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Eliminating Bias in Classify-Analyze Approaches for Latent Class Analysis
Structural Equation Modeling: A Multidisciplinary Journal ( IF 6 ) Pub Date : 2014-09-04 , DOI: 10.1080/10705511.2014.935265
Bethany C Bray 1 , Stephanie T Lanza 2 , Xianming Tan 3
Affiliation  

Despite recent methodological advances in latent class analysis (LCA) and a rapid increase in its application in behavioral research, complex research questions that include latent class variables often must be addressed by classifying individuals into latent classes and treating class membership as known in a subsequent analysis. Traditional approaches to classifying individuals based on posterior probabilities are known to produce attenuated estimates in the analytic model. We propose the use of a more inclusive LCA to generate posterior probabilities; this LCA includes additional variables present in the analytic model. A motivating empirical demonstration is presented, followed by a simulation study to assess the performance of the proposed strategy. Results show that with sufficient measurement quality or sample size, the proposed strategy reduces or eliminates bias.

中文翻译:

消除潜在类别分析的分类分析方法中的偏差

尽管最近潜在类别分析 (LCA) 的方法论取得了进步,并且其在行为研究中的应用迅速增加,但通常必须通过将个人分类为潜在类别并按照随后的分析中已知的类别成员身份处理来解决包含潜在类别变量的复杂研究问题. 已知基于后验概率对个体进行分类的传统方法会在分析模型中产生衰减估计。我们建议使用更具包容性的 LCA 来生成后验概率;此 LCA 包括存在于分析模型中的其他变量。提出了一个激励性的实证论证,然后进行了模拟研究,以评估所提出策略的性能。结果表明,在具有足够的测量质量或样本量的情况下,
更新日期:2014-09-04
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