ABCD Analyses
Examples of longitudinal analysis methods using data from the ABCD Study® dataset.
Learn more about the ABCD Study→Filters
Featured Tutorials
LMM: Random Intercept
Estimate linear mixed models with random intercepts to capture person-specific baselines, separating within- and between-subject variance in repeated ABCD measurements.
LMM: Random Slopes
Extend random-intercept LMMs by adding random slopes, enabling individualized change trajectories and richer inferences for ABCD longitudinal outcomes.
LGCM: Basic
Introduce latent growth curve modeling to estimate average emotional suppression trajectories, growth rates, and individual variability across repeated ABCD assessments.
GEE: Basic
Fit population-averaged generalized estimating equations for binary outcomes, choose working correlation structures, and interpret marginal effects for clustered ABCD observations.
GLMM: Basic
Build generalized linear mixed models for clustered count data, specify random effects, handle overdispersion, and interpret conditional estimates for ABCD longitudinal outcomes.
Residualized Change Score
Use residualized change score regression to isolate within-person change while adjusting for baseline levels in ABCD longitudinal analyses.
Common Workflows
Tutorials grouped by research question
Multi-Group & Nested Models
3 tutorials
Multi-Group & Nested Models
3 tutorials
Growth & Change
4 tutorials
Growth & Change
4 tutorials
Non-Continuous Outcomes
4 tutorials
Non-Continuous Outcomes
4 tutorials
Time-Varying Effects
3 tutorials
Time-Varying Effects
3 tutorials
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