Sum Scores in Twin Growth Curve Models: Practicality Versus Bias

Sum Scores in Twin Growth Curve Models: Practicality Versus Bias.

To study behavioral or psychiatric phenotypes, multiple indices of the behavior or disorder are often collected that are thought to best reflect the phenotype. Combining these items into a single score (e.g. a sum score) is a simple and practical approach for modeling such data, but this simplicity can come at a cost in longitudinal studies, where the relevance of individual items often changes...

Latent Growth Curve Models

Growth curve mixture models

doi: 10.3969/j.issn.1002-0829.2012.06.009 Division of Biostatistics, Thomas Jefferson University, Philadelphia, Pennsylvania, USA *Correspondence: [email protected] Psychiatric studies often collect longitudinal data to characterize the natural history of disease in a cohort or to evaluate the effect of behavioral or pharmaceutical interventions. For example, in a recent partially ra...

Applying Biometric Growth Curve Models to Developmental Synchronies in Cognitive Development: The Louisville Twin Study.

Biometric latent growth curve models were applied to data from the LTS in order to replicate and extend Wilson's (Child Dev 54:298-316, 1983) findings. Assessments of cognitive development were available from 8 measurement occasions covering the period 4-15 years for 1032 individuals. Latent growth curve models were fit to percent correct for 7 subscales: information, similarities, arithmetic, ...

Growth Curve Models with Categorical Outcomes

Motivated by the limited available literature on the treatment of longitudinal binary and ordinal outcomes in a growth modeling framework, the goal of this entry is to provide an accessible and practical introduction of this topic for a criminological audience. The parameterization of categorical latent growth models is explained by integrating aspects of the more familiar conventional latent g...