Date Approved


Degree Type

Open Access Senior Honors Thesis

Department or School


First Advisor

Dean Lauterbach, Ph.D.

Second Advisor

Rusty McIntyre, Ph.D.

Third Advisor

Ellen Koch, Ph.D.


Typically, the ability of individuals to regulate their behaviors and emotions improves over time. However, prior research has not examined possible heterogeneity in self-regulation skills from birth to age 16. This study examined trajectories of self-regulation using growth mixture modeling and tested the relationship between child maltreatment (i.e., number of maltreatment allegations) and trajectory group membership, growth parameters, and group formation. Subjects (N = 1354) were drawn from the Consortium for Longitudinal Studies of Child Abuse and Neglect (LONGSCAN). Tests of unconditional models (i.e., those without covariates) with 1-5 classes supported a 4-class solution (consistently good, consistently poor, improving, and worsening). Tests of conditional models with total number of maltreatment allegations serving as a covariate supported a 2-class solution (improving and worsening). Tests of conditional models that incorporated a time-varying covariate found multiple well-fitting models with no clear ‘winner.' A goal of this study was to examine the relationship between number of maltreatment allegations and self-regulation using three data analytic techniques (1-step, R3 step, and Time- Varying). Findings indicated that incorporating the number of maltreatment allegations in the model altered the optimal number of classes and was predictive of class membership. Importantly, this study showed that, in order to have a complete understanding of the relationship between number of maltreatment allegations and self-regulation, a variety of data analytic techniques are required.