Method evidence record
Bayesian Ex Post Facto Design
Bayesian ex post facto design investigates possible causal relationships among variables that have already occurred, without researcher manipulation of those variables, and quantifies uncertainty about those relationships using Bayesian statistical inference. The researcher selects groups that differ on an outcome or a presumed cause after the fact, then uses prior knowledge and observed data together — via Bayes' theorem — to estimate credible effect sizes, group differences, or predictors.
Evidence statusSources recorded, not reviewedBibliographic sources are present. Claim-level evidence review has not been performed.
Source record
Recorded
Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.
Bayesian Ex Post Facto Research Design
Taxonomic method recordprocess-pipelineresearch-design
Kruschke, J. K. (2015). Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan (2nd ed.). Academic Press. · ISBN 978-0124058880
Open full method2 recorded citations, copied from the method source record.
Curated claims
Assessed
Claims persisted in the evidence ledger, each with its own assessment.
No curated claims yet
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Related methods
Suggested5
Generated from the method graph and shown as machine-suggested relations. No evidence claim is inferred.
- See alsoBayesian Inference
machine-suggestedRelational suggestion, not evidence.
- Taxonomic bucketCausal-Comparative Research
machine-suggestedRelational suggestion, not evidence.
- Taxonomic bucketEx Post Facto Design
machine-suggestedRelational suggestion, not evidence.
- Same method familyPropensity Score Matching
machine-suggestedRelational suggestion, not evidence.
- Same method familyRetrospective Cohort Study
machine-suggestedRelational suggestion, not evidence.