Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

A Comparison of Estimation Methods for the Rasch Model

Version 1 : Received: 27 February 2021 / Approved: 1 March 2021 / Online: 1 March 2021 (13:16:08 CET)

How to cite: Robitzsch, A. A Comparison of Estimation Methods for the Rasch Model. Preprints 2021, 2021030011 (doi: 10.20944/preprints202103.0011.v1). Robitzsch, A. A Comparison of Estimation Methods for the Rasch Model. Preprints 2021, 2021030011 (doi: 10.20944/preprints202103.0011.v1).

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.

Subject Areas

Rasch model; item parameter estimation; maximum likelihood estimation; item response model

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