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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: Statistical modellingRemove filterClear all

2 resources found

Statistical modelling · Video and course · Graduate · methods prerequisites

Statistical Rethinking · 2023

Richard McElreath · 2023

Reading target
Focus on lecture 6, Good & Bad Controls, and lecture 12, Multilevel Models; the author links the full course.
Prerequisites
Probability, regression, R and willingness to simulate data.
Platform-designed exercise
Simulate grouped data; compare complete, separate and partial pooling, including uncertainty for small groups.
Method boundary
Adjustment depends on causal structure; complexity or better fit does not establish causality.
Access
Public lectures, slides and code; textbook obtained separately. This selection uses the fixed 2023 edition.

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Source checked 2026-10-25 · Video addresses verified from author pages; recordings were not reviewed end to end.

Statistical modelling · Papers and scholar essays · Graduate · methods prerequisites

Bayesian Workflow

Andrew Gelman, Aki Vehtari et al. · 2020

Reading target
Model building, checking, computational diagnostics and comparison as an iterative workflow.
Prerequisites
Bayesian inference, MCMC basics and a probabilistic programming tool.
Platform-designed exercise
Submit prior and posterior predictive plots and a revision log tying each change to a failure.
Method boundary
Passing diagnostics supports computation and checking, not the sampling or identification assumptions.
Access
Open preprint; use after a probability course.

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Source checked 2026-10-25