ended questions (quantitative) with open-ended questions (qualitative) in surveys can provide both measurable data and rich descriptive insights.
• Case Studies with Statistical Analysis: Researchers can conduct case studies (qualitative) while also collecting quantitative data to support their findings, offering a more robust analysis.
• Focus Groups with Follow-Up Surveys: After conducting focus groups (qualitative), researchers can administer surveys (quantitative) to a larger population to validate the insights gained.

Challenges and Considerations

While qualitative and quantitative data offer distinct advantages, researchers must also be aware of the challenges and considerations associated with each type:

Challenges of Qualitative Data

The challenges of qualitative data are multifaceted and can significantly impact the research process. Here are some of the primary challenges faced by researchers when working with qualitative data:

Subjectivity and Bias

One of the most significant challenges in qualitative research is the inherent subjectivity involved in data collection and analysis. Researchers’ personal beliefs, assumptions, and experiences can influence their interpretation of data.

Data Overload

Qualitative research often generates large volumes of data, which can be overwhelming. This data overload can make it challenging to identify key themes and insights. Researchers may struggle to manage and analyse vast amounts of qualitative data, leading to potential insights being overlooked.

Lack of Structure

Qualitative data is often unstructured, making it difficult to analyse systematically. The absence of a predefined format can lead to challenges in drawing meaningful conclusions from the data.

Time-Consuming Nature

Qualitative analysis can be extremely time-consuming, especially when dealing with extensive data sets. The process of collecting, transcribing, and analysing qualitative data often requires significant time and resources, which can be a barrier for researchers.

Challenges of Quantitative Data

Quantitative data provides objective, measurable evidence, it also faces challenges in capturing the full complexity of human experiences, maintaining data accuracy, and avoiding misinterpretation of statistical results. Integrating qualitative data can help overcome some of these limitations.

Limits in Capturing Complexity

Quantitative data, by its nature, can oversimplify complex phenomena and miss important nuances that qualitative data can capture. The focus on numerical measurements may not fully reflect the depth and richness of human experiences and behaviours.

Chances for Misinterpretation

Numbers can be twisted or misinterpreted if not analysed properly. Researchers must be cautious in interpreting statistical results, as correlation does not imply causation. Poor knowledge of statistical analysis can negatively impact the analysis and interpretation of quantitative data.

Influence of Measurement Errors

Due to the numerical nature of quantitative data, even small measurement errors can skew the entire dataset. Inaccuracies in data collection methods can lead to drawing incorrect conclusions from the analysis.

Lack of Context

Quantitative experiments often do not take place in natural settings. The data may lack the context and nuance that qualitative data can provide to fully explain the phenomena being studied.

Sample Size Limitations

Small sample sizes in quantitative studies can reduce the reliability of the data. Large sample sizes are needed for more accurate statistical analysis. This also affects the ability to generalise findings to wider populations.

Confirmation Bias

Researchers may miss observing important phenomena due to their focus on testing pre-determined hypotheses rather than generating new theories. The confirmation bias inherent in hypothesis testing can limit the discovery of unexpected insights.

In conclusion, understanding the distinctions between qualitative and quantitative data is essential for effective research and Data Analysis . Each type of data serves unique purposes and is suited to different contexts, making it crucial for researchers to select the appropriate methods based on their research objectives.

By integrating both qualitative and quantitative data, researchers can gain a more comprehensive understanding of complex phenomena, leading to richer insights and more informed decision-making.

As the landscape of research continues to evolve, the ability to effectively utilise and integrate both types of data will remain a valuable skill for researchers and analysts alike.

Frequently Asked Questions

What is the primary difference between qualitative and quantitative data.

The primary difference is that qualitative data is descriptive and non-numerical, focusing on understanding qualities and experiences, while quantitative data is numerical and measurable, focusing on quantifying variables and testing hypotheses.

When Should I Use Qualitative Data in My Research?

Qualitative data is best used when exploring new topics, understanding complex behaviours, or generating hypotheses, particularly when context and depth are important.

Can Qualitative and Quantitative Data Be Used Together?

Yes, integrating qualitative and quantitative data can provide a more comprehensive understanding of a research question, allowing researchers to validate findings and gain richer insights.

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Progress in remote sensing and gis-based fdi research based on quantitative and qualitative analysis.

1. Introduction

2. research methods and data, 2.1. research methods, 2.2. data sources and screening, 2.3. data processing, 3. subject categories and publication trends, 3.1. subject evolution, 3.2. trends in the number and cited times of published papers, 4. the intellectual structure, 4.1. quantitative analysis, 4.2. qualitative analysis, 4.2.1. macro-environmental research at national, regional, and city scales, 4.2.2. global industrial development and layout, 4.2.3. research on global value chains, 4.2.4. micro-information geography of tncs, 4.2.5. internationalization and commercialization of geo-information industry, 4.2.6. multiple data and interdisciplinary approaches, 5. discussions and conclusions, data availability statement, acknowledgments, conflicts of interest.

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Li, Z. Progress in Remote Sensing and GIS-Based FDI Research Based on Quantitative and Qualitative Analysis. Land 2024 , 13 , 1313. https://doi.org/10.3390/land13081313

Li Z. Progress in Remote Sensing and GIS-Based FDI Research Based on Quantitative and Qualitative Analysis. Land . 2024; 13(8):1313. https://doi.org/10.3390/land13081313

Li, Zifeng. 2024. "Progress in Remote Sensing and GIS-Based FDI Research Based on Quantitative and Qualitative Analysis" Land 13, no. 8: 1313. https://doi.org/10.3390/land13081313

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