ERIC Number: EJ1162519
Record Type: Journal
Publication Date: 2017
Abstractor: As Provided
Five Methods for Estimating Angoff Cut Scores with IRT
Wyse, Adam E.
Educational Measurement: Issues and Practice, v36 n4 p16-27 Win 2017
This article illustrates five different methods for estimating Angoff cut scores using item response theory (IRT) models. These include maximum likelihood (ML), expected a priori (EAP), modal a priori (MAP), and weighted maximum likelihood (WML) estimators, as well as the most commonly used approach based on translating ratings through the test characteristic curve (i.e., the IRT true-score (TS) estimator). The five methods are compared using a simulation study and a real data example. Results indicated that the application of different methods can sometimes lead to different estimated cut scores, and that there can be some key differences in impact data when using the IRT TS estimator compared to other methods. It is suggested that one should carefully think about their choice of methods to estimate ability and cut scores because different methods have distinct features and properties. An important consideration in the application of Bayesian methods relates to the choice of the prior and the potential bias that priors may introduce into estimates.
Descriptors: Cutting Scores, Item Response Theory, Bayesian Statistics, Maximum Likelihood Statistics, Test Items, Test Bias, Evaluation Methods, Simulation, Comparative Analysis
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Publication Type: Journal Articles; Reports - Research
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