The Annals of Applied Statistics
- Ann. Appl. Stat.
- Volume 10, Number 2 (2016), 925-945.
Unmixing Rasch scales: How to score an educational test
One of the important questions in the practice of educational testing is how a particular test should be scored. In this paper we consider what an appropriate simple scoring rule should be for the Dutch as a second language test consisting of listening and reading items. As in many other applications, here the Rasch model which allows to score the test with a simple sumscore is too restrictive to adequately represent the data. In this study we propose an exploratory algorithm which clusters the items into subscales each fitting a Rasch model and thus provides a scoring rule based on observed data. The scoring rule produces either a weighted sumscore based on equal weights within each subscale or a set of sumscores (one for each of the subscales). An MCMC algorithm which enables to determine the number of Rasch scales constituting the test and to unmix these scales is introduced and evaluated in simulations. Using the results of unmixing, we conclude that the Dutch language test can be scored with a weighted sumscore with three different weights.
Ann. Appl. Stat., Volume 10, Number 2 (2016), 925-945.
Received: May 2015
Revised: February 2016
First available in Project Euclid: 22 July 2016
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Bolsinova, Maria; Maris, Gunter; Hoijtink, Herbert. Unmixing Rasch scales: How to score an educational test. Ann. Appl. Stat. 10 (2016), no. 2, 925--945. doi:10.1214/16-AOAS919. https://projecteuclid.org/euclid.aoas/1469199899
- Supplement A: Supplement to “Unmixing Rasch scales: How to score an educational test.”. We provide the proof of identification of the multi-scale Rasch model in Section 1, details of the Gibbs Sampler for estimating the model in Section 2, details on approximating the likelihood of the model in Section 3, results of additional simulation studies in Section 4, and details on estimation of the model with fixed correlation parameters in Section 5.