Abstract
This paper presents an analysis of repeated ordinal outcomes arising from two psychological studies. The first case is a repeated measures analysis of variance; the second is a mixed-effects regression in a longitudinal design. In both, the subject-specific variation is modeled by including random effects in the linear predictor (inside a link function) of a generalized linear model. The NLMIXED procedure in SAS is used to fit the mixed-effects models for the categorical response data. The presentation emphasizes the parallel between the model specifications and the SAS statements. The purpose of this paper is to facilitate the use of mixed-effects models in the analysis of repeated ordinal outcomes.
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References
Abramowitz, M., &Stegun, I. A. (1972).Handbook of mathematical functions. New York: Dover.
Agresti, A, Booth, J. G., Hobert, J. P., &Caffo, B. (2000). Random effects modeling of categorical response data.Sociological Methodology,30, 27–80.
Bender, R., &Benner, A. (2000). Calculating ordinal regression models in SAS and S-Plus.Biometrical Journal,42, 677–699.
Bryk, A. S., &Raudenbusch, S. W. (1992).Hierarchical linear models. Thousand Oaks, CA: Sage.
Fielding, A. (1999). Why use arbitrary points scores? Ordered categories in models of educational progress.Journal of the Royal Statistical Society A,162, 303–328.
Green, D. M., &Swets, J. A. (1974).Signal detection theory and psychophysics. Huntington, NY: Krieger.
Guo, G., &Zhao, H. (2000). Multilevel modeling for binary data.Annual Review of Sociology,26, 441–462.
Hedeker, D., &Gibbons, R. D. (1996). A computer program for mixed-effects ordinal regression analysis.Computer Methods & Programs in Biomedicine,49, 157–176.
Lorr, M., &Klett, C. J. (1966).Inpatient multidimensional psychiatric scale: Manual. Palo Alto, CA: Consulting Psychologist Press.
McCullagh, P. (1980). Regression model for ordinal data.Journal of the Royal Statistical SocietyB, 42, 109–127.
McCulloch, C. E., &Searle, S. R. (2001).Generalized, linear, and mixed models. New York: Wiley.
Pinheiro, J. C., &Bates, D. M. (1995). Approximations to the log-likelihood function in the nonlinear mixed-effects model.Journal of Computational and Graphical Statistics,4, 12–35.
Randall, J. H. (1989). The analysis of sensory data by generalized linear models.Biometrical Journal,31, 781–793.
Raudenbusch, S. W., Bryk, A. S., Cheong, Y. F., &Congdon, R. T. (2000).HLM5: Hierarchical linear and nonlinear modeling. Chicago: Scientific Software International.
SAS Institute (2000).SAS/STAT user’s guide (Version 8). Cary, NC: Author.
Sheu, C.-F., & Heathcote, A. (2001). A nonlinear regression approach to estimating signal detection models for rating data.Behavioral Research Methods, Instruments, & Computers,33, 108–114.
Spiegelhalter, D. J., Thomas, A, Best, N. G., &Gilks, W. (1997).BUGS: Bayesian inference using Gibbs sampling (Version 0.60). Cambridge: Medical Research Council Biostatistic Unit.
Ware, J. H. (1985). Linear models for the analysis of longitudinal studies.American Statistician,39, 95–101.
Wolfinger, R. (1999).Fitting nonlinear mixed models with the new NLMIXED procedure (SUGI 24 Conference Proceedings, Paper 287). Cary, NC: SAS Institute.
Yang, M., Rashbash, J., &Goldstein, H. (1998).MLwiN macros for advanced multilevel modeling (V2.0). London: Institute of Education.
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Sheu, CF. Fitting mixed-effects models for repeated ordinal outcomes with the NLMIXED procedure. Behavior Research Methods, Instruments, & Computers 34, 151–157 (2002). https://doi.org/10.3758/BF03195436
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DOI: https://doi.org/10.3758/BF03195436