Title: A Modified Graded Response Model to Account for Guessing in
Multiple-Choice Items

Author: David Magis

Affiliation: University of Liege, Belgium

Abstract: 
This presentation focuses on multiple-choice items without any correction or
control for guessing. Most often, item responses are recoded as binary outcomes
so that dichotomous logistic item response models (such as the 2PL or 3PL
models) can be used to estimate ability levels. This is however at the cost of
loosing information from collapsing all distractors into a single FALSE
category. Polytomous IRT models could be considered to overcome that drawback
but none of the classical models are suitable to incorporate guessing into
account. The purpose of this talk is to introduce a modification to the graded
response model (Samejima, 1969) that can take guessing into account. The
modification consists in introducing lower and upper asymptotes to the
cumulative probability curves. After a brief theoretical and technical
description, practical implementation using the R package mirt (Chalmers, 2012)
is discussed. Primary results from a simulation study are eventually presented
and compared to traditional IRT scoring methods for scoring multiple-choice
items.