The utility of item response modeling in marketing research

Item response modeling (IRM/IRT) has been known to marketing scholars for a number of years. However, with the exception of some notable and important applications in international (cross-cultural) marketing and consumer behavior, even a cursory reading of marketing journals reveals a general lack of interest in applying IRM, despite its ability to provide highly useful measurement-related information. To address and hopefully remedy the paucity of adoption, we offer an application-oriented discussion of the utility of IRM for marketing and related business research to enable researchers to utilize the strengths and realize the benefits of this methodology in their empirical work. After a short discussion of the history of IRM, we focus on its fundamentals within a modern statistical framework based on the generalized linear model and closely related non-linear factor analysis. We then engage major concepts of IRM, including item characteristic curve, local independence, and dimensionality, as well as parameter estimation and information functions. The popular one- and two-parameter logistic models are next discussed, as is the issue of model selection. Several polytomous item response models are subsequently dealt with, followed by a discussion of multidimensional IRM and data illustrations of item response models using widely available software. References to exemplar marketing applications are provided along the way, and a discussion of limitations of IRM concludes the article.