Agent Skills
› brycewang-stanford/Awesome-Journal-Skills
› joap-study-design
joap-study-design
GitHub针对JAP期刊标准，提供研究设计与测量优化建议。聚焦构建效度、因果推断、共同方法偏差（CMV）控制、多层数据建模及样本量论证，强化数据收集前的设计严谨性，不生成代码。
Trigger Scenarios
规划研究或多项研究包的设计
撰写预注册或预分析计划
应对审稿人对CMV、因果推断或测量的质疑
论证特定分析层级的样本量
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill joap-study-design -g -y
SKILL.md
Frontmatter
{
"name": "joap-study-design",
"description": "Use when designing studies and measurement for a Journal of Applied Psychology (JAP) manuscript so they meet the journal's high bar on construct validity, causal inference, common-method variance, nested\/multilevel data, and sample-size justification. Strengthens the design and measurement plan; it does not write code."
}
Study Design & Measurement (joap-study-design)
JAP holds measurement and design to an exacting standard. The recurring killers are common-method variance (CMV), weak causal warrants (cross-sectional single-source data), unmodeled nesting, and construct validity gaps. This skill hardens the design before data collection, where most of these problems can actually be solved.
When to trigger
- Planning a study, a multi-study package, or a measurement strategy
- Writing a preregistration / pre-analysis plan
- A reviewer questioned CMV, causal inference, measurement, nesting, or power
- Justifying sample size at the relevant level of analysis
Design standards
- Construct validity first. Use validated measures; report reliability and, where the construct is new or contested, provide validity evidence (CFA, convergent/discriminant, measurement invariance across groups/time). A weak measure dooms an otherwise good design.
- Earn the causal claim. Cross-sectional single-source correlation rarely suffices. Strengthen with temporal separation (multi-wave), multiple sources (self + supervisor + objective), experimental or quasi-experimental legs, or a field experiment.
- Design against CMV. Build in procedural remedies (temporal/source/measurement separation, protected anonymity) and plan statistical checks; declare the strategy up front. Post hoc Harman's single-factor test alone is treated as insufficient at JAP.
- Model the nesting. If employees are nested in teams/units/firms, justify N at each level, report ICC(1)/ICC(2) and r_wg for aggregated constructs, and use multilevel models — do not ignore dependence or aggregate away the structure without justification.
- Justify sample size at the right level. Power for the effect that carries the claim (e.g., the cross-level interaction or indirect effect), not just the total N; for multilevel designs, the L2 sample size usually constrains power.
Common-method variance — the JAP design playbook
| Remedy | Type | Note |
|---|---|---|
| Temporal separation (multi-wave) | procedural | predictor and outcome at different waves |
| Source separation (self + other/objective) | procedural | the strongest single defense |
| Measurement/context separation | procedural | different scales/formats for predictor vs outcome |
| Protected anonymity, balanced items | procedural | reduces consistency and acquiescence bias |
| Marker variable / CFA marker technique | statistical | plan a theoretically unrelated marker in advance |
| ULMC (unmeasured latent method construct) | statistical | report alongside, not instead of, procedural remedies |
Sample-size justification — worked example (illustrative)
For the servant-leadership package, justify N at the level the hypotheses live, before collecting.
Multilevel field study (2-2-2 / 2-1-2 mediation):
Constraint: 74 teams (L2) drives power for the team-level indirect effect.
Power target: 80% for the indirect effect (Monte Carlo power for multilevel
mediation), assuming a path ≈ .25, b path ≈ .30, ICC(1) ≈ .15.
Result: target ≥ 70 teams, ~8 members each → ~560–620; we collect 612 in 74.
Lab experiment (causal leg):
Between-subjects, two conditions; power for the interaction (H3 boundary),
N ≈ 240 at 80%, alpha .05; fixed-N, no optional stopping.
Aggregation: report ICC(1), ICC(2), r_wg(j) to justify team-level aggregation
of psychological safety; preregister exclusion rules.
Pre-data lockdown checklist
| Degree of freedom | Lock before data? | Where it lives |
|---|---|---|
| Hypotheses + direction + level | yes | preregistration |
| Measures (all scales, all items) | yes | preregistration (prevents scale cherry-picking) |
| CMV remedies (procedural + planned statistical) | yes | design + preregistration |
| Aggregation rules (ICC/r_wg thresholds) | yes | analysis plan |
| Exclusion rules (careless responding, attrition) | yes | preregistration |
| Covariates / model form | yes | analysis plan |
| Exploratory analyses | allowed, labeled | reported separately, post hoc |
Design-stage reviewer pushback and the venue fix
- "Cross-sectional, same-source — common method bias" → add temporal/source separation or an experimental leg; declare procedural remedies, not just a Harman's test.
- "You ignored nesting" → model multilevel structure; report ICC(1)/ICC(2)/r_wg; justify aggregation.
- "Measure validity unclear" → report reliability, CFA fit, and invariance; cite scale provenance.
- "Underpowered for the cross-level effect" → repower at the constraining level; report the Monte Carlo
power analysis (handoff to
joap-data-analysis).
Execution bridge (StatsPAI / Stata MCP)
Estimate and audit the design, don't only describe 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.
detect_design→recommend→ fit withas_handle=true→audit_result.- Observational causal claims: staggered DiD (
callaway_santanna/sun_abraham+bacon_decomposition+honest_did_from_result); IV (effective_f_test+anderson_rubin_ci); RDD (rdrobust+mccrary_test). - Experiments: randomization-based inference,
romano_wolffor many-outcome family-wise control, andmediatefor mediation (not naive controlling-away). - Sensitivity:
oster_delta/sensemakrfor observational claims.
Report the effect size in interpretable units; route the full battery to the appendix/supplement. A run end-to-end (synthetic data, real returns) is in the JF execution walkthrough.
Anti-patterns
- Cross-sectional single-source self-report as the sole evidentiary base
- CMV addressed only by a post hoc Harman's single-factor test
- Nested data analyzed as if independent, or aggregated without ICC/r_wg justification
- New or modified measures with no validity evidence
- Sample size justified by total N while the carrying effect lives at L2
Output format
【Construct validity】reliability + CFA/invariance evidence? [Y/N]
【Causal warrant】temporal / multi-source / experimental leg present? [Y/N]
【CMV】procedural remedies + planned statistical check declared? [Y/N]
【Nesting】levels, ICC/r_wg, multilevel model justified? [Y/N/NA]
【Sample size】powered for the carrying effect at the right level? [Y/N]
【Next】joap-data-analysis
Supplementary resources
../../resources/external_tools.md— Mplus/lavaan/lme4, Monte Carlo power, CMV-marker and invariance tools../../resources/official-source-map.md— measurement, design, and reporting expectations
Version History
- 1839142 Current 2026-07-05 13:26
