devpsych-data-analysis

Research

Use when analyzing and reporting results for a Developmental Psychology (APA) manuscript. The journal expects analyses that model developmental change correctly — growth-curve/multilevel/SEM, mediation/moderation, measurement invariance — with effect sizes and confidence intervals, JARS-compliant disclosure, and a clear confirmatory/exploratory split. Guides analysis norms; it does not fabricate results.

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Source SKILL.md: https://github.com/brycewang-stanford/Awesome-Journal-Skills/blob/HEAD/Developmental-Psychology-Skills/skills/devpsych-data-analysis/SKILL.md

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Data Analysis (devpsych-data-analysis)

Developmental Psychology holds analyses to a developmental and a credibility standard at once: the model must actually capture change (not just a cross-sectional snapshot), and reporting must meet JARS — effect sizes with confidence intervals, full disclosure, and a clean confirmatory vs. exploratory split. The most common fatal error is interpreting trajectories without first establishing that the construct is measured the same way across ages.

When to trigger

Fitting growth-curve / multilevel / SEM models, or mediation/moderation of developmental effects
A reviewer asked for measurement invariance, effect sizes, intervals, or attrition handling
Reconciling preregistered developmental hypotheses with exploratory trajectory findings
Preparing analysis scripts and a data dictionary for deposit

Reporting norms Developmental Psychology expects

Model change correctly. Use the method the claim requires: latent growth / multilevel models for trajectories, SEM for latent constructs, cross-lagged / RI-CLPM for reciprocal effects, mediation/moderation for mechanism and moderated change. State time coding and centering.
Establish measurement invariance first. Test configural → metric → scalar across ages/waves before interpreting mean change; report partial invariance honestly if full scalar fails.
Effect sizes + uncertainty. Report a standardized or unstandardized effect size and confidence intervals for major results — slope estimates, interactions, indirect effects — not just stars.
Handle missing data and attrition principledly. Use FIML or multiple imputation; report the attrition analysis (completers vs. dropouts) and the missingness assumption.
JARS disclosure. Report how sample size was determined, all exclusions and reasons, all conditions and measures; keep confirmatory and exploratory analyses clearly separated.
Reproducibility. Provide analysis scripts and a data dictionary; the numbers should regenerate in a fresh session (see devpsych-open-science-and-transparency).

Worked micro-example (illustrative numbers)

A preregistered three-wave latent-growth study (ages 4, 6, 8; N = 300, 18% attrition) of effortful control, testing maternal scaffolding as a driver of the growth slope.

Invariance (reported first):
  configural fit good; metric and scalar invariance hold across waves
  (ΔCFI < .01) → mean change is interpretable.
Confirmatory (preregistered):
  Latent slope > 0: b = 0.42/year, 95% CI [0.31, 0.53] (within-person growth).
  Scaffolding × time: b = 0.18, 95% CI [0.07, 0.29] (steeper growth with
  higher wave-1 scaffolding).
  Missing data: FIML; MAR; completers and dropouts did not differ on baseline
  covariates (attrition analysis in supplement).
Exploratory (labeled):
  RI-CLPM suggests child→parent effects in later waves; reported as
  exploratory and flagged for confirmation in a future sample.

Why this passes scrutiny: invariance is reported before the growth claim; every developmental parameter carries an effect size and a CI; missingness is modeled, not deleted; the reciprocal-effects finding is honestly demoted to exploratory.

Analysis-stage reviewer pushback and the venue fix

Reviewer pushback	What it signals here	Developmental Psychology fix
"Is the construct the same at each age?"	invariance not tested	report configural→metric→scalar before interpreting change
"You deleted dropouts"	attrition bias	refit with FIML/MI; add the completers-vs-dropouts analysis
"ANOVA on age groups for a change claim"	wrong model for the claim	fit a growth/multilevel model on within-person data
"Stars, no effect size"	pre-reform reporting	report slope/interaction effect sizes with CIs
"Is this confirmatory?"	HARKing concern	point to preregistration; relabel post hoc trajectories exploratory

Calibration anchors

A clean latent-growth slope with a tight CI, on an invariant measure, beats an age-group ANOVA with stars — the venue's currency is credible change, not a snapshot contrast.
Prefer estimation language ("effortful control grew 0.42/year, 95% CI [...]") to "significant effect of age." Bare p-value sentences read as thin here.
When attrition is non-trivial, state the missingness assumption and show the trajectory is robust to a reasonable alternative (e.g., pattern-mixture sensitivity), rather than implying complete data.

Anti-patterns

Interpreting mean change without establishing measurement invariance
Listwise deletion or ignoring differential attrition
Using age-group ANOVA to support a within-person change claim
p-values and stars with no effect sizes or confidence intervals
HARKing exploratory trajectory shapes into confirmatory hypotheses

Output format

【Model】growth / multilevel / SEM / cross-lagged / mediation-moderation — matches the change claim?
【Invariance】configural→metric→scalar tested before interpreting change? [Y/N]
【Main result】effect size + confidence interval + meaning
【Missing data】FIML/MI + attrition analysis reported? [Y/N]
【Confirmatory vs exploratory】clearly separated (JARS)? [Y/N]
【Reproducible】scripts + data dictionary + fresh-session check? [Y/N]
【Next】devpsych-tables-figures

Supplementary resources

../../resources/external_tools.md — lavaan, Mplus, lme4/nlme, semTools invariance, mice, effect-size tooling
../../resources/official-source-map.md — JARS statistical and disclosure requirements