Qualitative data analysis digs into the meaning behind words and experiences. It's all about finding patterns and themes in things like interviews or observations. This chapter breaks down different ways researchers make sense of all that rich, messy data.

From coding interviews to developing theories, there are lots of approaches to choose from. The key is picking the right method for your research question and really getting to know your data inside and out.

Qualitative Analysis Methods

Thematic and Content Analysis Approaches

• Thematic analysis identifies, analyzes, and interprets patterns within qualitative data
  • Involves searching for recurring themes or ideas across a dataset
  • Researchers familiarize themselves with data, generate initial codes, search for themes, review themes, define and name themes, and produce the report
• Content analysis systematically categorizes and quantifies textual data
  • Examines both manifest (visible, surface) and latent (underlying meaning) content
  • Can be used for analyzing documents, interview transcripts, or media content
  • Involves developing a coding scheme, coding the data, and analyzing the results

Grounded Theory and Phenomenological Analysis

• Grounded theory develops theories from systematic analysis of data
  • Researchers collect and analyze data simultaneously, using constant comparison
  • Theory emerges from the data rather than being predetermined
  • Involves open coding, axial coding, and selective coding to develop core categories
• Phenomenological analysis explores individuals' lived experiences of a phenomenon
  • Focuses on understanding the essence of a shared experience
  • Researchers bracket their own experiences and assumptions
  • Involves reading transcripts, identifying significant statements, formulating meanings, and developing theme clusters

Key Analysis Techniques

Coding and Comparative Methods

• Coding assigns labels or tags to segments of data to categorize and organize information
  • Open coding breaks down data into discrete parts for comparison and conceptualization
  • Axial coding reassembles data by making connections between categories
  • Selective coding integrates categories to form a central explanatory concept
• Constant comparative method continuously compares data to identify similarities and differences
  • Involves comparing incidents applicable to each category
  • Integrating categories and their properties
  • Delimiting the theory
  • Writing the theory

Data Saturation and Triangulation

• Data saturation occurs when no new information or themes emerge from additional data collection
  • Indicates that sufficient data has been collected to answer research questions
  • Helps determine when to stop collecting data in qualitative studies
  • Achieved through iterative process of data collection and analysis
• Triangulation uses multiple data sources, methods, or perspectives to enhance credibility
  • Data triangulation uses different data sources (interviews, observations, documents)
  • Methodological triangulation employs multiple research methods
  • Investigator triangulation involves multiple researchers analyzing the same data
  • Theory triangulation applies different theoretical perspectives to interpret data