TY - JOUR
T1 - Comparing the performance of improved classify-analyze approaches for distal outcomes in latent profile analysis
AU - Dziak, John J.
AU - Bray, Bethany C.
AU - Zhang, Jieting
AU - Zhang, Minqiang
AU - Lanza, Stephanie T.
N1 - Publisher Copyright:
© 2016 Hogrefe Publishing.
PY - 2016/10
Y1 - 2016/10
N2 - 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.
AB - 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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U2 - 10.1027/1614-2241/a000114
DO - 10.1027/1614-2241/a000114
M3 - Article
C2 - 28630602
AN - SCOPUS:85002913948
SN - 1614-1881
VL - 12
SP - 107
EP - 116
JO - Methodology
JF - Methodology
IS - 4
ER -