Research Methods & Statistics
A working reference for research methodology: choosing a study design, calculating sample size and power, running quantitative and qualitative analysis, and establishing measurement validity.
Ask about Research Methods & Statistics
Answers are drawn from this topic and the rest of the CASRAI corpus, with a link to every source.
Answers are AI-generated from CASRAI’s own published pages and can be wrong, so check the linked sources before relying on one; your question is logged without personal data — never sold, never used to train a third-party model — to show us what CASRAI is missing, so please do not type personal or confidential details. How we use this
Research methodology is the set of decisions that determine what a study can legitimately claim. Those decisions are made in a specific order, and each one constrains the next: the research question shapes the design, the design determines what analysis is appropriate, and the quality of measurement bounds what any analysis can recover. A study with an elegant statistical model built on an unvalidated instrument is not a strong study; it is a precise answer to an unreliable question.
This section is organised around that sequence rather than around statistical techniques in isolation. It is written for researchers designing and defending their own studies, and for the research administrators, reviewers and methodologists who assess them.
How this section is organised
Eight sub-sections follow the arc of a study from design through analysis and reporting:
- Study design — randomised controlled trials, cohort and case-control studies, cross-sectional and longitudinal designs, and quasi-experimental approaches, with the validity threats specific to each.
- Sampling and statistical power — power analysis and sample size calculation before data collection, and the sampling strategies that determine what a sample can generalise to.
- Quantitative analysis — regression, ANOVA, t-tests, confidence intervals, effect sizes, and the assumptions each technique depends on.
- Qualitative methods — thematic analysis, grounded theory, phenomenology and ethnography, with the rigour criteria qualitative work is judged against.
- Research paradigms and mixed methods — the ontological and epistemological assumptions underneath any chosen method, and how quantitative and qualitative strands are combined.
- Measurement, reliability and validity — whether an instrument measures what it claims to, including internal consistency, test–retest reliability and instrument development.
- Survey research — questionnaire construction, scale design, response rates and sampling frames.
- Methods fundamentals — the orienting vocabulary that the sub-sections above build on.
Where evidence synthesis lives
Systematic reviews, meta-analysis, scoping reviews and PRISMA reporting are covered under Scholarly Publishing, in its evidence-synthesis section, rather than duplicated here. Evidence synthesis is a form of secondary research with its own reporting standards and publication conventions, and it sits more naturally alongside publication practice than alongside primary study design.
Method choice is a reporting obligation, not just a design one
Most reporting guidelines are organised by study design: CONSORT for randomised trials, STROBE for observational studies, PRISMA for systematic reviews, ARRIVE for animal research. Choosing a design therefore also chooses the checklist a manuscript will be assessed against, and increasingly the checklist a funder or journal requires at submission. Pages in this section note the applicable reporting standard alongside the method itself, because in practice the two are inseparable.
More guides in this cluster
Showing 9 of 119 guides directly — the rest are organised into the topic hubs above.
Likert Scale Survey Design & Statistical Analysis Guide
How to design and analyse Likert data: the difference between a Likert item and a Likert scale, how 5-point, 7-point and forced-choice formats behave, and when the ordinal-versus-interval question decides between non-parametric and parametric tests.
Cohort Study vs Case-Control Study: Research Guide
How cohort and case-control designs differ for epidemiology and clinical research: which one starts from exposure and which from outcome, when relative risk or an odds ratio is the right measure, and how each handles rare disease, confounding and STROBE reporting.
Likert Scale: Construction, Examples & Analysis
How to build a Likert item or scale correctly — unipolar vs. bipolar, how many response points, worked example items, and the ordinal-vs-interval analysis debate.
Survey Question Types: A Practical Guide with Examples
A worked-example catalogue of survey question types: multiple choice and multi-select, dichotomous, category, filter/skip-logic, Likert/rating/ranking, matrix, open-ended, demographic, and attention-check items, with a quick-reference table.
What Is a Control Group? Types, Ethics, and How to Choose One
A control group supplies the counterfactual that lets researchers attribute an observed change to an intervention rather than to time, expectation, or chance. This guide covers the main types of control, the ethics of choosing one under the Declaration of Helsinki, randomization and blinding, control groups outside clinical trials, and what to do when a true control group isn’t possible.
Symbolic Interactionism: A Research Paradigm for Qualitative Study Design
Symbolic interactionism holds that meaning arises through social interaction and shapes action. This guide covers Blumer’s three premises, Mead’s theory of the self, key concepts (Thomas theorem, looking-glass self, dramaturgy, labelling theory), the Chicago vs. Iowa schools, and what the framework means for qualitative research design.
Inductive vs. Deductive vs. Abductive Reasoning in Research
A complete comparison of inductive, deductive, and abductive reasoning in research: what each means, how they map to real study designs, and why conflating an inductively generated hypothesis with a deductively pre-specified one (HARKing) is a research-integrity problem, not just a stylistic one.
Closed-Ended Questions in Research: Types & Examples
Closed-ended survey questions explained: what they are, closed vs. open-ended, the six main types (dichotomous, multiple choice, Likert, rating, ranking, checklist) with worked examples, when to use each, and how to code the data.
Questionnaire Design: Writing Survey Questions That Work
A construction guide to the questionnaire-design decisions that determine data quality: question wording, response-option and Likert scale design, question order, sensitive-question handling, pretesting, and the scale-vs-independent-items distinction.
Comparisons in this cluster
Last reviewed:
