Bayesian computational methods for inequality constrained latent class analysis

New Developments in Categorical Data Analysis for the Social and Behavioral Sciences

Volumn , Issue , 2004, Pages 52-69

  Laudy, Olav ^a   Boom, Jan ^a   Hoijtink, Herbert ^a  

^a UTRECHT UNIVERSITY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84917063243     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4324/9781410612021     Document Type: Chapter

References (23)

1. Boom, J., Hoijtink, H., & Kunnen, S. (2001). Rules in the balance: Classes, strategies or rules for the balance scale task. Cognitive Development, 16, 717-735.
2. Clogg, C.C. (1981). New developments in latent structure analysis. In D. J.Jackson & E.F.Borgatta (Eds.), Factor analysis and measurement in sociological research (pp. 215-246). Beverly Hills, CA: Sage.
3. Croon, M.A. (1990). Latent class analysis with ordered latent classes. British Journal of Mathematical and Statistical Psychology, 43, 171-192.
4. Everitt, B.S. (1988). A Monte Carlo investigation of the likelihood ratio test for number of classes in latent class analysis. Multivariate Behavioral Research, 23, 531-538.
5. Gelfand, A.E., Smith, A.F.M., & Lee, T. (1992). Bayesian analysis of constrained parameter and truncated data problems using Gibbs sampling. Journal of the American Statistical Association, 87, 523-532.
6. Gelman, A., Meng, X.L., & Stern, H.S. (1996). Posterior predictive assessment of model fitness via realized discrepancies (with discussion). Statistica Sinica, 6, 733-807.
7. Goodman, L.A. (1974). The analysis of systems of qualitative variables when some of the variables are unobservable. Part I: A modified latent structure approach. American Journal of Sociology, 79, 1179-1295.
8. Haberman, S.J. (1988). Analysis of quantitative data, vol 2. New developments. New York: Academic Press.
9. Hoijtink, H., & Molenaar, I.W. (1997). A multidimensional item response model:Constrained latent class analysis using the Gibbs sampler and posterior predictive checks. Psychometrika, 62, 171-180.
10. Hoijtink, H. (1998). Constrained latent class analysis using the Gibbs sampler and posterior predictive p-values: Applications to educational testing. Statistica Sinica, 8, 691-711.
11. Hoijtink, H. (2001). Confirmatory latent class analysis: Model selection using Bayes factors and (pseudo) likelihood ratio statistics. Multivariate Behavioral Research, 16, 717-735.
12. Jansen, B.R.J., & Van der Maas, H.L.J. (1997). Statistical test of rule assessment methodology by latent class analysis. Developmental Review, 17, 321-357.
13. Lin, T.H., & Dayton, C.M. (1997). Model selection information criteria for non nested latent class models. Journal of Educational and Behavioral Statistics, 22, 249-264.
14. Kass, R.E., & Raftery, A.E. (1995). Bayes factors. Journal of the American Statistical Association, 90, 773-795.
15. Meng, X.L. (1994). Posterior predictive p-values. The Annals of Statistics, 22, 1142-1160.
16. Normandeau, S., Larivee, S., Roulin, J., & Longeot, F. (1989). The balance-scale dilemma: Either the subject or the experimenter muddles through. Journal of Genetic Psychology 150, 237-250.
17. Rubin, D.B. (1984). Bayesianly justifiable and relevant frequency calculations for the applied statistician. The Annals of Statistics, 12, 1151-1172.
18. Schwarz, G. (1978). Estimating the dimension of a model. The Annals of Statistics, 6, 461-464.
19. Sellke, T., Bayarri, M.J., & Berger, J.O. (2001). Calibration of p-values for testing precise null hypotheses. American Statistician 55, 62-71.
20. Siegler, R.S. (1981). Developmental sequences within and between concepts. Monographs of the Society for Research in Child Development. 46(2, Serial No. 189).
21. Siegler, R.S., & Chen, Z. (2002). Development of rules and strategies: Balancing the old and the new. Journal of Experimental Child Psychology, 81, 446-457.
22. Vermunt, J.K. (1997). Log-linear models for event histories. Thousand Oakes, CA: Sage.
23. Wilkenings, F., & Anderson, N.H. (1982). Comparison of two rule-assessment methodologies for studying cognitive development and knowledge structure. Psychological Bulletin, 92, 215-237.