Biomedical subjects

F Hamagami

Publications and source records attributed to F Hamagami.

3 recordsLinked to original sources

PubMed2000

Age-related change in personality disorder trait levels between early adolescence and adulthood: a community-based longitudinal investigation.

OBJECTIVE: To investigate change in personality disorder (PD) traits between early adolescence and early adulthood among individuals in the community. METHOD: PD traits were assessed in 1983 (mean age = 14), 1985-86 (mean age = 16) and 1992 (mean age = 22) in a representative community sample of 816 youths. RESULTS: Overall, PD traits declined 28% during both adolescence and early adulthood. PD traits were moderately stable during the first 2-year interval, and were as stable as they have been reported to be among adults over similar intervals. PD trait stability declined slightly as the inter-assessment interval increased. Adolescents with PDs tended to have elevated PD traits during early adulthood. CONCLUSION: PD traits tend to decline steadily in prevalence during adolescence and early adulthood. However, adolescents with PDs often have elevated PD traits as young adults, and the stability of PD traits appears to be similar during adolescence and early adulthood.

Adolescent↗

PubMed1991

Structural modeling of mixed longitudinal and cross-sectional data.

In this paper we describe some mathematical and statistical models for dealing with changes over age. We concentrate specifically on the use of a structural equation modeling (SEM) approach (using computer programs like LISREL) to deal with issues of: (1) group differences in regression parameters, (2) differences in longitudinal and cross-sectional results, (3) differences due to longitudinal attrition, and (4) mixtures of these problems. To illustrate these ideas we use data from a previous study of hypertension and intellectual abilities (from Schultz, Elias, Robbins, Streeten, and Blakeman, 1986).

Adult↗

PubMedDate not supplied

Modeling incomplete longitudinal and cross-sectional data using latent growth structural models.

In this paper we describe some mathematical and statistical models for identifying and dealing with changes over age. We concentrate specifically on the use of a latent growth structural equation model approach to deal with issues of: (1) latent growth models of change, (2) differences in longitudinal and cross-sectional results, and (3) differences due to longitudinal attrition. This is a methodological paper using simulated data, but we base our models on practical and conceptual principles of modeling change in developmental psychology. Our results illustrate both benefits and limitations using structural models to analyze incomplete longitudinal data.

Aging↗