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cogpsych-data-analysis
Use when analyzing data and fitting/comparing models for a Cognitive Psychology (Elsevier) manuscript. The journal expects principled model fitting and comparison (AIC/BIC/Bayes factors), parameter and model recovery, (generalized) linear mixed models or hierarchical Bayesian estimation where apt, and effect sizes with uncertainty — all reproducible from shared code. Guides the analysis and modeling; it does not fabricate results.
Cognitive-Psychology-Skills/skills/cogpsych-data-analysis/SKILL.md
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# Data Analysis & Model Fitting (cogpsych-data-analysis)
Cognitive Psychology holds analyses to a **model-based** standard: fit the formal model, **compare it
to rivals with principled criteria**, demonstrate that parameters and models are **recoverable**, use
**mixed models or hierarchical Bayesian estimation** where the design demands it, and report **effect
sizes with uncertainty** for behavioral results — all regenerable from deposited code. This is the
experiment-to-model-fit loop that defines the venue.
## When to trigger
- Fitting the formal model and comparing it to rival accounts
- Running the behavioral analyses (mixed models, hierarchical Bayesian, contrasts)
- A reviewer asked for model comparison, recovery, robustness, or fuller disclosure
- Preparing analysis/model code and a data dictionary for deposit