A note on research approaches: for Housing Studies students

A note on research approaches: for Housing Studies students

 

Describe the following research approaches in the context of doing Housing Studies dissertation projects:

Category 1 (methods)

1.1. Qualitative research
1.2. Quantitative research

1.3 Mixed methods research

Category 2 (reasoning)
2.1 Inductive approach
2.2 Deductive approach
2.3 Abductive approach

 

In Housing Studies dissertation projects, research approaches are typically grouped into methods (qualitative vs quantitative) and reasoning logics (inductive, deductive, abductive). Each shapes how you frame questions, collect data, and build arguments.

Category 1: Research Methods

1.1 Qualitative research

Qualitative research in Housing Studies focuses on understanding meanings, experiences, and social processes related to housing, rather than measuring frequencies or averages.

Typical features in Housing Studies dissertations:

• Data types: In-depth interviews, focus groups, participant observation, field notes, policy documents, media texts, and sometimes visual or spatial materials (e.g. photos of estates, floor plans).
• Common designs:
• Case studies of a housing estate, co-housing project, or regeneration scheme.
• Ethnographic work in neighbourhoods or temporary housing.
• Narrative or “story-sharing” approaches to capture residents’ lived experiences of home, displacement, or community.
• Grounded theory to develop new concepts about, for example, housing justice or informal housing practices.
• Analysis techniques: Thematic analysis, content analysis, narrative analysis, discourse analysis, and sometimes semiotic or textual analysis of policy and media.
• Strengths for Housing Studies:
• Captures complex, context-specific realities (e.g. how residents experience estate renewal, gentrification, or public housing allocation).
• Generates rich, detailed explanations of why and how housing outcomes occur, not just that they occur.
• Useful when existing theory is limited or when studying under-researched groups (e.g. homeless youth in transitional housing).
• Limitations:
• Findings are usually not statistically generalisable to all housing contexts; instead, they offer transferable insights when contexts are similar.
• More vulnerable to researcher bias; requires careful reflexivity and transparent documentation of decisions.

In a part-time Housing Studies dissertation, qualitative methods are often chosen when the research question is “how” or “why” something happens in a particular housing setting, and when depth and nuance matter more than breadth.

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1.2 Quantitative research

Quantitative research in Housing Studies uses numerical data and statistical techniques to describe patterns, test relationships, and sometimes make predictions about housing phenomena.

Typical features in Housing Studies dissertations:

• Data types:
• Secondary data: census data, housing surveys, transaction records, rental listings, administrative registers.
• Primary data: structured questionnaires with closed-ended items, rating scales, or experimental designs.
• Common designs and techniques:
• Descriptive statistics to profile housing conditions, affordability, or demographic patterns.
• Regression models (e.g. hedonic pricing models) to estimate how property characteristics affect prices or rents.
• Comparative statistical analysis across cities, regions, or policy regimes.
• Large-scale survey analysis to test hypotheses about, for example, the impact of housing policy on satisfaction or mobility.
• Strengths for Housing Studies:
• Enables generalisation (within defined populations) and comparison across places and time.
• Useful for testing specific hypotheses derived from existing theories (e.g. “higher housing cost burden reduces residential satisfaction”).
• Supports policy evaluation by quantifying effects (e.g. changes in affordability after a subsidy reform).
• Limitations:
• May overlook contextual nuances and lived experiences behind the numbers.
• Dependent on data quality and the appropriateness of measures (e.g. how “affordability” is defined and operationalised).

Quantitative approaches are particularly suitable when your Housing Studies dissertation aims to measure the scale, distribution, or statistical relationships of housing variables, or to test theory-driven hypotheses.

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1.3 Mixed methods research

Mixed methods research in Housing Studies deliberately combines qualitative and quantitative methods within a single project to gain a more comprehensive and nuanced understanding of housing issues than either approach could achieve alone.

Core idea:
You collect and analyse both numerical data (e.g. surveys, administrative records, property data) and textual or visual data (e.g. interviews, focus groups, documents, observations), then integrate the findings to address your research questions.

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Typical designs in Housing Studies dissertations

Common mixed-methods designs include:

• Convergent (parallel) design:
• Qualitative and quantitative data are collected at roughly the same time, analysed separately, then compared or merged to see where they confirm, complement, or contradict each other.
• Example: A survey measuring residents’ satisfaction with a regeneration project, conducted alongside in-depth interviews exploring why people feel satisfied or dissatisfied.
• Explanatory sequential design:
• Quantitative first, then qualitative to explain or elaborate on the statistical results.
• Example: You first analyse census or survey data showing sharp affordability stress in certain districts, then interview households in those areas to understand coping strategies and decision-making.
• Exploratory sequential design:
• Qualitative first, to explore a phenomenon and inform the design of a subsequent quantitative phase.
• Example: You conduct focus groups with public housing tenants to identify key dimensions of “housing quality”, then develop and test a structured questionnaire based on those dimensions across a larger sample.
• Case-study mixed methods:
• Within a single housing estate, programme, or policy, you combine multiple data sources (e.g. interviews, surveys, policy documents, property data) to build a rich, multi-layered analysis.
• Example: A dissertation on a Housing First programme might combine administrative data on homelessness outcomes with qualitative interviews and observations of participants’ experiences.

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Why use mixed methods in Housing Studies?

Mixed methods are particularly valuable in Housing Studies because housing problems are often multi-dimensional (economic, social, spatial, policy-related) and affect different groups in different ways.

Key benefits:

• More comprehensive understanding:
• Quantitative data show patterns, scale, and relationships (e.g. who is affected, how much, where).
• Qualitative data reveal meanings, processes, and mechanisms (e.g. how people experience policies, why they make certain housing choices).
• Contextualised findings:
• Numbers are interpreted in light of local social, cultural, and policy contexts uncovered through interviews, documents, or observation.
• Improved validity through triangulation:
• Using multiple methods and data sources can cross-check findings and reduce the risk that results are artefacts of one method alone.
• Better support for policy and practice:
• Policymakers often want both evidence of impact (quantitative) and insight into how and why interventions work or fail (qualitative).

Illustrative examples from Housing Studies:

• A study on affordable housing combined survey data on community outcomes with in-depth interviews and focus groups, finding positive impacts on health and education and identifying the mechanisms behind these effects.
• Research on Housing First programmes integrated administrative data on homelessness with qualitative interviews and observations, showing effectiveness in reducing homelessness and highlighting key success factors.
• A dissertation on publicly assisted affordable rental housing used closed-ended surveys and open-ended interviews to examine social acceptance and attitudes towards such housing.

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Challenges and limitations

Mixed methods are powerful but demanding, especially for part-time dissertations with time and resource constraints.

Common challenges:

• Methodological complexity:
• You need competence in both qualitative and quantitative techniques, and in how to integrate them coherently.
• Time and resource intensity:
• Collecting and analysing two types of data usually takes more time, effort, and sometimes funding than a single-method project.
• Integration difficulties:
• It can be challenging to meaningfully connect qualitative and quantitative findings rather than simply reporting them side by side.
• Weak integration can lead to a “two studies in one” feel, rather than a genuinely mixed-methods design.
• Potential biases:
• If one component is much stronger or more dominant, or if integration is poorly handled, the overall validity can suffer.

For a Housing Studies dissertation, mixed methods are most appropriate when your research question explicitly requires both breadth and depth, and when you can realistically manage the additional workload within your programme’s timeline.

  

Category 2: Reasoning Approaches

Reasoning approaches describe how you move between theory and data when building your argument.

2.1 Inductive approach

An inductive approach moves from specific observations to broader patterns, concepts, or theories.

In Housing Studies dissertations:

• You start with empirical data (e.g. interview transcripts, field notes, documents) without a strong pre-existing theoretical framework dictating what you must find.
• Through coding and interpretation, you identify recurring themes or categories (e.g. “sense of home”, “fear of displacement”, “informal support networks”).
• These themes are then developed into concepts or a tentative theory about the housing phenomenon under study.
• Commonly associated with interpretivist philosophies and qualitative methods, especially grounded theory and some case-study work.

Example:
You interview residents in several public housing estates in Hong Kong about their experiences of estate management. From their narratives, you derive a new conceptual framework of “resident agency in high-density public housing”, rather than testing an existing model.

Inductive reasoning is valuable when existing theories do not fully explain the housing issue you are studying, or when you want to generate new, context-sensitive insights.

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2.2 Deductive approach

A deductive approach moves from general theory to specific hypotheses and then to empirical testing.

In Housing Studies dissertations:

• You begin with existing theories or models (e.g. housing affordability models, residential mobility theory, capability approach to housing justice).
• From these, you derive testable hypotheses (e.g. “Higher housing cost burden is associated with lower subjective well-being among low-income renters”).
• You then collect quantitative (or sometimes structured qualitative) data to test whether the hypotheses are supported.
• Commonly associated with positivist or post-positivist philosophies and quantitative methods, though structured qualitative designs can also be deductive.
• Example:
  Using the capability approach, you hypothesise that access to secure tenure increases residents’ perceived housing capabilities. You test this using survey data on tenure type and capability indicators across several neighbourhoods.

Deductive reasoning is appropriate when your goal is to test, refine, or challenge existing theories in a housing context, especially where measurable variables and clear hypotheses can be specified.

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2.3 Abductive approach

An abductive approach involves iteratively moving between data and theory to develop the most plausible explanation for surprising or complex findings.

In Housing Studies dissertations:

• You may start with an empirical puzzle (e.g. why some low-income households remain in high-cost areas despite apparent affordability stress).
• You consider existing theories, but also allow new interpretations to emerge as you engage with the data.
• You cycle between:
• Observations (qualitative, quantitative, or mixed)
• Existing theoretical ideas
• New or revised explanations that better fit the evidence
• This approach is closely linked to pragmatism and critical realism, and often supports mixed-methods designs.

Example:
You find that some residents in a regeneration area report improved well-being despite displacement. Existing displacement theories cannot fully explain this. Through abductive reasoning, you combine interview insights (e.g. improved services, stronger social networks) with quantitative data on service access to propose a revised understanding of “beneficial displacement” under specific policy conditions.

Abduction is especially useful in Housing Studies when:

• The phenomenon is complex or contradictory (e.g. mixed outcomes of housing policies).
• Existing theories offer partial or conflicting explanations.
• You want to develop practically useful, context-sensitive explanations that can inform policy and practice.

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How these approaches combine in practice

In many Housing Studies dissertations, especially at MBA or master’s level, you will see combinations:

• Qualitative + inductive: Exploring residents’ experiences to generate new concepts about housing quality or community.
• Quantitative + deductive: Testing hypotheses about affordability, prices, or policy impacts using survey or administrative data.
• Mixed methods + abductive: Using both surveys and interviews to explain complex housing outcomes and refine theory in light of unexpected findings.

How mixed methods relate to reasoning approaches

Mixed methods often align well with an abductive reasoning approach, where you move iteratively between data and theory to develop the most plausible explanation for complex housing phenomena.

·        Quantitative results may highlight patterns that qualitative data help explain.

·        Qualitative insights may suggest new variables or relationships to test quantitatively.

·        Together, they support theory refinement rather than simple description or single-theory testing.

Your choice should be driven by your research question, the state of existing theory, the type of data you can access, and what kind of contribution (descriptive, explanatory, theoretical, or practical) you aim to make in Housing Studies.