Fig 1.
Latent growth curve model (panel A) and biometric latent growth curve model (panel B) of proactive aggression. Proactive aggression is illustrated here, the same models are used for reactive aggression. Naming scheme of the parameters: The letter refers to the biometric component, the first number refers to the destination of an arrow, and the last number to the origin of an arrow. For example, a21 indicate a link from the 1st genetic component to the 2nd latent variable (here a slope).
Table 1.
Hypotheses suggested by the biometric decomposition of intercepts (baseline level) and slopes (developmental change) of PA and RA.
Fig 2.
Cholesky decomposition of growth parameters in a bivariate latent growth curve model of proactive and reactive aggression.
Naming scheme of the parameters: The letter refers to the biometric component, the first number refers to the destination of an arrow, and the last number to the origin of an arrow. For example, a31 indicate a link from the 1st genetic component to the 3rd latent variable (here, PA’s slope).
Table 2.
Means, phenotypic correlations, between-subtype correlations and intraclass correlations.
Table 3.
Fit statistics for the multivariate latent growth model.
Table 4.
Standardized portion of the phenotypes variance associated with genetic, shared and nonshared environmental factors in the multivariate biometric latent growth curve model (%).