Citation

Merians AN, Baker MR, Frazier P, Lust K. Child Abuse Negl. 2019; 87: 51-64.

Affiliation

Boynton Health, University of Minnesota Twin Cities, United States.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.chiabu.2018.07.020

PMID

30064695

Abstract

The aims of this study were to identify latent classes of adverse childhood experiences (ACEs) in a large sample of college students (N = 8997), investigate the relations between ACEs classes and life functioning, and compare results using latent class analysis to analyses using cumulative risk scores. Nine types of ACEs were assessed (three types of child abuse and six types of household dysfunction). Outcomes were self-report measures of mental health, physical health, alcohol consequences, and academic performance. Latent class analysis (LCA) results indicated that four classes fit the data best across random halves of the sample and were labeled High ACEs, Moderate Risk of Non-Violent Household Dysfunction, Emotional and Physical Child Abuse, and Low ACEs. Comparing across latent classes, the largest differences in outcomes were between the High ACEs and Low ACEs classes. There were no differences in outcomes between the Moderate Risk of Non-Violent Household Dysfunction and Emotional and Physical Child Abuse classes. The largest between-class differences were found for mental health and the smallest differences were found for academic performance. Comparing results using LCA latent classes and cumulative ACEs scores, the differences between the High and Low ACEs latent classes were similar to the differences between those with zero ACEs and those with 5 or more ACEs. Both approaches also accounted for roughly equivalent amounts of variance in all outcomes. Thus, latent class and cumulative risk analyses provided similar results with regard to predicting outcomes of interest among college students.

Copyright © 2018 Elsevier Ltd. All rights reserved.

Language: en

Keywords

Adverse childhood experiences; College students; Cumulative risk; Latent class analysis; Mental health