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

1. 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.
2. Establish measurement invariance first. Test configural → metric → scalar across ages/waves before interpreting mean change; report partial invariance honestly if full scalar fails.
3. Effect sizes + uncertainty. Report a standardized or unstandardized effect size and confidence intervals for major results — slope estimates, interactions, indirect effects — not just stars.
4. Handle missing data and attrition principledly. Use FIML or multiple imputation; report the attrition analysis (completers vs. dropouts) and the missingness assumption.
5. JARS disclosure. Report how sample size was determined, all exclusions and reasons, all conditions and measures; keep confirmatory and exploratory analyses clearly separated.
6. 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