Analyzing Dyadic Data with IRT Models

A Dyadic IRT Model

We propose a dyadic Item Response Theory (dIRT) model for measuring interactions of pairs of individuals when the responses to items represent the actions (or behaviors, perceptions, etc.) of each individual (actor) made within the context of a dyad formed with another individual (partner). Examples of its use include the assessment of collaborative problem solving or the evaluation of intra-team dynamics. The dIRT model generalizes both Item Response Theory models for measurement and the Social Relations Model for dyadic data. The responses of an actor when paired with a partner are modeled as a function of not only the actor's inclination to act and the partner's tendency to elicit that action, but also the unique relationship of the pair, represented by two directional, possibly correlated, interaction latent variables. Generalizations are discussed, such as accommodating triads or larger groups. Estimation is performed using Markov-chain Monte Carlo implemented in Stan, making it straightforward to extend the dIRT model in various ways. Specifically, we show how the basic dIRT model can be extended to accommodate latent regressions, multilevel settings with cluster-level random effects, as well as joint modeling of dyadic data and a distal outcome. A simulation study demonstrates that estimation performs well. We apply our proposed approach to speed-dating data and find new evidence of pairwise interactions between participants, describing a mutual attraction that is inadequately characterized by individual properties alone.

Assessment of Collaborative Problem Solving Based on Process Stream Data: A New Paradigm for Extracting Indicators and Modeling Dyad Data

As one of the important 21st-century skills, collaborative problem solving (CPS) has aroused widespread concern in assessment. To measure this skill, two initiative approaches have been created: the human-to-human and human-to-agent modes. Between them, the human-to-human interaction is much closer to the real-world situation and its process stream data can reveal more details about the cognitive processes. The challenge for fully tapping into the information obtained from this mode is how to extract and model indicators from the data. However, the existing approaches have their limitations. In the present study, we proposed a new paradigm for extracting indicators and modeling the dyad data in the human-to-human mode. Specifically, both individual and group indicators were extracted from the data stream as evidence for demonstrating CPS skills. Afterward, a within-item multidimensional Rasch model was used to fit the dyad data. To validate the paradigm, we developed five online tasks following the asymmetric mechanism, one for practice and four for formal testing. Four hundred thirty-four Chinese students participated in the assessment and the online platform recorded their crucial actions with time stamps. The generated process stream data was handled with the proposed paradigm. Results showed that the model fitted well. The indicator parameter estimates and fitting indexes were acceptable, and students were well differentiated. In general, the new paradigm of extracting indicators and modeling the dyad data is feasible and valid in the human-to-human assessment of CPS. Finally, the limitations of the current study and further research directions are discussed.