IRT for voting advice applications: a multi-dimensional test that is adaptive and interpretable

Abstract

AbstractVoting advice applications rely on user input to match user preferences to political parties or candidates. Providing the input can be time-consuming, which may have a negative effect on participation. For individuals who are under time constraints or who are affected by survey fatigue, the participation threshold may be lowered if there is an option to conclude the test without answering all question items. The test result should ideally be close to the result that the participant would have gotten after answering the full battery of questions. We propose a method that allows respondents to conclude a VAA early and still get results with sufficient accuracy.The method proposed here extends the Graded Response Model and the Maximum Information Criterion, used in Item Response Theory. The aim of the method is to allow the user to control the length of the test. Furthermore, we want a simpler interpretation of multidimensional parameter estimates than we get from traditional MIRT. To achieve this, we propose an algorithm for adaptive IRT capable of selecting from a pool of items that belong to separate unidimensional scales. Using both simulated data and response data from a voting advice application project, we evaluate the accuracy of shorter tests implemented with our adaptive method. When only a few test items are answered, our proposed method outperforms a static-order IRT test of equal length in identifying the best match. We expect that implementation of the method can increase participation and engagement in voting advice applications.