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Chapter 13: Qualitative Analysis - Coggle Diagram
Chapter 13: Qualitative Analysis
Definition:
Analysis of qualitative data such as interview transcripts; focuses on understanding a phenomenon rather than predicting/explaining.
Characteristics:
Dependent on researcher’s skills and knowledge of social context.
Emphasizes creative and investigative mindset.
Content Analysis
Example: Sentiment analysis (e.g., analyzing online reviews).
Criticism: Lack of systematic procedures for replication.
Process:
Unitizing: Identifying separable units of analysis.
Coding: Applying concepts to text segments.
Analysis: Determining theme frequency, context, relationships.
Sampling relevant texts.
Model for Procedure: Schilling’s spiral model with five levels:
Condensed protocols.
Preliminary category system.
Coded protocols.
Interpretations.
Raw text data.
Definition: Systematic analysis of text content.
Software Tools for Qualitative Analysis
Functions: Automate coding, organize and process large text volumes.
Limitations: Inability to decipher context/meaning behind words/phrases.
Examples: ATLAS.ti.5, NVivo, QDA Miner.
Hermeneutic Analysis
Applications: Literature, religion, law.
Key Philosophers: Martin Heidegger, Hans-Georg Gadamer.
Concept: Hermeneutic circle – iterative process between part (text) and whole (context).
Definition: Interpretive technique focusing on understanding the text in its socio-historic context.
Grounded Theory
Key Contributors: Glaser and Strauss (1967), Strauss and Corbin (1990).
Process:
Open Coding:
Identifying key concepts within textual data.
Concepts are linked to text portions.
Grouping similar concepts into higher-order categories.
Axial Coding:
Assembling categories into causal relationships/hypotheses.
Identifying conditions, actions/interactions, and consequences.
Selective Coding:
Identifying a central category and relating it to other categories.
Continual refinement until theoretical saturation.
Definition: Inductive technique for interpreting recorded data to build theories.
Techniques: Memoing, storylining, concept mapping.
Conclusions
Importance of creating a validated coding schema.
Challenges with automated analysis and potential for misinterpretation.