Agent skill

Joap Data Analysis

by brycewang-stanford in brycewang-stanford/Awesome-Journal-Skills

A skill your agent uses when analyzing and reporting results for a Journal of Applied Psychology (JAP) manuscript using SEM, multilevel (HLM) models, mediation/moderation, or meta-analysis.

MITAuto-check passedData & Analytics

Install Joap Data Analysis

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill joap-data-analysis -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills joap-data-analysis --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy

$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/Journal-of-Applied-Psychology-Skills/skills/joap-data-analysis .claude/skills/joap-data-analysis && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Paste into Claude Code

Install the "joap-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Applied-Psychology-Skills/skills/joap-data-analysis into .claude/skills/joap-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "joap-data-analysis", then confirm the skill loads.

Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

$skill-installer

$skill-installer install https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Applied-Psychology-Skills/skills/joap-data-analysis

Type this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill joap-data-analysis -a codex

Project install goes to .agents/skills/; add -g for ~/.codex/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills joap-data-analysis --agent codex

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/Journal-of-Applied-Psychology-Skills/skills/joap-data-analysis .agents/skills/joap-data-analysis && rm -rf skills-src

Use ~/.agents/skills/ instead of .agents/skills for a personal install.

Codex skills documentation · loads skills from .agents/skills/

Paste into Codex

Install the "joap-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Applied-Psychology-Skills/skills/joap-data-analysis into .agents/skills/joap-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "joap-data-analysis", then confirm the skill loads.

Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill joap-data-analysis -a cursor

Project install goes to .agents/skills/; add -g for ~/.cursor/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills joap-data-analysis --agent cursor

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/Journal-of-Applied-Psychology-Skills/skills/joap-data-analysis .cursor/skills/joap-data-analysis && rm -rf skills-src

Use ~/.cursor/skills/ instead of .cursor/skills for a personal install.

Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/

Paste into Cursor

Install the "joap-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Applied-Psychology-Skills/skills/joap-data-analysis into .cursor/skills/joap-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "joap-data-analysis", then confirm the skill loads.

Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

gemini skills install

$ gemini skills install https://github.com/brycewang-stanford/Awesome-Journal-Skills.git --path Journal-of-Applied-Psychology-Skills/skills/joap-data-analysis

--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill joap-data-analysis -a gemini-cli

Project install goes to .agents/skills/; add -g for ~/.gemini/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills joap-data-analysis --agent gemini-cli

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/Journal-of-Applied-Psychology-Skills/skills/joap-data-analysis .gemini/skills/joap-data-analysis && rm -rf skills-src

Use ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.

Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/

Paste into Gemini CLI

Install the "joap-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Applied-Psychology-Skills/skills/joap-data-analysis into .gemini/skills/joap-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "joap-data-analysis", then confirm the skill loads.

Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills joap-data-analysis

Installs for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill joap-data-analysis -a github-copilot

Project install goes to .agents/skills/; add -g for ~/.copilot/skills/.

Manual copy

$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/Journal-of-Applied-Psychology-Skills/skills/joap-data-analysis .github/skills/joap-data-analysis && rm -rf skills-src

Use ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.

GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/

Paste into GitHub Copilot

Install the "joap-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Applied-Psychology-Skills/skills/joap-data-analysis into .github/skills/joap-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "joap-data-analysis", then confirm the skill loads.

GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

skills CLI

$ npx skills add brycewang-stanford/Awesome-Journal-Skills --skill joap-data-analysis -a opencode

OpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.

GitHub CLI

$ gh skill install brycewang-stanford/Awesome-Journal-Skills joap-data-analysis --agent opencode

Project scope by default (.agents/skills/); add --scope user for a personal install.

Manual copy

$ git clone --depth 1 https://github.com/brycewang-stanford/Awesome-Journal-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/Journal-of-Applied-Psychology-Skills/skills/joap-data-analysis .opencode/skills/joap-data-analysis && rm -rf skills-src

Use ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.

OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/

Paste into OpenCode

Install the "joap-data-analysis" agent skill from https://github.com/brycewang-stanford/Awesome-Journal-Skills/tree/main/Journal-of-Applied-Psychology-Skills/skills/joap-data-analysis into .opencode/skills/joap-data-analysis/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "joap-data-analysis", then confirm the skill loads.

OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.

Facts

Skill name: joap-data-analysis
GitHub stars: 1.2k
Token cost: ~1.9k tokens
SKILL.md length: 685 words
Files: 1
Skills in repo: 2,387
Repo updated: Sep 27, 2026
First seen: Oct 7, 2026
Licence: MIT

At a glance

A skill your agent uses when analyzing and reporting results for a Journal of Applied Psychology (JAP) manuscript using SEM, multilevel (HLM) models, mediation/moderation, or meta-analysis.

Works in 7 steps: Measurement before structure. Report the… → Effect sizes + uncertainty. Give… → Mediation done right. Report the… → …
Analyzing and reporting results for a Journal of Applied Psychology (JAP) manuscript using SEM
SKILL.md covers When to trigger, Reporting norms JAP expects, Worked micro-example… and Analysis-stage reviewer…, plus 5 more sections
Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Joap Data Analysis is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when analyzing and reporting results for a Journal of Applied Psychology (JAP) manuscript using SEM, multilevel (HLM) models, mediation/moderation, or meta-analysis. JAP requires effect sizes with confidence intervals, model-based indirect effects with bootstrap CIs, fit indices, full disclosure, and a clean confirmatory/exploratory split. Guides analysis norms; it does not fabricate results.

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Data & Analytics, covering Data analysis, Statistics and Dispute resolution. The repository describes itself as: Journal-specific Claude Code/Codex skill packs covering mainstream journals — AER, QJE, Nature, Cell, 管理世界, 经济研究 & 200+ more — your fast track to getting published. ｜覆盖主流期刊的… The licence is MIT.

When your agent uses it

Analyzing and reporting results for a Journal of Applied Psychology (JAP) manuscript using SEM
Multilevel (HLM) models
Mediation/moderation

Example prompts

“/joap-data-analysis”

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

Measurement before structure. Report the measurement model (CFA fit: χ²/df, CFI, TLI, RMSEA,
Effect sizes + uncertainty. Give standardized and/or unstandardized estimates **with confidence
Mediation done right. Report the indirect effect with a bootstrap (or Monte Carlo) CI (not
Multilevel correctly. Report ICC(1)/ICC(2), center predictors appropriately (group-mean vs
Full disclosure. Report how sample size was determined, all exclusions (careless responding,
Meta-analysis (if applicable). Report the model (random vs fixed), heterogeneity (Q, I²),
Reproducibility. Provide scripts and a codebook; results should regenerate from shared data in a

What it can do on your machine

Read from SKILL.md and the folder at commit 932eb23. It shows what the files ask for, not the result of running them.

Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Joap Data Analysis loads about 1.9k tokens when it runs. Until then it costs about 105 tokens; SKILL.md has 685 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it: ~105
When it runs · the whole SKILL.md, loaded when a task matches: ~1.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from brycewang-stanford/Awesome-Journal-Skills at commit 932eb23, republished under its MIT licence (© brycewang-stanford). 685 words, ~1,908 tokens.

Download SKILL.mdSave it as .claude/skills/joap-data-analysis/SKILL.md (or your agent's skills folder).

name: joap-data-analysis
description: Use when analyzing and reporting results for a Journal of Applied Psychology (JAP) manuscript using SEM, multilevel (HLM) models, mediation/moderation, or meta-analysis. JAP requires effect sizes with confidence intervals, model-based indirect effects with bootstrap CIs, fit indices, full disclosure, and a clean confirmatory/exploratory split. Guides analysis norms; it does not fabricate results.

Data Analysis (joap-data-analysis)

JAP analyses must be model-appropriate, fully reported, and reproducible. The house toolkit is SEM/CFA, multilevel (HLM) models, mediation and moderation with proper inference, and meta-analysis. The journal expects effect sizes with confidence intervals, fit indices, bootstrap CIs for indirect effects, full disclosure of how data were handled, and a clean confirmatory vs. exploratory separation, with data and code shareable under TOP.

When to trigger

Specifying and reporting the main and supporting analyses
A reviewer asked for fit indices, indirect-effect CIs, robustness, or disclosure
Reconciling preregistered analyses with exploratory follow-ups
Preparing analysis scripts and a data/codebook for deposit

Reporting norms JAP expects

Measurement before structure. Report the measurement model (CFA fit: χ²/df, CFI, TLI, RMSEA, SRMR) and reliability/AVE before interpreting the structural model; report measurement invariance when comparing groups or waves.
Effect sizes + uncertainty. Give standardized and/or unstandardized estimates with confidence intervals for key paths — not just p-values and stars.
Mediation done right. Report the indirect effect with a bootstrap (or Monte Carlo) CI (not only the Baron-Kenny steps or a Sobel z); for multilevel mediation use the appropriate (e.g., 1-1-1, 2-1-1, 2-2-1) decomposition and within/between separation.
Multilevel correctly. Report ICC(1)/ICC(2), center predictors appropriately (group-mean vs grand-mean), model random effects, and justify the estimator; do not run OLS on nested data.
Full disclosure. Report how sample size was determined, all exclusions (careless responding, attrition) and reasons, all conditions, and all measures. Label confirmatory vs. exploratory.
Meta-analysis (if applicable). Report the model (random vs fixed), heterogeneity (Q, I²), artifact corrections, publication-bias checks, and a transparent coding protocol.
Reproducibility. Provide scripts and a codebook; results should regenerate from shared data in a fresh session (see joap-open-science-and-transparency).

Worked micro-example (illustrative numbers)

The servant-leadership package: a multilevel mediation in the field and a causal test in the lab.

Measurement model (field, N = 612 in 74 teams)
  CFA: χ²/df = 2.1, CFI = .96, TLI = .95, RMSEA = .045, SRMR = .04
  Reliabilities ω: leadership .91, safety .88, performance .87
  Aggregation: ICC(1) = .16, ICC(2) = .77, r_wg(j) = .85 → team-level OK
Confirmatory (preregistered) — multilevel mediation (2-2-2)
  a (leadership→safety) = .42 [.27, .57]; b (safety→performance) = .31 [.14, .48]
  Indirect = .13, 95% Monte Carlo CI [.05, .23] → mediation supported
  Direct (leadership→performance | safety) = .09 [-.06, .24], ns
Confirmatory (preregistered) — lab experiment (causal leg)
  Servant vs control on safety: d = 0.46, 95% CI [0.21, 0.71]
  H3 boundary: interaction with interdependence, ΔR² = .03, CI excludes 0
Exploratory (labeled): voice as a serial L1 mediator surfaced post hoc;
  reported as exploratory, flagged for confirmation in future work.

Why this passes JAP scrutiny: the measurement model is reported before structure; the indirect effect carries a bootstrap/Monte Carlo CI; nesting is modeled and aggregation justified; the experimental leg supplies causal warrant; and the post hoc serial path is honestly demoted to exploratory.

Analysis-stage reviewer pushback and the venue fix

Reviewer pushback	What it signals	JAP fix
"Mediation by Sobel/steps only"	outdated inference	report indirect effect + bootstrap/Monte Carlo CI
"OLS on nested data"	dependence ignored	multilevel model; report ICC, centering, random effects
"No fit indices / measurement model"	construct validity unchecked	report CFA fit + reliability before structure
"Which exclusions were preregistered?"	forking-paths concern	disclosure table: rule, count, preregistered vs post hoc, result with/without
"Is this confirmatory?"	HARKing concern	point to the preregistration; relabel post hoc analyses exploratory
"Reviewer 2 couldn't rerun your code"	reproducibility gate	ship a fresh-session run log (see open-science skill)

Show full SKILL.md (240 more words)Show less

Calibration anchors

Report measurement before structure: a beautiful path model on a misfitting measurement model convinces no JAP reviewer.
Prefer estimation language with CIs to dichotomous "significant/not"; bare p-value sentences read as pre-reform.
For mediation and cross-level effects, the interval on the carrying effect is the result — design and report so that interval is defensible.

Execution bridge (StatsPAI / Stata MCP)

Run the battery, don't just enumerate it. Full map: execution-with-mcp. JAP is organizational psychology — multilevel survey/field data and experiments; cluster at the right level and apply mediation/moderation discipline.

Many outcomes / specifications: romano_wolf (step-down FWER) or benjamini_hochberg — report the adjusted threshold.
OVB sensitivity: oster_delta / sensemakr.
Inference: wild_cluster_bootstrap (few clusters), twoway_cluster / conley; multilevel data → cluster at the right level.
Re-fit off one handle: audit_result(result_id) lists the missing checks and the exact suggest_function for each.
Exhibits: etable / did_summary_to_latex from the handle — no retyped numbers.

Keep the decisive checks in the body and the exhaustive battery in the supplement. See the executed chain in the JF execution walkthrough.

Anti-patterns

p-values and stars with no effect size, CI, or fit indices
Mediation claimed via Baron-Kenny steps or Sobel z without a bootstrap/Monte Carlo CI
Ignoring nesting (OLS on multilevel data) or aggregating without ICC/r_wg justification
Selectively reporting conditions, measures, or exclusions (undisclosed flexibility)
HARKing exploratory findings into confirmatory hypotheses
Code that does not reproduce the reported numbers

Output format

【Measurement model】CFA fit + reliability + invariance reported? [Y/N]
【Main result】effect size(s) + CI(s) + meaning
【Mediation/multilevel】indirect-effect bootstrap/Monte Carlo CI; nesting modeled? [Y/N/NA]
【Disclosure】N-determination + all exclusions + all conditions + all measures? [Y/N]
【Confirmatory vs exploratory】clearly separated? [Y/N]
【Reproducible】scripts + codebook + fresh-session check? [Y/N]
【Next】joap-tables-figures

Supplementary resources

../../resources/external_tools.md — Mplus, lavaan, lme4/nlme, psych, metafor/metaSEM, bootstrap/Monte Carlo tools
../../resources/official-source-map.md — statistical and disclosure requirements

Files

Just SKILL.md in Journal-of-Applied-Psychology-Skills/skills/joap-data-analysis of brycewang-stanford/Awesome-Journal-Skills.

Open the folder on GitHubat commit 932eb23

Compare with similar skills

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Questions about Joap Data Analysis

What does Joap Data Analysis do?

A skill your agent uses when analyzing and reporting results for a Journal of Applied Psychology (JAP) manuscript using SEM, multilevel (HLM) models, mediation/moderation, or meta-analysis. Joap Data Analysis is an agent skill from brycewang-stanford/Awesome-Journal-Skills. Use when analyzing and reporting results for a Journal of Applied Psychology (JAP) manuscript using SEM, multilevel (HLM) models, mediation/moderation, or meta-analysis.

When should I use Joap Data Analysis?

Joap Data Analysis fits situations like: analyzing and reporting results for a Journal of Applied Psychology (JAP) manuscript using SEM; multilevel (HLM) models; mediation/moderation.

How do I install Joap Data Analysis in Claude Code?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill joap-data-analysis -a claude-code`. Or copy the skill folder (Journal-of-Applied-Psychology-Skills/skills/joap-data-analysis in brycewang-stanford/Awesome-Journal-Skills) into .claude/skills/joap-data-analysis in your project. Claude Code loads it when a task matches its description.

How do I install Joap Data Analysis in Codex?

Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill joap-data-analysis -a codex`. Or copy the skill folder (Journal-of-Applied-Psychology-Skills/skills/joap-data-analysis in brycewang-stanford/Awesome-Journal-Skills) into .agents/skills/joap-data-analysis in your project. Codex loads it when a task matches its description.

Can I use Joap Data Analysis in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill joap-data-analysis -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/joap-data-analysis, .gemini/skills/joap-data-analysis, .github/skills/joap-data-analysis and .opencode/skills/joap-data-analysis in your project.

What does Joap Data Analysis need to run?

SKILL.md names no scripts, command-line tools or credentials: Joap Data Analysis is instructions for the agent only.

Does Joap Data Analysis access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Joap Data Analysis safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Joap Data Analysis use?

Joap Data Analysis is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Joap Data Analysis use?

About 1.9k tokens (SKILL.md is roughly 7.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Joap Data Analysis?

Skills that share tags, products or a category with Joap Data Analysis: Matlab (zLanqing/codex-claude-academic-skills, 4.7k stars), Eqtl Catalogue Region Fetch (ClawBio/ClawBio, 1.2k stars), CSV Data Analysis (5zjk5/prompt-engineering, 127 stars) and Meridian MMM Model Building (google/meridian, 1.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Joap Data Analysis?

brycewang-stanford (a GitHub user) maintains it in brycewang-stanford/Awesome-Journal-Skills, which has 1,228 GitHub stars. The repository holds 2,387 skills in this directory. The repository was last updated on September 27, 2026.

Source: brycewang-stanford/Awesome-Journal-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.

Install Joap Data Analysis

Facts

At a glance

What it does

When your agent uses it

Example prompts

Workflow steps

What it can do on your machine

Tool permissions

Runs code

Network

Credentials

Context cost

Safety

SKILL.md

Data Analysis (joap-data-analysis)

When to trigger

Reporting norms JAP expects

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

Files

Compare with similar skills

Similar skills

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Related

Questions about Joap Data Analysis