--- name: devpsych-study-design description: Use when designing studies for a Developmental Psychology (APA) manuscript so they can actually support a developmental-change claim. Covers age-appropriate experimental and longitudinal designs, age vs. cohort confounds, attrition, measurement invariance across ages, sample-size justification, and ethics with minors and vulnerable populations. Strengthens the design and pre-analysis plan; it does not write code. --- # Study Design (devpsych-study-design) A developmental design must support a claim about **change**, not just measure something at different ages. Developmental Psychology reviewers probe the four threats that are specific to this field: **age vs. cohort confounds**, **attrition**, **measurement invariance across ages**, and **age-appropriate ethics and task validity**. This skill hardens the design before data collection. ## When to trigger - Planning a cross-sectional, longitudinal, accelerated, micro-genetic, or experimental developmental study - Writing a preregistration / pre-analysis plan for developmental work - A reviewer questioned age confounds, attrition, invariance, power, or ethics with minors - Justifying sample size for a growth model or an age interaction ## Developmental design standards 1. **Match the design to the change claim.** - *Cross-sectional* age comparison: cheap, but age is confounded with **cohort** and with **selection/era**; you can describe age differences, not within-person change. - *Longitudinal*: supports within-person change but introduces **attrition** and **retest** effects. - *Accelerated longitudinal (cohort-sequential)*: overlapping age cohorts to cover a wide span fast — state the convergence assumption. - *Micro-genetic*: dense repeated observation to catch the process of change; justify sampling rate. 2. **Address age vs. cohort.** For any age effect, say what could be cohort/era instead, and how the design (or covariates, or a sequential design) addresses it. Do not call a cross-sectional age difference "development" without this. 3. **Plan for measurement invariance.** The same instrument can mean different things at different ages. Pre-specify a **configural → metric → scalar** invariance test across ages/waves; interpret change only at the level of invariance you establish (handoff to `devpsych-data-analysis`). 4. **Plan for attrition.** Estimate expected dropout, design retention, pre-specify the missing-data model (FIML/MI) and an attrition (MCAR/MAR) analysis comparing completers vs. dropouts. 5. **Justify sample size.** Power for the *change* parameter you care about — a growth slope, an age × condition interaction, or a cross-lagged path — not just a group mean difference. State the assumed effect size and its source. 6. **Age-appropriate ethics and validity.** Child **assent** plus parental/guardian **consent**; age-appropriate measures (a task valid at 4 and at 8); special protections for vulnerable populations. ## Pre-data lockdown checklist (developmental) | Degree of freedom | Lock before data? | Where it lives | |-------------------|-------------------|----------------| | Hypotheses + developmental form (slope/interaction) | yes | preregistration | | Age bands / waves / spacing | yes | preregistration | | Measurement-invariance test plan | yes | analysis plan | | Inclusion/exclusion + attrition handling (FIML/MI) | yes | preregistration | | Time coding / centering for growth models | yes | analysis plan | | Covariates (incl. cohort/SES) and model form | yes | analysis plan | | Exploratory trajectory analyses | allowed, labeled | reported separately | ## Sample-size justification — worked example (illustrative) For a three-wave latent-growth study (ages 4, 6, 8), justify N for the *slope* and the scaffolding × time interaction, not a t-test. ``` Target parameter: latent slope variance + scaffolding × time interaction. Method: Monte Carlo power simulation (lavaan/Mplus) under a plausible growth model with 20% per-wave attrition and FIML. Result: N = 300 at wave 1 gives ~85% power for the interaction at the smallest developmentally meaningful slope difference; precision goal is a slope-CI half-width small enough to sign the trajectory. Invariance: configural→metric→scalar tested across waves before growth is interpreted; partial scalar invariance plan if a few intercepts differ. Attrition: MAR assumed; completers-vs-dropouts compared on baseline covariates. ``` ## Design-stage reviewer pushback and the venue fix - "This is a cohort effect, not development" → add a sequential element or model cohort; soften to age-difference language if you cannot separate them. - "Is the construct the same at every age?" → pre-specify and report measurement invariance; interpret change only at the invariance level achieved. - "Attrition could bias the trajectory" → report the attrition analysis and a principled missing-data model, not listwise deletion. - "Task isn't valid across this age range" → justify age-appropriateness or use age-anchored measures. ## Anti-patterns - Calling a cross-sectional age difference "developmental change" - Interpreting trajectories without testing measurement invariance - Listwise deletion / ignoring differential attrition - Powering for a mean difference when the claim is a growth slope or an age interaction - Consent without child assent; one task used across ages it is not valid for ## Output format ``` 【Design】cross-sectional / longitudinal / accelerated / micro-genetic / experiment 【Change claim supportable】age vs. cohort addressed? [Y/N] 【Invariance plan】configural→metric→scalar across ages/waves? [Y/N] 【Attrition plan】expected dropout + missing-data model + attrition analysis? [Y/N] 【Sample size】N + power for the change parameter (slope/interaction) 【Ethics】consent + child assent + age-appropriate measures? [Y/N] 【Next】devpsych-data-analysis ``` ## Supplementary resources - [`../../resources/external_tools.md`](../../resources/external_tools.md) — Mplus/lavaan, `simr`, power simulation, longitudinal design references - [`../../resources/official-source-map.md`](../../resources/official-source-map.md) — JARS design/reporting requirements and ethics policy