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A Mixed-Effects Regression Model for Longitudinal Multivariate Ordinal Data

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  • Li C. Liu
  • Donald Hedeker

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  • Li C. Liu & Donald Hedeker, 2006. "A Mixed-Effects Regression Model for Longitudinal Multivariate Ordinal Data," Biometrics, The International Biometric Society, vol. 62(1), pages 261-268, March.
    • Handle: RePEc:bla:biomet:v:62:y:2006:i:1:p:261-268
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2005.00408.x
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    References listed on IDEAS

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    1. Robert Gibbons & R. Bock, 1987. "Trend in correlated proportions," Psychometrika, Springer;The Psychometric Society, vol. 52(1), pages 113-124, March.
    2. Geoff Masters, 1982. "A rasch model for partial credit scoring," Psychometrika, Springer;The Psychometric Society, vol. 47(2), pages 149-174, June.
    3. R. Bock & Murray Aitkin, 1981. "Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm," Psychometrika, Springer;The Psychometric Society, vol. 46(4), pages 443-459, December.
    4. Sophia Rabe-Hesketh & Anders Skrondal & Andrew Pickles, 2002. "Reliable estimation of generalized linear mixed models using adaptive quadrature," Stata Journal, StataCorp LP, vol. 2(1), pages 1-21, February.
    5. Johannes Berkhof & Tom A. B. Snijders, 2001. "Variance Component Testing in Multilevel Models," Journal of Educational and Behavioral Statistics, , vol. 26(2), pages 133-152, June.
    6. Jason Roy & Xihong Lin, 2000. "Latent Variable Models for Longitudinal Data with Multiple Continuous Outcomes," Biometrics, The International Biometric Society, vol. 56(4), pages 1047-1054, December.
    7. Raymond J. Adams & Mark Wilson & Margaret Wu, 1997. "Multilevel Item Response Models: An Approach to Errors in Variables Regression," Journal of Educational and Behavioral Statistics, , vol. 22(1), pages 47-76, March.
    8. Jean-Paul Fox & Cees Glas, 2001. "Bayesian estimation of a multilevel IRT model using gibbs sampling," Psychometrika, Springer;The Psychometric Society, vol. 66(2), pages 271-288, June.
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    Citations

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    Cited by:

    1. Rana, Subrata & Roy, Surupa & Das, Kalyan, 2018. "Analysis of ordinal longitudinal data under nonignorable missingness and misreporting: An application to Alzheimer’s disease study," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 62-77.
    2. Chan, Moon-tong & Yu, Dalei & Yau, Kelvin K.W., 2015. "Multilevel cumulative logistic regression model with random effects: Application to British social attitudes panel survey data," Computational Statistics & Data Analysis, Elsevier, vol. 88(C), pages 173-186.
    3. Steele, Fiona & Clarke, Paul & Leckie, George & Allan, Julia & Johnston, Derek, 2017. "Multilevel structural equation models for longitudinal data where predictors are measured more frequently than outcomes: an application to the effects of stress on the cognitive function of nurses," LSE Research Online Documents on Economics 64893, London School of Economics and Political Science, LSE Library.
    4. Keunbaik Lee & Michael J. Daniels, 2007. "A Class of Markov Models for Longitudinal Ordinal Data," Biometrics, The International Biometric Society, vol. 63(4), pages 1060-1067, December.
    5. Fiona Steele & Paul Clarke & George Leckie & Julia Allan & Derek Johnston, 2017. "Multilevel structural equation models for longitudinal data where predictors are measured more frequently than outcomes: an application to the effects of stress on the cognitive function of nurses," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(1), pages 263-283, January.
    6. Lin, Kuo-Chin, 2010. "Goodness-of-fit tests for modeling longitudinal ordinal data," Computational Statistics & Data Analysis, Elsevier, vol. 54(7), pages 1872-1880, July.
    7. Yu-Zhu Tian & Man-Lai Tang & Wai-Sum Chan & Mao-Zai Tian, 2021. "Bayesian bridge-randomized penalized quantile regression for ordinal longitudinal data, with application to firm’s bond ratings," Computational Statistics, Springer, vol. 36(2), pages 1289-1319, June.
    8. Chaubert, F. & Mortier, F. & Saint André, L., 2008. "Multivariate dynamic model for ordinal outcomes," Journal of Multivariate Analysis, Elsevier, vol. 99(8), pages 1717-1732, September.
    9. Schliep Erin M. & Schafer Toryn L. J. & Hawkey Matthew, 2021. "Distributed lag models to identify the cumulative effects of training and recovery in athletes using multivariate ordinal wellness data," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 17(3), pages 241-254, September.

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