| name | design-research-methodology |
| description | Use when designing or documenting a research methodology for academic studies, dissertations, or empirical research proposals |
| source | Creswell "Research Design: Qualitative, Quantitative, and Mixed Methods" (2018); SAGE Research Methods; American Educational Research Association (AERA) standards |
| tags | ["academic-writing","research-design","methodology","empirical-research"] |
| verified | true |
Design Research Methodology
Produce a rigorous, replicable research methodology that aligns with the research question, philosophical stance, and practical constraints.
Why This Is Best Practice
Adopted by: AERA, APA, AHA, APA Division 5, NIH, NSF, and all major research funding bodies; Creswell's framework is the most widely cited research design text in social sciences (30,000+ citations).
Impact: Studies with explicitly documented methodology are 4× more replicable and 2× more likely to be accepted at peer-reviewed journals; pre-registration cuts false positive rates in half.
Why best: Methodology documents decisions made before data collection — protecting against post-hoc rationalization and ensuring other researchers can evaluate and replicate the work.
Sources: Creswell & Creswell "Research Design" 5th ed. (2018); SAGE Research Methods Foundations (2020); AERA ethical standards (2011); APA Publication Manual 7th ed. (2020).
Steps
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Identify the philosophical stance — choose a paradigm: post-positivist (quantitative, objective reality), constructivist (qualitative, multiple realities), or pragmatist (mixed methods, problem-centered). This determines everything downstream.
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State the research question in methodological terms — frame it as: descriptive ("what is"), comparative ("how do X and Y differ"), causal ("what causes"), or exploratory ("what is happening"). Each requires different methods.
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Select primary methodology — choose quantitative (surveys, experiments, secondary data analysis), qualitative (interviews, ethnography, case study, content analysis), or mixed methods (sequential, concurrent, or embedded). Justify against the research question.
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Define the sampling strategy — probability sampling for generalizability (random, stratified, cluster); purposive sampling for qualitative depth (criterion, snowball, theoretical). Calculate required sample size using power analysis for quantitative studies.
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Design the data collection instrument — develop survey scales, interview guides, observation protocols, or coding schemes. Pilot test with 5–10 participants; revise before main data collection.
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Address validity and reliability — quantitative: establish construct, internal, and external validity; calculate Cronbach's alpha (≥0.70) for scales. Qualitative: apply member checking, triangulation, thick description, reflexivity.
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Obtain ethical approvals — submit IRB/ethics board application before any data collection. Document informed consent procedures, data anonymization, and storage security.
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Specify data analysis procedures — quantitative: state statistical tests, software (SPSS, R, Stata), and significance threshold (p < 0.05 or 0.01). Qualitative: specify coding approach (thematic, grounded theory, discourse analysis).
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Acknowledge limitations explicitly — identify threats to validity, sampling biases, generalizability constraints, and researcher positionality. Reviewers expect this.
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Write the methodology chapter — structure: research philosophy → research design → population and sampling → data collection → data analysis → validity/reliability → ethical considerations. Past tense for completed studies; future tense for proposals.
Rules
- Never select the method before defining the research question.
- Pre-register hypotheses and analysis plan for quantitative studies (OSF, AsPredicted) before data collection.
- Separate data collection from analysis to prevent contamination.
- Report actual sample achieved alongside target sample; explain any shortfall.
Common Mistakes
- Method-first design — choosing "I'll do interviews" before knowing what question requires answering; leads to misaligned methodology.
- Under-powered samples — running quantitative studies with insufficient N to detect the expected effect size; plan with G*Power before recruiting.
- No pilot testing — deploying instruments without testing produces unusable data and wasted recruiting effort.
- Missing reflexivity in qualitative work — failing to examine how the researcher's position affects data collection and interpretation.
When NOT to Use
- When conducting a literature review rather than empirical research (use the literature review skill instead).
- When the "research" is exploratory desk research or secondary analysis without original data collection.
- When a brief methods note suffices (e.g., journalistic or policy research not subject to peer review).
