Comparing the Performance of Improved Classify-Analyze Approaches For Distal Outcomes in Latent Profile Analysis.

Journal: Methodology : European journal of research methods for the behavioral & social sciences
Published Date: Dec 5, 2016

Abstract

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, & 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.

Authors

  • John J Dziak
    The Methodology Center, The Pennsylvania State University, University Park, PA, USA.
  • Bethany C Bray
    The Methodology Center, The Pennsylvania State University, University Park, PA, USA.
  • Jieting Zhang
    College of Psychology and Sociology, Shenzhen University, Guangdong, China.
  • Minqiang Zhang
    Center for Studies of Psychological Application, School of Psychology, South China Normal University, Guangdong, China.
  • Stephanie T Lanza
    The Methodology Center, The Pennsylvania State University, University Park, PA, USA.

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