title: Sex differences in the latent class structure of alcohol use disorder: Does (dis)aggregation of indicators matter?
abstract: Many researchers have argued for a differential presentation of alcohol use disorder (AUD) between men and women. Latent class analysis is the most commonly used analytic technique for modeling AUD subcategories, and latent class analyses have supported a variety of class structures of AUD. This article examines whether these differential results are, in part, an artifact of whether researchers have (a) analyzed men and women in the same analysis and (b) aggregated item-level symptoms into AUD diagnostic criteria prior to analysis. These related methodological issues are examined using Wave 2 data from the National Epidemiologic Survey of Alcohol and Related Conditions (N = 22,177). Direct comparison of results when the sexes are modeled separately or together shows that women are classified differently depending on whether men are included in the analysis. A comparison of disaggregated item-level symptoms and aggregated AUD criteria suggests that aggregating data remove a subgroup, individuals who exhibit tolerance but are normative on all other AUD symptoms, which is of theoretical and clinical interest. Consequently, basic methodological issues that are rarely systematically studied appear to be important determinants of studies seeking to determine whether male and female alcoholism are structurally isomorphic.
authors.affiliation: Department of Psychological Sciences, University of Missouri.
authors.forename: Emilie M
authors.initials: EM
authors.lastname: Shireman
authors.affiliation: Department of Psychological Sciences, University of Missouri.
authors.forename: Douglas
authors.initials: D
authors.lastname: Steinley
authors.affiliation: Department of Psychological Sciences, University of Missouri.
authors.forename: Kenneth
authors.initials: K
authors.lastname: Sher
mesh_terms: D019973:Alcohol-Related Disorders / Q000175:diagnosis* / Q000453:epidemiology*; D015982:Bias; D003625:Data Collection / Q000379:methods*; D016206:Databases, Bibliographic / Q000706:statistics & numerical data; D005260:Female; D006306:Health Surveys; D006801:Humans; D008297:Male; D008609:Mental Status Schedule; D012727:Sex Characteristics