Using data augmentation to obtain standard errors and conduct hypothesis tests in latent class and latent transition analysis
Using data augmentation to obtain standard errors and conduct hypothesis tests in latent class and latent transition analysis
复制标题
DOI：
10.1037/1082-989x.10.1.84
复制
发表时间：
2005-03-01
影响因子：
7
通讯作者：
Flaherty, BP
中科院分区：
文献类型：
--
作者：
Lanza, ST;Collins, LM;Flaherty, BP
潜在类别分析（LCA）提供了一种识别由多个分类指标测量的人群中的亚组混合的方法。潜在转移分析（LTA）是一种LCA，有助于解决纵向数据中随时间推移的阶段顺序变化的研究问题。这两种方法在社会科学中使用的频率越来越高。本文的目的是说明数据扩充（DA），马尔可夫链蒙特卡罗程序，可用于获得LCA和LTA模型的参数估计和标准误差。通过使用DA，不仅可以构建关于标准模型参数的假设检验，还可以构建参数组合，从而提供了巨大的灵活性。DA是证明了一个例子，涉及测试的种族差异，性别差异，和种族X性别的互动发展中的青少年问题行为。
Latent class analysis (LCA) provides a means of identifying a mixture of subgroups in a population measured by multiple categorical indicators. Latent transition analysis (LTA) is a type of LCA that facilitates addressing research questions concerning stage-sequential change over time in longitudinal data. Both approaches have been used with increasing frequency in the social sciences. The objective of this article is to illustrate data augmentation (DA), a Markov chain Monte Carlo procedure that can be used to obtain parameter estimates and standard errors for LCA and LTA models. By use of DA it is possible to construct hypothesis tests concerning not only standard model parameters but also combinations of parameters, affording tremendous flexibility. DA is demonstrated with an example involving tests of ethnic differences, gender differences, and an Ethnicity X Gender interaction in the development of adolescent problem behavior.