Fitting a Thurstonian IRT model to forced-choice data using Mplus

To counter response distortions associated with the use of rating scales (a.k.a. Likert scales), items can be presented in a comparative fashion, so that respondents are asked to rank the items within blocks (forced-choice format). However, classical scoring procedures for these forced-choice designs lead to ipsative data, which presents psychometric challenges that are well described in the literature. Recently, Brown and Maydeu-Olivares (Educational and Psychological Measurement 71: 460–502, 2011a) introduced a model based on Thurstone’s law of comparative judgment, which overcomes the problems of ipsative data. Here, we provide a step-by-step tutorial for coding forced-choice responses, specifying a Thurstonian item response theory model that is appropriate for the design used, assessing the model’s fit, and scoring individuals on psychological attributes. Estimation and scoring is performed using Mplus, and a very straightforward Excel macro is provided that writes full Mplus input files for any forced-choice design. Armed with these tools, using a forced-choice design is now as easy as using ratings.