research techniques qualitative quantitative and mixed methods approaches for engineers

• Research Techniques

Qualitative, Quantitative and Mixed Methods Approaches for Engineers

• © 2023
• Habeeb Adewale Ajimotokan 0

Department of Mechanical Engineering, University of Ilorin, Ilorin, Nigeria

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• Reports on how to develop a research plan
• Step-by-step instructions for conducting engineering research and gaining a patent
• Lists generic and soft skills required for success in engineering research

Part of the book series: SpringerBriefs in Applied Sciences and Technology (BRIEFSAPPLSCIENCES)

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Table of contents (5 chapters)

Front matter, introduction and basic concepts of engineering research.

Habeeb Adewale Ajimotokan

Research Problem and Questions

Use of libraries, literature search and review, developing a research plan, project report writing and presentations.

• Engineering Research
• Research Approaches
• Research Designs
• Research Processes

About this book

This book provides a hands-on guide towards conducting state-of-the-art engineering research and gaining a patent. It lists pragmatic, step-by-step instructions that cover every stage in engineering research and patent gaining, from choosing a topic to the presentation of research outcomes or patent application. The topics include the introduction and basic concepts of engineering research; research problem and questions; use of libraries, literature search and review; developing a research plan; research data collection methods, analysis and interpretation; project report writing and presentations; and inventions and patents. This book is ideal for engineering undergraduate and postgraduate students and/or first-time or novice researchers and academics intending to launch their research studies and careers.

Authors and Affiliations

About the author.

Habeeb Adewale Ajimotokan is a Senior Lecturer at the University of Ilorin, Ilorin, Nigeria, in the Department of Mechanical Engineering. His research interests include catalysis for energy and bioproducts; renewable energy and systems; heat recovery-to-power systems; material energy efficiency and sustainability; process simulation and optimisation; engineering education; and Nigerian content development.

Bibliographic Information

Book Title : Research Techniques

Book Subtitle : Qualitative, Quantitative and Mixed Methods Approaches for Engineers

Authors : Habeeb Adewale Ajimotokan

Series Title : SpringerBriefs in Applied Sciences and Technology

DOI : https://doi.org/10.1007/978-3-031-13109-7

Publisher : Springer Cham

eBook Packages : Engineering , Engineering (R0)

Copyright Information : The Author(s), under exclusive license to Springer Nature Switzerland AG 2023

Softcover ISBN : 978-3-031-13108-0 Published: 20 September 2022

eBook ISBN : 978-3-031-13109-7 Published: 19 September 2022

Series ISSN : 2191-530X

Series E-ISSN : 2191-5318

Edition Number : 1

Number of Pages : XV, 76

Number of Illustrations : 2 b/w illustrations, 2 illustrations in colour

Topics : Engineering/Technology Education , Research Skills

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Research Techniques: Qualitative, Quantitative and Mixed Methods Approaches for Engineers

About this ebook, about the author.

Habeeb Adewale Ajimotokan is a Senior Lecturer at the University of Ilorin, Ilorin, Nigeria, in the Department of Mechanical Engineering. His research interests include catalysis for energy and bioproducts; renewable energy and systems; heat recovery-to-power systems; material energy efficiency and sustainability; process simulation and optimisation; engineering education; and Nigerian content development.

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Chapter 3: Developing a Research Question

3.5 Quantitative, Qualitative, & Mixed Methods Research Approaches

Generally speaking, qualitative and quantitative approaches are the most common methods utilized by researchers. While these two approaches are often presented as a dichotomy, in reality it is much more complicated. Certainly, there are researchers who fall on the more extreme ends of these two approaches, however most recognize the advantages and usefulness of combining both methods (mixed methods). In the following sections we look at quantitative, qualitative, and mixed methodological approaches to undertaking research. Table 2.3 synthesizes the differences between quantitative and qualitative research approaches.

Quantitative Research Approaches

A quantitative approach to research is probably the most familiar approach for the typical research student studying at the introductory level. Arising from the natural sciences, e.g., chemistry and biology), the quantitative approach is framed by the belief that there is one reality or truth that simply requires discovering, known as realism. Therefore, asking the “right” questions is key. Further, this perspective favours observable causes and effects and is therefore outcome-oriented. Typically, aggregate data is used to see patterns and “truth” about the phenomenon under study. True understanding is determined by the ability to predict the phenomenon.

Qualitative Research Approaches

On the other side of research approaches is the qualitative approach. This is generally considered to be the opposite of the quantitative approach. Qualitative researchers are considered phenomenologists, or human-centred researchers. Any research must account for the humanness, i.e., that they have thoughts, feelings, and experiences that they interpret of the participants. Instead of a realist perspective suggesting one reality or truth, qualitative researchers tend to favour the constructionist perspective: knowledge is created, not discovered, and there are multiple realities based on someone’s perspective. Specifically, a researcher needs to understand why, how and to whom a phenomenon applies. These aspects are usually unobservable since they are the thoughts, feelings and experiences of the person. Most importantly, they are a function of their perception of those things rather than what the outside researcher interprets them to be. As a result, there is no such thing as a neutral or objective outsider, as in the quantitative approach. Rather, the approach is generally process-oriented. True understanding, rather than information based on prediction, is based on understanding action and on the interpretive meaning of that action.

Table 3.3 Differences between quantitative and qualitative approaches (from Adjei, n.d).

Note: Researchers in emergency and safety professions are increasingly turning toward qualitative methods. Here is an interesting peer paper related to qualitative research in emergency care.

Qualitative Research in Emergency Care Part I: Research Principles and Common Applications by Choo, Garro, Ranney, Meisel, and Guthrie (2015)

Interview-based Qualitative Research in Emergency Care Part II: Data Collection, Analysis and Results Reporting.

Research Methods for the Social Sciences: An Introduction Copyright © 2020 by Valerie Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Qualitative Research

Quantitative research, mixed methods.

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Research is based on finding a solution to a problem. There are various methods of formulating a research design for a study. Two broad approaches of data collection and interpretation are qualitative and quantitative research. Qualitative research focuses in understanding a research query on a humanistic approach and generates non-numerical data.  Below are books that explain how to create a qualitative study and the statistical techniques used in analyzing the data.

There are six major types of qualitative research design:

• Phenomenology
• Ethnography
• Grounded Theory
• Analyzing Qualitative Data Publication Date: 2010
• Big data for qualitative research by Kathy A. Mills Publication Date: 2019
• Fundamentals of qualitative research: a practical guide by Kakali Bhattacharya Publication Date: 2017
• Introduction to qualitative research methods: a guidebook and resource by Steven J. Taylor, Robert Bogdan, and Majorie L. DeVault Publication Date: 2016
• Qualitative Research in Business Publication Date: 2015
• Qualitative marketing research: a practical text for understanding consumers by Dominika Maison Publication Date: 2019

The quantitative research method is based on numerical data. This type of research is concerned with the organization, analysis, interpretation and presentation of numerical data. It is important to use the appropriate statistical test(s) for analysis of the data. Below are books that explain how to create a quantitative study and the statistical techniques used in analyzing the data.

There are four major types of qualitative research design:

• Descriptive
• Correlational
• Causal-Comparative/Quasi-Experimental
• Experimental Research
• Best practices in data cleaning : a complete guide to everything you need to do before and after collecting your data by Jason W. Osborne Publication Date: 2013
• Best practices in quantitative methods by Jason W. Osborne Publication Date: 2008
• Quantitative techniques in business, management and finance : a case-study approach by Umeshkumar Dubey, D P Kothari and G K Awari Publication Date: 2017
• SAGE quantitative research methods by W. Paul Vogt Publication Date: 2011
• Teaching quantitative methods : getting the basics right by Geoff Payne and Malcolm Williams Publication Date: 2011
• SAGE Research Methods Datasets This link opens in a new window A collection of teaching datasets that can be used to support the teaching of quantitative and qualitative analytical methods used in the social sciences. These are datasets taken from larger national and international data sources, cleaned and reduced in size and complexity for teaching and self-study purposes, perfect for researchers, learning a new method, or brushing up on a familiar one. Published by: SAGE Publications
• SAGE Research Methods Cases This link opens in a new window A collection of case studies of real social research, specially commissioned and designed to help you understand abstract methodological concepts in practice. Published by: SAGE Publications

Mixed methods research design (MMR) is a type of research where the researcher(s) combine elements of qualitative and quantitative research approaches. For example, use of qualitative and quantitative viewpoints, data collection, analysis, or inference techniques for the broad purposes of breadth and depth of understanding and corroboration of the data.

Below are books that explain how to create a mixed methods research study and the statistical techniques used in analyzing the data.

• Blending qualitative and quantitative research methods in theses and dissertations by R. Murray Thomas Publication Date: 2003
• Challenging the qualitative-quantitative divide : explorations in case-focused causal analysis by Barry Cooper Publication Date: 2012
• Integrating Qualitative and Quantitative Methods: a pragmatic approach by David L. Morgan Publication Date: 2017
• Selecting the right analyses for your data : quantitative, qualitative, and mixed methods by W. Paul Vogt Publication Date: 2014
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Quantitative, qualitative, and mixed research methods in engineering education

2009, Journal of Engineering …

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Mirka Koro-Ljungberg

Background Methodology refers to the theoretical arguments that researchers use in order to justify their research methods and design. There is an extensive range of well established methodologies in the educational research literature of which a growing subset is beginning to be used in engineering education research. Purpose In this article it is shown that a more explicit engagement with methodologies, particularly with those that are currently only emerging in engineering education research, is important in order that these researchers can broaden the focus of research questions that they are able to address. Scope/Method A series of seven methodologies is outlined and for each an exemplar paper is analyzed in order to demonstrate the methodology in operation and also to highlight its particular contribution. These methodologies are: Case Study, Grounded Theory, Ethnography, Action 2 Research, Phenomenography, Discourse Analysis, Narrative Analysis. It is noted that many of the exemplar papers use more than one methodology in combination. Conclusions A consideration of the research findings of the exemplar papers shows that collectively these methodologies might allow the research community to be able to better address questions around key challenges such as students‘ responses to innovative pedagogies, diversity issues in engineering, and the changing requirements on engineering graduates in the 21st century.

Canadian Engineering Education Association

Explicit discussion of methodology is important to better understand how knowledge claims are made in a field. In light of a methodological taxonomy, this exploratory review paper examined the research topics and methodologies that were used in a sample of 142 articles published in 2018 by four major engineering education journals. The analysis reveals that engineering education research exhibits varied profiles in different engineering education journals. It also identifies several patterns and trends in the current state of engineering education research. The findings will not only provide novice engineering education researchers with a snapshot, yet an illustrating view, of the emerging field of EER but also offer a starting point to examine critical questions in the field of EER, such as quality and rigor.

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Combining qualitative and quantitative research within mixed method research designs: A methodological review

Ulrika Östlund.

a Division of Nursing, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden

b Institute for Applied Health Research/School of Health, Glasgow Caledonian University, United Kingdom

Yvonne Wengström

c Division of Nursing, Department or Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden

Neneh Rowa-Dewar

d Public Health Sciences, University of Edinburgh, United Kingdom

It has been argued that mixed methods research can be useful in nursing and health science because of the complexity of the phenomena studied. However, the integration of qualitative and quantitative approaches continues to be one of much debate and there is a need for a rigorous framework for designing and interpreting mixed methods research. This paper explores the analytical approaches (i.e. parallel, concurrent or sequential) used in mixed methods studies within healthcare and exemplifies the use of triangulation as a methodological metaphor for drawing inferences from qualitative and quantitative findings originating from such analyses.

This review of the literature used systematic principles in searching CINAHL, Medline and PsycINFO for healthcare research studies which employed a mixed methods approach and were published in the English language between January 1999 and September 2009.

In total, 168 studies were included in the results. Most studies originated in the United States of America (USA), the United Kingdom (UK) and Canada. The analytic approach most widely used was parallel data analysis. A number of studies used sequential data analysis; far fewer studies employed concurrent data analysis. Very few of these studies clearly articulated the purpose for using a mixed methods design. The use of the methodological metaphor of triangulation on convergent, complementary, and divergent results from mixed methods studies is exemplified and an example of developing theory from such data is provided.

A trend for conducting parallel data analysis on quantitative and qualitative data in mixed methods healthcare research has been identified in the studies included in this review. Using triangulation as a methodological metaphor can facilitate the integration of qualitative and quantitative findings, help researchers to clarify their theoretical propositions and the basis of their results. This can offer a better understanding of the links between theory and empirical findings, challenge theoretical assumptions and develop new theory.

What is already known about the topic?

• • Mixed methods research, where quantitative and qualitative methods are combined, is increasingly recognized as valuable, because it can potentially capitalize on the respective strengths of quantitative and qualitative approaches.
• • There is a lack of pragmatic guidance in the research literature as how to combine qualitative and quantitative approaches and how to integrate qualitative and quantitative findings.
• • Analytical approaches used in mixed-methods studies differ on the basis of the sequence in which the components occur and the emphasis given to each, e.g. parallel, sequential or concurrent.

What this paper adds

• • A trend for conducting parallel analysis on quantitative and qualitative data in healthcare research is apparent within the literature.
• • Using triangulation as a methodological metaphor can facilitate the integration of qualitative and quantitative findings and help researchers to clearly present both their theoretical propositions and the basis of their results.
• • Using triangulation as a methodological metaphor may also support a better understanding of the links between theory and empirical findings, challenge theoretical assumptions and aid the development of new theory.

1. Introduction

Mixed methods research has been widely used within healthcare research for a variety of reasons. The integration of qualitative and quantitative approaches is an interesting issue and continues to be one of much debate ( Bryman, 2004 , Morgan, 2007 , Onwuegbuzie and Leech, 2005 ). In particular, the different epistemological and ontological assumptions and paradigms associated with qualitative and quantitative research have had a major influence on discussions on whether the integration of the two is feasible, let alone desirable ( Morgan, 2007 , Sale et al., 2002 ). Proponents of mixed methods research suggest that the purist view, that quantitative and qualitative approaches cannot be merged, poses a threat to the advancement of science ( Onwuegbuzie and Leech, 2005 ) and that while epistemological and ontological commitments may be associated with certain research methods, the connections are not necessary deterministic ( Bryman, 2004 ). Mixed methods research can be viewed as an approach which draws upon the strengths and perspectives of each method, recognising the existence and importance of the physical, natural world as well as the importance of reality and influence of human experience ( Johnson and Onquegbuzie, 2004 ). Rather than continue these debates in this paper, we aim to explore the approaches used to integrate qualitative and quantitative data within healthcare research to date. Accordingly, this paper focuses on the practical issues of conducting mixed methods studies and the need to develop a rigorous framework for designing and interpreting mixed methods studies to advance the field. In this paper, we will attempt to offer some guidance for those interested in mixed methods research on ways to combine qualitative and quantitative methods.

The concept of mixing methods was first introduced by Jick (1979) , as a means for seeking convergence across qualitative and quantitative methods within social science research ( Creswell, 2003 ). It has been argued that mixed methods research can be particularly useful in healthcare research as only a broader range of perspectives can do justice to the complexity of the phenomena studied ( Clarke and Yaros, 1988 , Foss and Ellefsen, 2002 , Steckler et al., 1992 ). By combining qualitative and quantitative findings, an overall or negotiated account of the findings can be forged, not possible by using a singular approach ( Bryman, 2007 ). Mixed methods can also help to highlight the similarities and differences between particular aspects of a phenomenon ( Bernardi et al., 2007 ). Interest in, and expansion of, the use of mixed methods designs have most recently been fuelled by pragmatic issues: the increasing demand for cost effective research and the move away from theoretically driven research to research which meets policymakers’ and practitioners’ needs and the growing competition for research funding ( Brannen, 2009 , O’Cathain et al., 2007 ).

Tashakkori and Creswell (2007) broadly define mixed methods research as “research in which the investigator collects and analyses data, integrates the findings and draws inferences using both qualitative and quantitative approaches” (2007:3). In any mixed methods study, the purpose of mixing qualitative and quantitative methods should be clear in order to determine how the analytic techniques relate to one another and how, if at all, the findings should be integrated ( O’Cathain et al., 2008 , Onwuegbuzie and Teddlie, 2003 ). It has been argued that a characteristic of truly mixed methods studies are those which involve integration of the qualitative and quantitative findings at some stage of the research process, be that during data collection, analysis or at the interpretative stage of the research ( Kroll and Neri, 2009 ). An example of this is found in mixed methods studies which use a concurrent data analysis approach, in which each data set is integrated during the analytic stage to provide a complete picture developed from both data sets after data has been qualitised or quantitised (i.e. where both forms of data have been converted into either qualitative or quantitative data so that it can be easily merged) ( Onwuegbuzie and Teddlie, 2003 ). Other analytic approaches have been identified including; parallel data analysis, in which collection and analysis of both data sets is carried out separately and the findings are not compared or consolidated until the interpretation stage, and finally sequential data analysis, in which data are analysed in a particular sequence with the purpose of informing, rather than being integrated with, the use of, or findings from, the other method ( Onwuegbuzie and Teddlie, 2003 ). An example of sequential data analysis might be where quantitative findings are intended to lead to theoretical sampling in an in depth qualitative investigation or where qualitative data is used to generate items for the development of quantitative measures.

When qualitative and quantitative methods are mixed in a single study, one method is usually given priority over the other. In such cases, the aim of the study, the rationale for employing mixed methods, and the weighting of each method determine whether, and how, the empirical findings will be integrated. This is less challenging in sequential mixed methods studies where one approach clearly informs the other, however, guidance on combining qualitative and quantitative data of equal weight, for example, in concurrent mixed methods studies, is rather less clear ( Foss and Ellefsen, 2002 ). This is made all the more challenging by a common flaw which is to insufficiently and inexplicitly identify the relationships between the epistemological and methodological concepts in a particular study and the theoretical propositions about the nature of the phenomena under investigation ( Kelle, 2001 ).

One approach to combining different data of equal weight and which facilitate clear identification of the links between the different levels of theory, epistemology, and methodology could be to frame triangulation as a ‘methodological metaphor’, as argued by Erzberger and Kelle (2003) . This can help to; describe the logical relations between the qualitative and quantitative findings and the theoretical concepts in a study; demonstrate the way in which both qualitative and quantitative data can be combined to facilitate an improved understanding of particular phenomena; and, can also be used to help generate new theory ( Erzberger and Kelle, 2003 ) (see Fig. 1 ). The points of the triangle represent theoretical propositions and empirical findings from qualitative and quantitative data while the sides of the triangle represent the logical relationships between these propositions and findings. The nature and use of the triangle depends upon the outcome from the analysis, whether that be convergent , where qualitative and quantitative findings lead to the same conclusion; complementary, where qualitative and quantitative results can be used to supplement each other or; divergent , where the combination of qualitative and quantitative results provides different (and at times contradictory) findings. Each of these outcomes requires a different way of using the triangulation metaphor to link theoretical propositions to empirical findings ( Erzberger and Kelle, 2003 ).

Illustrating the triangulation triangle ( Erzberger and Kelle, 2003 )

1.1. Purpose of this paper

In the following paper, we identify the analytical approaches used in mixed methods healthcare research and exemplify the use of triangulation ( Erzberger and Kelle, 2003 ) as a methodological metaphor for drawing inferences from qualitative and quantitative findings. Papers reporting on mixed methods studies within healthcare research were reviewed to (i) determine the type of analysis approach used, i.e. parallel, concurrent, or sequential data analysis and, (ii) identify studies which could be used to illustrate the use of the methodological metaphor of triangulation suggested by Erzberger and Kelle (2003) . Four papers were selected to illustrate the application of the triangulation metaphor on complementary, convergent and divergent outcomes and to develop theory.

This literature review has used systematic principles ( Cochrane, 2009 , Khan, 2001 ) to search for mixed methods studies within healthcare research. The first search was conducted in September 2009 in the data bases CINAHL, Medline and PsycINFO on papers published in English language between 1999 and 2009. To identify mixed methods studies, the search terms (used as keywords and where possible as MeSH terms) were: “mixed methods”, “mixed research methods”, “mixed research”, “triangulation”, “complementary methods”, “concurrent mixed analysis” and “multi-strategy research.” These terms were searched individually and then combined (with OR). This resulted in 1896 hits in CINAHL, 1177 in Medline and 1943 in PsycINFO.

To focus on studies within, or relevant to, a healthcare context the following search terms were used (as keywords or as MeSH terms and combined with OR): “health care research”; “health services research”; and “health”. These limits applied to the initial search (terms combined with AND) resulted in 205 hits in Medline and 100 hits in PsycINFO. Since this combination in CINAHL only limited the search results to 1017; a similar search was conducted but without using the search term triangulation to capture mixed methods papers; resulting in 237 hits. In CINAHL the search result on 1017 papers was further limited by using “interventions” as a keyword resulting in 160 papers also selected to be reviewed. Moreover; in Medline the mixed methods data set was limited by the MeSH term “research” resulting in 218 hits and in PsycINFO with “intervention” as keyword or MeSH term resulting in 178 hits.

When duplicates were removed the total numbers of papers identified were 843. The abstracts were then reviewed by each author and those identified as relevant to the review were selected to be retrieved and reviewed in full text. Papers were selected based on the following inclusion criteria: empirical studies; published in peer review journals; healthcare research (for the purpose of this paper defined as any study focussing on participants in receipt, or involved in the delivery, of healthcare or a study conducted within a healthcare setting, e.g. different kinds of care, health economics, decision making, and professionals’ role development); and using mixed methods (defined as a study in which both qualitative and quantitative data were collected and analysed ( Halcomb et al., 2009b ). To maintain rigour, a random sample (10%) of the full text papers was reviewed jointly by two authors. Any disagreements or uncertainties that arose between the reviewers regarding their inclusion or in determining the type of analytic approach used were resolved through discussion between the authors.

In addition to the criteria outlined above, papers were excluded if the qualitative element constituted a few open-ended questions in a questionnaire, as we would agree with previous authors who have argued such studies do not strictly constitute a mixed methods design ( Kroll and Neri, 2009 ). Papers were also excluded if they could not be retrieved in full text via the library services at the University of Edinburgh, Glasgow Caledonian University or the Karolinska Institutet, or did not adequately or clearly describe their analytic strategy, for example, failing to report how the qualitative and quantitative data sets were analysed individually and, where relevant, how these were integrated. See Table 1 for reasons for the exclusion of subsequent papers.

Reasons for exclusion.

A second search was conducted within the databases of Medline, PsychInfo and Cinahl to identify studies which have specifically used Erzberger and Kelle's (2003) triangulation metaphor to frame the description and interpretation of their findings. The term ‘triangulation metaphor’ (as keywords) and author searches on ‘Christian Erzberger’ and ‘Udo Kelle’ were conducted. Three papers, published by Christian Erzberger and Udo Kelle, were identified in the PsychInfo databases but none of these were relevant to the purpose of this review. There were no other relevant papers identified in the other two databases.

168 Papers were included in the final review and reviewed to determine the type of mixed analysis approach used, i.e. parallel, concurrent, or sequential mixed analysis. Four of these papers (identified from the first search on mixed methods studies and healthcare research) were also used to exemplify the use of the methodological metaphor of triangulation ( Erzberger and Kelle, 2003 ). Data was extracted from included papers accordingly in relation to these purposes.

In total, 168 papers were included in our review. The majority of these studies originated in the USA ( n  = 63), the UK ( n  = 39) and Canada ( n  = 19), perhaps reflecting the considerable interest and expertise in mixed methods research within these countries. The focus of the studies included in the review varied significantly and the populations studied included both patients and healthcare professionals.

3.1. Analytic approaches

Table 2 illustrates the types of analytic approaches adopted in each of the studies included in the review. The most widely used analytic approach ( n  = 98) was parallel analysis ( Creswell and Plano Clark, 2007 ). However, very few of the studies employing parallel analysis clearly articulate their purpose for mixing qualitative and quantitative data, the weighting (or priority) given to the qualitative and quantitative data or the expected outcomes from doing so, mirroring previous research findings ( O’Cathain et al., 2008 ). The weighting, or priority, of the qualitative and quantitative data in a mixed methods study is dependent upon various factors including; the aims of the study and whether the purpose is, for example, to contextualise quantitative data using qualitative data or to use qualitative data to inform a larger quantitative approach such as a survey. Nonetheless, the omission of this statement makes it difficult to determine which data set the conclusions have been drawn from and the role of, or emphasis on, each approach. Therefore, is of importance for authors to clearly state this in their papers ( Creswell and Plano Clark, 2007 ). A number of studies had also used sequential data analysis ( n  = 46), where qualitative approaches were visibly used to inform the development of both clinical tools (e.g. Canales and Rakowski, 2006 ) and research measures and surveys (e.g. Beatty et al., 2004 ) or where quantitative surveys were supplemented by and issues further explored using qualitative approaches (e.g. Abadia and Oviedo, 2009 , Cheng, 2004 , Halcomb et al., 2008 ).

Included papers illustrating their analytical approach and country of origin.

Most notably, with only 20 included studies using a concurrent approach to data analysis, this was the least common design employed. Compared to the studies using a parallel or sequential approach, the authors of concurrent studies more commonly provided an explanation for their purpose of using a mixed methods design in their study, e.g. how it addressed a gap or would facilitate and advance the state of knowledge (e.g. Bussing et al., 2005 , Kartalova-O’Doherty and Tedstone Doherty, 2009 ). Despite this, there remained a lack of clarity within these studies about the weighting given to, and priority of, each method. Consequently, the importance and relevance of the findings produced by each approach and how these have informed their conclusions and interpretation is lacking. In four of the included papers a combination of approaches to data analysis (i.e. sequential and concurrent, parallel and concurrent, or sequential and parallel) were used. In the next section, we have selected papers to illustrate the methodological metaphor of triangulation ( Erzberger and Kelle, 2003 ).

3.2. Using the methodological metaphor of triangulation

We have selected four papers from our review ( Lukkarinen, 2005 , Midtgaard et al., 2006 , Shipman et al., 2008 , Skilbeck et al., 2005 ) to illustrate how the methodological metaphor of triangulation ( Erzberger and Kelle, 2003 ) can be applied to mixed methods studies. Each of these studies has been used to illustrate how the metaphor of triangulation can be applied to studies producing: (i) complementary findings, (ii) convergent findings, and (iii) divergent findings. In the following section, we demonstrate how the application of the metaphor can be used as a framework both to develop theory and to facilitate the interpretation of the findings from mixed methods studies and their conclusions in each of these scenarios, and how using the metaphor can help to promote greater clarity of the study's purpose, its theoretical propositions, and the links between data sets.

3.2.1. Triangulating complementary results

To exemplify the use of the methodological metaphor of triangulation ( Erzberger and Kelle, 2003 ) for drawing inferences from complementary results, we have drawn on the results of a UK based study by Shipman et al. (2008) ( Fig. 2 ). In the UK, members of district nursing teams (DNs) provide most nursing care to people at home in the last year of life. Following concerns that inadequate education might limit the confidence of some DNs to support patients and their carers’ at home, and that low home death rates may in part be related to this, the Department of Health (DH) identified good examples of palliative care educational initiatives for DNs and invested in a 3-year national education and support programme in the principles and practice of palliative care. Shipman et al.’s study evaluates whether the programme had measurable effects on DN knowledge and confidence in competency in the principles and practice of palliative care. The study had two parts, a summative (concerned with outcomes) quantitative component which included ‘before and after’ postal questionnaires which measured effects on DNs’ ( n  = 1280) knowledge, confidence and perceived competence in the principles and practice of palliative care and a formative (concerned with process) qualitative component which included semi-structured focus groups and interviews with a sub-sample of DNs ( n  = 39).

Illustrating the use of triangulation ( Erzberger and Kelle, 2003 ) on complementary results in the study by Shipman et al. (2008) .

While their theoretical proposition may not be explicitly stated by the authors, there is clearly an implicit theoretical proposition that the educational intervention would improve DNs knowledge and confidence (theoretical proposition 1, Fig. 2 ). This was supported by the quantitative findings which showed significant improvement in the district nurses confidence in their professional competence post intervention. Qualitative results supported and complemented the quantitative findings as the district nurses described several benefits from the program including greater confidence in tackling complex problems and better communication with patient and carers’ because of greater understanding of the reasons for symptoms. Thus, a complementary theoretical proposition (theoretical proposition 2, Fig. 2 ) can be deduced from the qualitative findings: the DN's better understanding of factors contributing to complex problems and underlying reasons for symptoms led to improved confidence in competence raised from district nurses increased understanding.

Fig. 2 illustrates the theoretical propositions, the empirical findings from qualitative and quantitative data and the logical relationships between these. Theoretical proposition 1 is supported by the quantitative findings. From qualitative findings, a complementary theoretical proposition (theoretical proposition 2) can be stated explaining the process that led to the DNs improved confidence in competence.

3.2.2. Triangulating convergent results

To illustrate how the methodological metaphor of triangulation can be used to draw inferences from convergent findings, we have drawn on the example of a Danish study by Midtgaard et al. (2006) ( Fig. 3 ). This study was conducted to explore experiences of group cohesion and changes in quality of life (QoL) among people ( n  = 55) who participated in a weekly physical exercise intervention (for six weeks) during treatment for cancer. The study, conducted in a Danish hospital, involved the use of structured QoL questionnaires, administered at baseline and post intervention (at six weeks) to determine changes in QoL and health status, and qualitative focus groups, conducted post intervention (at six weeks), to explore aspects of cohesion within the group. With regards to the theoretical proposition of the study ( Fig. 3 ), group cohesion was seen as essential to understand the processes within the group that facilitated the achievement of desired outcomes and the satisfaction of affective needs as well as promoting a sense of belonging to the group itself.

Illustrating the use of triangulation ( Erzberger and Kelle, 2003 ) on convergent results in the study by Midtgaard et al. (2006) .

This proposition was deductively tested in an intervention where patients exercised in mixed gender groups of seven to nine members during a nine hour weekly session over a six week period and was supported by both the empirical quantitative and qualitative findings. The quantitative data showed significant improvements in peoples’ emotional functioning, social functioning and mental health. The qualitative data showed how the group setting motivated the individuals to pursue personal endeavors beyond physical limitations, that patients used each others as role models during ‘down periods’ and how exercising in a group made individuals feel a sense of obligation to train and to do their best. This subsequently helped to improve their social functioning which in turn satisfied their affective needs, improving their improved emotional functioning and mental health.

Fig. 3 illustrates the theoretical propositions, empirical findings from qualitative and quantitative data and the logical relationships between these. Both the quantitative and qualitative findings, demonstrating improvements in participants’ emotional and social functioning and their mental health, can be attributed to the nature of group cohesion within the programme as expected.

3.2.3. Triangulating divergent results

Qualitative and quantitative results that seem to contradict each other are often explained as resulting from methodological error. However, instead of a methodological flaw, a divergent result could be a consequence of the inadequacy of the theoretical concepts used. This may indicate the need for changing or developing the theoretical concepts involved ( Erzberger and Kelle, 2003 ). The following example of using the methodological metaphor of triangulation ( Erzberger and Kelle, 2003 ) for drawing inferences from divergent results is intended as an example rather than an attempt to change the theoretical concept involved. In a study by Skilbeck et al. (2005) ( Fig. 4 ), some results were found to be divergent which was explained as resulting from the use of inadequate questionnaires. We do not wish to critique their conclusion; rather we intend to simply offer an alternative interpretation for their findings.

Illustrating the use of triangulation ( Erzberger and Kelle, 2003 ) on divergent results using the study by Skilbeck et al. (2005) .

The study aimed to explore family carers’ expectations and experiences of respite services provided by one independent hospice in North England. This hospice provides inpatient respite beds specifically for planned respite admission for a two-week period. Referrals were predominated from general practitioners and patients and their carers were offered respite care twice a year, during the study this was reduced to once a year for each patient. Data was collected prior to respite admission and post respite care by semi-structured interviews and using the Relative Stress Scale inventory (RSSI), a validated scale to measure relative distress in relation to caring. Twenty-five carers were included but pre- and post-data were completed by 12 carers. Qualitative data was analysed by using a process of constant comparison and quantitative data by descriptive and comparative statistical analysis.

No clear theoretical proposition was stated by the authors, but from the definition of respite care it is possible to deduce that ‘respite care is expected to provide relief from care-giving to the primary care provider’ (theoretical proposition 1, Fig. 4 ). This proposition was tested quantitatively by pre- and post-test using the RSSI showing that the majority of carers experienced either a negative or no change in scores following the respite stay (no test of significance was stated). Accordingly, the theoretical proposition was not supported by the quantitative empirical data. The qualitative empirical results, however, were supportive in showing that most of the carers considered respite care to be important as it enabled them to have a break and a rest from ongoing care-responsibilities. From this divergent empirical data it could be suggested changing or developing the original theoretical proposition. It seems that respite care gave the carers relief from their care-responsibilities but not from the distress carers experienced in relation to caring (measured by the used scale). We would therefore suggest that in order to lessen distress related to caring, other types of support is also needed which would change the theoretical proposition as suggested (theoretical proposition 2).

Fig. 4 illustrates the theoretical propositions, empirical findings from qualitative and quantitative data and the logical relationships between these. Theoretical proposition 1 was not supported by the quantitative findings (indicated in Fig. 4 by the broken arrow), but the qualitative findings supported this proposition. From these divergent empirical findings, the theoretical proposition could accordingly be changed and developed. Respite care seemed to provide relief from carers’ on-going care-responsibilities, but other types of support need to be added to provide relief from distress experienced (theoretical proposition 2).

3.2.4. Triangulation to produce theoretical propositions

Methodological triangulation has also been applied to illustrate how theoretical propositions can be produced by drawing on the findings from a Finnish study by Lukkarinen (2005) ( Fig. 5 ). The purpose of this longitudinal study was to describe, explain and understand the subjective health related quality of life (QoL) and life course of people with coronary artery disease (CAD). A longitudinal quantitative study was undertaken during the year post treatment and 19 individuals also attended thematic interviews one year after treatment. This study is one of the few studies that clearly defines theoretical underpinnings for both the selected methods and their purpose, namely “to obtain quantitatively abundant average information about the QoL of CAD patients and the changes in it as well as the patients’ individual, unique experiences of their respective life situations” ( Lukkarinen, 2005 :622).

Illustrating the use of triangulation ( Erzberger and Kelle, 2003 ) to develop theory from the study by Lukkarinen (2005) .

The results of the quantitative analysis showed that the male and female CAD patients in the youngest age group had the poorest QoL. While patients’ QoL improved in the dimensions of pain, energy and mobility it deteriorated on dimensions of social isolation, sleep and emotional reactions. From the viewpoint of methodological triangulation used in the study the aim of the quantitative approach was to observe changes in QoL at the group level and also explore correlations of background factors to QoL. The qualitative approach generated information concerning both QoL in the individuals’ life situation and life course and the individuals’ rehabilitation. Both the quantitative and the qualitative analysis showed the youngest CAD patients to have the poorest psychosocial QoL. The results obtained using qualitative methods explained the quantitative findings and offered new insight into the factors related to poor psychosocial QoL, which could be used to help develop theoretical propositions around these. Patients at risk of poorer QoL were those with an acute onset of illness at a young age that led to an unexpected termination of career, resulting in financial problems, and worries about family. This group also experienced lack of emotional support (especially the females with CAD) and were concerned for the illness that was not alleviated by treatment. The interviews and the method of phenomenological psychology therefore helped to gain insight into the participants’ situational experience of QoL and life course, not detectable by the use of a questionnaire.

Fig. 5 illustrates the theoretical propositions, empirical findings from qualitative and quantitative data and the relationships between these. The use of the mixed methods approach enabled a clearer understanding to emerge in relation to the lived experience of CAD patients and the factors that were related to poor QoL. This understanding allows new theoretical propositions about these issues to be developed and further explored, as depicted at the theoretical level.

4. Discussion

As the need for, and use of, mixed methods research continues to grow, the issue of quality within mixed methods studies is becoming increasingly important ( O’Cathain et al., 2008 , O’Cathain et al., 2007 ). Similarly, the need for guidance on the analysis and integration of qualitative and quantitative data is a prominant issue ( Bazeley, 2009 ). This paper firstly intended to review the types of analytic approaches (parallel, concurrent or sequential data analysis) that have been used in mixed methods studies within healthcare research. As identified in previous research ( O’Cathain et al., 2008 ), we found that the majority of studies included in our review employed parallel data analysis in which the different analyses are not compared or consolidated until the full analysis of both data sets have been completed. A trend to conduct separate analysis on quantitative and qualitative data is apparent in mixed methods healthcare studies, despite the fact that if the data were correlated, a more complete picture of a particular phenomenon may be produced ( Onwuegbuzie and Teddlie, 2003 ). If qualitative and quantitative data are not integrated during data collection or analysis, the findings may be integrated at the stage of interpretation and conclusion.

Although little pragmatic guidance exists within the wider literature, Erzberger and Kelle (2003) have published some practical advice, on the integration of mixed methods findings. For mixed methodologists, the ‘triangulation metaphor’ offers a framework to facilitate a description of the relationships between data sets and theoretical concepts and can also assist in the integration of qualitative and quantitative data ( Erzberger and Kelle, 2003 ). Yet despite the fact that the framework was published in 2003 within Tashakkori and Teddlie's (2003) seminal work, the Handbook for Mixed Methods in Social and Behavioural Research, our search revealed that it has received little application within the published body of work around mixed methods studies since its publication. This is surprising since mixed methodologists are acutely aware of the lack of guidance with regards to the pragmatics and practicalities of conducting mixed methods research ( Bryman, 2006 , Leech et al., 2010 ). Furthermore, there have been frequent calls to move the field of mixed methods away from the “should we or shouldn’t we” debate towards the practical application, analysis and integration of mixed methods and its’ findings and what we can learn from each other's work and advice. Consequently, we have a state of ambiguity and instability in the field of mixed methods in which mixed methodologists find themselves lacking appropriate sources or evidence to draw upon with which to facilitate the future design, conduct and interpretation of mixed methods studies. It is for these reasons that we, in this paper, also intended to identify and select studies that could be used as examples for the application of Erzberger and Kelle's (2003) triangulation metaphor.

When reviewing the studies it was clear that the majority of theoretical assumptions were implicit, rather than explicitly stated by authors. Wu and Volker (2009) previously acknowledged that while studies undoubtedly have a theoretical basis in their literature reviews and the nature of their research questions, they often fail to clearly articulate a particular theoretical framework. This is unfortunate as theory can help researchers to clarify their ideas and also help data collection, analysis and to improve the study's rigour ( Wu and Volker, 2009 ). When using triangulation as a methodological metaphor ( Erzberger and Kelle, 2003 ), researchers are encouraged to articulate their theoretical propositions and to validate their conclusions in relation to the chosen theories. Theory can also guide researchers when defining outcome measures . Should the findings not support the chosen theory, as shown in our examples on complementary and divergent results, researchers can modify or expand their theory accordingly and new theory may be developed ( Wu and Volker, 2009 ). It is therefore our belief that using triangulation as a methodological metaphor in mixed methods research can also benefit the design of mixed method studies.

Like other researchers ( O’Cathain et al., 2008 ), we have also found that most of the papers reviewed lacked clarity in whether the reported results primarily stemmed from qualitative or quantitative findings. Many of the papers were even less clear when discussing their results and the basis of their conclusions. The reporting of mixed methods studies is notoriously challenging, but clarity and transparency are, at the very least, crucial in such reports ( O’Cathain, 2009 ). Using triangulation as a methodological metaphor ( Erzberger and Kelle, 2003 ) may be one way of addressing this lack of clarity by explicitly showing the types of data that researchers have based their interpretations on. It may even help address some of the issues raised in the debate on the feasibility of integrating research methods and results stemming from different epistemological and ontological assumptions and paradigms ( Morgan, 2007 , Sale et al., 2002 ). In order to carry out methodological triangulation researchers also need to identify and observe the consistency and adequacy of the two methods, positivistic and phenomenological regarding the research questions, data collection, methods of analysis and conclusions.

While we used systematic principles in our search for mixed methods studies in healthcare research, we cannot claim to have included all such studies. In many cases, reports of mixed methods studies are subjected to ‘salami slicing’ by researchers and hence the conduct of, and findings from, individual approaches are addressed in separate papers. Since these papers are often not indexed as a ‘mixed method’ study, they are undoubtedly more difficult to identify. Furthermore, different terminologies are used to describe and index mixed methods studies within the electronic databases ( Halcomb and Andrew, 2009a ), making it challenging to be certain that all relevant studies were captured in this review. However, the studies included in this review should give a sufficient overview of the use of mixed analysis in healthcare research and most importantly, they enable us to make suggestions for the future design, conduct, interpretation and reporting of mixed methods studies. It is also important to emphasise that we have based our triangulation examples on the data published but have no further knowledge of the analysis and findings undertaken by the authors. The examples should thus be taken as examples and not alternative explanations or interpretations.

Mixed methods research within healthcare remains an emerging field and its use is subject to much debate. It is therefore particularly important that researchers clearly describe their use of the approach and the conclusions made to improve transparency and quality within mixed methods research. The use of triangulation as a methodological metaphor ( Erzberger and Kelle, 2003 ) can help researchers not only to present their theoretical propositions but also the origin of their results in an explicit way and to understand the links between theory, epistemology and methodology in relation to their topic area. Furthermore it has the potential to make valid inferences, challenge existing theoretical assumptions and to develop or create new ones.

Conflict of interest

None declared.

Ethical approval

Not required.

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Chapter 15. Mixed Methods

Introduction.

Where deep ethnography (chapter 14) is a tradition that relies on naturalistic techniques of data collection, foregrounding the specificity of a particular culture and site, there are other times when researchers are looking for approaches that allow them to make use of some of the analytical techniques developed by statisticians and quantitative researchers to generalize the data they are collecting. Rather than push into a deeper understanding of a culture through thick interpretive descriptions, these researchers would rather abstract from a sufficiently large body of cases (or persons) to hazard predictions about a connection, relationship, or phenomenon. You may already have some experience learning basic statistical techniques for analyzing large data sets. In this chapter, we describe how some research harnesses those techniques to supplement or augment qualitative research, mixing methods for the purpose of building stronger claims and arguments. There are many ways this can be done, but perhaps the most common mixed methods research design involves the use of survey data (analyzed statistically via descriptive cross-tabs or fairly simple regression analyses of large number probability samples) plus semistructured interviews. This chapter will take a closer look at mixed methods approaches, explain why you might want to consider them (or not), and provide some guidance for successful mixed methods research designs.

What Is It? Triangulation, Multiple Methods, and Mixed Methods

First, a bit of nomenclature. Mixed methods can be understood as a path toward triangulation . Triangulation is a way of strengthening the validity of a study by employing multiple forms of data, multiple investigators, multiple theoretical perspectives, or multiple research methods. Let’s say that Anikit wants to know more about how first-year college students acclimate to college. He could talk to some college students (conduct interviews) and also observe their behavior (fieldwork). He is strengthening the validity of his study by including multiple forms of data. If both the interview and the observations indicate heavy reliance on peer networks, a reported finding about the importance of peers would be more credible than had he only interviewed students or only observed them. If he discovers that students say one thing but do another (which is pretty common, after all), then this, too, becomes an interesting finding (e.g., Why do they forget to talk about their peers when peers have so much observable influence?). In this case, we say that Anikit is employing multiple forms of data, or even that he relies on multiple methods. But he is not, strictly speaking, mixing data. Mixed methods refer specifically to the use of both quantitative and qualitative research methods. If Anikit were to supplement his interviews and/or observations with a random sample of one thousand college students, he would then be employing a mixed methods approach. Although he might not get the rich details of how friends matter in the survey, the large sample size allows statistical analyses of relationships among variables, perhaps showing which groups of students are more likely to benefit from strong peer networks. So to summarize, both multiple methods and mixed methods are forms of research triangulation, [1] but mixed methods include mixing both qualitative and quantitative research elements.

Mixed methods techniques, then, are pretty unique. Where many qualitative researchers have little interest in statistical generalizability, and many quantitative researchers undervalue the importance of rich descriptions of singular cases, the mixed methods researcher has an open mind about both approaches simultaneously. And they use the power of both approaches to build stronger results: [2]

Quantitative (mainly deductive) methods are ideal for measuring pervasiveness of “known” phenomena and central patterns of association, including inferences of causality. Qualitative (mainly inductive) methods allow for identification of previously unknown processes, explanations of why and how phenomena occur, and the range of their effects (Pasick et al. 2009). Mixed methods research, then, is more than simply collecting qualitative data from interviews, or collecting multiple forms of qualitative evidence (e.g., observations and interviews) or multiple types of quantitative evidence (e.g., surveys and diagnostic tests). It involves the intentional collection of both quantitative and qualitative data and the combination of the strengths of each to answer research questions . ( Creswell et al. 2011:5 ; emphases added)

Why Use Mixed Methods?

As with all methodological choices, the answer depends on your underlying research questions and goals. Some research questions are better answered by the strengths of the mixed methods approach. Small ( 2011 ) discusses the use of mixed methods as a confirmation or complement of one set of findings from one method by another. Creswell and Clark ( 2017:8ff .) note the following situations as being particularly aided by combining qualitative and quantitative data collection and analysis: (1) when you need to obtain both more complete (need for qualitative) and more corroborated (need for quantitative) information; (2) when you need to explain (need for qualitative) initial results (quantitative); (3) when you need to do an exploratory study (need for qualitative) before you can really create and administer a survey or other instrument (quantitative); (4) when you need to describe and compare different types of cases to get a more holistic understanding of what is going on; (5) when you need (or very much want!) to include participants in the study, adding in qualitative elements as you build a quantitative design; (6) when you need all the tools at your disposal to develop, implement, and evaluate a program.

Please note what is not included in this list: because you can . Mixed methods research is not always preferable, even if in general it makes your study “stronger.” Strength is not the only criterion for quality or value. I have met many students in my career who assume that the mixed methods approach is optimal because it includes both qualitative and quantitative research. That is the wrong way of looking at things. Mixed methods are optimal when and only when they fit the necessities of your research question (e.g., How can I corroborate this interesting finding from my interviews so that proper solutions can be fashioned?) or underlying goal (e.g., How can I make sure to include the people in this program as participants of the study?).

If you are just starting out and learning your way through designing your first study, mixed methods are not default requirements. As you will see in the next section on design, mixed methods studies often happen sequentially rather than consecutively, so I recommend you start with the study that has the most meaning to you, the one that is the most compelling. Later on, if you want to add (mix) another approach for the sake of strength or validity or “corroboration” (if you are adding quantitative) or “explanation” (if you are adding qualitative), you can always do that then, after the completion of your first study.

Segue: Historical Interlude

For those interested in a little history, one could make the case that mixed methods research in the social sciences actually predates the development of either quantitative or qualitative research methods. The very first social scientists (what we call “social science” in the West, which is itself a historical construct, as many other peoples have been exploring meaning and interpretation of the social world for centuries if not millennia) often employed a mélange of methods to address their research questions. For example, the first sociologists in the US operating out of the “Chicago School” of the early twentieth century surveyed neighborhoods, interviewing people, observing demographic subcultures, and making tallies of everything from the numbers of persons in households to what languages were being spoken. They learned many of these techniques from early statisticians and demographers in Europe—people like Charles Booth ( 1902 ), who surveyed neighborhoods in London, and Frédéric Le Play, who spent decades examining the material conditions of the working classes across Europe, famously including family “budgets” along with interviews and observations (see C. B. Silver 1982). The renowned American sociologist W. E. B. Du Bois, who was the first Black man to earn a PhD from Harvard University, also conducted one of the very first mixed methods studies in the US, The Philadelphia Negro ( 1899 ). This work mapped every Black residence, church, and business in Philadelphia’s Seventh Ward and included observations and details on family structure and occupation (similar to Booth’s earlier work on London). Continuing through the 1930s and 1940s, “community studies” were conducted by teams of researchers who basically tallied everything they could find about the particular town or city they chose to work in and performed countless interviews, months and years of fieldwork, and detailed mappings of community relationships and power relations. One of the most famous of these studies includes the “Middletown” studies conducted by Robert and Helen Lynd ( 1929 , 1937 ).

As statistical analysis progressed after World War II alongside the development of the technology that allowed for ever faster computations, quantitative research emerged as a separate field. There was a lot to learn about how to conduct statistical analyses, and there were more refinements in the creation of large survey instruments. Qualitative research—the observations and interviews at the heart of naturalistic inquiry—became a separate field for different kinds of researchers. One might even say qualitative research languished at the expense of new developments of quantitative analytical techniques until the 1970s, when feminist critiques of positivist social science emerged, casting doubt on the superiority of quantitative research methods. The rise of interdisciplinarity in recent decades combined with a lessening of the former harsh critique of quantitative research methods and the “paradigm wars” ( Small 2011 ) has allowed for an efflorescence of mixed methods research, which is where we are today.

Mixed-Methods Research Designs

Returning from our historical interlude to the list of possible uses of mixed methods, we now confront the question of research design. If we are using more than one method, how exactly do we do this, and when ? The how and the when will depend largely on why we are using mixed methods. For example, if we want to corroborate findings emerging from interviews, then we obviously begin with interviews and follow with, perhaps, a large survey. On the other hand, if we are seeking to explain findings generated from a survey, we begin with that survey and add interviews or observations or focus groups after its completion. And if we are seeking to include participants in the research design itself, we may want to work concurrently, interviewing and holding focus groups as surveys are administered. So it all depends on why we have chosen to use mixed methods.

We can think of our choices here in terms of three possibilities. The first, called sequential explanatory , begins with quantitative data (collection) and then follows with qualitative data (collection). After both are collected, interpretations are made. The second, called sequential exploratory , begins the other way around, with qualitative followed by quantitative. After both are collected, interpretations are made. The third, called concurrent triangulation , conceives of both quantitative and qualitative elements happening concurrently. In practice, one may still happen before the other, but one does not follow the other. The data then converge, and from that convergence, interpretations are made.

In sequential explanatory design (figure 15.1), we are asking ourselves, “In what ways do the qualitative findings explain the quantitative results?” ( Creswell et al. 2017 ). This design thus gives some priority to the quantitative data. The qualitative data, collected after the quantitative data, is used to provide a better understanding of the research problem and then the quantitative data alone.

Often, this means providing some context or explaining meanings and motivations behind the correlations found in the quantitative data. For example, in my research on college students ( Hurst 2019 ), I found a statistical correlation between upper-middle-class female students and study abroad. In other words, and stating this rather baldly, class*gender could be used to predict who studied abroad. But I couldn’t fully explain why, given the survey data I had collected. [3] To answer these (and other) questions that the survey results raised, I began interviewing students and holding focus groups. And it was through these qualitative forms of data collection that I found a partial answer: upper-middle-class female students had been taught to see study abroad as a final “finishing” component of their education in a way that other students simply had not. They often had mothers who had done the same. And they clearly saw connections here to the kinds of well-traveled cosmopolitan adults they wanted to become.

In sequential exploratory design (figure 15.2), we are asking ourselves, “In what ways do the quantitative findings generalize (or confirm) the qualitative results?” ( Creswell et al. 2018 ). This design thus gives some priority to the qualitative data. The quantitative data, collected after the qualitative data, is used to confirm the findings.

This approach is ideal for developing new instruments or when a researcher intends to generalize findings from a qualitative study to different groups or populations. The American Sociological Association (ASA) Task Force on First-Generation and Working-Class Persons wanted to understand how class background may have played a role in the success of sociology graduate students and faculty. Because this was a relatively new research question, the task force began by conducting several focus groups, asking general questions about how class might have affected careers in sociology. Based on several recurring findings (e.g., high debt burdens, mentorship, feelings of fit), the task force developed a survey instrument that it then administered to more than one thousand sociologists, thus generalizing the preliminary findings and providing corroboration of some of the key variables at play.

In concurrent triangulation design (figure 15.3), neither the quantitative nor the qualitative component takes precedence. Although in practice one might precede the other in time, neither is the tail that wags the dog, so to speak. They are both the dog. The general of this design is to better understand or deepen one’s understanding of the phenomenon under study. The goal is to obtain different but complementary data that strengthen (validate) the overall results.

These designs might be either nested or nonnested . In a nested design , a subsample of an original randomized sample is used for further interviews or observation. A common nested design form is where in-depth interviews are conducted with a subsample of those who filled out a survey. Nonnested designs occur when it is impractical or impossible to recruit the same individuals that took place in the survey. The research I conducted for my book Amplified Advantage ( Hurst 2019 ) is an example of this. I supplemented a large national survey of college students and recent college graduates with interviews and focus groups of similar college students and graduates who were not participants in the study (or who may have been randomly selected as participants but without my knowledge or linking their data). Nonnested designs are much more flexible than nested designs, but they eliminate the possibility of linking data across methods.

As with all research design, it is important to think about how best to address your particular research question. There are strengths and weaknesses of each design. Sequential design allows for the collection and analysis of different methods separately, which can make the process more manageable. Sequential designs are relatively easy to implement, design, and report. Sequential exploratory designs allow you to contextualize and generalize qualitative findings to larger samples, while sequential explanatory designs enable you to gain a deeper understanding of findings revealed by quantitative data analysis. All sequential design takes a lot of time, however. You are essentially doubling your research. This is why I do not recommend these approaches to undergraduate students or graduate students in master’s programs. In contrast, concurrent designs, whose dual methods may be conducted simultaneously, may be conducted more quickly. However, as a practical matter, you will probably end up focusing first on one data collection method and then the other, so the time saved might be minimal. [4] Concurrent design can also preclude following up on interesting findings that emerge from one side of the study, and the abbreviated form may prevent clarification of confusing issues that arise during analysis. If the results are contradictory or diverge, it may also be difficult to integrate the data. You might end up with more questions to pursue for further study and not much conclusive to say at the end of all your work.

Finally, there is what I will call here the recursive design model (figure 15.4), in which you combine both explanatory and exploratory sequential design.

This design is currently being used by the ASA task force mentioned above. The first stage of data collection involved several focus groups. From these focus groups, we constructed a survey that we administered to ASA members. The focus group survey could be viewed as an example of exploratory sequential design. As the surveys were being analyzed, we added a nested set of interviews with persons who had taken the survey and who indicated a willingness to participate in this later stage of data collection. These interviews then help explain some of the findings from the survey. The entire process takes several years, however, and involves multiple researchers!

Advanced: Crossover Design

Small’s ( 2011 ) review of the state of mixed methods research argues that mixed methods are being increasingly adopted in social science research. In addition to sequential and concurrent research designs, where quantitative and qualitative data work to either confirm or complement each other, he sets forth examples of innovative designs that go further toward truly blending the special techniques and strengths of both quantitative and qualitative methods. [5] Written in 2011, I have seen scant evidence so far that these blended techniques are becoming well established, but they are promising. As new software programs for data analysis emerge, along with increased computing power, there will be greater opportunities for crossover work. Perhaps you can take up the charge and attempt one of these more innovative approaches yourself.

Here is Small’s ( 2011:73ff .) list of innovative crossover research design:

• Network analyses of narrative textual data . Here, researchers use techniques of network analysis (typically quantitative) and apply them to narratives (qualitative), coding stories as separate “nodes” and then looking for connections between those nodes, as is done in network analysis.
• Sequence analyses of narrative textual data . Here, techniques of event structure analysis and optimal matching (designed for analysis of quantitative data) are applied to narratives (qualitative data). The narratives are reconceived as a series of events, and then causal pathways between these events are mapped. This allows for identification of crucial turning points as well as “nonsignificant” events that just happened.
• Quantitative analyses of semantic (meaning) elements of narrative textual data . The basic distinction between quantitative (data in the form of numbers) and qualitative (date in the form of words) gets blurred here, as words themselves and their meanings and contexts are coded numerically. I usually strongly advise beginning students to do this, as what often happens is that they begin to think quantitatively about the data, flattening it considerably. However, if done with full attention to meaning and context, the power of computing/analytical software may strengthen the coding process.
• Narrative analyses of large-n survey data. In contrast to the first three designs listed above, where quantitative techniques were applied to qualitative data, we now come to a situation where the reverse takes place. Here we have a large data set, either coded numerically or “raw” with various choice options for each question posed. Rather than read the data set as a series of factors (variables) whose relationship one explores through statistical analyses, the researcher creates a narrative from the survey responses, contextualizing the answers rather than abstracting them. [6]
• Regression-based analyses of small-n or narrative textual data. This is by far the most common crossover method and the reverse of the fourth example. Many qualitative software analysis programs now include basic quantitative analytical functions. The researcher can code interview transcripts and fieldnotes in such a way that allows for basic cross-tabulations, simple frequency statistics, or even basic regression analyses. Transcripts and fieldnotes can generate “variables” for such analyses.

Despite the promise of blending methods in this way, the possibility of doing damage to one’s study by discounting the particular values of either quantitative or qualitative approaches is a real one. Unlike mixed methods, where the two approaches work separately (even when designed to concur in time), crossover research blends or muddies the two. Small ( 2011 ) warns, “At a minimum, the application of techniques should not be fundamentally contrary to the epistemological principles from which they are derived or to the technical problems for which they were intended” ( 76 ). When employing any of these designs or blending approaches, it is very important to explain clearly and fully what one’s aims are and how the analysis has proceeded, as this allows others to evaluate the appropriateness of the design for the questions posed.

Further Readings

Cech, Erin. 2021. The Trouble with Passion: How Searching for Fulfillment at Work Fosters Inequality . Berkeley, CA: University of California Press.* Cech combines surveys with interviews to explore how people think about and talk about job searches and careers.

Cooper, Kristy S. 2014. “Eliciting Engagement in the High School Classroom: A Mixed-Methods Examination of Teaching Practices.” American Educational Research Journal 51(2):363–402. An example of using multilevel regression analyses with both interviews and observations to ascertain how best to engage students.

Creswell, John W., and J. David Creswell. 2018. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . Thousand Oaks, CA: SAGE. Essential textbook for mixed-methods research.

Edin, Kathryn, and Maureen A. Pirog. 2014. “Special Symposium on Qualitative and Mixed-Methods for Policy Analysis.” Journal of Policy Analysis and Management 33(2):345–349. A good overview of the strengths of mixed-methods research, which, the authors argue, make it particularly well suited for public policy analysis.

Hurst, Allison L. 2019. Amplified Advantage: Going to a “Good” College in an Era of Inequality . Lanham, MD: Rowman & Littlefield: Lexington Books..* Employs a national survey of recent graduates of small liberal arts colleges combined with interviews, focus groups, and archival data to explore how class background affects college outcomes.

Johnson, R. Burke, and Anthony J. Onwuegbuzie. 2004. “Mixed Methods Research: A Research Paradigm Whose Time Has Come.” Educational Researcher 33(7):14–26. Takes a pragmatic approach and provides a framework for designing and conducting mixed-methods research.

Klinenberg, Eric. 2015. Heat Wave: A Social Autopsy of Disaster in Chicago . Chicago: University of Chicago Press.* A great read and could not be more timely. Klinenberg uses a combination of fieldwork, interviews, and archival research to investigate why some neighborhoods experience greater mortality than others.

Lynd, Robert, and Helen Lynd. 1929. Middletown: A Study in American Culture . New York: Harcourt, Brace.* This early mixed-methods study of a “typical” American city was a pioneering work in sociology. The husband-and-wife team seemingly explores every aspect of life in the city, mapping social networks, surveying attitudes and beliefs, talking to people about their expectations and lives, and observing people going about their everyday business. Although none of the techniques are very sophisticated, this remains a classic example of pragmatic research.

Lynd, Robert, and Helen Lynd. 1937. Middletown in Transition . New York: Harcourt, Brace. The follow-up to the Lynds’ original study of a small American city. More theoretical and critical than the first volume.

Markle, Gail. 2017. “Factors Influencing Achievement in Undergraduate Social Science Research Methods Courses: A Mixed Methods Analysis.” Teaching Sociology 45(2):105–115.* Examines the factors that influence student achievement using an initial survey with follow-up interviews.

Matthews, Wendy K. 2017. “‘Stand by Me’: A Mixed Methods Study of a Collegiate Marching Band Members’ Intragroup Beliefs throughout a Performance Season.” Journal of Research in Music Education 65(2):179–202.* The primary method here is focus groups, but the author also employed multivariate analysis of variance (MANOVA) to shore up the qualitative findings.

Monrad, Merete. 2013. “On a Scale of One to Five, Who Are You? Mixed Methods in Identity Research.” Acta Sociologica 56(4):347–360. A call to employ mixed methods in identity research.

Silver, Catherine Bodard. 1982. Frédéric Le Play on Family, Work and Social Change . Chicago: University of Chicago Press. For anyone interested in the historic roots of mixed-methods research, the work of Frédéric Le Play is essential. This biography is a good place to start.

Small, Mario Luis. 2011. “How to Conduct a Mixed Methods Study: Recent Trends in a Rapidly Growing Literature.” Annual Review of Sociology 37:57–86. A massive review of recent mixed-methods research, distinguishing between mixed-data-collection studies, which combine two or more kinds of data, and mixed-data-analysis studies, which combine two or more analytical strategies. Essential reading for graduate students wanting to use mixed methods.

• To extend this notion of triangulation a little further: if Anikit enlisted the help of Kanchan to interpret the observations and interview transcripts, he would be strengthening the validity of the study through multiple investigators, another form of triangulation having nothing at all to do with what methods are employed. He could also bring in multiple theoretical frameworks—say, Critical Race Theory and Bourdieusian field analysis—as a form of theoretical triangulation. ↵
• If stronger is your aim, that is. For many qualitative researchers, verisimilitude, or the truthfulness of a presentation, is a more desirable aim than strength in the sense of validity. ↵
• Actually, I could do a fair amount of testing on other variables’ relationships to this finding: students who had gone far away to college (more than five hundred miles) were significantly more likely to study abroad, for example, as were students who majored in arts and humanities courses. But I still missed any way of getting at personal motivations or how individuals explained these motivations. That is the part a survey is just never going to fully get at, no matter how well or numerous the questions asked. ↵
• The big exception here is when you are relying on data that has already been collected and is ready for analysis, as in the case of large survey data sets like the General Social Survey. In that case, it is not too time consuming to design a mixed methods study that uses (nonnested) interviews to supplement your analyses of survey data. ↵
• I refer to these as blended methods rather than mixed methods because the epistemological positions and science claims, usually rather distinct from quantitative (more positivistic) and qualitative (more naturalistic), blur considerably. ↵
• I admit that trained first as a qualitative researcher, this has always been my impulse when confronting a large survey data set. ↵

A research design that employs both quantitative and qualitative methods, as in the case of a survey supplemented by interviews.

The process of strengthening a study by employing multiple methods (most often, used in combining various qualitative methods of data collection and analysis).  This is sometimes referred to as data triangulation or methodological triangulation (in contrast to investigator triangulation or theory triangulation).  Contrast mixed methods .

A mixed-methods design that conceives of both quantitative and qualitative elements happening concurrently.  In practice, one may still happen before the other, but one does not follow the other.  The data then converge and from that convergence interpretations are made.  Compare sequential exploratory design and sequential explanatory design .

A mixed-methods design that begins with quantitative data collection followed by qualitative data collection, which helps “explain” the initial quantitative findings.  Compare sequential exploratory design and concurrent triangulation .

A mixed-methods design that begins with qualitative data collection followed by quantitative data collection.  In this case, the qualitative data suggests factors and variables to include in the quantitative design.  Compare sequential explanatory design and concurrent triangulation .

A form of mixed-methods design in which a subsample of an original randomized sample is used for further interviews or observation.

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

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Understanding Q-Methodology: Bridging the Gap Between Qualitative and Quantitative Research

By  Stella Smith, Ph.D.

Introduction

Among the myriad of methodologies, Q-methodology stands out as a unique approach that offers a nuanced understanding of subjectivity while maintaining the rigor of quantitative analysis (Damio, 2016; Herrington & Coogan, 2011). On April 2nd, the Research Methodology Group hosted a webinar focused on Q-methodology Essentials. In this blog post, we delve into the essence of Q-methodology, exploring its principles, applications, and significance in contemporary research. We will end with some suggestions for how to learn more about Q-methodology.

Q-methodology

Seeks to uncover subjective viewpoints or perspectives on a particular topic by systematically analyzing individuals' rankings of statements or items

What is Q-Methodology?

Q-methodology, developed by British physicist and psychologist William Stephenson, is a research technique that combines elements of both qualitative and quantitative methodologies (Stephenson,1953). At its core, Q-methodology seeks to uncover subjective viewpoints or perspectives on a particular topic by systematically analyzing individuals' rankings of statements or items (Sandling, 2022; Van Exel & De Graaf, 2005). Unlike traditional surveys or interviews, which aim to capture consensus or frequency of responses, Q-methodology focuses on understanding the diversity of opinions within a given population.

Principles of Q-Methodology

Central to Q-methodology is the notion of "subjectivity" – recognizing that individuals interpret the world differently based on their unique experiences, beliefs, and values. The process typically involves three main steps:

Statement Generation: Researchers compile a set of statements or items relevant to the topic under study. These statements should cover a wide range of viewpoints and perspectives to capture the diversity within the population.

Q-Sorting: Participants are presented with the statements and asked to rank them according to their level of agreement or preference. This process, known as Q-sorting, requires participants to make subjective judgments about the statements based on their personal viewpoints.

Factor Analysis: The Q-sort data from multiple participants are then subjected to factor analysis, a statistical technique that identifies patterns or "factors" representing clusters of similar viewpoints. Through factor analysis, researchers can uncover underlying dimensions of opinion within the dataset.

Applications of Q-Methodology

Q-methodology has found applications across various disciplines, including psychology, sociology, political science, and market research. Some common areas of application include exploring subjective perceptions, understanding stakeholder perspectives and market segmentation.

Significance of Q-Methodology

What distinguishes Q-methodology is its ability to reconcile the richness of qualitative data with the rigor of quantitative analysis. By acknowledging the subjective nature of human perception while employing robust statistical techniques, Q-methodology offers a holistic approach to understanding complex social phenomena (Herrington & Coogan, 2011).

Moreover, Q-methodology provides a platform for amplifying marginalized voices and uncovering minority viewpoints that may be overlooked in traditional research approaches. By embracing diversity and embracing subjectivity, Q-methodology fosters a more inclusive and comprehensive understanding of the world around us.

Want to know more?

Check out the full webinar on Q-methodology which is uploaded to the  Research and Methodology Group Teams  site. 

Schedule an  office hours appointment  with a methodologist to discuss your Q-methodology design.

Review the  Qmethod  website and  Operant Subjectivity - The International Journal of Q Methodology

Damio, S. M. (2016). Q Methodology: An Overview and Steps to Implementation. Asian Journal of  University Education, 12(1), 105.

Herrington, N., &, Coogan, J. (2011). Q methodology: an overview. Research in Teacher   Education, 1(2), 24-28.

Sandling, J. (2022). Q Methodology: Complete Beginner’s Guide. Available at   https://jonathansandling.com/q-methodology-complete-beginners-guide/

Stephenson W. The study of behavior: Q-technique and its methodology. Chicago: University of Chicago Press. 1953

Van Exel, J., & De Graaf, G. (2005). Q methodology: A sneak preview. Available at https://www.betterevaluation.org/tools-resources/q-methodology-sneak-preview

Stella Smith, Ph.D.

ABOUT THE AUTHOR

Dr. Stella Smith serves as the Associate University Research Chair for Center for Educational and Instructional Technology Research (CEITR).  She is also an Assistant Professor of Qualitative Research at Prairie View A&M University. A qualitative researcher, Dr. Stella Smith's scholarly interests focus on the experiences of  African American females in leadership in higher education; diversity, equity and inclusion of underserved populations in higher education, and P–20 educational pipeline alignment.  Dr. Smith is a strong advocate for social justice and passionate about creating asset based pathways of success for underserved students.

Dr. Smith was recognized with a 2014 Dissertation Award from the American Association of Blacks in Higher Education and as part of the 2019 class of 35 Outstanding Women Leaders in Higher Education by Diverse Issues in Higher Education. Dr. Smith earned her PhD in Educational Administration with a portfolio in Women and Gender Studies from The University of Texas at Austin.

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