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.
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.
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.
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.
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.
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.
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.
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.
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.
