Mixed Methods Approach Model in Research

Purpose and type of research Mixed methods

According to Creswell, Plano Clark, Gutmann, and Hanson (2003), mixed methods research involve “the collection or analysis of quantitative and qualitative data in one study in which data are collected simultaneously or sequentially, given priority, and involve data integration”.  

This definition underscores some of the characteristics of mixed methods research design and data collection.  First, quantitative and qualitative data are collected simultaneously or sequentially.  Second, quantitative and qualitative data may receive equal emphasis, or one type of data may have priority over another.

Decisions regarding data priority are indirect.  Creswell et al.  (2003) provide several criteria that researchers should consider when determining priorities between quantitative and qualitative data.  

These criteria include studies cited in the literature review;  research question;  the amount of data collected;  length of discussion about data types;  and preferences of the target audience.

The part that most expresses Creswell's definition, in my view, is the part about 'data integration'.  The authors emphasize that the mixed methods approach is not about incorporating different types of data and data analysis methods into one study.  Different types of data need to be closely integrated to answer the research questions posed.  The importance of data integration is also emphasized in the applied linguistics literature.

When discussing the characteristics of mixed methods, J. D. Brown (2014) argues that mixed methods research should use “quantitative and qualitative methods systematically in complementary relationships to reinforce one another”.  He also claims that mixed methods must have 'a deliberate mixing of quantitative and qualitative methods.  

Therefore, only collecting and analyzing quantitative and qualitative data does not qualify the study as a mixed methods study.  In contrast, mixed methods research is about using quantitative and qualitative methods systematically in such a way that they reinforce each other's results and interpretations.  If quantitative and qualitative data do not interact in a certain way, it is not mixed methods.  

To be called mixed methods, a study must have a strategic and purposeful integration of the two methods.  Through data integration, mixed methods can respond to research questions in a more meaningful way than quantitative or qualitative methods alone.

While there is uniformity in the definitions and benefits of mixed methods research, there are different types of mixed methods designs (Creswell et al., 2003):

  1. Sequential explanation: Quantitative data is collected first, followed by qualitative data which can explain the findings from the quantitative data (for example, after assessing pragmatic competence at the group level, following up on several participants to gain an understanding of their characteristics).
  2. Sequential exploration: Qualitative data is collected first, followed by quantitative data to interpret qualitative findings.  (eg collecting interview data to identify recurring themes and then using those themes to develop survey items).
  3. Sequential transformative: Quantitative or qualitative data are collected sequentially for the purpose of changing existing policies or practices.
  4. Concurrent triangulation: Quantitative and qualitative data are collected simultaneously with equal emphasis and used to cross-validate findings.
  5. Concurrent nesting: Quantitative and qualitative data are collected simultaneously, but one type of data is more dominant than another.  The less dominant data is nested (embedded) within the more dominant data and used to enrich the data description.
  6.  Concurrently transformative: Quantitative and qualitative data are collected simultaneously with the same emphasis, and used for the purpose of changing existing policies or practices.

There are a number of benefits to mixed methods research.  According to Tashakkori and Teddlie (2003, mixed methods can "answer research questions that other methodologies cannot" and what's more, mixed methods can yield "better and stronger conclusions" based on the results. This strength is understandable given that mixed methods are a combination of two methods that follow different philosophical and methodological orientations.

The quantitative approach takes a confirmatory approach and is used for the purpose of confirming (or disproving) the hypothesis, while the qualitative approach is exploratory.  By combining the two, researchers can test hypotheses, and at the same time they can explore the meaning behind (dis)confirmed hypotheses.  For example, in instructed pragmatic studies, researchers often compare several treatment conditions for their effectiveness in producing learning outcomes.

Once one condition is proven to be more effective than another, the researcher can explore why a particular condition is effective by insinuating the elements involved in the condition and the participants (e.g., focus on form, interaction, and learner characteristics such as motivation).  A qualitative approach is helpful for understanding the many elements in a system.  

Mixed methods can lead to stronger conclusions because triangulation of data is embedded in the design.  Part of the research process is collecting data and drawing conclusions based on that data.  Conclusions drawn from data become stronger when there is more evidence, i.e. more data.  Hence, mixed methods help researchers achieve a comprehensive understanding of complex phenomena.


Taguchi, N. (2018).  Description and explanation of pragmatic development: Quantitative, qualitative, and mixed methods research.  Systems, 75, 23-32.

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