Doctoral Dissertations

Date of Award


Degree Type


Degree Name

Doctor of Philosophy


Social Work

Major Professor

David Patterson

Committee Members

William Nugent, Samuel MacMaster, Cindy Davis, Marian W. Roman


Over the past 20 years researchers and health care practitioners have come to realize in addition to high prevalence rates, individuals with co-occurring disorders did not represent a homogeneous group (Drake, et al., 1998: 2001; Lehman, et al., 1994: 2000; Mueser, et al., 2000). It is essential to consider the heterogeneity of co-occurring disorders when considering new treatment modalities. Thus, it becomes pivotal to identify these differences for treatment approaches and program goals. Research shows that heterogeneity of treatment populations can be reduced through empirically-derived homogeneous groups based on multivariate analysis (Ries, et al., 1993; Lehman et al., 2000; Mueser, et al., 2000).

The purpose of the current study was to address a significant void in knowledge on the heterogeneity of co-occurring disorders by determining if homogeneous subgroups exist within an outpatient population presenting for treatment and if so how many groups exist and what makes up group membership. Identification of subgroups can provide a mechanism to better understand the interrelationships between determinants that contribute to the etiology and problem severity at an individual and group level. Secondly, in an effort to improve service delivery, empirically-derived subgroups hold important clinical implications for treatment models.

The exploratory research was conducted through a retrospective analysis seeking a parsimonious model of subgroups made up of individuals with co-occurring disorders entering an outpatient program using a latent class analysis (LCA). The best fitting statistical model in the latent class analysis was one in which the overall sample was composed of three (3) subgroups. The three-class model that included alcohol use, illegal drug use, education level and serious depression was identified as best fitting the data.

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