A Comparison of Mixture Modeling Approaches in Latent Class Models With External Variables Under Small Samples.

Unkyung No, Sehee Hong
Author Information
  1. Unkyung No: Korea University, Seoul, Korea, Republic of Korea.
  2. Sehee Hong: Korea University, Seoul, Korea, Republic of Korea.

Abstract

The purpose of the present study is to compare performances of mixture modeling approaches (i.e., one-step approach, three-step maximum-likelihood approach, three-step BCH approach, and LTB approach) based on diverse sample size conditions. To carry out this research, two simulation studies were conducted with two different models, a latent class model with three predictor variables and a latent class model with one distal outcome variable. For the simulation, data were generated under the conditions of different sample sizes (100, 200, 300, 500, 1,000), entropy (0.6, 0.7, 0.8, 0.9), and the variance of a distal outcome (homoscedasticity, heteroscedasticity). For evaluation criteria, parameter estimates bias, standard error bias, mean squared error, and coverage were used. Results demonstrate that the three-step approaches produced more stable and better estimations than the other approaches even with a small sample size of 100. This research differs from previous studies in the sense that various models were used to compare the approaches and smaller sample size conditions were used. Furthermore, the results supporting the superiority of the three-step approaches even in poorly manipulated conditions indicate the advantage of these approaches.

Keywords

References

  1. Cancer Epidemiol Biomarkers Prev. 2007 Jul;16(7):1422-7 [PMID: 17627007]
  2. J Adolesc. 2008 Aug;31(4):499-517 [PMID: 17904631]
  3. J Abnorm Child Psychol. 2009 Jul;37(5):693-704 [PMID: 19263212]
  4. Depress Anxiety. 2013 May;30(5):489-96 [PMID: 23281049]
  5. Psychol Addict Behav. 2014 Mar;28(1):257-67 [PMID: 23772759]
  6. Struct Equ Modeling. 2013 Jan;20(1):1-26 [PMID: 25419096]
  7. Multivariate Behav Res. 2001 Oct 1;36(4):611-37 [PMID: 26822184]
  8. Psychol Med. 1994 Feb;24(1):41-51 [PMID: 8208893]

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