A note on the steps involved in analyzing interview transcripts with theories: for Housing Studies students

A note on the steps involved in analyzing interview transcripts with theories: for Housing Studies students (information from perplexity.ai)

My question 1: "Regarding the task of qualitative data analysis for a Housing Studies dissertation project, I have a specific query about the steps involved in conducting theory-driven analysis (with both inductive and deductive reasoning) on a semi-structure interview transcript. The aim is both to contribute to theory evaluation/ enhancement as well as to generate some concrete actionable knowledge. Could you offer a straightforward set of analysis steps to conduct such an analysis, using an illustrative example on the following two cases:

Case 1: to use 2 to 3 housing studies theories to analyze 4 interview transcripts of the Hong Kong Gen Zs' concern about housing needs and the impacts of the weak economy.

Case 2: to use 2 to 3 housing studies theories to analyze 4 interview transcripts on "the impacts of the 2025 Hong Kong Government Housing Policy on the housing market and the confidence of Gen Z's confidence on home purchase in 2025".."

The reply: "Let's clarify your goals for the qualitative analysis in your Housing Studies dissertation. You want a clear, step-by-step set of actions for performing theory-driven analysis on semi-structured interview transcripts, blending both inductive and deductive reasoning. The aim is twofold: to contribute to theory evaluation/enhancement and to generate concrete actionable insights.

I'll tailor the steps for your two cases:

• Case 1: Analysis of 4 interviews with Hong Kong Gen Z on housing needs and economic impact using 2-3 housing theories.

• Case 2: Analysis of 4 interviews on the 2025 Hong Kong government housing policy's impact on the housing market and Gen Z home-purchase confidence, using 2-3 housing theories.

Before I provide this practical framework, could you share your current familiarity with qualitative data analysis, especially theory-driven methods? This helps me pitch the explanation at your level.

The system question: How comfortable are you with theory-driven qualitative data analysis (blending inductive and deductive reasoning) on interview transcripts?

My response: I have some basic grasp of how to employ academic theories to conduct explanatory and evaluative analysis; I also have some basic ideas on how to conduct coding analysis (of the grounded theory) and thematic analysis for qualitative data analysis.

Based on a comprehensive review of qualitative data analysis literature with emphasis on theory-driven approaches, here is a straightforward, stepwise method using combined inductive (bottom-up) and deductive (top-down) reasoning for your Housing Studies dissertation project—which includes both theory evaluation/enhancement and generating actionable knowledge.

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General Theory-Driven Qualitative Analysis Steps

1. Data Preparation and Familiarization

   • Transcribe semi-structured interviews verbatim.

   • Immerse yourself in the data by multiple readings of transcripts.

   • Take initial notes on impressions, potential codes, and theoretical concepts.

2. Initial Coding (Open Coding - inductive)

   • Label meaningful segments of text related to housing concerns or policy impacts without forcing codes initially.

   • Capture participants’ perspectives using participants' own words where possible.

   • Generate a broad set of codes reflecting emerging themes.

3. Apply Theoretical Frameworks (Deductive Coding)

   • Select 2-3 relevant housing theories as lenses for analysis (e.g., housing affordability theory, life course theory, theory of economic vulnerability).

   • Develop a coding scheme or template based on key constructs from these theories.

   • Apply this template to the data to identify confirmatory, contradictory, or nuanced insights.

4. Iterate Between Inductive and Deductive Processes

   • Adjust codes and themes iteratively, allowing new concepts to emerge (inductive) while simultaneously testing and refining theoretical applications (deductive).

   • Use memo writing to reflect on how findings support, modify, or challenge existing theories.

5. Theme Development and Organization

   • Group initial codes into basic themes.

   • Cluster basic themes into broader organizing themes that reflect either theoretical constructs or emerging phenomena.

   • Identify a global (overarching) theme that integrates findings across participants and theories.

6. Cross-Case Analysis

   • Compare and contrast themes across different interview transcripts to identify patterns, divergences, and conditions influencing perceptions or impacts.

7. Generate Theory-Related Insights and Actionable Knowledge

   • Evaluate how empirical data supports or challenges housing theories.

   • Highlight new theoretical contributions or refinements.

   • Derive concrete policy-relevant insights and recommendations based on findings.

8. Validation and Reflexivity

   • Validate findings by peer debriefing, member checks (if possible), and searching for negative cases.

   • Reflect on researcher biases and their influence on data interpretation.

   • Document the analytic process transparently.

9. Present Results

   • Use illustrative quotes from interviews to anchor themes.

   • Present thematic networks or models that show relationships among themes and theories.

   • Clearly distinguish between theoretical contributions and practical implications.

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Illustrative Example Applications

Case 1: Hong Kong Gen Z's Housing Needs and Economy Impact

• Theories to apply: Housing affordability theory, life course theory, and economic vulnerability framework.

• Steps:

  • Code interview transcripts openly for concerns on housing affordability, aspirations, and perceived economic constraints.

  • Apply theory-based codes to examine how economic downturn impacts Gen Z housing expectations and strategies.

  • Identify emergent themes such as “delayed homeownership,” “financial insecurity,” and “prioritization of rental flexibility.”

  • Contrast how well each theory explains different elements (e.g., life course theory explains timing of home purchase; economic vulnerability explains access barriers).

  • Synthesize themes into organizing frameworks reflecting both theory confirmation and contextual modifications.

  • Generate actionable insights on targeted housing policies or financial support mechanisms adapted for Gen Z.

Case 2: Impact of 2025 HK Government Housing Policy on Market and Gen Z Confidence

• Theories to apply: Housing policy analysis framework, market confidence theory, behavioral economic theory.

• Steps:

  • Inductively identify perceptions and attitudes toward new policy from interview transcripts.

  • Deductively code for theoretical constructs such as policy effectiveness, market confidence indicators, and behavioral responses.

  • Develop themes around policy awareness, perceived accessibility, distrust or optimism toward market changes.

  • Evaluate policy impacts on both market behavior (e.g., buying intentions) and psychological confidence in homeownership.

  • Draw conclusions on theory applicability and suggest enhancements, e.g., integrating psychological confidence with policy impact models.

  • Propose concrete policy recommendations based on patterns in confidence and market reactions among Gen Z.

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This approach balances inductive discovery with deductive testing of theories, ensuring rigorous, theory-informed yet data-driven qualitative analysis that contributes to scholarly theory and practical knowledge for housing issues."

A collection of blog notes on using chatgpt for research purpose.