Fit Statistics for Latent Class Models.

Emerging adulthood is a period associated with increases in mental health problems, with those who have faced adverse life experiences (ALEs; adversity experienced during childhood and adulthood) being at greater risk for poor mental health outcomes. Experiencing multiple ALEs is associated with worse outcomes; however, limited research exists that looks at how patterns of ALEs relate to various mental health outcomes among emerging adults. The present study sought to understand patterns of co-occurring ALEs and their relationship to symptom severity of various mental health outcomes (e.g., depression, anxiety, substance use) by utilizing a person-centered approach (i.e., Latent Class Analysis; LCA). Data from 442 emerging adults from a university in Southern California were analyzed using Latent Class Analysis to identify various classes of ALEs. Analysis of variance with Bonferroni-adjusted post-hoc test was utilized to assess whether classes related to an array of mental health outcomes. The use of LCA suggested that a five-class mode fit the data best: (1) Low Adversity, (2) Witnessing Adversity, (3) Experiencing Death, (4) Child Maltreatment and Adult Victimization, and (5) High Adversity. Individuals in the High Adversity and Child Maltreatment and Adult Victimization classes had the highest average severity levels on most mental health outcomes relative to those in the other classes. These findings point to the importance of examining the specificity of adverse experiences rather than using the standard cumulative risk approach. Research implications include further assessment of specific co-morbidities of ALEs and their impact on mental health to establish consistency, as well as examining the weight of individual ALEs in predicting mental health problems.