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.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cogpsych-data-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 ## Reporting norms Cognitive Psychology expects 1. **Fit and compare models, don't just fit one.** Report fit for your model **and** the rival(s) under matched flexibility; compare with **AIC/BIC**, cross-validation, or **Bayes factors** as appropriate, and say what the comparison licenses. 2. **Show recovery.** Demonstrate **parameter recovery** (can the fitting proce
- When to trigger
- Reporting norms Cognitive Psychology expects
- Robustness
- Worked micro-example (illustrative numbers)
- Analysis-stage reviewer pushback and the venue fix
- Calibration anchors
- Execution bridge (StatsPAI / Stata MCP)
- Anti-patterns
- Output format
- Supplementary resources
What does the cogpsych-data-analysis skill do?
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.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill cogpsych-data-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.