Data Analysis

When analysing data, qualitative researchers typically use text. The goal of qualitative data analysis is to assemble or reconstruct the data in a meaningful and understandable way that is transparent, rigorous, and remains “true” to the participants’ stories. Although qualitative data analysis is inductive and focused on meaning, methods of analysis vary in aim and have ontological and epistemological foundations.There are two key approaches to analysing qualitative data: inductive analysis and deductive analysis.

Inductive analysis involves coding data without trying to fit it into a pre-existing coding frame or the researcher’s analytic preconceptions.
Deductive analysis is driven by theoretical interest and may provide a more detailed analysis of specific aspects of the data, although it tends to produce a less detailed description of the overall data.

Another concept that we have to consider is data saturation. Data saturation refers to the point in qualitative data collection when no new information, themes, or insights are emerging from the data. It occurs when additional interviews or data sources begin to generate repetition rather than new understanding. Saturation signals that the researcher has gathered sufficient depth and breadth of data to meaningfully answer the research question, and therefore, further collection is unlikely to change the analysis.

Types of qualitative data analysis

There are different data analyses used in healthcare research, including content analysis, discourse analysis, thematic analysis, interpretive phenomenological analysis, narrative analysis, and grounded theory analysis. The common ones include content analysis, discourse analysis, and thematic analysis, and as beginner researchers, we will focus on these.

Content analysis

Content analysis is a method of unobtrusively investigating large volumes of textual material to detect trends and patterns in words used, their frequency, their connections, and the structures and discourses of communication. It transforms qualitative input into quantitative data by quantifying words, messages, or concepts and analysing the relationships between them. The goal of content analysis is to explain the features of a document’s content by examining who says what, to whom, and to what effect.

Content analysis typically involves four stages: decontextualization, recontextualization, categorization, and compilation.

• Decontextualization: The researcher familiarizes themselves with the data (e.g., reading through transcripts) and breaks it into meaning units with assigned codes.

• Recontextualization: The researcher checks that all relevant content aligns with the study’s aim, revisits the text, and adds any missed meaningful data.

• Categorization: The codes are condensed and grouped into categories and themes.

• Compilation: The researcher synthesizes the findings and begins the writing process.

Discourse analysis

Discourse analysis investigates language in use rather than psychological factors such as attitudes or memories. It studies language in terms of construction and function and views language as a tool for producing social reality. Thus, discourse analysis explores how particular concerns are constructed in people’s narratives and the variability in these accounts, examining how language shapes social experience. In healthcare, discourse analysis can be used to examine communication between doctors or nurses and patients, interprofessional interactions, or in-depth interviews about lay health beliefs.

Thematic analysis

Thematic analysis is a technique for identifying, examining, and reporting patterns (themes) within data. It involves generating codes or units of analysis that emerge from the data and is the most commonly used method in qualitative research.

Thematic analysis follows six iterative phases:

1. Familiarizing yourself with the data

2. Generating initial codes

3. Searching for themes

4. Reviewing themes

5. Defining and naming themes

6. Producing the report

Because thematic analysis is flexible and accessible, it is particularly suitable for new researchers, as it does not require deep theoretical or technical expertise.

Remixed from:

• An Introduction to Research Methods for Undergraduate Health Profession Students by Bunmi Malau-Aduli and Faith Alele (2023), James Cook University, published under a CC BY NC 4.0 license.
  • Chapter 4: Qualitative Research

References