Bray Bethany C, Lanza Stephanie T, Tan Xianming
The Methodology Center, Penn State.
The Methodology Center, Penn State ; College of Health and Human Development, Penn State.
Struct Equ Modeling. 2015 Jan;22(1):1-11. doi: 10.1080/10705511.2014.935265.
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.
尽管潜在类别分析（LCA）在方法学上取得了最新进展，且其在行为研究中的应用迅速增加，但包含潜在类别变量的复杂研究问题通常必须通过将个体分类到潜在类别中，并在后续分析中将类别归属视为已知来解决。基于后验概率对个体进行分类的传统方法在分析模型中会产生衰减估计。我们建议使用更具包容性的LCA来生成后验概率；这种LCA包括分析模型中存在的其他变量。本文给出了一个激励性的实证示范，随后进行了一项模拟研究，以评估所提出策略的性能。结果表明，在有足够的测量质量或样本量的情况下，所提出的策略可减少或消除偏差。
