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Research path

Research methods and scholar examples

Research training beyond introductory study: evidence synthesis, causal design, modelling, qualitative analysis and reproducible experiments.

Start with your research question, choose a method, study original materials and produce work others can examine. Sources are mainly in English, with bilingual reading tasks.

Six methods workbooksScholars, courses and sourcesYouTube and talks

Learn through your research project

Find a question, read papers and learn with AI

Eight units with methods, worked examples and free primary sources. Practise here or offline, take work to your AI, then import feedback and revise.

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Choose a research task

The workbooks and review criteria are platform-designed exercises for projects or group discussion, not assignments set by the source authors.

Questions and contributions: distinguish competing explanations

Compare candidate questions, map claims to evidence and draft a proposal that can be challenged.

Method · example · deliverable · review criteria →

Evidence synthesis: protocols, bias and disagreement

Move from rerunnable searches to synthesis; separate reporting, study quality and comparability.

Method · example · deliverable · review criteria →

Causal design: estimands, DAGs and sample justification

Define the target trial and identification assumptions before estimation, then simulate where the design fails.

Method · example · deliverable · review criteria →

Bayesian workflow: generative models, diagnostics and revision

Connect priors, sampling diagnostics, predictive checks and sensitivity analysis in an auditable workflow.

Method · example · deliverable · review criteria →

Reflexive thematic analysis: from extracts to interpretation

State epistemology and positionality, trace codes into themes and revise interpretations using counterexamples.

Method · example · deliverable · review criteria →

Reproducible experiments: baselines, fair comparisons and failure audits

Fix splits, comparison budgets and sources of variability so another researcher can rebuild the main result.

Method · example · deliverable · review criteria →

Scholar examples and professional resources

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Task: Causal identificationRemove filterClear all

2 resources found

Causal identification · Textbook · Graduate · methods prerequisites

Causal Inference: What If

Miguel A. Hernán & James M. Robins · 2020 · 作者页持续修订 / living author edition

Reading target
Progresses from causal questions without models to modelling and complex longitudinal data.
Prerequisites
Conditional probability, regression and observational study basics.
Platform-designed exercise
Write a target-trial table: population, intervention, comparator, time zero, outcome and estimand.
Method boundary
Observational data require explicit identification assumptions; more covariates do not guarantee less bias.
Access
Book, data and associated code provided free by the author.

Open workbook and review criteria →

Source checked 2026-10-25

Causal identification · Textbook · Graduate · methods prerequisites

Sample Size Justification

Daniël Lakens · 2022 · 配套在线章节 / companion chapter

Reading target
Justify sample size against inferential goals, effects of interest, power, precision and resources.
Prerequisites
Effect sizes, confidence intervals and hypothesis testing.
Platform-designed exercise
Before seeing main results, specify an effect of interest, sample requirements under alternatives and attrition allowance.
Method boundary
Do not mechanically plan from an inflated pilot effect or justify a design with observed post-hoc power.
Access
Public author chapter with a companion tool.

Open workbook and review criteria →

Source checked 2026-10-25