Consequences of fitting nonidentified latent class models

Structural Equation Modeling

Volumn 19, Issue 1, 2012, Pages 1-15

  Abar, Beau ^a   Loken, Eric ^a  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

Bias; Boundary solutions; Identification; Latent class analysis; Posterior probabilities

Indexed keywords

BIAS; LATENT CLASS; LATENT CLASS ANALYSIS; LATENT CLASS MODEL; OBSERVED DATA; PARAMETER ESTIMATE; PARAMETER SPACES; POSTERIOR PROBABILITY;

IDENTIFICATION (CONTROL SYSTEMS); PARAMETER ESTIMATION;

BEHAVIORAL RESEARCH;

EID: 84865628428     PISSN: 10705511     EISSN: None     Source Type: Journal    
DOI: 10.1080/10705511.2012.634701     Document Type: Article

References (31)

1. Abar, B., & Loken, E. (2010). Self-regulated learning and self-directed study in a pre-college sample. Learning and Individual Differences, 20, 25-29.
2. Asparouhov, T., & Muthén, B. O. (2007). Wald test of mean equality for potential latent class predictors in mixture modeling. Retrieved from http://www.statmodel.com/download/MeanTest1.pdf
3. Caldwell, L. L., Bradley, S., & Coffman, D. (2009). A person-centered approach to individualizing a school-based universal preventive intervention. American Journal of Drug and Alcohol Abuse, 35(4), 214-219.
4. Clark, S. L., & Muthén, B. O. (2009). Relating latent class analysis results to variables not included in the analysis.
5. Clogg, C. C. (1995). Latent class models: Recent developments and prospects for the future. In G. Arminger, C. C. Clogg, & M. E. Sobel (Eds.), Handbookof statistical modeling for the social and behavioralsciences (pp. 311-352). New York, NY: Plenum.
6. Coffman, D. L., Patrick, M. E., Palen, L. A., Rhoades, B. L., & Ventura, A. K. (2007). Why do high school seniors drink? Implications for a targeted approach to intervention. Prevention Science, 8, 1-8.
7. Dayton, C. M. (1998). Latent class scaling models. Thousand Oaks, CA: Sage.
8. Dayton, C. M., & Macready, G. B. (1988). Concomitant-variablelatent class models. Journalof the American Statistical Association, 83, 173-178.
9. Feldman, B. J., Masyn, K. E., & Conger, R. D. (2009). New approaches to studying problem behaviors: A comparison of methods for modeling longitudinal, categorical adolescent drinking data. Developmental Psychology, 45(3), 652-676.
10. Formann, A. K. (2003). Latent class model diagnosis from a frequentist point of view. Biometrics, 59, 189-196.
11. Gerber, M., Wittekind, A., Grote, G., & Staffelbach, B. (2009). Exploring types of career orientation: A latent class analysis approach. Journal of Vocational Behavior, 75, 303-318.
12. Goodman, L. A. (1974). Exploratory latent structure analysis using both identifiable and unidentifiable models. Biometrika, 61, 215-231.
13. Hagenaars, J. A. (1993). Loglinear models with latent variables. London, UK: Sage.
14. Lanza, S. T., Collins, L. M., Lemmon, D. R., & Schafer, J. L. (2007). PROC LCA: A SAS procedure for latent class analysis. Structural Equation Modeling, 14, 671-694.
15. Laska, M. N., Pasch, K. E., Lust, K., Story, M., & Ehlinger, E. (2009). Latent class analysis of lifestyle characteristics and health risk behaviors among college youth. Prevention Science, 10, 376-386.
16. Lazarsfeld, P. F., & Henry, N. W. (1968). Latent structure analysis. Boston, MA: Houghton Mifflin.
17. Lo, Y., Mendell, N. R., & Rubin, D. B. (2001). Testing the number of components in a normal mixture. Biometrika, 88, 767-778.
18. Loken, E. (2004). Using latent class analysis to model temperament types. Multivariate Behavioral Research, 39, 625-652.
19. Magidson, J., & Vermunt, J. K. (2001). Latent class factor and cluster models, bi-plots and tri-plots and related graphical displays. Sociological Methodology, 31, 223-264.
20. Molenaar, P. C. M., & von Eye, A. (1994). On the arbitrary nature of latent variables. In A. von Eye & C. C. Clogg (Eds.), Latent variable analysis (pp. 226-242). Thousand Oaks, CA: Sage.
21. Muthén, B. O., & Muthén, L. K. (2000). Integrating person-centered and variable-centered analysis: Growth mixture modeling with latent trajectory classes. Alcoholism: Clinical and Experimental Research, 24, 882-891.
22. Muthén, L. K., & Muthén, B. O. (1998-2007). Mplus user's guide (5th ed.). Los Angeles, CA: Muthén & Muthén.
23. Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2006). Deciding on the number of classes in latent class analysis and growth mixture modeling. A Monte Carlo simulation study. Structural Equation Modeling, 14, 535-569.
24. Nylund, K., Bellmore, A., Nishina, A., & Graham, S. (2007). Subtypes, severity, and structural stability of peer victimization: What does latent class analysis say? Child Development, 78, 1706-1722.
25. Pastor, D. A., Barron, K. E., Miller, B. J., & Davis, S. L. (2007). A latent profile analysis of college students' achievement goal orientation. Contemporary Educational Psychology, 32, 8-47.
26. Roeder, K., Lynch, K. G., & Nagin, D. S. (1999). Modeling uncertainty in latent class membership: A case study in criminology. Journal of the American Statistical Association, 94, 766-776.
27. Schwartz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461-464.
28. van der Heijden, P. G. M., Dessens, J., & Bockenholt, U. (1996). Estimating the concomitant-variable latent-class model with the EM algorithm. Journal of Educational and Behavioral Statistics, 21, 215-229.
29. Van Leeuwen, K. G., Mervielde, I., Braet, C., & Bosmans, G. (2004). Child personality and parental behavior as moderators of behavior: Variable-and person-centered approaches. Developmental Psychology, 40, 1028-1046.
30. Vermunt, J. K., & Magidson, J. (2005). Technical guide for Latent Gold 4.0: Basic and advanced. Belmont, MA: Statistical Innovations.
31. Wang, C. P., Brown, C. H., & Bandeen-Roche, K. (2005). Residual diagnostics for growth mixture models: Examining the impact of preventive intervention on multiple trajectories of aggressive behavior. Journal of the American Statistical Association, 100, 1054-1076.