Childhood Prevalence and Latent Classes of Behavioral Issues in the Adolescent Brain Cognitive Development-Social Development Dataset

This study sought to identify latent classes of early-life behavioral issues among a sample of youth and compare to previous results obtained from a justice-involved youth sample. The first wave of the Adolescent Brain Cognitive Development-Social Development Study was analyzed. Latent class analysis was used to identify within-person latent clusters of behavioral issues (violence, stealing, substance use, being disruptive, cheating). A three-class model best fit the data. These three classes were characterized by low prevalence of all behaviors (Low), high prevalence of stealing (Stealing Only), and high prevalence of cheating and violence (Cheating and Violence). This finding demonstrated some similarities to classes identified in a justice-involved youth sample, but also some distinct differences.

Authors

Institutions

Publication Details

Journal
Crime & Delinquency
Published
2024-09-26
DOI
https://doi.org/10.1177/00111287241285582
Primary Topic
Child and Adolescent Psychosocial and Emotional Development
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Childhood Prevalence and Latent Classes of Behavioral Issues in the Adolescent Brain Cognitive Development-Social Development Dataset

H. Virginia McCoy, Thomas Walter Wojciechowski, Lia Ahonen
Crime & Delinquency
Child and Adolescent Psychosocial and Emotional Development
article

Childhood Prevalence and Latent Classes of Behavioral Issues in the Adolescent Brain Cognitive Development-Social Development Dataset

H. Virginia McCoy, Thomas Walter Wojciechowski, Lia Ahonen
article en

Abstract

This study sought to identify latent classes of early-life behavioral issues among a sample of youth and compare to previous results obtained from a justice-involved youth sample. The first wave of the Adolescent Brain Cognitive Development-Social Development Study was analyzed. Latent class analysis was used to identify within-person latent clusters of behavioral issues (violence, stealing, substance use, being disruptive, cheating). A three-class model best fit the data. These three classes were characterized by low prevalence of all behaviors (Low), high prevalence of stealing (Stealing Only), and high prevalence of cheating and violence (Cheating and Violence). This finding demonstrated some similarities to classes identified in a justice-involved youth sample, but also some distinct differences.

Crime & Delinquency
University of Pittsburgh (US), The University of Texas at Austin (US), Michigan State University (US)
Openalex Percentile: Top 77%
Child and Adolescent Psychosocial and Emotional Development
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.