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A comprehensive simulation study of estimation methods for the Rasch model. / Robitzsch, Alexander.

in: Stats, Jahrgang 4, Nr. 4, 01.10.2021, S. 814-836.

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@article{20cbd9cb35604aaa81eb5d622b8b7bcb,
title = "A comprehensive simulation study 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 systematically compared through a comprehensive simulation study: Different alternatives of joint maximum likelihood (JML) estimation, different alternatives of marginal maximum likelihood (MML) estimation, conditional maximum likelihood (CML) estimation, and several limited information methods (LIM). The type of ability distribution (i.e., nonnormality), the number of items, sample size, and the distribution of item difficulties were systematically varied. Across different simulation conditions, MML methods with flexible distributional specifications can be at least as efficient as CML. Moreover, in many situations (i.e., for long tests), penalized JML and JML with ε adjustment resulted in very efficient estimates and might be considered alternatives to JML implementations currently used in statistical software. Moreover, minimum chi-square (MINCHI) estimation was the best-performing LIM method. These findings demonstrate that JML estimation and LIM can still prove helpful in applied research. ",
keywords = "Methodological research and method development, Rasch model, estimation methods, nonnormality",
author = "Alexander Robitzsch",
year = "2021",
month = oct,
day = "1",
doi = "10.3390/stats4040048",
language = "English",
volume = "4",
pages = "814--836",
journal = "Stats",
issn = "2571-905X",
publisher = "MDPI",
number = "4",

}

RIS

TY - JOUR

T1 - A comprehensive simulation study of estimation methods for the Rasch model

AU - Robitzsch, Alexander

PY - 2021/10/1

Y1 - 2021/10/1

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 systematically compared through a comprehensive simulation study: Different alternatives of joint maximum likelihood (JML) estimation, different alternatives of marginal maximum likelihood (MML) estimation, conditional maximum likelihood (CML) estimation, and several limited information methods (LIM). The type of ability distribution (i.e., nonnormality), the number of items, sample size, and the distribution of item difficulties were systematically varied. Across different simulation conditions, MML methods with flexible distributional specifications can be at least as efficient as CML. Moreover, in many situations (i.e., for long tests), penalized JML and JML with ε adjustment resulted in very efficient estimates and might be considered alternatives to JML implementations currently used in statistical software. Moreover, minimum chi-square (MINCHI) estimation was the best-performing LIM method. These findings demonstrate that JML estimation and LIM can still prove helpful in applied research.

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 systematically compared through a comprehensive simulation study: Different alternatives of joint maximum likelihood (JML) estimation, different alternatives of marginal maximum likelihood (MML) estimation, conditional maximum likelihood (CML) estimation, and several limited information methods (LIM). The type of ability distribution (i.e., nonnormality), the number of items, sample size, and the distribution of item difficulties were systematically varied. Across different simulation conditions, MML methods with flexible distributional specifications can be at least as efficient as CML. Moreover, in many situations (i.e., for long tests), penalized JML and JML with ε adjustment resulted in very efficient estimates and might be considered alternatives to JML implementations currently used in statistical software. Moreover, minimum chi-square (MINCHI) estimation was the best-performing LIM method. These findings demonstrate that JML estimation and LIM can still prove helpful in applied research.

KW - Methodological research and method development

KW - Rasch model

KW - estimation methods

KW - nonnormality

U2 - 10.3390/stats4040048

DO - 10.3390/stats4040048

M3 - Journal article

VL - 4

SP - 814

EP - 836

JO - Stats

JF - Stats

SN - 2571-905X

IS - 4

ER -

ID: 1696700