Analyzing dyadic data with IRT models

Abstract

Dyadic data frequently occur in social sciences and numerous techniques have been developed for their analysis. The most prominent methods involve using regression, path, and structural equation models. The present contribution extends these approaches by considering Item Response Theory (IRT) Models. Two pivotal dyadic data analysis models, the Actor-Partner Interdependence Model (APIM) and the Common Fate Model (CFM), are built using the Multidimensional Random Coefficients Multinomial Logit Model (MRCMLM). This approach combines the advantages of dyadic data analysis with a model for discrete data, thus allowing for categorical items while drawing inferences based on the estimated true scores on an interval scale.