A Comparison of Statistics for Assessing Model Invariance in Latent Class Analysis

• 作者: Holmes Finch
• 来源期刊: 统计学期刊(英文)
• 学科: 医学
• 关键词: LATENT Class ANALYSIS MODEL INVARIANCE Information Indices
• 年，卷（期）: 2015,（3）
• 页码范围: 191-210
• 页数: 20页
• 分类号: R73

摘要

Latent class analysis (LCA) is a widely used statistical technique for identifying subgroups in the population based upon multiple indicator variables. It has a number of advantages over other unsupervised grouping procedures such as cluster analysis, including stronger theoretical underpinnings, more clearly defined measures of model fit, and the ability to conduct confirmatory analyses. In addition, it is possible to ascertain whether an LCA solution is equally applicable to multiple known groups, using invariance assessment techniques. This study compared the effectiveness of multiple statistics for detecting group LCA invariance, including a chi-square difference test, a bootstrap likelihood ratio test, and several information indices. Results of the simulation study found that the bootstrap likelihood ratio test was the optimal invariance assessment statistic. In addition to the simulation, LCA group invariance assessment was demonstrated in an application with the Youth Risk Behavior Survey (YRBS). Implications of the simulation results for practice are discussed.