how do quantitative and qualitative research work together

Qualitative vs Quantitative Research Methods & Data Analysis

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

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

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

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

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

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

• Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed numerically. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.
• Qualitative research gathers non-numerical data (words, images, sounds) to explore subjective experiences and attitudes, often via observation and interviews. It aims to produce detailed descriptions and uncover new insights about the studied phenomenon.

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What Is Qualitative Research?

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

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

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

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

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

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

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

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

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

Qualitative Methods

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

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

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

Here are some examples of qualitative data:

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

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

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

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

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

Qualitative Data Analysis

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

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

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

Key Features

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

Limitations of Qualitative Research

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

Advantages of Qualitative Research

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

What Is Quantitative Research?

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

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

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

Quantitative Methods

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

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

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

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

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

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

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

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

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

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

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

Quantitative Data Analysis

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

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

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

Limitations of Quantitative Research

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

Advantages of Quantitative Research

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

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

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

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

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

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

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

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

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

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

Further Information

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

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Three techniques for integrating data in mixed methods studies

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• Peer review
• Alicia O’Cathain , professor 1 ,
• Elizabeth Murphy , professor 2 ,
• Jon Nicholl , professor 1
• 1 Medical Care Research Unit, School of Health and Related Research, University of Sheffield, Sheffield S1 4DA, UK
• 2 University of Leicester, Leicester, UK
• Correspondence to: A O’Cathain a.ocathain{at}sheffield.ac.uk
• Accepted 8 June 2010

Techniques designed to combine the results of qualitative and quantitative studies can provide researchers with more knowledge than separate analysis

Health researchers are increasingly using designs that combine qualitative and quantitative methods, and this is often called mixed methods research. 1 Integration—the interaction or conversation between the qualitative and quantitative components of a study—is an important aspect of mixed methods research, and, indeed, is essential to some definitions. 2 Recent empirical studies of mixed methods research in health show, however, a lack of integration between components, 3 4 which limits the amount of knowledge that these types of studies generate. Without integration, the knowledge yield is equivalent to that from a qualitative study and a quantitative study undertaken independently, rather than achieving a “whole greater than the sum of the parts.” 5

Barriers to integration have been identified in both health and social research. 6 7 One barrier is the absence of formal education in mixed methods research. Fortunately, literature is rapidly expanding to fill this educational gap, including descriptions of how to integrate data and findings from qualitative and quantitative methods. 8 9 In this article we outline three techniques that may help health researchers to integrate data or findings in their mixed methods studies and show how these might enhance knowledge generated from this approach.

Triangulation protocol

Researchers will often use qualitative and quantitative methods to examine different aspects of an overall research question. For example, they might use a randomised controlled trial to assess the effectiveness of a healthcare intervention and semistructured interviews with patients and health professionals to consider the way in which the intervention was used in the real world. Alternatively, they might use a survey of service users to measure satisfaction with a service and focus groups to explore views of care in more depth. Data are collected and analysed separately for each component to produce two sets of findings. Researchers will then attempt to combine these findings, sometimes calling this process triangulation. The term triangulation can be confusing because it has two meanings. 10 It can be used to describe corroboration between two sets of findings or to describe a process of studying a problem using different methods to gain a more complete picture. The latter meaning is commonly used in mixed methods research and is the meaning used here.

The process of triangulating findings from different methods takes place at the interpretation stage of a study when both data sets have been analysed separately (figure ⇓ ). Several techniques have been described for triangulating findings. They require researchers to list the findings from each component of a study on the same page and consider where findings from each method agree (convergence), offer complementary information on the same issue (complementarity), or appear to contradict each other (discrepancy or dissonance). 11 12 13 Explicitly looking for disagreements between findings from different methods is an important part of this process. Disagreement is not a sign that something is wrong with a study. Exploration of any apparent “inter-method discrepancy” may lead to a better understanding of the research question, 14 and a range of approaches have been used within health services research to explore inter-method discrepancy. 15

Point of application for three techniques for integrating data in mixed methods research

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The most detailed description of how to carry out triangulation is the triangulation protocol, 11 which although developed for multiple qualitative methods, is relevant to mixed methods studies. This technique involves producing a “convergence coding matrix” to display findings emerging from each component of a study on the same page. This is followed by consideration of where there is agreement, partial agreement, silence, or dissonance between findings from different components. This technique for triangulation is the only one to include silence—where a theme or finding arises from one data set and not another. Silence might be expected because of the strengths of different methods to examine different aspects of a phenomenon, but surprise silences might also arise that help to increase understanding or lead to further investigations.

The triangulation protocol moves researchers from thinking about the findings related to each method, to what Farmer and colleagues call meta-themes that cut across the findings from different methods. 11 They show a worked example of triangulation protocol, but we could find no other published example. However, similar principles were used in an iterative mixed methods study to understand patient and carer satisfaction with a new primary angioplasty service. 16 Researchers conducted semistructured interviews with 16 users and carers to explore their experiences and views of the new service. These were used to develop a questionnaire for a survey of 595 patients (and 418 of their carers) receiving either the new service or usual care. Finally, 17 of the patients who expressed dissatisfaction with aftercare and rehabilitation were followed up to explore this further in semistructured interviews. A shift of thinking to meta-themes led the researchers away from reporting the findings from the interviews, survey, and follow-up interviews sequentially to consider the meta-themes of speed and efficiency, convenience of care, and discharge and after care. The survey identified that a higher percentage of carers of patients using the new service rated the convenience of visiting the hospital as poor than those using usual care. The interviews supported this concern about the new service, but also identified that the weight carers gave to this concern was low in the context of their family member’s life being saved.

Morgan describes this move as the “third effort” because it occurs after analysis of the qualitative and the quantitative components. 17 It requires time and energy that must be planned into the study timetable. It is also useful to consider who will carry out the integration process. Farmer and colleagues require two researchers to work together during triangulation, which can be particularly important in mixed methods studies if different researchers take responsibility for the qualitative and quantitative components. 11

Following a thread

Moran-Ellis and colleagues describe a different technique for integrating the findings from the qualitative and quantitative components of a study, called following a thread. 18 They state that this takes place at the analysis stage of the research process (figure ⇑ ). It begins with an initial analysis of each component to identify key themes and questions requiring further exploration. Then the researchers select a question or theme from one component and follow it across the other components—they call this the thread. The authors do not specify steps in this technique but offer a visual model for working between datasets. An approach similar to this has been undertaken in health services research, although the researchers did not label it as such, probably because the technique has not been used frequently in the literature (box)

An example of following a thread 19

Adamson and colleagues explored the effect of patient views on the appropriate use of services and help seeking using a survey of people registered at a general practice and semistructured interviews. The qualitative (22 interviews) and quantitative components (survey with 911 respondents) took place concurrently.

The researchers describe what they call an iterative or cyclical approach to analysis. Firstly, the preliminary findings from the interviews generated a hypothesis for testing in the survey data. A key theme from the interviews concerned the self rationing of services as a responsible way of using scarce health care. This theme was then explored in the survey data by testing the hypothesis that people’s views of the appropriate use of services would explain their help seeking behaviour. However, there was no support for this hypothesis in the quantitative analysis because the half of survey respondents who felt that health services were used inappropriately were as likely to report help seeking for a series of symptoms presented in standardised vignettes as were respondents who thought that services were not used inappropriately. The researchers then followed the thread back to the interview data to help interpret this finding.

After further analysis of the interview data the researchers understood that people considered the help seeking of other people to be inappropriate, rather than their own. They also noted that feeling anxious about symptoms was considered to be a good justification for seeking care. The researchers followed this thread back into the survey data and tested whether anxiety levels about the symptoms in the standardised vignettes predicted help seeking behaviour. This second hypothesis was supported by the survey data. Following a thread led the researchers to conclude that patients who seek health care for seemingly minor problems have exceeded their thresholds for the trade-off between not using services inappropriately and any anxiety caused by their symptoms.

Mixed methods matrix

A unique aspect of some mixed methods studies is the availability of both qualitative and quantitative data on the same cases. Data from the qualitative and quantitative components can be integrated at the analysis stage of a mixed methods study (figure ⇑ ). For example, in-depth interviews might be carried out with a sample of survey respondents, creating a subset of cases for which there is both a completed questionnaire and a transcript. Cases may be individuals, groups, organisations, or geographical areas. 9 All the data collected on a single case can be studied together, focusing attention on cases, rather than variables or themes, within a study. The data can be examined in detail for each case—for example, comparing people’s responses to a questionnaire with their interview transcript. Alternatively, data on each case can be summarised and displayed in a matrix 8 9 20 along the lines of Miles and Huberman’s meta-matrix. 21 Within a mixed methods matrix, the rows represent the cases for which there is both qualitative and quantitative data, and the columns display different data collected on each case. This allows researchers to pay attention to surprises and paradoxes between types of data on a single case and then look for patterns across all cases 20 in a qualitative cross case analysis. 21

We used a mixed methods matrix to study the relation between types of team working and the extent of integration in mixed methods studies in health services research (table ⇓ ). 22 Quantitative data were extracted from the proposals, reports, and peer reviewed publications of 75 mixed methods studies, and these were analysed to describe the proportion of studies with integrated outputs such as mixed methods journal articles. Two key variables in the quantitative component were whether the study was assessed as attempting to integrate qualitative or quantitative data or findings and the type of publications produced. We conducted qualitative interviews with 20 researchers who had worked on some of these studies to explore how mixed methods research was practised, including how the team worked together.

Example of a mixed methods matrix for a study exploring the relationship between types of teams and integration between qualitative and quantitative components of studies* 22

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The shared cases between the qualitative and quantitative components were 21 mixed methods studies (because one interviewee had worked on two studies in the quantitative component). A matrix was formed with each of the 21 studies as a row. The first column of the matrix contained the study identification, the second column indicated whether integration had occurred in that project, and the third column the score for integration of publications emerging from the study. The rows were then ordered to show the most integrated cases first. This ordering of rows helped us to see patterns across rows.

The next columns were themes from the qualitative interview with a researcher from that project. For example, the first theme was about the expertise in qualitative research within the team and whether the interviewee reported this as adequate for the study. The matrix was then used in the context of the qualitative analysis to explore the issues that affected integration. In particular, it helped to identify negative cases (when someone in the analysis doesn’t fit with the conclusions the analysis is coming to) within the qualitative analysis to facilitate understanding. Interviewees reported the need for experienced qualitative researchers on mixed methods studies to ensure that the qualitative component was published, yet two cases showed that this was neither necessary nor sufficient. This pushed us to explore other factors in a research team that helped generate outputs, and integrated outputs, from a mixed methods study.

Themes from a qualitative study can be summarised to the point where they are coded into quantitative data. In the matrix (table ⇑ ), the interviewee’s perception of the adequacy of qualitative expertise on the team could have been coded as adequate=1 or not=2. This is called “quantitising” of qualitative data 23 ; coded data can then be analysed with data from the quantitative component. This technique has been used to great effect in healthcare research to identify the discrepancy between health improvement assessed using quantitative measures and with in-depth interviews in a randomised controlled trial. 24

We have presented three techniques for integration in mixed methods research in the hope that they will inspire researchers to explore what can be learnt from bringing together data from the qualitative and quantitative components of their studies. Using these techniques may give the process of integration credibility rather than leaving researchers feeling that they have “made things up.” It may also encourage researchers to describe their approaches to integration, allowing them to be transparent and helping them to develop, critique, and improve on these techniques. Most importantly, we believe it may help researchers to generate further understanding from their research.

We have presented integration as unproblematic, but it is not. It may be easier for single researchers to use these techniques than a large research team. Large teams will need to pay attention to team dynamics, considering who will take responsibility for integration and who will be taking part in the process. In addition, we have taken a technical stance here rather than paying attention to different philosophical beliefs that may shape approaches to integration. We consider that these techniques would work in the context of a pragmatic or subtle realist stance adopted by some mixed methods researchers. 25 Finally, it is important to remember that these techniques are aids to integration and are helpful only when applied with expertise.

Summary points

Health researchers are increasingly using designs which combine qualitative and quantitative methods

However, there is often lack of integration between methods

Three techniques are described that can help researchers to integrate data from different components of a study: triangulation protocol, following a thread, and the mixed methods matrix

Use of these methods will allow researchers to learn more from the information they have collected

Cite this as: BMJ 2010;341:c4587

Funding: Medical Research Council grant reference G106/1116

Competing interests: All authors have completed the unified competing interest form at www.icmje.org/coi_disclosure.pdf (available on request from the corresponding author) and declare financial support for the submitted work from the Medical Research Council; no financial relationships with commercial entities that might have an interest in the submitted work; no spouses, partners, or children with relationships with commercial entities that might have an interest in the submitted work; and no non-financial interests that may be relevant to the submitted work.

Contributors: AOC wrote the paper. JN and EM contributed to drafts and all authors agreed the final version. AOC is guarantor.

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

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Combine qualitative and quantitative data

Using a combination of qualitative and quantitative data can improve an evaluation by ensuring that the limitations of one type of data are balanced by the strengths of another.

This will ensure that understanding is improved by integrating different ways of knowing. Most evaluations will collect both quantitative data (numbers) and qualitative data (text, images), however it is important to plan in advance how these will be combined.

When data are gathered

Qualitative and quantitative data are gathered at the same time.

For example, a closed-ended questionnaire to many service users is done at the same time as semi-structured observations of the service center.

Sequencing is one way of combining qualitative and quantitative data by alternating between them.

When data are combined

Component design is an approach to mixed methods evaluation that conducts qualitative components of the evaluation separately to quantitative components and then combines the data at the time of report writing.  

Integrated Design is an approach to mixed options evaluation where qualitative and quantitative data are integrated into an overall design.  

Purpose of combining data

‘Enriching’ is achieved by using qualitative work to identify issues or obtain information on variables not obtained by quantitative surveys. 

‘Examining’ refers to generating hypotheses from qualitative work to be tested through the quantitative approach.

‘Explaining’ involves using qualitative work to understand unanticipated results from quantitative data.  

In principle, this mechanism may operate in either direction – from qualitative to quantitative approaches or vice versa.

Triangulation facilitates validation of data through cross verification from more than two sources.

This guide, written by Michael Bamberger for InterAction outlines the elements of a mixed methods approach with particular reference to how it can be used in an impact evaluation.

This technical note from the US Agency for International Development (USAID) provides an overview of using a mixed-methods approach for evaluation.

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Quantitative and Qualitative Research Methods

• First Online: 03 January 2022

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Quantitative research uses methods that seek to explain phenomena by collecting numerical data, which are then analysed mathematically, typically by statistics. With quantitative approaches, the data produced are always numerical; if there are no numbers, then the methods are not quantitative. Many phenomena lend themselves to quantitative methods because the relevant information is already available numerically. Qualitative methods provide a mechanism to provide answers based on the collection of non-numerical data ‘i.e words, actions, behaviours’. Both quantitative and qualitative methodologies are important in medical imaging and radiation therapy.   In some instances, both quantitative and qualitative approaches can be combined into a mixed-methods approach. This chapter discusses all methodological approaches to research from both medical imaging and radiation therapy perspectives.  

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England, A. (2021). Quantitative and Qualitative Research Methods. In: Seeram, E., Davidson, R., England, A., McEntee, M.F. (eds) Research for Medical Imaging and Radiation Sciences. Springer, Cham. https://doi.org/10.1007/978-3-030-79956-4_5

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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.

Reviewed by:

Post written by: Smith Alex

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Qualitative vs. quantitative data analysis: How do they differ?

Learning analytics have become the cornerstone for personalizing student experiences and enhancing learning outcomes. In this data-informed approach to education there are two distinct methodologies: qualitative and quantitative analytics. These methods, which are typical to data analytics in general, are crucial to the interpretation of learning behaviors and outcomes. This blog will explore the nuances that distinguish qualitative and quantitative research, while uncovering their shared roles in learning analytics, program design and instruction.

What is qualitative data?

Qualitative data is descriptive and includes information that is non numerical. Qualitative research is used to gather in-depth insights that can't be easily measured on a scale like opinions, anecdotes and emotions. In learning analytics qualitative data could include in depth interviews, text responses to a prompt, or a video of a class period. 1

What is quantitative data?

Quantitative data is information that has a numerical value. Quantitative research is conducted to gather measurable data used in statistical analysis. Researchers can use quantitative studies to identify patterns and trends. In learning analytics quantitative data could include test scores, student demographics, or amount of time spent in a lesson. 2

Key difference between qualitative and quantitative data

It's important to understand the differences between qualitative and quantitative data to both determine the appropriate research methods for studies and to gain insights that you can be confident in sharing.

Data Types and Nature

Examples of qualitative data types in learning analytics:

• Observational data of human behavior from classroom settings such as student engagement, teacher-student interactions, and classroom dynamics
• Textual data from open-ended survey responses, reflective journals, and written assignments
• Feedback and discussions from focus groups or interviews
• Content analysis from various media

Examples of quantitative data types:

• Standardized test, assessment, and quiz scores
• Grades and grade point averages
• Attendance records
• Time spent on learning tasks
• Data gathered from learning management systems (LMS), including login frequency, online participation, and completion rates of assignments

Methods of Collection

Qualitative and quantitative research methods for data collection can occasionally seem similar so it's important to note the differences to make sure you're creating a consistent data set and will be able to reliably draw conclusions from your data.

Qualitative research methods

Because of the nature of qualitative data (complex, detailed information), the research methods used to collect it are more involved. Qualitative researchers might do the following to collect data:

• Conduct interviews to learn about subjective experiences
• Host focus groups to gather feedback and personal accounts
• Observe in-person or use audio or video recordings to record nuances of human behavior in a natural setting
• Distribute surveys with open-ended questions

Quantitative research methods

Quantitative data collection methods are more diverse and more likely to be automated because of the objective nature of the data. A quantitative researcher could employ methods such as:

• Surveys with close-ended questions that gather numerical data like birthdates or preferences
• Observational research and record measurable information like the number of students in a classroom
• Automated numerical data collection like information collected on the backend of a computer system like button clicks and page views

Analysis techniques

Qualitative and quantitative data can both be very informative. However, research studies require critical thinking for productive analysis.

Qualitative data analysis methods

Analyzing qualitative data takes a number of steps. When you first get all your data in one place you can do a review and take notes of trends you think you're seeing or your initial reactions. Next, you'll want to organize all the qualitative data you've collected by assigning it categories. Your central research question will guide your data categorization whether it's by date, location, type of collection method (interview vs focus group, etc), the specific question asked or something else. Next, you'll code your data. Whereas categorizing data is focused on the method of collection, coding is the process of identifying and labeling themes within the data collected to get closer to answering your research questions. Finally comes data interpretation. To interpret the data you'll take a look at the information gathered including your coding labels and see what results are occurring frequently or what other conclusions you can make. 3

Quantitative analysis techniques

The process to analyze quantitative data can be time-consuming due to the large volume of data possible to collect. When approaching a quantitative data set, start by focusing in on the purpose of your evaluation. Without making a conclusion, determine how you will use the information gained from analysis; for example: The answers of this survey about study habits will help determine what type of exam review session will be most useful to a class. 4

Next, you need to decide who is analyzing the data and set parameters for analysis. For example, if two different researchers are evaluating survey responses that rank preferences on a scale from 1 to 5, they need to be operating with the same understanding of the rankings. You wouldn't want one researcher to classify the value of 3 to be a positive preference while the other considers it a negative preference. It's also ideal to have some type of data management system to store and organize your data, such as a spreadsheet or database. Within the database, or via an export to data analysis software, the collected data needs to be cleaned of things like responses left blank, duplicate answers from respondents, and questions that are no longer considered relevant. Finally, you can use statistical software to analyze data (or complete a manual analysis) to find patterns and summarize your findings. 4

Qualitative and quantitative research tools

From the nuanced, thematic exploration enabled by tools like NVivo and ATLAS.ti, to the statistical precision of SPSS and R for quantitative analysis, each suite of data analysis tools offers tailored functionalities that cater to the distinct natures of different data types.

Qualitative research software:

NVivo: NVivo is qualitative data analysis software that can do everything from transcribe recordings to create word clouds and evaluate uploads for different sentiments and themes. NVivo is just one tool from the company Lumivero, which offers whole suites of data processing software. 5

ATLAS.ti: Similar to NVivo, ATLAS.ti allows researchers to upload and import data from a variety of sources to be tagged and refined using machine learning and presented with visualizations and ready for insert into reports. 6

SPSS: SPSS is a statistical analysis tool for quantitative research, appreciated for its user-friendly interface and comprehensive statistical tests, which makes it ideal for educators and researchers. With SPSS researchers can manage and analyze large quantitative data sets, use advanced statistical procedures and modeling techniques, predict customer behaviors, forecast market trends and more. 7

R: R is a versatile and dynamic open-source tool for quantitative analysis. With a vast repository of packages tailored to specific statistical methods, researchers can perform anything from basic descriptive statistics to complex predictive modeling. R is especially useful for its ability to handle large datasets, making it ideal for educational institutions that generate substantial amounts of data. The programming language offers flexibility in customizing analysis and creating publication-quality visualizations to effectively communicate results. 8

Applications in Educational Research

Both quantitative and qualitative data can be employed in learning analytics to drive informed decision-making and pedagogical enhancements. In the classroom, quantitative data like standardized test scores and online course analytics create a foundation for assessing and benchmarking student performance and engagement. Qualitative insights gathered from surveys, focus group discussions, and reflective student journals offer a more nuanced understanding of learners' experiences and contextual factors influencing their education. Additionally feedback and practical engagement metrics blend these data types, providing a holistic view that informs curriculum development, instructional strategies, and personalized learning pathways. Through these varied data sets and uses, educators can piece together a more complete narrative of student success and the impacts of educational interventions.

Master Data Analysis with an M.S. in Learning Sciences From SMU

Whether it is the detailed narratives unearthed through qualitative data or the informative patterns derived from quantitative analysis, both qualitative and quantitative data can provide crucial information for educators and researchers to better understand and improve learning. Dive deeper into the art and science of learning analytics with SMU's online Master of Science in the Learning Sciences program . At SMU, innovation and inquiry converge to empower the next generation of educators and researchers. Choose the Learning Analytics Specialization to learn how to harness the power of data science to illuminate learning trends, devise impactful strategies, and drive educational innovation. You could also find out how advanced technologies like augmented reality (AR), virtual reality (VR), and artificial intelligence (AI) can revolutionize education, and develop the insight to apply embodied cognition principles to enhance learning experiences in the Learning and Technology Design Specialization , or choose your own electives to build a specialization unique to your interests and career goals.

For more information on our curriculum and to become part of a community where data drives discovery, visit SMU's MSLS program website or schedule a call with our admissions outreach advisors for any queries or further discussion. Take the first step towards transforming education with data today.

• Retrieved on August 8, 2024, from nnlm.gov/guides/data-glossary/qualitative-data
• Retrieved on August 8, 2024, from nnlm.gov/guides/data-glossary/quantitative-data
• Retrieved on August 8, 2024, from cdc.gov/healthyyouth/evaluation/pdf/brief19.pdf
• Retrieved on August 8, 2024, from cdc.gov/healthyyouth/evaluation/pdf/brief20.pdf
• Retrieved on August 8, 2024, from lumivero.com/solutions/
• Retrieved on August 8, 2024, from atlasti.com/
• Retrieved on August 8, 2024, from ibm.com/products/spss-statistics
• Retrieved on August 8, 2024, from cran.r-project.org/doc/manuals/r-release/R-intro.html#Introduction-and-preliminaries

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How to combine qualitative and quantitative data for better outcomes

Qualitative and quantitative data are two very different types of information that both have their strengths and weaknesses.

What if you could use one type of information to make up for what the other type lacks? That’s where combining qualitative and quantitative data comes into play!

This article will help break down what kind of metrics you can combine, how it can be done effectively, and why this strategy ends up producing better outcomes overall.

Before jumping straight into the topic, let us look through a quick description of Qualitative and quantitative data.

Qualitative data Quantitative Data How To Merge Qualitative and Quantitative Data? The Steps Involved in Merging of Qualitative and Quantitative Data Types of Combining Both Qualitative and Quantitative Data Benefits of Merging Qualitative and Quantitative Data Conclusion

Qualitative data

Qualitative data means dealing with subjective things like emotions, desires, and so on. This type usually comes from focus groups or interviews because it cannot be measured by standard instruments.

Qualitative data is also referred to as “soft data” or nonnumerical facts. It cannot be measured by standard instruments and is gathered from focus groups, interviews, and questionnaires

Qualitative data deals with subjective things like emotions, desires, etc. It can’t be measured by a standard instrument and it comes from focus groups or interviews.

Because this type of research deals with people’s feelings which are difficult to measure numerically.

Quantitative data

Quantitative data means dealing with  objective facts  that have been captured through numeric representation. For instance, measurements in numbers values on different scales, counts of events observations made about an object using instruments designed for the purpose.

In other words – numerical information is measurable.

Quantitative data is the type of data that comes  from surveys where you ask people for their opinions, like closed questions with multiple choice answers.

Quantitative data can also come from numbers stored in an event. For example: if there was a crisis and we wanted to know how many people were affected by it then we could get this information through census data.

How to merge qualitative and quantitative data?

Qualitative data helps you to get insights into your target audience .

On the other hand, quantitative research will let you know more specific information such as the demographics and behaviors of customers.

And when we combine both qualitative and quantitative data, it becomes easier for us to understand our customer base better and then improve product or service accordingly.

What is the process for merging? Let’s find out in the next section.

The steps involved in merging of qualitative and quantitative data

1. identify your qualitative and quantitative research goals:.

Before you start integrating any data, it is important to know the  goal of merging.

Do you want to get deep insights about customers’ perceptions or just a general idea?

Set up clear objectives for both types of data collection processes.

2. Define variables:

Determine what type of information can be merged such as demographics, psychographics, attitudes, and so on.

Once this step is complete then merge two datasets by matching all the relevant fields so that there will not be any duplicates in the final dataset.

And do some clean-up work if necessary before sending out analysis results to stakeholders.

Remember to keep only those variables that help understand the customer base and remove unnecessary ones otherwise it will confuse you later.

3. Validate data:

Don’t forget about  data validation  because it helps to avoid mistakes when working with big sets of data.

4. Merge them easily:

Using Excel  or similar software like SPSS (Statistical Package for Social Science) makes it possible to merge qualitative and quantitative data.

5. Use a common identifier:

Include the same variable in both datasets so that it becomes easy to match them up. Variables like ID or some other unique identifier for each record.

Then run the analysis and see how you can use combined data sets together.

6. Keep different categories of responses separate:

There would be more than one type of response coming from qualitative research.

In such scenarios, keep those variables separately on an excel sheet before merging with the quantitative dataset.

For example, if customers have rated the product on a scale of 0 – 100 for three attributes. Like:  price, color & size then all these rating scales should be kept separate on the first row itself after importing into the SPSS software file.

Another best practice would be to keep all customer responses in separate columns since they are different variables.

For example, if customers have responded to the question “How much do you enjoy using product X?” with a number between 0 – 100 then put it on its column so this value can be easily changed into a binary response variable where 0= does not enjoy at all & 100 = enjoys very much.

7. Merge quantitative data set after testing for missing values:

After merging your qualitative and quantitative datasets make sure that both of these datasets should be free from any kind of missing or incorrect values (read more here).

At times some research participants may fail to respond to questions asked by researchers which leads them towards missing value.

If there is no way possible to get back to those respondents who have missed taking the survey, then it is advised to remove those missing values from the dataset.

We have thoroughly learned about the steps involved in combining the two types of data. Now it’s time to explore the types of merging the two data sets.

Types of combining both qualitative and quantitative data

There are several different ways you can combine your results:

The first way is an “and” approach where researchers look at both sets of findings side by side and compare them against each other for similarities or differences within their work. This method gives a great overall view of how people feel about something compared to something else that was found out via another survey or experiment just like it.

The second type is called the “plus” approach which involves looking at all gathered data collectively for an even more accurate picture of the participants’ feelings. This method is particularly helpful when researchers are unable to survey or interview all their participants in person. This can happen for several reasons including limited time and money allocated for research purposes.

For example, if a business used both of these methods to gather information about their customers, they would be able to see the differences and similarities between two types of people or regions more accurately.

The third type is called the “minus” approach where only one set of findings need to be looked at. Because it shows what people did not feel about something compared to another finding that was gathered via surveys or experiments just like it.

A perfect example would be if you had two different studies done on how voters feel about political candidates running in elections. However, some data has been left out either accidentally or deliberately by someone involved with both studies. Hence, this technique comes into play here as well helping us find out what was missed.

The fourth type is called the “statistical” approach and this method is one of the most commonly used. Using a statistical methodology that someone who has minimal training in statistics can use to combine both qualitative and quantitative data. It results in better outcomes. Meaning, any average person can do it as long as they have access to or know where to find both sets of findings.

This process will show us if there are differences between groups being compared, their averages together with how strong those differences might be using measures. For example, percentages along with what kinds of relationships exist among them all so we know exactly what happened during an experiment or survey.

In other words, this technique tells us if something important occurred outside our expectations based on prior research given certain conditions were met.

Ever wondered what are the benefits of combining methods? Don’t worry we have got you covered.

Benefits of merging qualitative and quantitative data

The helps in achieving clarity of the information. The audit trail for analysis to find out how it was done, what were the steps taken to achieve this outcome.

Transparency  is fostered as there is nothing hidden from view if someone wants to go through each step and see where a mistake has been made.

It improves the ability to explore all possible options for hypothesis generation and testing because we can use both systems side by side so that no stone remains unturned when exploring different possibilities with regard to the subject at hand.

It helps in understanding the system to which the data is being applied in a better manner.

It helps in a better comprehension of the  client’s perspective . It helps to understand if they are satisfied with what you have done or not, and how it could be made more effective.

It provides a clear picture of where there is room for improvement so that efforts can be directed towards those areas which need them most urgently.

This leads to efficient use of resources as well as the time available for achieving results.

It also ensures the proper allocation of resources required without wastage. Well, that is because these activities will remain focused on ones that produce positive outcomes instead of wasting energy on things that do not matter much at this point.

Merging quantitative data with qualitative data gives us detailed information about both systems side by side to find out how each one interacts with the other.

Merging both types of data is beneficial for analysis as well as predictive purposes because it allows us to make projections about the future with a fair degree of accuracy and clarity.

This merger technique also helps us understand how each one fits into the big picture and what role it plays for better business decisions making process.

The benefits of this method are so many, including being able to use current employees’ knowledge about market needs, consumer behavior, and more., tracking changes in perception over time by comparing past results with present ones, the list goes on.

Merging both types of data gives you detailed information which allows you to make predictions about future events and with clarity. It takes time, but the benefits are worth it. All you have to do is follow these simple steps and enjoy your insights.

Having understood how to combine qualitative and quantitative data along with its benefits, we hope you can choose how you want to use this technique for your research or personal projects.

This blog consists of a comprehensive guide to implement this strategy that you can apply practically.

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5 Reasons To Combine Qualitative And Quantitative Research

Qualitative  and  quantitative  market research approaches are designed to give you very different perspectives, even if you are using them with the same audience. Qualitative research gives you rich, detailed, and often emotionally driven insights based on the personal views of those you interview – for example, what do people feel about your product? In contrast, quantitative surveys give you a broader, full view, based on hard statistics – i.e. what % of people like or dislike your product?

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Advantages of using both qualitative and quantitative research methods

Given that both qualitative and quantitative market research provides vital ingredients of the understanding you are looking for (the Why and the What), combining them should deliver significant benefits, enabling you to compare and contrast results and gain much deeper insights.

However, traditionally this hasn’t been achievable due to deeply rooted industry perceptions of the different purposes of each methodology. Firstly, qualitative and quantitative methods are often seen as providing opposing viewpoints, with the former a more open style, based on the power of human interaction, and the latter more closed and metrics-driven. This meant they were seen as requiring different skill sets and to meet different needs, leading to specialism in one or the other. This, in turn, meant gaining a combined and universal view proved to be complex and costly.

Thanks to recent advances in market research technology, in many cases these challenges can now be overcome. Here are five ways that using qualitative and quantitative research together delivers real benefits:

1. The power of online research

Previously all qualitative research had to be carried out face-to-face through focus groups. The growth of digital channels provides new and more accessible ways of gauging qualitative insights, such as through online communities or online focus groups where you can share and show information, stimulus, and materials with your audience. Well-designed online communities allow you to collect quantitative data through quick polls and surveys , from the same audience, in a unified way.

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2. How to gain a holistic picture

Bringing qualitative and quantitative market research together through one, unified online platform enables you to gain a holistic picture. It means you can have a multi-stage discussion where you can validate a hypothesis, gain an understanding of it (through qualitative research) then widen your scope to get statistical data (quantitative), before testing a solution through further qualitative exploration. This creates a virtuous circle – all in the same system and with the same audience.

3. The benefits of combining the Why and the What

You don’t need to run a multi-stage process to gain value from combining qualitative and quantitative market research. You can simply use the two methodologies together to gain deeper insight into particular questions. For example, recent research found that  83% of e-commerce shoppers add products to their online carts , but don’t then finalize the transaction, costing businesses millions in lost revenue. Rather than merely quantifying the challenge, retailers could add a qualitative dimension to find out the precise reasons why a shopper didn’t buy, giving a much richer and more directly actionable insight into consumer behavior.

4. How automation drives agility

Previously a large part of the complexity of bringing qualitative and quantitative research together was that they used different systems to collect, analyze and report results. Put very simply, budgets didn’t stretch to both so you had to choose. Technology can now provide a single platform that handles both, lowering time to results through automation and reducing cost, complexity, and resources. Qualitative research was previously very time-consuming and labor-intensive – online qualitative projects are considerably faster.

5. Connect more deeply with your audience

At a time of growing competition, brands realize that they need to build strong connections to their customers if they are to retain their loyalty. This means creating a deeper understanding based on empathy, and combining qualitative and quantitative market research enables you to build a more human, emotional connection to your audience, but also take direct action to address their needs.

When is qualitative or quantitative research used?

In the past combining qualitative and quantitative market research approaches was both difficult and costly. And while there are still some areas where face-to-face qualitative research is the only answer, more and more business challenges can benefit from bringing the two methodologies together through online platforms that deliver the What and the Why together in one place. Through this, you can uncover the full story, provide deeper insights, build a narrative around customer needs, ultimately driving better, more informed decision-making.

Want to learn how you can benefit from combining qualitative and quantitative market research? Find out more on our market research page .

If you’d like to  sign up for a free trial with our market research tool or know more about how to create effective surveys  click here.  We’d love to hear from you!

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A Deep Dive into How Quantitative and Qualitative Research Work Together

• By Somnath Banerjee
• Business Research and Analysis

The world of research can be daunting if you do not know the right path to take. While quantitative research is focused on numbers and concrete stats, there’s also qualitative research, which looks beyond the data and is focused on emotions, perceptions, and more. Both are concrete ways to gain meaningful insights for your business. However, understanding which one is best for you and when to use either can be perplexing at times.

We have taken this opportunity to shed light on both research approaches. Through this blog, we will go through both methods in detail, understand the differences, and the correct way to deploy them together. If you depend on market research and trends for your business, this blog will enlighten you on various aspects of quantitative and qualitative approaches. So come along for an enriching experience in the complex world of quantitative and qualitative research.

Table of Contents

Interpreting quantitative and qualitative approaches.

In this section we will take a closer look at quantitative and qualitative research approaches and different aspects of to have a better understanding of both the approaches.

Quantitative Research

Quantitative research is a data-driven approach to gathering market intelligence . It deals with concrete numbers and breaks your research into smaller, quantifiable parts. This approach focuses on the measurable variables to extract actionable insights. You will need quantitative data for this approach to work. Thus, businesses extensively take approaches to gather all types of data on their customers. Some of the most popular ways to collect this data include – 

• Surveys:

Sending questionnaires to your customers/prospects is a great way to gather the trends and patterns shaping your industry. This also helps you understand your customers’ pain points or requirements and tailor solutions accordingly. 

• Industry-Based Experiments

Industry-based experiments allow you to test the effectiveness of your marketing campaign, a product idea, or any new collateral you might’ve designed. This method helps you test your ideas before you put them out in the real world.

• Observational Studies

Many researchers base their research on observational data, such as buying patterns and traffic patterns. This method helps predict any possible pattern that can emerge and optimize your efforts to capitalize on that data.

For the quantitative approach to yield the desired outcome, statistical data analysis must be used. This will help you clearly understand your findings and draw conclusions based on the data you have collected.

Qualitative Research

Qualitative research is a method based predominantly on perceptions and subjective experiences. While the quantitative approach deals with many concrete stats and numbers, the qualitative approach revolves a lot around words, pictures, and sometimes even sounds. Depending upon your business, you can use any of the below methods to gather information for your qualitative research –

• Personal Interviews : Interviews or one-on-one discussions are great sources to gather individual perceptions and experiences, which can be instrumental in shaping your marketing and product development efforts.
• Focus Groups : Focus groups are great sources to gather shared perspectives and experiences, presenting a fair understanding of anything business-related. It can be market trends , industry insights, or just customer experiences in general. 
• Social Media/Web Analysis : We live in the age of social media, where all types of content are available to gauge customer opinions, feedback, and sentiments. These are incredibly useful for understanding customer sentiments around your brand/product.

The methods above are just some of your business’s most effective qualitative research methods. However, there are other methods that you can deploy or invent based on your business requirements.

Tools and Techniques for Quantitative & Qualitative Analysis

Quantitative analysis: understanding the numbers.

While quantitative data is essential for any business, it’s easy to get lost in the sea of data if not used properly. You need to know the tools and techniques to understand the numbers correctly. Here are a few methods you can leverage to understand the numbers better. 

• Descriptive Statistics: This powerful tool allows you to delve into your data, providing a clear and precise summary of key features such as:
• Central Tendency: This helps you find your data clusters by giving you measures such as mean (average), median (middle value), and mode (most frequent value).
• Variability: Variability helps you understand the extent of your data spread. By knowing the variability of your data, you can quickly figure out the values and the spread of your dataset.
• Frequency Tables and Histograms: These visuals will help you picture how many different values are in your dataset.
• Inferential Statistics: This robust method is your key to drawing reliable and valid conclusions about your data, even beyond its immediate scope. 
• Hypothesis Testing: This involves building a hypothesis based on your data and conducting statistical tests to establish your hypothesis. This will help you understand whether any patterns developed are real or purely hypothetical. 
• Confidence Analysis: This technique allows you to understand the level of confidence your customers and prospects have in your product or brand.

Qualitative Analysis: Understanding the Emotions

Qualitative research goes beyond data, and hence, it is equally vital for businesses looking to better understand their prospects and customers. 

• Emerging Themes : You can use your customer data in the form of social posts or interviews to extract any possible emerging themes around your business. 
• Narrative Analysis : By analyzing the narrative surrounding your business or product, you can understand the general sentiment about your business and identify the key points that are swaying the narrative in your favor or against you.

It is essential to understand that to utilize your customer dataset fully, you need to leverage both quantitative and qualitative analysis approaches. They are complementary to each other and will help you have a comprehensive research analysis of your data.

Collaborative Research is the Way To Go

Now that we have understood the difference between quantitative and qualitative research methodologies and the tools and techniques required to complete it, both approaches have strengths and limitations. If you want to harness the power of research for your business, you need to make sure that you have a collaborative approach to your market research. Only with a blended approach will you be able to embrace the power of concrete numbers and stories together.

However, if you are new to market research and need assistance, we have a proficient pool of experts to help you get started. Just write to us at  [email protected] , and we will connect you with our expert to kickstart your journey.

Somnath Banerjee

Related posts, virtual captives – an emerging trend, understanding market research: a guide to informed decisions, leveraging market intelligence companies for strategic advantage: a guide for business strategy professionals, account profiling – create an ideal customer profile, building a data-driven culture: how market research & intelligence improve decision making, market intelligence and the art of staying ahead of competition, get in touch.

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• 1 University of Nebraska Medical Center
• 2 GDB Research and Statistical Consulting
• 3 GDB Research and Statistical Consulting/McLaren Macomb Hospital
• PMID: 29262162
• Bookshelf ID: NBK470395

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. Instead of collecting numerical data points or intervening or introducing treatments just like in quantitative research, qualitative research helps generate hypothenar to further investigate and understand quantitative data. Qualitative research gathers participants' experiences, perceptions, and behavior. It answers the hows and whys instead of how many or how much. It could be structured as a standalone study, purely relying on qualitative data, or part of mixed-methods research that combines qualitative and quantitative data. This review introduces the readers to some basic concepts, definitions, terminology, and applications of qualitative research.

Qualitative research, at its core, asks open-ended questions whose answers are not easily put into numbers, such as "how" and "why." Due to the open-ended nature of the research questions, qualitative research design is often not linear like quantitative design. One of the strengths of qualitative research is its ability to explain processes and patterns of human behavior that can be difficult to quantify. Phenomena such as experiences, attitudes, and behaviors can be complex to capture accurately and quantitatively. In contrast, a qualitative approach allows participants themselves to explain how, why, or what they were thinking, feeling, and experiencing at a particular time or during an event of interest. Quantifying qualitative data certainly is possible, but at its core, qualitative data is looking for themes and patterns that can be difficult to quantify, and it is essential to ensure that the context and narrative of qualitative work are not lost by trying to quantify something that is not meant to be quantified.

However, while qualitative research is sometimes placed in opposition to quantitative research, where they are necessarily opposites and therefore "compete" against each other and the philosophical paradigms associated with each other, qualitative and quantitative work are neither necessarily opposites, nor are they incompatible. While qualitative and quantitative approaches are different, they are not necessarily opposites and certainly not mutually exclusive. For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. For example, say a quantitative analysis has determined a correlation between length of stay and level of patient satisfaction, but why does this correlation exist? This dual-focus scenario shows one way in which qualitative and quantitative research could be integrated.

Copyright © 2024, StatPearls Publishing LLC.

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Conflict of interest statement

Disclosure: Steven Tenny declares no relevant financial relationships with ineligible companies.

Disclosure: Janelle Brannan declares no relevant financial relationships with ineligible companies.

Disclosure: Grace Brannan declares no relevant financial relationships with ineligible companies.

• Introduction
• Issues of Concern
• Clinical Significance
• Enhancing Healthcare Team Outcomes
• Review Questions

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• Qualitative Research

Quantitative Research

Qualitative vs quantitative research: better together.

2 November 2023

By Brynn Harris

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I don’t know about you, but I’m super bored. Bored with the same old qualitative vs quantitative research debate. The whole… “I can do anything you can do better. I can do anything better than you. No you can’t! Yes, I can!” …song and dance. 

Here’s how it typically goes:

• Qualitative research is better because you study a smaller group of people and drill deeper into why people think, feel, and do what they do. 
• No, quantitative research is better because it uses larger sample sizes to ensure statistical validity so we can generalize to a larger population. 
• Qualitative research is better because it uses more natural and human data collection techniques, like interviews and focus groups, to explore people’s underlying attitudes and emotions to get to richer insight.
• No, quantitative research is better because it uses structured surveys to help reduce bias and human subjectivity. 
• Qualitative research is better because it can be used at the early stages of research when little is known about a topic to generate hypotheses and refine research questions.
• No, quantitative research is better because it’s confirmatory and can validate or test hypotheses so clients can make data-driven decisions and forecast trends.
• Qualitative research is better because findings are presented in narrative form, using quotes and models, so clients can relate to the story on a more human level.
• No, quantitative research is better because findings are presented in tables, charts, and graphs, so learnings are much easier for clients to interpret and share.

Back and forth… and back and forth… like a superfluous tennis match I am not at all interested in watching. Please tell me this isn’t still happening. It’s nearly 2024. I can’t, I just can’t. It’s exhausting . 

In fact this very topic, “qualitative vs quantitative research”, according to our friend Google, has been referenced about 435,000,000 times. How original. So let’s start where I would guess 99% of these articles started from… and look at what makes qualitative and quantitative research different (cue standard definitions in any marketing research 101 textbook that has ever been published).   

What is qualitative research? 

Traditionally , “qualitative research” is defined as a research method that focuses on exploring and understanding the underlying meanings, motivations, and behaviors of individuals or groups. It focuses on gathering subjective and descriptive (aka non-numerical) data about human behavior, attitudes, and perceptions, related to products, services, brands, or marketing strategies. It aims to understand the “why” and “how” behind people’s choices and behaviors by digging into their lived experiences.

Researchers tend to use methods like in-depth interviews, focus groups, and ethnography to collect qualitative data. These methods encourage people to express themselves freely and provide detailed responses, allowing researchers to explore people’s emotions, motivations, and decision-making processes.

What is quantitative research? 

Again traditionally , “quantitative research” is defined as a systematic empirical investigation that gathers data to provide evidence for understanding, explaining, and predicting human phenomena. It’s primarily concerned with obtaining numeric information, such as numerical values and percentages to understand and measure various aspects of behavior.

Researchers and consultants tend to use structured surveys, and other standardized data collection methods, that aim to gather objective data that allows for numerical analysis and statistical testing. It is often used to answer questions related to “how much,” “how many,” or “to what extent.”” 

If you’ve been in the marketing research world as long as I have, these very standard definitions of qualitative and quantitative research will not be new news to you. Even though the world has changed, clients’ needs have changed, people have changed, the defining characteristics of these two research dinosaurs haven’t changed a heck of a lot within the wider world of research. However, at The Sound, we have a slightly different take. More on that later. 

So if the differences are obvious and the use cases are clear… as an old qualie, naturally my first question is… why the debate? Why qualitative research vs quantitative research? Why do we keep pitting them against each other? Like two kids at the playground fighting for the last swing. Well, when two people (or in this case two research disciplines) continue to battle it out, it often stems from 3 things:

• Insecurity. Like most other industries, the demand for marketing research services fluctuates based on economic conditions. During economic downturns or periods of uncertainty (uh, like now!), businesses are forced to reduce their marketing budgets… because they too are trying to survive the shit storm.
• Scarcity. Smaller marketing budgets leads to less comprehensive research, reducing the number of opportunities available… hence increasing competition between agencies (and methodologies). This competition contributes to a perception of scarcity, particularly for small to medium sized businesses.
• Change. The marketing landscape is constantly evolving, with new channels, technologies (um, hello AI), and market behaviors emerging. Professionals who stay updated on industry trends and adapt to new methodologies are laughing… those who don’t risk falling by the wayside.

So yes, we’ve all been on the struggle bus. But let’s not lose the plot here. A lot of these things we can’t control as research agencies and professionals but what we can control is our reaction to them (you can thank my therapist for that one). So let’s do something different and talk about how they are the same.

What do qualitative research and quantitative research have in common?

The irony of it all is that qualitative research and quantitative research have more in common than one might expect. Let’s take a moment to drill… it… all… the… way… down… to… the… basics.

As researchers and consultants, what do we always start with? Well, it’s typically two things:

• A client with a business objective.
• A group of people to study.   

Let’s start with the first one. A client with a business objective. Clients tend to come to a research partner with a desire to do something. To develop a new product, acquire a new audience, launch a new campaign, whatever it is. And in order to do this thing they want to do they need information to make decisions. Because making uninformed decisions is just… well, not very smart. 

That’s where the people part comes in. In order to gather the information, we need to go straight to the source. People. The people who buy the dish soap, use the food delivery service, or download the dating app. And until Elon Musk invents a sure-fire way to read people’s minds in order to get information from people (scary), we need to ask them questions. Questions about their thoughts, feelings, behaviors, experiences, perceptions, desires, fears, insecurities… all of it.

• How are you feeling about your life right now?
• What is the main idea behind Concept F?
• What grocery stores do you prefer to shop at?
• Why haven’t you purchased the iPhone 15 Pro?

The reality is, regardless of whether you’re running qualitative research or quantitative research, the questions are typically the same. It’s how we ask the question that can differ. Sometimes it’s an open ended question during an in-home ethnography in someone’s living room. And sometimes it’s a closed ended question on the 5-pt likert scale. It doesn’t really matter. It’s still a human being asking a human being a question so that another human being can make a decision that will impact even more human beings. Picking up what I’m putting down?

So whether it’s qualitative research or quantitative research, our intentions don’t change (or at least they shouldn’t change). We are both trying to understand people to help our clients make business decisions. As Simon Sinek says “If you don’t understand people, you don’t understand business.” But what does change is our perspective . The perspective from which we approach understanding people.

From a quantitative research perspective, we tend to look at people from the top down . Quant starts by listening to the stories of 1000 people at the same time… then we break it down, examine the pieces, and put it back together again.

From a qualitative research perspective, we tend to look at people from the ground up. Qual starts by listening to one person’s story, then another, then another… then we analyze for themes and ladder up to insights.

But both lead to the same place. Insight about people . And insight leads to strategic recommendations. And strategic recommendations lead to smart decision making. And smart decision making leads to better products and services for people . Don’t you love it when things come full circle? 

Qualitative research vs quantitative research… So, which is better then?

The answer is, both are better. More specifically, both are better together . Here’s what I mean. You would never suggest someone smell a rose with their eyes closed. Or taste a fresh strawberry while plugging their nose. Because essentially you would be cutting them off from having the full experience of whatever it is they’re doing. 

Similarly, quantitative research and qualitative research offer a unique perspective of the same phenomenon. So by finding ways to employ both quantitative and qualitative research, we are opening ourselves (and our client) up to more stimulus, more data, more insight… and more opportunities to take action. This is what helps all of us become better research partners and marketers. 

In fact, when we look more closely at this “qualitative vs quantitative research debate”… their differences complement each other. Each naturally compensates for what the other doesn’t provide. 

As such, it is important that we don’t get stuck in old research tropes around what qualitative and quantitative research is and isn’t, and what each can and can’t do. Qualitative research is more than just subjective storytelling. It is the rigorous analysis of hundreds of data points. And quantitative research can do more than just tell clients how much or how many. It can reveal insight into why people do the things they do or feel the way they feel. At The Sound we believe that behind every data point (whatever form it comes in… numbers, words, picture, video, voice) is a person , a story to be told. A human story. 

In summary, just like any situation where we use multiple vantage points to examine the same phenomenon… there are huge advantages to using both quantitative and qualitative research . Here’s just a few of them:

• Enhances Understanding: Exploring an audience from two different research perspectives provides us with a more comprehensive and nuanced understanding of their lived experience… which leads to deeper insights for our clients… and greater empathy!
• Improves Problem Solving: Exploring an audience from two different research perspectives can reveal solutions or approaches to solving a client’s business problem we would have never spotted alone. It gives us an opportunity to brainstorm together, weigh out different viewpoints, and help our clients make more informed and well-rounded decisions. 
• Reduces Bias: Exploring an audience from two different research perspectives, we are less likely to be influenced by our personal biases or preconceptions. We can use each other to poke (and fill) holes in our findings and develop a more honest, comprehensive story.  
• Sparks Innovation: Exploring an audience from two different research perspectives can foster innovation and help us come up with novel ideas that cater to a broader audience.
• Increases Adaptability: Exploring an audience from two different research perspectives can make us more adaptable as researchers and consultants. We can adjust your strategies, plans, and approaches based on new information… and most importantly, we can learn from each other. 

So, you see? We both like puzzles. People puzzles. It’s just how qualitative research and quantitative research approach solving them is a bit different. So, let’s drop the pretense. Drop the debate. Drop the drama. And recognize that we need both quantitative research and qualitative research to work together to help our clients achieve their business outcomes. 

Have some research questions you’d like answered? We’re here to help.  

Written By:

Brynn harris, keep reading.

August 26, 2024

From Academia to Industry: Qualitative Research in the Digital Age

Identity, Technology, Qualitative Research - My PhD in Communications enhances my work at The Sound, helping brands understand people in a tech-driven world.

August 18, 2024

Building a Customer Journey that you’ll use

There are few exploratory project requests that come in as complicated to work through as a customer journey. On the surface, it seems like it...

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Broadening horizons: Integrating quantitative and qualitative research

Health research usually employs quantitative, often experimental, methods to study clinical conditions and outcomes. The use of qualitative methods in this type of research is much less common. However, we contend that qualitative research, in combination with quantitative research, can play an important role generating an improved understanding of disease, health and health care.

Data collected in qualitative research are usually in narrative rather than numerical form, such as the transcript of an unstructured, in-depth interview. Analysis of qualitative data organizes, summarizes and interprets these nonnumerical observations. The goal of qualitative research is the development of concepts that help clarify phenomena in natural, rather than experimental, settings, giving due emphasis to the meanings, experiences and views of all the participants being studied. For example, to understand why some members of ethnic minorities have refused tuberculosis treatment, qualitative, culturally sensitive interviews may be much more informative than standardized quantitative interviews.

Both quantitative and qualitative research have weaknesses that to some degree are compensated for by the strengths of the other. Quantitative research is very well suited to establishing cause-and-effect relationships, to testing hypotheses and to determining the opinions, attitudes and practices of a large population, whereas qualitative research lends itself very well to developing hypotheses and theories and to describing processes such as decision making or communication processes. Quantitative research generates factual, reliable outcome data that are usually generalizable to some larger populations, and qualitative research produces rich, detailed and valid process data based on the participant’s, rather than the investigator’s, perspectives and interpretations ( 1 ).

Quantitative research is usually deductive, relying on experimental and survey methods to test specific hypotheses based on general principles. It is strong in inductive reasoning, building and expanding theories concerning relationships among phenomena. In the actual practice of scientific research, theory and research interact through a never-ending cycle of deduction, induction, deduction, induction and so forth ( 2 ). By combining quantitative and qualitative methods, a degree of comprehensiveness may be achieved that neither approach, if used alone, can achieve. For example, to target populations of children who are not being immunized for common childhood infectious diseases, it is critical to quantify the existence of a low rate of immunization. However, to intervene to rectify the identified problem, it is important to explore why parents are not having their children vaccinated. Qualitative interviews are most appropriate for this purpose.

The nature of inquiry is similar in both quantitative and qualitative research, it is couched in the human desire to understand and explain behaviour and events, their components, antecedents, corollaries and consequences. If differences among researchers exist, it is not because they aspire to different ends, but because they have operationalized their methods for reaching those ends differently ( 3 ).

Even though both approaches are different from one another, one is not necessarily inferior to the other. However, qualitative research is often considered to be lacking scientific rigour. Unfortunately, the standard strategies used to enhance validity, reliability and objectivity in quantitative research are not always relevant to qualitative research. Fortunately, more attention is being paid to strategies to enhance the quality of the data and interpretations collected through qualitative research ( 4 , 5 ). An example of a commonly used approach is triangulation, which refers to data collection in which evidence is deliberately sought from a wide range of different, independent sources and often by different means (for instance, comparing oral testimony with written records) ( 4 ).

The rigid demarcation between the two types of research has not encouraged interaction between the two camps. However, there are some good reasons for combining qualitative and quantitative research. First, a researcher may wish to explore an issue to understand what the relevant variables are or to develop hypotheses that can then be studied or tested in quantitative research. This way of combining the two approaches is also used in the development of scales or questionnaires. For example, qualitative techniques, such as observation, in-depth interviews or focus groups, can provide a description and understanding of a situation or behaviour. At their most basic, these techniques can be used simply to discover the most comprehensive terms or words to use in a subsequent survey questionnaire ( 6 ). Second, qualitative research also may follow quantitative research with the aim of explaining the quantitative results. For example, designing and evaluating an effective health campaign promoting influenza vaccinations faces multimethod challenges. To determine whether the campaign works so that the strategy can be effectively used again, it is not only important to identify how many people received shots, but also why and how they decided to get vaccinated (ie, linking process to outcome). Third, qualitative and quantitative research can be combined to enhance the validity of the results, much the same as in triangulation, but now using both quantitative and qualitative approaches, for their combined strength, rather than using one method to validate the result of the other. For example, overall validity would be enhanced through the use of a multimethods approach in instances where measuring the technical accuracy of a diagnostic or treatment intervention was important alongside an understanding of patient response to or acceptance of such a diagnostic test or treatment protocol ( 7 ).

Several barriers may prevent the further development of integrated research, such as lack of expertise, time and funding, mutual prejudices, and publication bias. If methodological integration is going to progress, a number of changes in the current environment are needed. These include acceptance and refinement of the underlying paradigms of qualitative and quantitative research, recognition by funding agencies of the need for both perspectives and a willingness to allocate sufficient resources, awareness by editorial boards of the importance of publishing multimethods research, training of researchers in both paradigms, encouragement of teamwork and promotion of mutual acceptance and respect by the adherents of each approach.

Given the multiple challenges facing health researchers today, broadening the horizons of health research to embrace the use and benefits of both quantitative and qualitative analysis is a methodological advance that must be supported and nurtured.

Quantitative Data Analysis: Everything You Need to Know

11 min read

Does the thought of quantitative data analysis bring back the horrors of math classes? We get it.

But conducting quantitative data analysis doesn’t have to be hard with the right tools. Want to learn how to turn raw numbers into actionable insights on how to improve your product?

In this article, we explore what quantitative data analysis is, the difference between quantitative and qualitative data analysis, and statistical methods you can apply to your data. We also walk you through the steps you can follow to analyze quantitative information, and how Userpilot can help you streamline the product analytics process. Let’s get started.

• Quantitative data analysis is the process of using statistical methods to define, summarize, and contextualize numerical data.
• Quantitative analysis is different from a qualitative one. The first deals with numerical data and focuses on answering “what,” “when,” and “where.” However, a qualitative analysis relies on text, graphics, or videos and explores “why” and “how” events occur.
• Pros of quantitative data analysis include objectivity, reliability, ease of comparison, and scalability.
• Cons of quantitative metrics include the data’s limited context and inflexibility, and the need for large sample sizes to get statistical significance.
• The methods for analyzing quantitative data are descriptive and inferential statistics.
• Choosing the right analysis method depends on the type of data collected and the specific research questions or hypotheses.
• These are the steps to conduct quantitative data analysis: 1. Defining goals and KPIs . 2. Collecting and cleaning data. 3. Visualizing the data. 4. Identifying patterns . 5. Sharing insights. 6. Acting on findings to improve decision-making.
• With Userpilot , you can auto-capture in-app user interactions and build analytics dashboards . This tool also lets you conduct A/B and multivariate tests, and funnel and cohort analyses .
• Gather and visualize all your product analytics in one place with Userpilot. Get a demo .

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What is quantitative data analysis?

Quantitative data analysis is about applying statistical analysis methods to define, summarize, and contextualize numerical data. In short, it’s about turning raw numbers and data into actionable insights.

The analysis will vary depending on the research questions and the collected data (more on this below).

Quantitative vs qualitative data analysis

The main difference between these forms of analysis lies in the collected data. Quantitative data is numerical or easily quantifiable. For example, the answers to a customer satisfaction score (CSAT) survey are quantitative since you can count the number of people who answered “very satisfied”.

Qualitative feedback , on the other hand, analyzes information that requires interpretation. For instance, evaluating graphics, videos, text-based answers, or impressions.

Another difference between quantitative and qualitative analysis is the questions each seeks to answer. For instance, quantitative data analysis primarily answers what happened, when it happened, and where it happened. However, qualitative data analysis answers why and how an event occurred.

Quantitative data analysis also looks into identifying patterns , drivers, and metrics for different groups. However, qualitative analysis digs deeper into the sample dataset to understand underlying motivations and thinking processes.

Pros of quantitative data analysis

Quantitative or data-driven analysis has advantages such as:

• Objectivity and reliability. Since quantitative analysis is based on numerical data, this reduces biases and allows for more objective conclusions. Also, by relying on statistics, this method ensures the results are consistent and can be replicated by others, making the findings more reliable.
• Easy comparison. Quantitative data is easily comparable because you can identify trends , patterns, correlations, and differences within the same group and KPIs over time. But also, you can compare metrics in different scales by normalizing the data, e.g., bringing ratios and percentages into the same scale for comparison.
• Scalability. Quantitative analysis can handle large volumes of data efficiently, making it suitable for studies involving large populations or datasets. This makes this data analysis method scalable. Plus, researchers can use quantitative analysis to generalize their findings to broader populations.

Cons of quantitative data analysis

These are common disadvantages of data-driven analytics :

• Limited context. Since quantitative data looks at the numbers, it often strips away the data from the context, which can show the underlying reasons behind certain trends. This limitation can lead to a superficial understanding of complex issues, as you often miss the nuances and user motivations behind the data points.
• Inflexibility. When conducting quantitative research, you don’t have room to improvise based on the findings. You need to have predefined hypotheses, follow scientific methods, and select data collection instruments. This makes the process less adaptable to new or unexpected findings.
• Large sample sizes necessary. You need to use large sample sizes to achieve statistical significance and reliable results when doing quantitative analysis. Depending on the type of study you’re conducting, gathering such extensive data can be resource-intensive, time-consuming, and costly.

Quantitative data analysis methods

There are two statistical methods for reviewing quantitative data and user analytics . However, before exploring these in-depth, let’s refresh these key concepts:

• Population. This is the entire group of individuals or entities that are relevant to the research.
• Sample. The sample is a subset of the population that is actually selected for the research since it is often impractical or impossible to study the entire population.
• Statistical significance. The chances that the results gathered after your analysis are realistic and not due to random chance.

Here are methods for analyzing quantitative data:

Descriptive statistics

Descriptive statistics, as the name implies, describe your data and help you understand your sample in more depth. It doesn’t make inferences about the entire population but only focuses on the details of your specific sample.

Descriptive statistics usually include measures like the mean, median, percentage, frequency, skewness, and mode.

Inferential statistics

Inferential statistics aim to make predictions and test hypotheses about the real-world population based on your sample data.

Here, you can use methods such as a T-test, ANOVA, regression analysis, and correlation analysis.

Let’s take a look at this example. Through descriptive statistics, you identify that users under the age of 25 are more likely to skip your onboarding. You’ll need to apply inferential statistics to determine if the result is statistically significant and applicable to your entire ’25 or younger’ population.

How to choose the right method for your quantitative data analysis

The type of data that you collect and the research questions that you want to answer will impact which quantitative data analysis method you choose. Here’s how to choose the right method:

Determine your data type

Before choosing the quantitative data analysis method, you need to identify which group your data belongs to:

• Nominal —categories with no specific order, e.g., gender, age, or preferred device.
• Ordinal —categories with a specific order, but the intervals between them aren’t equal, e.g., customer satisfaction ratings .
• Interval —categories with an order and equal intervals, but no true zero point, e.g., temperature (where zero doesn’t mean “no temperature”).
• Ratio —categories with a specific order, equal intervals, and a true zero point, e.g., number of sessions per user .

Applying any statistical method to all data types can lead to meaningless results. Instead, identify which statistical analysis method supports your collected data types.

Consider your research questions

The specific research questions you want to answer, and your hypothesis (if you have one) impact the analysis method you choose. This is because they define the type of data you’ll collect and the relationships you’re investigating.

For instance, if you want to understand sample specifics, descriptive statistics—such as tracking NPS —will work. However, if you want to determine if other variables affect the NPS, you’ll need to conduct an inferential analysis.

The overarching questions vary in both of the previous examples. For calculating the NPS, your internal research question might be, “Where do we stand in customer loyalty ?” However, if you’re doing inferential analysis, you may ask, “How do various factors, such as demographics, affect NPS?”

6 steps to do quantitative data analysis and extract meaningful insights

Here’s how to conduct quantitative analysis and extract customer insights :

1. Set goals for your analysis

Before diving into data collection, you need to define clear goals for your analysis as these will guide the process. This is because your objectives determine what to look for and where to find data. These goals should also come with key performance indicators (KPIs) to determine how you’ll measure success.

For example, imagine your goal is to increase user engagement. So, relevant KPIs include product engagement score , feature usage rate , user retention rate, or other relevant product engagement metrics .

2. Collect quantitative data

Once you’ve defined your goals, you need to gather the data you’ll analyze. Quantitative data can come from multiple sources, including user surveys such as NPS, CSAT, and CES, website and application analytics , transaction records, and studies or whitepapers.

Remember: This data should help you reach your goals. So, if you want to increase user engagement , you may need to gather data from a mix of sources.

For instance, product analytics tools can provide insights into how users interact with your tool, click on buttons, or change text. Surveys, on the other hand, can capture user satisfaction levels . Collecting a broad range of data makes your analysis more robust and comprehensive.

3. Clean and visualize your data

Raw data is often messy and contains duplicates, outliers, or missing values that can skew your analysis. Before making any calculations, clean the data by removing these anomalies or outliers to ensure accurate results.

Once cleaned, turn it into visual data by using different types of charts , graphs, or heatmaps . Visualizations and data analytics charts make it easier to spot trends, patterns, and anomalies. If you’re using Userpilot, you can choose your preferred visualizations and organize your dashboard to your liking.

4. Identify patterns and trends

When looking at your dashboards, identify recurring themes, unusual spikes, or consistent declines that might indicate data analytics trends or potential issues.

Picture this: You notice a consistent increase in feature usage whenever you run seasonal marketing campaigns . So, you segment the data based on different promotional strategies. There, you discover that users exposed to email marketing campaigns have a 30% higher engagement rate than those reached through social media ads.

In this example, the pattern suggests that email promotions are more effective in driving feature usage.

If you’re a Userpilot user, you can conduct a trend analysis by tracking how your users perform certain events.

5. Share valuable insights with key stakeholders

Once you’ve discovered meaningful insights, you have to communicate them to your organization’s key stakeholders. Do this by turning your data into a shareable analysis report , one-pager, presentation, or email with clear and actionable next steps.

Your goal at this stage is for others to view and understand the data easily so they can use the insights to make data-led decisions.

Following the previous example, let’s say you’ve found that email campaigns significantly boost feature usage. Your email to other stakeholders should strongly recommend increasing the frequency of these campaigns and adding the supporting data points.

Take a look at how easy it is to share custom dashboards you built in Userpilot with others via email:

6. Act on the insights

Data analysis is only valuable if it leads to actionable steps that improve your product or service. So, make sure to act upon insights by assigning tasks to the right persons.

For example, after analyzing user onboarding data, you may find that users who completed the onboarding checklist were 3x more likely to become paying customers ( like Sked Social did! ).

Now that you have actual data on the checklist’s impact on conversions, you can work on improving it, such as simplifying its steps, adding interactive features, and launching an A/B test to experiment with different versions.

How can Userpilot help with analyzing quantitative data

As you’ve seen throughout this article, using a product analytics tool can simplify your data analysis and help you get insights faster. Here are different ways in which Userpilot can help:

Automatically capture quantitative data

Thanks to Userpilot’s new auto-capture feature, you can automatically track every time your users click, write a text, or fill out a form in your app—no engineers or manual tagging required!

Our customer analytics platform lets you use this data to build segments, trigger personalized in-app events and experiences, or launch surveys.

If you don’t want to auto-capture raw data, you can turn this functionality off in your settings, as seen below:

Monitor key metrics with customizable dashboards for real-time insights

Userpilot comes with template analytics dashboards , such as new user activation dashboards or customer engagement dashboards . However, you can create custom dashboards and reports to keep track of metrics that are relevant to your business in real time.

For instance, you could build a customer retention analytics dashboard and include all metrics that you find relevant, such as customer stickiness , NPS, or last accessed date.

Analyze experiment data with A/B and multivariate tests

Userpilot lets you conduct A/B and multivariate tests , either by following a controlled or a head-to-head approach. You can track the results on a dashboard.

For example, let’s say you want to test a variation of your onboarding flow to determine which leads to higher user activation .

You can go to Userpilot’s Flows tab and click on Experiments. There, you’ll be able to select the type of test you want to run, for instance, a controlled A/B test , build a new flow, test it, and get the results.

Use quantitative funnel analysis to increase conversion rates

With Userpilot, you can track your customers’ journey as they complete actions and move through the funnel. Funnel analytics give you insights into your conversion rates and conversion times between two events, helping you identify areas for improvement.

Imagine you want to analyze your free-to-paid conversions and the differences between devices. Just by looking at the graphic, you can draw some insights:

• There’s a significant drop-off between steps one and two, and two and three, indicating potential user friction .
• Users on desktops convert at higher rates than those on mobile or unspecified devices.
• Your average freemium conversion time is almost three days.

Leverage cohort analysis to optimize retention

Another Userpilot functionality that can help you analyze quantitative data is cohort analysis . This powerful tool lets you group users based on shared characteristics or experiences, allowing you to analyze their behavior over time and identify trends, patterns, and the long-term impact of changes on user behavior.

For example, let’s say you recently released a feature and want to measure its impact on user retention. Via a cohort analysis, you can group users who started using your product after the update and compare their retention rates to previous cohorts.

You can do this in Userpilot by creating segments and then tracking user segments ‘ retention rates over time.

Check how many users adopted a feature with a retention table

In Userpilot, you can use retention tables to stay on top of feature adoption . This means you can track how many users continue to use a feature over time and which features are most valuable to your users. The video below shows how to choose the features or events you want to analyze in Userpilot.

As you’ve seen, to conduct quantitative analysis, you first need to identify your business and research goals. Then, collect, clean, and visualize the data to spot trends and patterns. Lastly, analyze the data, share it with stakeholders, and act upon insights to build better products and drive customer satisfaction.

To stay on top of your KPIs, you need a product analytics tool. With Userpilot, you can automate data capture, analyze product analytics, and view results in shareable dashboards. Want to try it for yourself? Get a demo .

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Can I Combine Qualitative And Quantitative Methods?

Qualitative and Quantitative research provides a deep understanding of the problem in separate manner. Qualitative researches are often to be of understanding the personal views of the people, whereas quantitative researches are of concrete statistics and generalization of the population through the samples . However, there has been an increasing trend from early 90’s in combining the quantitative and qualitative research which in turns delivers significant insights about the problem. In this blog, I will list out the benefits of combining both researches to give meaningful results of the study.

• In earlier studies, the qualitative researches are being conducted face-to-face . However, with the advent of digital technologies, the qualitative research can be conducted through online mode where the respondents share the information which you need in less time. In addition, the qualitative and quantitative data can be collected with the same survey and with the same respondents in this online mode of data collection .
• Having the data collected from the online survey, now the researcher can develop a hypothesis of the problem whether it is of understanding the problem or validate a hypothesis of interest (qualitative) or to explore the scope of the problem (quantitative) with the same respondent group.
• Combining the qualitative and quantitative research often saves time in the case of market research analysis. For example, consider a online shopping survey, here the researcher wants to know the pattern of people purchasing the product and the pattern of people not purchasing the product from a website. The research problem is multi-fold, i.e, the people may add a product to the cart and leaves the website without shopping or the people may do a window shopping or the people may search for products for the new arrivals or the people may leave the website because of no trial experience without purchasing. Thus, the problem can be taken as both qualitative and quantitative in a single sample survey. Combining the two research may lead to reduce the time of data collection , reduce the cost, and resources.

As in the case of earlier example, the combination of both qualitative and quantitative research gives more deeper insights about the problem and it allow us to satisfy the customer needs in a timely manner with a proper decision through the statistical analysis .

The researches in early 80’s are often of either qualitative or quantitative in nature. However, you can optimize the research interest with mixing both at a time. Though it may not sounds good in certain area of research, but it becomes a common paradigm in many statistical practices. Qualitative and quantitative methods can be combined at various point of view, i.e either at the time of data collection or at the analysis of data . In practice, there are three different research designs to combine these both research in a single survey. They are

Explanatory Design

• Exploratory Design
• Parallel Convergence Design

Let’s see each of this in detail.

In an explanatory design , there are two possibilities of conducting a mixed research (i) the observations are recorded in qualitative manner and the analyses are conducted using quantitative manner by converting the text data into numbers say, number of times good opinion recorded or the number of times person purchased a product, and (ii) collect a large sample for quantitative study and from that collect a sub sample with small size and conduct qualitative research for getting more detailed opinions about the problem.

Let me explain you the second case with an example. Suppose, the researcher is interested in finding the shopping experience of 10 mobile websites and he/she constructed a questionnaire on the topic and conducted a large comparative study using quantitative measures. Then with the statistical results, he/she wants to explore the shopping experience in more precise manner. So, small samples of say, 15 respondents are chosen from the population and conducted a face-to-face interview and analysed the data in qualitative manner. Here the researcher used a new set of 15 participants for the study. However, one can select a sub sample from the previous samples and proceed with the analysis.

In an explanatory design, the study starts from qualitative to quantitative

Exploratory design is something like a pilot study. First we make use of small participants and test the research hypothesis and then develop the idea to a quantitative research.

Parallel Convergent Design

So far, we have seen that the data is being collected separately that is either quantitative or qualitative manner. Here, in the case of parallel convergent design , the qualitative and quantitative data is being collected simultaneously but independently and taken forward for the statistical data analysis . In analysing this kind of mixed data, the research interest often to compare and contrast the findings of the study and identify if there is any pattern in the data.

Suppose a company executive wants to identify patterns in the purchase of products who are shopping from an online store and who are shopping from the showroom. Thus, the company executive decided to conduct a qualitative research for the persons shopping from showroom and quantitative research for the online shopping. Then the executive combines and converge the findings to deliver meaning insights to develop the business standard.

In conclusion, If you still have a question of whether to combine the qualitative and quantitative research, then my answer is a Big Yes! you can combine these two according to the research needs and keep a clear track of the data is collected and the statistical analysis you use to deliver proper inference of the problem.

• Brewer, J. & Hunter, A. (1989) Multimethod research: A synthesis of styles. Newbury Park, CA: Sage.
• Bryman, A. (1992) Quantitative and qualitative research: further reflections on their integration. In Brannen, J. (ed) Mixing Methods: Qualitative and Quantitative Research. Aldershot: Avebury.
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