Can thematic analysis be used in mixed methods research?

Yes, thematic analysis can be effectively incorporated into mixed methods research.

Mixed methods research, characterized by collecting and analyzing both qualitative and quantitative data, seeks a more comprehensive understanding of a research problem than a single approach could provide.

Thematic analysis (TA) is a flexible method for identifying and analyzing patterns in qualitative data. It can be used with a range of theoretical and philosophical commitments, making it adaptable to different research designs.

TA is valuable in mixed methods research because it can reveal the “why” behind quantitative results. It provides a deeper understanding of the quantitative findings.

Researchers can use qualitative data to explain statistical relationships, contextualize findings, and explore the underlying mechanisms of observed phenomena.

For example, if a survey reveals a correlation between a certain personality trait and career success, thematic analysis of interview data could provide insights into how that trait manifests in different work environments and contributes to success.

Integrating Thematic Analysis in Mixed Methods Research

Integration, a key aspect of mixed methods research, involves intentionally combining quantitative and qualitative research to create interdependence and synergy between the two approaches.

There are multiple potential levels of integration, including at the design, methods, and representation levels.

1. Design:

Thematic analysis is compatible with a range of mixed methods designs, including convergent parallel, exploratory sequential, and explanatory sequential designs.

The choice of design depends on the research questions and the function of qualitative data within the study.

• Convergent parallel design involves simultaneously collecting quantitative and qualitative data, analyzing these datasets separately, and then merging the results for interpretation.
• Explanatory sequential design is used in research when you want to use one type of data to help explain the findings of another type.
• Exploratory sequential design is a type of research that involves two phases of data collection and analysis, with the qualitative phase coming first.

2. Method:

It’s crucial to ensure that all methods used work in synergy and support the research goals.

Method level integration combines data collection approaches in mixed methods research by using insights from one method to shape data collection in the other.

Approaches to integration:

• Thematic analysis of qualitative data can help shape the development of quantitative data collection tools or provide context for interpreting quantitative findings.
• When qualitative research informs quantitative approaches, researchers use interview themes and participant language to develop targeted survey items and measurement scales that better capture the studied phenomena.
• Conversely, quantitative findings can guide qualitative data collection by helping researchers refine interview questions, focus on statistically significant variables, or probe unexpected results.
• In concurrent integration, researchers simultaneously collect and analyze both data types, allowing for real-time adjustments to sampling strategies and measurement protocols based on emerging patterns and participant feedback.

Examples of integration:

• For example, thematic analysis of qualitative data collected alongside an RCT can illuminate participants’ experiences, perceptions of the intervention, and contextual factors influencing outcomes.
• Thematic analysis of qualitative data can inform the sampling strategy for the quantitative phase. For example, researchers could use themes from initial interviews to select specific participants for a subsequent survey, ensuring representation of diverse perspectives.

Importance of methodological rigor and transparency:

• Rationale and Purpose: Researchers should clearly articulate their rationale for using thematic analysis and how it contributes to achieving the study’s objectives.
• Transparency: Transparency in the analytical process, including coding procedures, theme development, and integration strategies, is crucial. This transparency enables other researchers to understand and evaluate the findings.
• Established Guidelines: Using established guidelines for thematic analysis, such as those outlined by Braun and Clarke (2006), enhances the rigor and trustworthiness of the findings.

3. Representation:

Qualitative data can illuminate the mechanisms behind observed statistical relationships, providing insights into how and why certain variables are associated with particular outcomes.

It can also provide context-specific information that helps to interpret the meaning of quantitative findings. Thematic analysis helps to explain and contextualize quantitative results.

Qualitative themes derived from thematic analysis can be woven into the narrative presentation of quantitative findings, offering deeper insights and understanding.

Several methods can be used to integrate qualitative and quantitative data:

• Integrating through data transformation involves converting one type of data (qualitative or quantitative) into the other type. For example, qualitative data may be converted into numerical counts, which are then integrated with other numerical data for analysis.
• Pattern matching in mixed methods research involves connecting qualitative themes to quantitative data points.
• Thematic analysis can identify themes that are then compared or related to quantitative data, generating new insights (meta-inferences).

Examples of these methods in practice:

• Researchers found that participants who frequently discussed workplace stress in interviews also scored higher on standardized burnout assessment scales (pattern matching).
• One approach is to examine a statistical profile of each theme. Another is to compare themes across different levels of quantitative variables. For example, researchers could compare themes from interviews of individuals with high grit scores to those with low grit scores.

Visual representations, such as tables or matrices, can be used to showcase the relationships between quantitative results and qualitative themes from thematic analysis, facilitating the understanding of complex relationships.

For example, thematic quote excerpts alongside relevant statistical graphs.