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%lrasch_mml: A SAS Macro for Marginal Maximum Likelihood Estimation in Longitudinal Polytomous Rasch Models

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  • Olsbjerg, Maja
  • Christensen, Karl Bang

Abstract

Item response theory models are often applied when a number items are used to measure a unidimensional latent variable. Originally proposed and used within educational research, they are also used when focus is on physical functioning or psychological wellbeing. Modern applications often need more general models, typically models for multidimensional latent variables or longitudinal models for repeated measurements. This paper describes a SAS macro that fits two-dimensional polytomous Rasch models using a specification of the model that is sufficiently flexible to accommodate longitudinal Rasch models. The macro estimates item parameters using marginal maximum likelihood estimation. A graphical presentation of item characteristic curves is included.

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  • Olsbjerg, Maja & Christensen, Karl Bang, 2015. "%lrasch_mml: A SAS Macro for Marginal Maximum Likelihood Estimation in Longitudinal Polytomous Rasch Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(c02).
    • Handle: RePEc:jss:jstsof:v:067:c02
    DOI: http://hdl.handle.net/10.18637/jss.v067.c02
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    1. David Thissen, 1982. "Marginal maximum likelihood estimation for the one-parameter logistic model," Psychometrika, Springer;The Psychometric Society, vol. 47(2), pages 175-186, June.
    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. Susan Embretson, 1991. "A multidimensional latent trait model for measuring learning and change," Psychometrika, Springer;The Psychometric Society, vol. 56(3), pages 495-515, September.
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