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

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Standard

%lrasch_mml : A SAS Macro for Marginal Maximum Likelihood Estimation in Longitudinal Polytomous Rasch Models. / Larsen, Maja Olsbjerg; Christensen, Karl Bang.

I: Journal of Statistical Software, Bind 67, Nr. Code Snippet 2, 07.10.2015, s. 1-24.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Larsen, MO & Christensen, KB 2015, '%lrasch_mml: A SAS Macro for Marginal Maximum Likelihood Estimation in Longitudinal Polytomous Rasch Models', Journal of Statistical Software, bind 67, nr. Code Snippet 2, s. 1-24. https://doi.org/10.18637/jss.v067.c02

APA

Larsen, M. O., & Christensen, K. B. (2015). %lrasch_mml: A SAS Macro for Marginal Maximum Likelihood Estimation in Longitudinal Polytomous Rasch Models. Journal of Statistical Software, 67(Code Snippet 2), 1-24. https://doi.org/10.18637/jss.v067.c02

Vancouver

Larsen MO, Christensen KB. %lrasch_mml: A SAS Macro for Marginal Maximum Likelihood Estimation in Longitudinal Polytomous Rasch Models. Journal of Statistical Software. 2015 okt. 7;67(Code Snippet 2):1-24. https://doi.org/10.18637/jss.v067.c02

Author

Larsen, Maja Olsbjerg ; Christensen, Karl Bang. / %lrasch_mml : A SAS Macro for Marginal Maximum Likelihood Estimation in Longitudinal Polytomous Rasch Models. I: Journal of Statistical Software. 2015 ; Bind 67, Nr. Code Snippet 2. s. 1-24.

Bibtex

@article{ef8e25479c2e4244a48fda77fcdeb13f,
title = "%lrasch_mml: A SAS Macro for Marginal Maximum Likelihood Estimation in Longitudinal Polytomous Rasch Models",
abstract = "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.",
keywords = "polytomous Rasch model, longitudinal Rasch model, marginal maximum likelihood (MML) estimation, item parameter drift, response dependence, SAS macro",
author = "Larsen, {Maja Olsbjerg} and Christensen, {Karl Bang}",
year = "2015",
month = oct,
day = "7",
doi = "10.18637/jss.v067.c02",
language = "English",
volume = "67",
pages = "1--24",
journal = "Journal of Statistical Software",
issn = "1548-7660",
publisher = "The Foundation for Open Access Statistics",
number = "Code Snippet 2",

}

RIS

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