Problems with the current categorical approach to classification used by the Diagnostic and Statistical Manual of Mental Disorders (DSM) have led to proposals that classify the emotional disorders (EDs; anxiety and mood disorders) using a dimensional-categorical system based on shared ED vulnerabilities and phenotypes. Such profile-based approaches have yet to be empirically evaluated, in part because a single multidimensional assessment of shared ED vulnerabilities and phenotypes amenable to profile-based classification has not been developed. The present studies aimed to provide an initial examination of a categorical-dimensional approach to ED classification (Study 1) as well as develop and evaluate a multidimensional self-report assessment of shared ED vulnerabilities and phenotypes (the Multidimensional Emotional Disorder Inventory [MEDI], Study 2). The samples consisted of 1,218 (Study 1) and 227 (Study 2) participants who presented for assessment and treatment at an outpatient ED treatment center. All participants were assessed using a semi-structured ED interview and a set of ED self-report questionnaires. The MEDI was completed only by the participants in Study 2.
Study 1 used mixture modeling to identify six unobserved groups (classes) of individuals sharing similar profiles across seven dimensional ED vulnerability and phenotype indicators. The external validity of the profiles was supported when related ED covariates were added to the solution. The incremental validity of the profiles was supported using hierarchical regression models; the profiles accounted for unique variance in ED outcomes beyond DSM diagnoses. In Study 2, exploratory structural equation modeling (ESEM) and confirmatory factor analysis were used to evaluate the factor structure of the MEDI. ESEM supported an eight-factor solution of a 47-item version of the MEDI. Differential magnitude of correlation analyses supported the convergent/discriminant validity of seven of the eight MEDI scales. A five-class (profile) solution, consistent with Study 1, was found when mixture modeling was applied to the MEDI scales. Collectively, the present studies provide compelling evidence in support of the development and utility of a hybrid dimensional-categorical profile approach to emotional disorder classification using multidimensional self-report assessment methods such as the MEDI.
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/14112 |
| Date | 22 January 2016 |
| Creators | Rosellini, Anthony Joseph |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
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