Quantitative Research Workflow

Problem and Positioning

Quantitative research often gets stuck when moving from topic to reviewable artifacts: hypotheses, variables, sample size, scales, data structure, and statistical tests tend to blur into one narrative. This skill splits the workflow into six phases, requires design before data inspection, and produces four separate files for replication and audit.

Core Workflow

• Research design: convert a topic into 1-3 testable RQs, specify H0 and H1, operationalize variables, choose a design type, and map each hypothesis to a statistical test.
• Sampling and instrument: define the target population, create 6-10 personas, justify sample size, and generate questionnaire.md with manipulation checks, DV, mediator, moderator, control, and demographic items.
• Data and analysis: generate or clean data.csv containing participant_id, condition variables, raw item scores, composite scores, and demographics; then output analysis_results.md in the order of descriptives, manipulation checks, reliability, main effects, interactions, mediation, moderation, and follow-up tests.
• Report writing: write report.md in APA style, summarizing key findings, implications, and limitations while referencing the other three deliverables instead of duplicating raw output.

Scope and Caveats

It is useful for reproducible experimental or survey analyses involving ANOVA, regression, mediation/moderation, and SEM. The skill stresses designing before analyzing, reporting non-significant results, reporting effect sizes, and checking parametric assumptions; it still requires real data collection and human judgment for validity when needed.