CHAPTER 2. MIXED METHODS RESEARCH DESIGNS

‘Mixed methods research is the type of research in which a researcher or team of researchers combines elements of qualitative and quantitative research approaches (e.g. use of qualitative and quantitative viewpoints, data collection, analysis, inference techniques) for the broad purposes of breadth and depth of understanding and corroboration’ (Johnson et al. 2007, p. 123).

Mixed methods research is gaining popularity in social sciences because it combines the strengths of both quantitative and qualitative research to address the complex social problems, which neither qualitative nor quantitative approaches on their own can properly address, while their combined use provides an expanded understanding of research problems (Creswell, 2009, p. 188). So, a mixed methods design is characterized by the combination of at least one qualitative and one quantitative research component (Schoonenboom & Johnson, 2017, p. 108).

Morse (1991) established a commonly used mixed methods notation system, in which the components are marked as qual and quan (or QUAL and QUAN to emphasize primacy) for qualitative and quantitative research respectively. Plus (+) sign refers to the concurrent implementation of components, and arrow (→) refers to sequential implementation of components. To ensure the equity of both research traditions, each abbreviation contains the equal number of letters, i.e. four (Schoonenboom & Johnson, 2017, p. 108).

Several primary characteristics that should be considered during a mixed methods design process include as follows: the purpose of mixing, theoretical drive, timing, point of integration, typological use, and degree of complexity (Schoonenboom & Johnson, 2017, p. 109).

Purpose: the overall goal of a mixed methods design is to expand and strengthen the conclusions of a study, thus making a contribution to the existing literature. A mixed methods research study should be of sufficient quality to answer the research questions, and achieve ‘multiple validation legitimation’ (Johnson & Christensen, 2017) by meeting the relevant combination of quantitative, qualitative and mixed methods validities in each research study. Based on an analysis of mixed methods designs, Green, Caracelli, and Graham (1989) proposed a classification of purposes, which is still popular, and includes as follows:

• triangulation – seeking convergence, corroboration, correspondence of results of different methods;
• complementarity – seeking elaboration, enhancement, illustration, clarification of the results from one method with the results from the other method;
• development – seeking to use the results of one method to help develop or inform the other method with regard to sampling, implementation, measurement decisions;
• initiation – seeking to discover the paradox and contradiction, new perspectives of frameworks, the recasting of questions and results from one method with questions or results from the other method;
• expansion – seeking to extend the breadth and range of inquiry by using different methods for different inquiry components (Schoonenboom & Johnson, 2017, p. 110).

It is important for a researcher to begin a study with at least one research question, and then carefully consider what the purposes of mixing are. One can use mixed methods to examine different aspects of a single research question, or one can use separate but related qualitative and quantitative research questions. Nevertheless, the mixing of methods, methodologies, and/or paradigms will help to answer the research questions, and make improvements over a more basic study design. Fuller and richer information will be obtained in a mixed methods study.

Theoretical drive: mixed methods research can have three different drives, as formulated by Johnson et al. (2007):

• qualitative dominant (or qualitatively driven) mixed methods research is the type of mixed research in which one relies on a qualitative view of the research process, while concurrently recognizing that the addition of quantitative data and approaches are likely to benefit the research project.
• quantitative dominant (or quantitatively driven) mixed methods research is the type of mixed research in which one relies on a quantitative view of the research process, while concurrently recognizing that the addition of qualitative data and approaches are likely to benefit the research project.
• the area around the center of the (qualitative-quantitative) continuum, i.e. equal status, is convenient for the person that self-identifies as a mixed methods researcher. This researcher takes as his or her starting point the logic and philosophy of mixed methods research. These mixed methods researchers are likely to believe that qualitative and quantitative data and approaches will add insights into most, if not all, research questions. The equal status research is most easily conducted when a research team is composed of qualitative, quantitative, and mixed methods researchers, who interact continually, and conduct a study to address one superordinate goal (Schoonenboom & Johnson, 2017, p. 113).

Timing: it has two aspects: simultaneity and dependence (Guest, 2013). Simultaneity forms the basis of the distinction between concurrent and sequential designs. In a sequential design, the quantitative component precedes the qualitative one, or vice versa. In a concurrent design, both components are executed (almost) simultaneously. In the notation of Morse (1991), concurrence is indicated by ‘+’ between components (e.g. QUAL + quan), while sequentiality is indicated with ‘→’ (QUAL → quan). It is possible to collect interview data and survey data of one inquiry simultaneously, and in that case, the research activities would be concurrent. It is also possible to conduct interviews after the survey data have been collected (or vice versa), and in that case, research activities are performed sequentially. The second aspect of timing is dependence. Two research components are dependent if the implementation of the second component depends on the results of data analysis in the first component. Two research components are independent if their implementation does not depend on the results of data analysis in the other component. A researcher can often choose whether to perform data analyses independently or not. A researcher can analyse interview data and questionnaire data of one inquiry independently, and in that case, the research activities would be independent. It is also possible to let the interview questions depend upon the outcomes of the analysis of the questionnaire data (or vice versa), and in that case, the research activities are performed dependently. It is up to the researcher to determine whether a concurrent-dependent design, a concurrent-independent design, a sequential-dependent design, or a sequential-independent design is needed to answer a particular research question or set of research questions in a given situation.

Point of integration: each true mixed methods study has at least one point of integration called the point of interface by Morse and Niehaus (2009) and Guest (2013), at which the qualitative and quantitative components are brought together. Having one or more points of integration is the distinguishing feature of a design based on multiple components. It is at this point that the components are ’mixed’, and hence the label ’mixed methods designs’. The term ’mixing’, however, is misleading as the components are not simply mixed, but have to be integrated very carefully. Determining where the point of integration will be, and how the results will be integrated, is an important, if not the most important, decision in the design of mixed methods research (Schoonenboom & Johnson, 2017, p. 115). Some primary ways of integrating the components are as follows:

• merging the two data sets,
• connecting from the analysis of one set of data to the collection of a second set of data,
• embedding one form of data within a larger design or procedure, and
• using a framework (theoretical or programme) to bind together the data sets (Creswell & Clark, 2011, p. 76).

Creswell (2009) provides the explanation of six main, commonly used mixed methods designs, which include as follows:

Sequential Explanatory Design 

It appeals to the researcher with strong quantitative inclination. Quantitative data collection and analysis are followed by qualitative data collection and analysis, which build on the results of the initial quantitative results. It is usually used when unexpected results arise from quantitative analyses, and qualitative data collection serves to examine the surprising results in more detail. It is easy to implement because the steps fall into separate stages, and therefore it is easy to describe and report.

Sequential Exploratory Design

First, qualitative data are collected and analysed, followed by a quantitative data collection and analysis that builds on the results of the first, qualitative phase. Quantitative data and results help to interpret qualitative findings, but the aim is to initially explore a phenomenon. It is appropriate when testing elements of an emergent theory resulting from the qualitative phase, as well as to generalize findings to different samples, or when an instrument needs to be developed.

Sequential Transformative Design

It is a two-phase project, but with a theoretical lens such as gender or race overlaying the sequential procedures. The initial phase can be either qualitative or quantitative, and is followed by the second phase, also either qualitative or quantitative, which builds on the earlier phase. The theoretical lens shapes the research question aimed at exploring a problem, and guides the study.

Sequential designs are visually presented in Figure 2.

The researcher collects both quantitative and qualitative data concurrently, and then compares the two databases to determine if there is a convergence, difference, or some combination of the two. This model generally uses separate quantitative and qualitative methods as a means to offset the weaknesses inherent within one method with the strengths of the other (or conversely, the strength of one adds to the strength of the other). The mixing during this approach, usually found in an interpretation or discussion section, is to actually merge the data (i.e. transform one type of data into the other type of data so that they can be compared easily) or integrate or compare the results of two databases side by side in a discussion. The concurrent data collection results in a shorter data collection time period as compared to the one of the sequential approaches because both the qualitative and quantitative data are gathered at the same time at the research site. This model also has a number of limitations. It requires great effort and expertise to adequately study a phenomenon with two separate methods. It can also be difficult to compare the results of two analyses using different forms of data.

Both qualitative and quantitative data are collected simultaneously, but this approach has a primary method that guides the project, and a secondary database that provides a supporting role in the procedures. Given less priority, the secondary method (qualitative or quantitative) is embedded or nested within the predominant method (qualitative or quantitative). The mixing of the data from the two methods is often done to integrate the information and compare one data source with the other, typically accomplished in the discussion section of a study. However, the data also may not be compared but reside side by side as two different pictures that provide an overall composite assessment of the problem. This would be the case when the researcher uses this approach to assess different research questions or different levels in an organization.

It is guided by the researcher’s use of a specific theoretical perspective, as well as the concurrent collection of both quantitative and qualitative data. This perspective can be based on ideologies such as critical theory, advocacy, participatory research, or a conceptual or theoretical framework. This perspective is reflected in the purpose or research questions of the study. It is the driving force behind all methodological choices, such as defining the problem, identifying the design and data sources, analysing, interpreting, and reporting results. The choice of a concurrent model, whether it is triangulation or embedded design, is made to facilitate this perspective.

Concurrent designs are visually presented in Figure 3.

Creswell, J. W. (2009). Research design qualitative, quantitative, and mixed methods approaches. Sage.

Greene, J. C., Caracelli, V. J., & Graham, W. F. (1989). Toward a conceptual framework for mixed-method evaluation designs. Educational Evaluation and Policy Analysis, 11(3), 255–274.

Guest, G. (2013). Describing mixed methods research: An alternative to typologies. Journal of Mixed Methods Research, 7(2), 141–151.

Johnson, R. B., & Christensen, L. B. (2017). Educational Research: Quantitative, qualitative, and mixed approaches. Sage.

Morse J. M. (1991). Evaluating qualitative research. Qualitative Health Research, 1(3), 283–286.

Morse J. M. & Niehaus, L. (2009). Mixed method design: Principles and procedures. Left Coast Press Inc.

Schoonenboom, J., & Johnson, R. B. (2017). How to construct a mixed methods research design. Kölner Zeitschrift für Soziologie und Sozialpsychologie, 69(2), 107–131.