A note on positivism and post-positivism: for Housing Studies students

A note on positivism and post-positivism: for Housing Studies students

Highlight 4 main ideas of positivism and offer 3 practice advices for part-time Housing Studies students doing dissertation projects; also do so for post-positivism. Finally, discuss how your positivism-based practices advices and that of post-positivism differ.

Positivism and post-positivism both treat housing conditions as a real, empirical field that can be investigated systematically. Their key difference is how confidently they think a researcher can know that reality: positivism aims to establish objective, general laws, whereas post-positivism seeks the most credible but always provisional explanation.

Positivism: four main ideas

1.    An objective reality exists independently of the researcher.
Positivism assumes that features of the social world—such as overcrowding, rent burden, residential satisfaction, housing turnover, or access to public transport—exist as observable facts, whether or not a researcher studies them.

2.    Valid knowledge is empirical and measurable.
The researcher should rely on systematic observation, numerical measurement, and data that can be checked by others. Abstract concepts must be operationalised: for example, “housing affordability” could be measured as housing expenditure as a percentage of household income; “residential satisfaction” could be measured through a standardised Likert-scale survey.

3.    Research should identify causal or law-like relationships.
Positivism seeks patterns that explain, predict, and potentially control outcomes. A typical question is: “To what extent does perceived estate-management quality affect residents’ overall residential satisfaction?” The aim is to test whether changes in one variable are associated with, or cause, changes in another. Positivism assumes that such relationships may be generalisable across relevant settings.

4.    The researcher should be detached, neutral, and value-free.
Positivists try to keep personal beliefs, political views, and interaction with participants from shaping the results. Standardised questionnaires, fixed procedures, probability sampling where feasible, and statistical analysis are intended to reduce subjective influence and support reliability and replicability.

Positivism: three practical advices

For a part-time Housing Studies dissertation—particularly one with a four-month schedule—a focused quantitative design is usually more manageable than an overly ambitious attempt to explain every social aspect of housing.

1.    Frame a narrow, measurable explanatory question.
State a clear population, location, outcome, and proposed explanatory factors. Avoid a broad question such as “What affects quality of life in Hong Kong public housing?” A more positivist and feasible version is:
“What is the relationship between perceived estate-management service quality, neighbourhood accessibility, and residential satisfaction among adult residents of selected public rental housing estates in Hong Kong?”
Define each concept before data collection and convert it into measurable indicators, ideally using items adapted from established housing or service-quality studies.

2.    Use a structured, standardised dataset and analyse it consistently.
A self-administered questionnaire, a properly coded document dataset, or secondary survey/ administrative data can fit a part-time schedule. Use the same questions, scale anchors, and instructions for every respondent. Prepare an analysis plan in advance: descriptive statistics first, then reliability checks for multi-item scales, followed by correlation, group comparison, or regression where the sample and data quality justify it. Positivist methods commonly include surveys, structured interviews, document coding, variable measurement, and statistical analysis.

3.    Build safeguards against error and bias into the design.
Pilot the questionnaire with a small number of people to identify ambiguous wording; use neutral questions rather than leading ones; explain the sampling logic; and record missing data and exclusions. Where possible, control for plausible confounding variables—for example, household income, age, household size, estate age, tenure type, or length of residence—before claiming that management quality “causes” satisfaction. In your write-up, distinguish an observed association from causal proof unless your design genuinely supports causal inference.

Post-positivism: four main ideas

1.    A real world exists, but it can only be known imperfectly.
Post-positivists retain realism: material housing conditions, institutional rules, and social outcomes are real. However, they reject the claim that researchers can observe these phenomena perfectly or reach final certainty. Findings therefore express the best available, probability-based explanation rather than unquestionable truth.

2.    All knowledge claims are fallible and open to refutation.
A theory cannot be conclusively verified by supportive evidence. Instead, it should be exposed to severe testing and possible disconfirmation. Thus, a non-significant or contradictory result is not automatically a dissertation failure; it may show that the proposed explanation does not hold under the stated conditions.

3.    Objectivity is an aspiration achieved through critical procedures, not a guaranteed personal state.
Post-positivism recognises that researchers’ theories, prior knowledge, assumptions, and values affect what they notice, measure, and interpret. The response is not to abandon rigour, but to make bias-reducing procedures explicit: transparent decisions, counter-explanations, peer review, careful sampling, and reflexive acknowledgement of assumptions.

4.    Multiple forms of evidence can strengthen a conclusion.
Quantitative methods remain important—often dominant—but post-positivists may use qualitative evidence and triangulation to test, qualify, or explain statistical patterns. For housing research, survey results may show whether management quality is associated with satisfaction, while interviews, complaint records, or policy documents help assess how, why, and under what conditions that relationship operates.

Post-positivism: three practical advices

1.    Develop hypotheses, but actively seek rival explanations.
For example, begin with:
H1: Higher perceived estate-management quality is associated with higher residential satisfaction.
Then identify alternatives before analysing the data: perhaps estate location, flat size, household income, building age, neighbour relationships, or expectations explain satisfaction more strongly. Your analysis should not merely look for evidence that supports H1; it should test whether the relationship persists after considering these alternatives. This reflects the post-positivist emphasis on falsification and warranted conclusions rather than proof.

2.    Use a small, purposeful mixed-methods design where it adds value.
For a time-constrained dissertation, a realistic design could combine:

o   An online questionnaire measuring satisfaction, management perceptions, accessibility, and household characteristics.

o   Six to ten semi-structured follow-up interviews with residents selected to represent contrasting survey responses—for example, highly satisfied and highly dissatisfied residents.

o   Secondary evidence such as estate-management reports, Housing Authority publications, complaint statistics, or relevant planning documents.

Do not add interviews merely to make the study look sophisticated. Give them a specific role: explaining unexpected statistical patterns, checking whether your survey measures capture residents’ actual concerns, or assessing alternative explanations.

3.    Report conclusions cautiously and transparently.
Include a short reflexive statement explaining your connection to the topic, assumptions, and potential influence on survey design or interview interpretation. Maintain an audit trail: versions of the questionnaire, sampling decisions, coding rules, excluded cases, and analytic choices. Write conclusions such as: “The evidence supports a probable positive association within this sample” rather than “Estate management definitively determines resident satisfaction.” Post-positivism regards research conclusions as credible and testable warrants, not final proof.

How the advice differs

Dimension	Positivism-based practice	Post-positivism-based practice
Central aim	Identify objective, measurable regularities and test a predefined relationship	Produce the strongest available explanation while recognising that it may be incomplete or wrong
Research question	Usually tightly focused on measurable variables and cause–effect relations	Still focused and empirical, but explicitly considers context, uncertainty, and competing explanations
Theory and hypotheses	Theory generates hypotheses to be tested, often with emphasis on confirmation or prediction	Theory generates hypotheses that should be exposed to potential falsification and revision
Preferred evidence	Standardised quantitative data: surveys, administrative data, experiments, structured coding	Quantitative data remain central, but qualitative and documentary evidence may be used for triangulation
Researcher role	Strives for detachment and value-free observation	Strives for objectivity while acknowledging that assumptions and prior knowledge influence inquiry
Treatment of bias	Control or eliminate bias through standardisation, sampling, measurement, and statistics	Control bias and also disclose, examine, and challenge it through reflexivity, transparency, and rival explanations
Nature of conclusion	May make stronger claims about general patterns, prediction, and causation when design permits	Uses qualified conclusions: probable, context-bounded, open to retesting, and subject to revision
Practical dissertation risk	Can become reductionist if complex housing experiences are forced into a few variables	Can become too broad if the student adds too many methods, perspectives, or data sources

In short, the positivist advice tells you to measure carefully, standardise the study, and test a focused relationship. The post-positivist advice tells you to do those things while also challenging your own explanation, checking it against different evidence, and presenting the result as a well-supported but provisional claim. For a part-time Housing Studies project, post-positivism is often especially practical when you want a primarily quantitative study but need enough contextual evidence to avoid simplistic conclusions about residents, estates, or housing policy.

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