Qualitative Data Analysis

Flashcards about qualitative data analysis, covering topics such as ethical concerns, data analysis spiral, data management, reading and memoing, coding, theming, interpretation, and data representation.

Qualitative Data Analysis

A complex process that focuses on diverse forms of data rather than numerical data.

Protection of Participants

Ensuring participants are not exposed to harm, and maintaining anonymity using composite profiles.

Ethical Disclosure of Findings

Ensuring research findings are truthful and accurate through member checking and avoiding misrepresentation of data.

Collaborative Interpretation

Involving participants and multiple researchers in the analysis process to avoid biased conclusions.

Data Analysis Spiral

A non-rigid approach to data analysis that evolves as research progresses, involving continuous engagement with data and refining earlier interpretations.

Key Stages of Qualitative Data Analysis

Managing data, reading and memoing emergent ideas, describing and classifying codes into themes, developing and assessing interpretations, and representing and visualizing the data.

Effective Data Management

Ensures researchers can efficiently retrieve, compare, and analyze their data, preventing confusion and loss of valuable information.

Naming System Conventions

Data form (interview, FGD, field notes, etc.), Participants ID (R1, R2, etc.), Date of data collection, and Location or context (if needed).

Database or Spreadsheet Organization

Cataloging files based on demographic info, data type, data collection date, and file location.

Reading and Memoing Emergent Ideas

Helps researchers immerse themselves in the data, identify initial themes, and track their analytical thinking throughout the study.

Immersing in the Data

Researchers should read transcripts several times, absorbing the details before breaking the data into parts.

Memoing

Writing notes, key phrases, and analytical thoughts alongside the data to serve as early interpretations.

Segment Memos

Capturing immediate thoughts about phrases or sections of the data.

Memoing During Coding

Refining and organizing the meaning behind codes during the coding process.

Memoing for Theme Development

Moving beyond individual codes to develop broader themes.

Memoing for Theory Development

Connecting findings to existing theories or developing new models.

Memoing for Final Report

Summarizing key insights and ensuring findings are well-supported before writing the research paper.

Key Questions in Examining Data

What is it, why, when, how, and when was it produced, and what meanings does the data convey?

Describing and Classifying Codes into Themes

Describing the data, coding the data by identifying key information, and classifying codes into themes by grouping related concepts.

Developing and Assessing Interpretation

Making sense of data, extracting larger meanings, and forming connections to existing knowledge.

Assessing Interpretation

Challenging interpretations with alternative explanations, peer checks, or comparison with existing literature.

Text-Based Representation

Narrative descriptions that summarize findings.

Matrices and Comparison Tables

Help researchers compare categories, themes, or groups in a structured way.

Hierarchical Tree Diagram

Organizes data by levels of abstraction, with broader themes at the top and more specific categories below.

Clustered Display and Pattern Recognition

Looking for clusters or relationships in data.