• Aggression;
  • classification;
  • epidemiology;
  • methodology;
  • statistics

Epidemiological surveys of child and adolescent mental disorders often rely on multiple informants to get a complete diagnostic picture. A consistent finding in the literature is that different informants often do not identify the same children as being disordered. However, because current strategies for estimating interinformant agreement often involve categorizing children using less than perfectly sensitive and/or specific symptoms, biased estimates of interinformant agreement are likely. The aim of this report was to illustrate how latent class analysis (LCA) can be used to model interinformant agreement in the absence of a “gold standard”. The proposed model consists of informant-specific latent variables each made up of two or more latent classes corresponding to different levels of symptomatology. Unlike most previous applications of LCA this model allows us to model the extent to which the prevalence of the disorder is the same across informants; and, in addition, the association between informants. The data set comes from a prospective longitudinal study of 2264 children from Québec (1155 boys and 1109 girls). In grade 2, teachers and mothers independently rated each child on three physical aggression behavior symptoms. We satisfactorily accounted for the cross-classification of the behavior symptoms by postulating the existence of two latent variables—one for each informant—each made up of three latent classes of children: low-, medium-, and high-aggressive. The results showed that the prevalence of low- and medium-aggressive children in the population differed from teacher to mother, but that the prevalence of high-aggressive children did not. We found that the association between teacher and mother was large and positive and did not vary according to the child's physical aggression state or gender; in contrast, the association between physical aggression and gender was not the same for mother and teacher. Limitations and other potential applications of the proposed model are discussed.