ABSTRACT
AIM: To examine the prospective relationship between externalizing and internalizing problems and cannabis use in early adolescence. MATERIALS AND METHODS: Data were used from the TRAILS study, a longitudinal cohort study of (pre)adolescents (n=1,449), with measurements at age 11.1 (T1), age 13.6 (T2) and age 16.3 (T3). Internalizing (withdrawn behaviour, somatic complaints and depression) and externalizing (delinquent and aggressive behaviour) problems were assessed at all data waves, using the Youth Self Report. Participants reported on cannabis use at the second and third wave. Path analysis was used to identify the temporal order of internalizing and externalizing problems and cannabis use. RESULTS: Path analysis showed no associations between cannabis use (T2-T3) and internalizing problems (T1-2-3). However, cannabis use and externalizing problems were associated (r ranged from .19-.58); path analysis showed that externalizing problems at T1 and T2 preceded cannabis use at T2 and T3, respectively. In contrast, cannabis use (T2) did not precede externalizing problems (T3). CONCLUSIONS: These results suggest that in early adolescence, there is no association between internalizing behaviour and cannabis use. There is an association between externalizing behaviour and cannabis use, and it appears that externalizing behaviour precedes cannabis use rather than the other way around during this age period.
Subject(s)
Marijuana Abuse/psychology , Marijuana Smoking/epidemiology , Stress, Psychological/epidemiology , Adolescent , Child , Child Behavior Disorders/chemically induced , Child Behavior Disorders/epidemiology , Child Behavior Disorders/psychology , Cohort Studies , Female , Humans , Internal-External Control , Longitudinal Studies , Male , Marijuana Abuse/epidemiology , Marijuana Smoking/psychology , Mental Disorders/chemically induced , Mental Disorders/etiology , Mental Disorders/pathology , Mental Disorders/psychology , Models, Theoretical , Parents/psychology , Social Class , Surveys and QuestionnairesABSTRACT
In this paper, we present a structural equation approach to modelling infant behaviour in the Strange Situation. A model was developed on a Dutch data set, and was subsequently cross-validated for an American data set containing the original Ainsworth data. Model building is reported in some detail as no previous similar analyses of the Strange Situation exist in the literature. The latent variables in the preferred model are stranger wariness, minimization or deactivation of attachment concerns, and maximization or hyperactivation of attachment concerns. Stranger wariness influences only the subsequent behaviour towards the mother, and behaviour in the second reunion episode is dependent on the same mother behaviour in the first reunion episode, and not on other mother behaviours. Structural equation modelling behaviour in the Strange Situation is shown to provide further insight into the dynamics of the procedure.
Subject(s)
Infant Behavior , Object Attachment , Psychological Tests , Psychometrics , Analysis of Variance , Cross-Cultural Comparison , Exploratory Behavior , Factor Analysis, Statistical , Female , Humans , Infant , Models, Psychological , Mother-Child Relations , Netherlands , Reproducibility of Results , United StatesABSTRACT
It is shown that, in large samples, the more parsimonious of two competing nested models yields an estimator of the common parameters that has smaller sampling variance. The use of parsimony as a criterion for choice between two otherwise acceptable models can thus be rationalized on the basis of precision of estimation.
Subject(s)
Models, Psychological , Models, Statistical , HumansABSTRACT
In factor analysis of variables with ordered categories, latent response variables are assumed. These latent response variables may be either discrete or continuous. Different assumptions regarding the latent response variables lead to different kinds of factor models. A convenient assumption is to postulate that the latent response variables are continuous and normally distributed. In this paper the relationship between two kinds of factor analysis is discussed. It is shown, mathematically, that factor analysis of data with integer values only (the category numbers) is very sensitive to the skewness of the manifest variables and the size of the factor loadings. This was also shown by Olsson (1979b) by a simulation study with "perfect" data. Further, in the paper we propose a simple estimation procedure for the factor model of categorical variables, in which it is assumed that the latent response variables are normally distributed.
