Abstract
Item analysis (IA) is commonly used to describe difficulty and discrimination indices of multiple true-false (MTF) questions. However, item analysis is basically a plain descriptive analysis with limited statistical value. Item response theory (IRT) can provide a better insight into the difficulty and discriminating ability of questions in a test. IRT consists of a collection of statistical models that allows evaluation of test items (questions) and test takers (examinees) at the same time. Specifically, this article focuses on two-parameter logistic IRT (2-PL IRT) model that is concerned with estimation of difficulty and discrimination parameters. This article shows how 2-PL IRT analysis is performed in R software environment, guides the interpretation of the IRT results and compares the results to IA on a sample of MTF questions.
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