Evaluation of Network Models for Ordinal Data

Network models have become a popular tool for studying multivariate dependencies in psychological data. The most popular models are the Ising model for binary data and the Gaussian Graphical Model (GGM) for continuous data. However, most cross-sectional data are in fact ordinal. For example, personality questionnaires are scored on Likert scales, symptoms are rated in ordered categories of severity, and opinions and attitudes are measured on scales ranging from strong disagreement to strong agreement. Recently, however, appropriate network models for ordinal data have been developed that eliminate the need to binarize the data or model ordinal variables as continuous. In this paper, we discuss existing network models for ordinal data that either use a latent continuous distribution or model ordinal variables as manifest variables. We then provide a large-scale simulation study that evaluates the absolute and relative performance of these ordinal network models and contrasts them with the misspecified GGM. Based on these results, we discuss the advantages and disadvantages of each method and provide guidance as to when each method is most appropriate.