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A comparison of estimation methods for the Rasch model. / Robitzsch, Alexander.

Book of short papers - SIS 2021. Hrsg. / Cira Perna; Nicola Salvati; Francesco Schirripa Spagnolo. Pearson, 2021. S. 157-162.

Publikationen: Beitrag in Sammelwerk/KonferenzbandBeitrag in SammelwerkForschungBegutachtung

Harvard

Robitzsch, A 2021, A comparison of estimation methods for the Rasch model. in C Perna, N Salvati & FS Spagnolo (Hrsg.), Book of short papers - SIS 2021. Pearson, S. 157-162.

APA

Robitzsch, A. (2021). A comparison of estimation methods for the Rasch model. in C. Perna, N. Salvati, & F. S. Spagnolo (Hrsg.), Book of short papers - SIS 2021 (S. 157-162). Pearson.

Vancouver

Robitzsch A. A comparison of estimation methods for the Rasch model. in Perna C, Salvati N, Spagnolo FS, Hrsg., Book of short papers - SIS 2021. Pearson. 2021. S. 157-162

Author

Robitzsch, Alexander. / A comparison of estimation methods for the Rasch model. Book of short papers - SIS 2021. Hrsg. / Cira Perna ; Nicola Salvati ; Francesco Schirripa Spagnolo. Pearson, 2021. S. 157-162

BibTeX

@inbook{8baabcdf6dc846f68607f6f2f7617c3d,
title = "A comparison of estimation methods for the Rasch model",
abstract = "The Rasch model is one of the most prominent item response models.In this article, different item parameter estimation methods for the Rasch model are compared through a simulation study. The type of ability distribution, the number of items, and sample sizes were varied. It is shown that variants of joint maximum likelihood estimation and conditional likelihood estimation are competitive to marginal maximum likelihood estimation. However, efficiency losses of limited-information estimation methods are only modest. It can be concluded that in empirical studies using the Rasch model, the impact of the choice of an estimation method with respect to item parameters is almost negligible for most estimation methods. Interestingly, this sheds a somewhat more positive light on old-fashioned joint maximum likelihood and limited information estimation methods.",
author = "Alexander Robitzsch",
year = "2021",
month = jun,
language = "English",
pages = "157--162",
editor = "Cira Perna and Nicola Salvati and Spagnolo, {Francesco Schirripa}",
booktitle = "Book of short papers - SIS 2021",
publisher = "Pearson",

}

RIS

TY - CHAP

T1 - A comparison of estimation methods for the Rasch model

AU - Robitzsch, Alexander

PY - 2021/6

Y1 - 2021/6

N2 - The Rasch model is one of the most prominent item response models.In this article, different item parameter estimation methods for the Rasch model are compared through a simulation study. The type of ability distribution, the number of items, and sample sizes were varied. It is shown that variants of joint maximum likelihood estimation and conditional likelihood estimation are competitive to marginal maximum likelihood estimation. However, efficiency losses of limited-information estimation methods are only modest. It can be concluded that in empirical studies using the Rasch model, the impact of the choice of an estimation method with respect to item parameters is almost negligible for most estimation methods. Interestingly, this sheds a somewhat more positive light on old-fashioned joint maximum likelihood and limited information estimation methods.

AB - The Rasch model is one of the most prominent item response models.In this article, different item parameter estimation methods for the Rasch model are compared through a simulation study. The type of ability distribution, the number of items, and sample sizes were varied. It is shown that variants of joint maximum likelihood estimation and conditional likelihood estimation are competitive to marginal maximum likelihood estimation. However, efficiency losses of limited-information estimation methods are only modest. It can be concluded that in empirical studies using the Rasch model, the impact of the choice of an estimation method with respect to item parameters is almost negligible for most estimation methods. Interestingly, this sheds a somewhat more positive light on old-fashioned joint maximum likelihood and limited information estimation methods.

M3 - Contribution to collected edition/anthology

SP - 157

EP - 162

BT - Book of short papers - SIS 2021

A2 - Perna, Cira

A2 - Salvati, Nicola

A2 - Spagnolo, Francesco Schirripa

PB - Pearson

ER -

ID: 1672728