Thematic Framework Analysis is a qualitative data analysis approach that combines the pattern-identification strengths of thematic analysis with the systematic structure and data management capabilities of framework analysis.
At its core, thematic analysis is known for its versatility, allowing qualitative researchers to identify, analyze, and report patterns within textual data. It's a flexible method for summarizing key features of a large body of data.
Conversely, Framework analysis offers a more systematic approach, enabling structured data management and a clear audit trail. It is particularly useful for large datasets and multi-disciplinary research teams, providing matrices to manage and organize data by theme and participant.
Therefore, Thematic Framework Analysis integrates these two methods. It involves:
- Identifying recurring themes and patterns across the dataset (borrowing from thematic analysis).
- Organizing and managing this thematic data within a structured framework, often using matrices, which facilitates systematic comparison and charting across cases and themes (borrowing from framework analysis).
This combined approach allows researchers to not only discover rich themes within their qualitative data but also to manage this data systematically, making the analysis process transparent and rigorous, particularly for large-scale projects.
Key Aspects
Combining thematic and framework analysis offers several benefits:
- Systematic Data Management: Framework's matrix structure helps organize themes identified through thematic analysis.
- Clear Audit Trail: The systematic process enhances transparency and allows others to follow the analytical steps.
- Efficient Comparison: Data charted in a framework matrix allows for easy comparison of themes across different participants or cases.
- Handling Large Datasets: The structured approach is well-suited for managing extensive qualitative data.
How It Works (Simplified)
Imagine you have interview data.
- Familiarization: Read and understand the data.
- Identifying Themes: Go through the data, highlighting or noting recurring ideas, concepts, or experiences (Thematic Analysis step).
- Creating the Framework: Develop categories or themes based on the identified patterns. Create matrices (Framework Analysis step) where rows represent cases (e.g., participants) and columns represent the themes.
- Charting Data: Summarize and enter relevant data extracts or analytical notes into the appropriate cells of the matrix for each participant and theme.
- Interpretation: Analyze the charted data, looking for patterns, variations, and relationships across themes and cases.
Example Use Case
Consider a study exploring patients' experiences with a chronic illness.
- Thematic Analysis Component: Researchers might identify themes like "coping strategies," "impact on daily life," "support systems," and "interaction with healthcare."
- Framework Analysis Component: They would create a matrix with patients as rows and these themes as columns. Data excerpts related to each theme for each patient would be charted in the matrix.
- Combined Benefit: This allows researchers to systematically compare how different patients discussed coping strategies or the varying impacts on their daily lives, providing a structured overview that is easy to navigate and analyze further.
This integrated method leverages the strengths of both approaches to provide a rigorous and manageable way to analyze qualitative data, particularly in studies with multiple participants or complex research questions.
