Mental disorders are complex, multifaceted phenomena that are associated with profound heterogeneity and comorbidity. Despite the heterogeneity of mental disorders, most are generally considered unitary dimensions. We argue that certain measurement practices, especially using too few indicators per construct, preclude the detection of meaningful multidimensionality. We demonstrate the implications of crude measurement for detecting construct multidimensionality with alcohol use disorder (AUD). To do so, we used a large sample of college heavy drinkers (N = 909) for whom AUD symptomology was thoroughly assessed (87 items) and a blend of confirmatory factor analysis, exploratory factor analysis, and hierarchical clustering. A unidimensional AUD model with one item per symptom criterion fit the data well, whereas a unidimensional model with all items fit the data poorly. Starting with an 11-item AUD model, model fit decreased and the variability in factor loadings increased as additional items were added to the model. Additionally, multidimensional models outperformed unidimensional ones in terms of variance explained in theoretically relevant external criteria. All told, we converged on a hierarchically organized model of AUD with three broad, transcriterial dimensions that reflected tolerance, withdrawal, and loss of control. In addition to introducing a hierarchical model of AUD, we propose that thorough assessment of psychological constructs paired with serious consideration of alternative, multidimensional structures can move past the deadlock of their unidimensional representations. General Scientific SummaryWe show how crude measurement can essentially determine the inferences we draw about the dimensionality of diagnostic constructs. We use alcohol use disorder as an example of this general argument because it is a heterogeneous construct that is assumed to reflect a unitary dimension. With thorough assessment, we propose a hierarchically organized, multidimensional account of alcohol use disorder.

The current article describes the adaptation of a measure of sexual orientation self-concept ambiguity (SSA) from an existing measure of general self-concept clarity. Latent “trait” scores of SSA reflect the extent to which a person's beliefs about their own sexual orientation are perceived as inconsistent, unreliable, or incongruent. Sexual minority and heterosexual women (n = 348), ages 18 to 30, completed a cross-sectional survey. Categorical confirmatory factor analysis guided the selection of items to form a 10-item, self-report measure of SSA. In the current report, we also examine (a) reliability of the 10-item scale score, (b) measurement invariance based on respondents' sexual identity status and age group, and (c) correlations with preexisting surveys that purport to measure similar constructs and theoretical correlates. Evidence for internal reliability, measurement invariance (based on respondent sex), and convergent validity was also investigated in an independent, validation sample. The lowest SSA scores were reported by women who self-ascribed an exclusively heterosexual or exclusively lesbian/gay sexual identity, whereas those who reported a bisexual, mostly lesbian/gay, or mostly heterosexual identity, reported relatively higher SSA scores.

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Affinity propagation is a message-passing-based clustering procedure that has received widespread attention in domains such as biological science, physics, and computer science. However, its implementation in psychology and related areas of social science is comparatively scant. In this paper, we describe the basic principles of affinity propagation, its relationship to other clustering problems, and the types of data for which it can be used for cluster analysis. More importantly, we identify the strengths and weaknesses of affinity propagation as a clustering tool in general and highlight potential opportunities for its use in psychological research. Numerical examples are provided to illustrate the method.

Psychiatric diagnoses are complex, multifaceted phenomena that are associated with profound heterogeneity and comorbidity. Despite the heterogeneity of psychiatric diagnoses, most are generally considered unitary dimensions. We argue that certain measurement practices, especially using too few indicators per construct, preclude the detection of meaningful multidimensionality. We demonstrate the implications of crude measurement for detecting multidimensionality within constructs using alcohol use disorder (AUD) as an example. To do so, we used a large sample of college heavy drinkers (N=909) for whom AUD symptomology was thoroughly assessed (87 items) to examine multidimensionality in AUD using a blend of confirmatory factor analysis, exploratory factor analysis, and hierarchical clustering. A unidimensional model of AUD fit the data poorly and was outperformed by a multidimensional model in terms of variance explained in theoretically-relevant external criteria. We converged on a hierarchically-organized model of AUD with three broad, transcriterial dimensions that reflected Tolerance, Withdrawal, and Loss of Control. In addition to introducing a hierarchical model of AUD, we offer that thorough, improved assessment of psychiatric diagnoses paired with serious consideration of alternative, multidimensional structures can move past the deadlock of unidimensional representations of psychiatric disorders.