Fitting polytomous Rasch models in SAS

Research output: Contribution to journalJournal articlepeer-review

Standard

Fitting polytomous Rasch models in SAS. / Christensen, Karl Bang.

In: Journal of Applied Measurement, Vol. 7, No. 4, 2006, p. 407-17.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Christensen, KB 2006, 'Fitting polytomous Rasch models in SAS', Journal of Applied Measurement, vol. 7, no. 4, pp. 407-17.

APA

Christensen, K. B. (2006). Fitting polytomous Rasch models in SAS. Journal of Applied Measurement, 7(4), 407-17.

Vancouver

Christensen KB. Fitting polytomous Rasch models in SAS. Journal of Applied Measurement. 2006;7(4):407-17.

Author

Christensen, Karl Bang. / Fitting polytomous Rasch models in SAS. In: Journal of Applied Measurement. 2006 ; Vol. 7, No. 4. pp. 407-17.

Bibtex

@article{e93b7350edf911ddbf70000ea68e967b,
title = "Fitting polytomous Rasch models in SAS",
abstract = "The item parameters of a polytomous Rasch model can be estimated using marginal and conditional approaches. This paper describes how this can be done in SAS (V8.2) for three item parameter estimation procedures: marginal maximum likelihood estimation, conditional maximum likelihood estimation, and pairwise conditional estimation. The use of the procedures for extensions of the Rasch model is also discussed. The accuracy of the methods are evaluated using a simulation study.",
author = "Christensen, {Karl Bang}",
note = "Keywords: Humans; Models, Psychological; Psychology",
year = "2006",
language = "English",
volume = "7",
pages = "407--17",
journal = "Journal of Applied Measurement",
issn = "1529-7713",
publisher = "J A M Press",
number = "4",

}

RIS

TY - JOUR

T1 - Fitting polytomous Rasch models in SAS

AU - Christensen, Karl Bang

N1 - Keywords: Humans; Models, Psychological; Psychology

PY - 2006

Y1 - 2006

N2 - The item parameters of a polytomous Rasch model can be estimated using marginal and conditional approaches. This paper describes how this can be done in SAS (V8.2) for three item parameter estimation procedures: marginal maximum likelihood estimation, conditional maximum likelihood estimation, and pairwise conditional estimation. The use of the procedures for extensions of the Rasch model is also discussed. The accuracy of the methods are evaluated using a simulation study.

AB - The item parameters of a polytomous Rasch model can be estimated using marginal and conditional approaches. This paper describes how this can be done in SAS (V8.2) for three item parameter estimation procedures: marginal maximum likelihood estimation, conditional maximum likelihood estimation, and pairwise conditional estimation. The use of the procedures for extensions of the Rasch model is also discussed. The accuracy of the methods are evaluated using a simulation study.

M3 - Journal article

C2 - 17068380

VL - 7

SP - 407

EP - 417

JO - Journal of Applied Measurement

JF - Journal of Applied Measurement

SN - 1529-7713

IS - 4

ER -

ID: 9997657