Spatially dependent Polytomous Item Response Theory Model with A Bayesian Nonparametric Approach

Music City Center 

The item response theory model (IRT) is the benchmark method for modeling individual response differences in survey data. For instance, in ecological data, it can assess how well individuals perform in species identification, taking into account both the difficulty of identifying specific species and environmental variables. In that regard, the three parameter item response model (U)sing (S)patially dependent item difficulties (3PLUS) provides a methodological approach that accounts for spatial dependencies in citizen science data while measuring users' abilities and item characteristics. Our contribution extends the 3PLUS model in two dimensions. First, we generalize the model to handle polytomous responses, expanding its applicability beyond binary outcomes. Second, we introduce Gaussian Process modeling for difficulty parameters, providing more flexibility in modeling spatial dependencies compared to the original conditional autoregressive prior specification. Through simulations and application to ecological citizen science data, we demonstrate more precise inference of item difficulties and participant abilities than the 3PLUS model.

Item response theory model

spatial dependency

Gaussian process

Latent variable modeling

Large-scale data

ecological data 

Social Statistics Section