Is adding more indicators to a latent class analysis beneficial or detrimental? Results of a Monte-Carlo study

Frontiers in Psychology

Volumn 5, Issue AUG, 2014, Pages

  Wurpts, Ingrid C ^a   Geiser, Christian ^b  

^a ARIZONA STATE UNIVERSITY   (United States)

^b UTAH STATE UNIVERSITY   (United States)

Author keywords

Covariates of class membership; Latent class analysis; Number and quality of indicators; Sample size

Indexed keywords

EID: 84906350385     PISSN: None     EISSN: 16641078     Source Type: Journal    
DOI: 10.3389/fpsyg.2014.00920     Document Type: Article

References (24)

1. Collins, L. M., and Wugalter, S. E. (1992). Latent class models for stage-sequential dynamic latent variables. Multivariate Behav. Res . 27, 131-157. doi: 10.1207/s15327906mbr2701_8
2. Crow, S. J., Swanson, S. A., Peterson, C. B., Crosby, R. D., Wonderlich, S. A., and Mitchell, J. E. (2011). Latent class analysis of eating disorders: relationship to mortality. J. Abnorm. Psychol . 121, 225-231. doi: 10.1037/a0024455
3. Finch, W. H., and Bronk, K. C. (2011). Conducting confirmatory latent class analysis using Mplus. Struct. Equ. Modeling 18, 132-151. doi: 10.1080/10705511.2011.532732
4. Galindo-Garre, F., and Vermunt, J. (2006). Avoiding boundary estimates in latent class analysis by Bayesian posterior mode estimation. Behaviormetrika 33, 43-59. doi: 10.2333/bhmk.33.43
5. Geiser, C., Lehmann, W., and Eid, M. (2006). Separating "rotators" from "non-rotators" in the mental rotations test: a multigroup latent class analysis. Multivariate Behav. Res . 41, 261-293. doi: 10.1207/s15327906mbr4103_2
6. Krull, J. L., and MacKinnon, D. P. (1999). Multilevel mediation modeling in group-based intervention studies. Eval. Rev . 23, 418-444. doi: 10.1177/0193841X9902300404
7. Langeheine, R., Pannekoek, J., and Van De Pol, F. (1996). Bootstrapping goodness-of-fit measures in categorical data analysis. Sociol. Methods Res . 24, 492-516. doi: 10.1177/0049124196024004004
8. Lubke, G., and Muthén, B. O. (2007). Performance of factor mixture models as a function of model size, covariate effects, and class-specific parameters. Struct. Equ. Modeling 14, 26-47. doi: 10.1207/s15328007sem1401_2
9. Marsh, H., Hau, K., Balla, J., and Grayson, D. (1998). Is more ever too much? The number of indicators per factor in confirmatory factory analysis. Multivariate Behav. Res . 33, 181-220. doi: 10.1207/s15327906mbr3302_1
10. Muthén, B. O. (2004). "Latent variable analysis: growth mixture modeling and related techniques for longitudinal data," in Handbook of Quantitative Methodology for the Social Sciences , ed D. Kaplan (Newbury Park, CA: Sage), 345-368. doi: 10.4135/9781412986311.n19
11. Muthén, L. K., and Muthén, B. O. (1998-2011). Mplus User's Guide , 6th Edn. Los Angeles, CA: Muthén & Muthén.
12. Muthén, L. K., and Muthén, B. O. (2002). How to use a Monte Carlo study to decide on sample size and determine power. Struct. Equ. Modeling 9, 599-620. doi: 10.1207/S15328007SEM0904_8
13. Nylund, K. L., Asparouhov, T., and Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Struct. Equ. Modeling 14, 535-569. doi: 10.1080/10705510701575396
14. Peugh, J., and Fan, X. (2013). Modeling unobserved heterogeneity using latent profile analysis: a Monte Carlo simulation. Struct. Equ. Modeling 20, 616-639. doi: 10.1080/10705511.2013.824780
15. Rosenthal, J. A. (1996). Qualitative descriptors of strength of association and effect size. J. Soc. Serv. Res . 21, 37-59. doi: 10.1300/J079v21n04_02
16. Steinley, D., and Brusco, M. J. (2011). Evaluating mixture modeling for clustering: recommendations and cautions. Psychol. Methods 16, 63. doi: 10.1037/a0022673
17. Tein, J. Y., Coxe, S., and Cham, H. (2013). Statistical power to detect the correct number of classes in latent profile analysis. Struct. Equ. Modeling 20, 640-657. doi: 10.1080/10705511.2013.824781
18. Tueller, S. J., Drotar, S., and Lubke, G. H. (2011). Addressing the problem of switched class labels in latent variable mixture model simulation studies. Struct. Equ. Modeling 18, 110-131. doi: 10.1080/10705511.2011.534695
19. Tueller, S. J., and Lubke, G. H. (2011). Evaluation of structural equation mixture models parmeter estimates and correct class assignment. Struct. Equ. Modeling 17, 165-192. doi: 10.1080/10705511003659318
20. Uebersax, J. (2000). A Brief Study of Local Maximum Solutions in Latent Class Analysis . Available online at: http://www.john-uebersax.com/stat/local.htm
21. Vermunt, J. K. (2003). Multilevel latent class models. Sociol. Methodol . 33, 213-239. doi: 10.1111/j.0081-1750.2003.t01-1-00131.x
22. Vermunt, J. K., and Magidson, J. (2004). "Latent class analysis," in The Sage Encyclopedia of Social Sciences Research Methods , eds M. S. Lewis-Beck, A. Bryman, and T. F. Liao (Thousand Oakes, CA: Sage Publications), 549-553.
23. Vermunt, J. K., and Magidson, J. (2005). "Structural equation models: mixture models," in Encyclopedia of Statistics in Behavioral Science , eds B. Everitt and D. Howell (Chichester: Wiley), 1922-1927. doi: 10.1002/0470013192.bsa600
24. von Oertzen, T., Hertzog, C., Lindenberger, U., and Ghisletta, P. (2010). The effect of multiple indicators on the power to detect inter-individual differences in change. Br. J. Math. Stat. Psychol . 63, 627-646. doi: 10.1348/000711010X486633