%lrasch_mml: A SAS Macro for Marginal Maximum Likelihood Estimation in Longitudinal Polytomous Rasch Models
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%lrasch_mml : A SAS Macro for Marginal Maximum Likelihood Estimation in Longitudinal Polytomous Rasch Models. / Larsen, Maja Olsbjerg; Christensen, Karl Bang.
In: Journal of Statistical Software, Vol. 67, No. Code Snippet 2, 07.10.2015, p. 1-24.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - %lrasch_mml
T2 - A SAS Macro for Marginal Maximum Likelihood Estimation in Longitudinal Polytomous Rasch Models
AU - Larsen, Maja Olsbjerg
AU - Christensen, Karl Bang
PY - 2015/10/7
Y1 - 2015/10/7
N2 - Item response theory models are often applied when a number items are used to measurea unidimensional latent variable. Originally proposed and used within educationalresearch, they are also used when focus is on physical functioning or psychological wellbeing.Modern applications often need more general models, typically models for multidimensionallatent variables or longitudinal models for repeated measurements. This paperdescribes a SAS macro that fits two-dimensional polytomous Rasch models using a specifi-cation of the model that is sufficiently flexible to accommodate longitudinal Rasch models.The macro estimates item parameters using marginal maximum likelihood estimation. Agraphical presentation of item characteristic curves is included.
AB - Item response theory models are often applied when a number items are used to measurea unidimensional latent variable. Originally proposed and used within educationalresearch, they are also used when focus is on physical functioning or psychological wellbeing.Modern applications often need more general models, typically models for multidimensionallatent variables or longitudinal models for repeated measurements. This paperdescribes a SAS macro that fits two-dimensional polytomous Rasch models using a specifi-cation of the model that is sufficiently flexible to accommodate longitudinal Rasch models.The macro estimates item parameters using marginal maximum likelihood estimation. Agraphical presentation of item characteristic curves is included.
KW - polytomous Rasch model
KW - longitudinal Rasch model
KW - marginal maximum likelihood (MML) estimation
KW - item parameter drift
KW - response dependence
KW - SAS macro
U2 - 10.18637/jss.v067.c02
DO - 10.18637/jss.v067.c02
M3 - Journal article
VL - 67
SP - 1
EP - 24
JO - Journal of Statistical Software
JF - Journal of Statistical Software
SN - 1548-7660
IS - Code Snippet 2
ER -
ID: 160407441