Journal
METHODOLOGY-EUROPEAN JOURNAL OF RESEARCH METHODS FOR THE BEHAVIORAL AND SOCIAL SCIENCES
Volume 12, Issue 4, Pages 107-116Publisher
PSYCHOPEN
DOI: 10.1027/1614-2241/a000114
Keywords
latent profile analysis; latent class analysis; distal outcome; three-step; classify-analyze
Funding
- National Institute on Drug Abuse (National Institutes of Health, United States) [P50 DA010075, P50 DA039838]
- Chinese National Social Science Foundation [BHA130053]
- Guangzhou Elementary Education Assessment Center [GZIT2013-ZB0465]
- Psychology Foundation of the Guangdong Philosophy and Social Science Foundation [09SXLQ001]
- Young Scholar Foundation in Humanities and Social Sciences by the Chinese Education Ministry [1201411413, 12YJC190016]
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Several approaches are available for estimating the relationship of latent class membership to distal outcomes in latent profile analysis (LPA). A three-step approach is commonly used, but has problems with estimation bias and confidence interval coverage. Proposed improvements include the correction method of Bolck, Croon, and Hagenaars (BCH; 2004), Vermunt's (2010) maximum likelihood (ML) approach, and the inclusive three-step approach of Bray, Lanza, and Tan (2015). These methods have been studied in the related case of latent class analysis (LCA) with categorical indicators, but not as well studied for LPA with continuous indicators. We investigated the performance of these approaches in LPA with normally distributed indicators, under different conditions of distal outcome distribution, class measurement quality, relative latent class size, and strength of association between latent class and the distal outcome. The modified BCH implemented in Latent GOLD had excellent performance. The maximum likelihood and inclusive approaches were not robust to violations of distributional assumptions. These findings broadly agree with and extend the results presented by Bakk and Vermunt (2016) in the context of LCA with categorical indicators.
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