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On heterogeneous latent class models with applications to the analysis of rating scores

Bibliographic reference Bertrand, Aurélie ; Hafner, Christian. On heterogeneous latent class models with applications to the analysis of rating scores. In: Computational Statistics, Vol. 29, no. 1-2, p. 307-330 (2014)
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  1. Albert Paul S., Random Effects Modeling Approaches for Estimating ROC Curves from Repeated Ordinal Tests without a Gold Standard, 10.1111/j.1541-0420.2006.00712.x
  2. Bandeen-roche Karen, Miglioretti Diana L., Zeger Scott L., Rathouz Paul J., Latent Variable Regression for Multiple Discrete Outcomes, 10.1080/01621459.1997.10473658
  3. Bandeen-Roche K., Huang G.-H., Munoz B., Rubin G. S., Determination of Risk Factor Associations with Questionnaire Outcomes: A Methods Case Study, 10.1093/oxfordjournals.aje.a009943
  4. Beath K (2011) RandomLCA: random effects latent class analysis. R package version 0.8-3.
  5. Bolck Annabel, Croon Marcel, Hagenaars Jacques, Estimating Latent Structure Models with Categorical Variables: One-Step Versus Three-Step Estimators, 10.1093/pan/mph001
  6. Collins Linda M., Fidler Penny L., Wugalter Stuart E., Long Jeffrey D., Goodness-of-Fit Testing for Latent Class Models, 10.1207/s15327906mbr2803_4
  7. Croissant Y (2011) Mlogit: multinomial logit model. R package version 0.2-1.
  8. Formann Anton K., Latent Class Model Diagnosis from a Frequentist Point of View, 10.1111/1541-0420.00023
  9. GOODMAN LEO A., Exploratory latent structure analysis using both identifiable and unidentifiable models, 10.1093/biomet/61.2.215
  10. Huang Guan-Hua, Bandeen-Roche Karen, Building an identifiable latent class model with covariate effects on underlying and measured variables, 10.1007/bf02295837
  11. Lange K (1995) A gradient algorithm locally equivalent to the EM algorithm. J Roy Stat Soc Ser B (Methodol) 57(2):425–437
  12. Lin T. H., Dayton C. M., Model Selection Information Criteria for Non-Nested Latent Class Models, 10.3102/10769986022003249
  13. Linzer DA, Lewis J (2011a) poLCA: Polytomous Variable Latent Class Analysis. R package version 1.3.1.
  14. Linzer Drew A., Lewis Jeffrey B., poLCA: AnRPackage for Polytomous Variable Latent Class Analysis, 10.18637/jss.v042.i10
  15. Malhotra N, Decaudin J-M, Bouguerra A (2007) Etudes marketing avec SPSS. Pearson Education France, Paris
  16. McCutcheon AL (1987) Latent class analysis. Sage University paper series on quantitative applications in the social sciences. Sage Publications, Beverly Hills
  17. Paulhus Delroy L., Measurement and Control of Response Bias, Measures of Personality and Social Psychological Attitudes (1991) ISBN:9780125902410 p.17-59, 10.1016/b978-0-12-590241-0.50006-x
  18. Qu Yinsheng, Tan Ming, Kutner Michael H., Random Effects Models in Latent Class Analysis for Evaluating Accuracy of Diagnostic Tests, 10.2307/2533043
  19. R Core Team (2013) R: a language and environment for statistical computing. R Foundation for statistical computing, Vienna, Austria.
  20. Reboussin Beth A., Ip Edward H., Wolfson Mark, Locally dependent latent class models with covariates: an application to under-age drinking in the USA, 10.1111/j.1467-985x.2008.00544.x
  21. Uebersax JS (2000) A practical guide to local dependence in latent class models.
  22. van Herk Hester, Poortinga Ype H., Verhallen Theo M. M., Response Styles in Rating Scales : Evidence of Method Bias in Data From Six EU Countries, 10.1177/0022022104264126
  23. Vermunt Jeroen K., Latent Class Modeling with Covariates: Two Improved Three-Step Approaches, 10.1093/pan/mpq025