New Approaches for Examining Associations With Latent Categorical Variables: Applications to Substance Abuse and Aggression
New Approaches for Examining Associations With Latent Categorical Variables: Applications to Substance Abuse and Aggression
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DOI：
10.1037/a0031487
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发表时间：
2014-03-01
影响因子：
3.4
通讯作者：
Capaldi, Deborah M.
中科院分区：
文献类型：
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作者：
Feingold, Alan;Tiberio, Stacey S.;Capaldi, Deborah M.
药物使用行为的评估通常包括分类变量，这些变量通常与使用逻辑回归或卡方分析的其他措施相关。当分类变量是潜在的（例如，从潜在类分析[LCA]中提取）时，观察值的分类通常用于从潜在变量中创建观察到的名义变量，以便在后续分析中使用。然而，最近的模拟研究发现，LCA先驱所倡导的这种经典的三步分析低估了潜在类别与其他变量的关联。检查这种联系的两种优选但未充分使用的替代方法是(a) 3步分析，它纠正了经典方法的低估偏差，以及(b) 1步分析。本文的目的是劝阻研究人员进行经典的三步分析，并促进使用所描述和比较的两种较新的方法。此外，这些新模型的应用——用于独立、依赖或两者都是潜在的分类变量——通过将药物滥用者的类别与亲密伴侣侵犯者的类别相关联的实质性分析来说明。
Assessments of substance use behaviors often include categorical variables that are frequently related to other measures using logistic regression or chi-square analysis. When the categorical variable is latent (e.g., extracted from a latent class analysis [LCA]), classification of observations is often used to create an observed nominal variable from the latent one for use in a subsequent analysis. However, recent simulation studies have found that this classical 3-step analysis championed by the pioneers of LCA produces underestimates of the associations of latent classes with other variables. Two preferable but underused alternatives for examining such linkages-each of which is most appropriate under certain conditions-are (a) 3-step analysis, which corrects the underestimation bias of the classical approach, and (b) 1-step analysis. The purpose of this article is to dissuade researchers from conducting classical 3-step analysis and to promote the use of the 2 newer approaches that are described and compared. In addition, the applications of these newer models-for use when the independent, the dependent, or both categorical variables are latent-are illustrated through substantive analyses relating classes of substance abusers to classes of intimate partner aggressors.