Conference Agenda

In recent years, the widespread adoption of computer-based testing has produced large volumes of data on examinee behavior. Beyond traditional binary indicators of correct responses, these datasets now typically include item-level response times, providing a richer and more informative perspective on the performance.
The joint modeling of response accuracy and response times has therefore attracted increasing attention, as the interaction between these two aspects can provide a clearer picture of the underlying phenomena.
Furthermore, such data commonly exhibit a hierarchical structure, where, for example, students are nested within classes or schools. Individuals in the same cluster may share unobserved characteristics, inducing heterogeneity at both cluster and individual levels. Consequently, appropriately accounting for this nested structure is essential to ensure valid and unbiased inference.
We propose a multilevel latent class response time model formulated using a normal-ogive parameterization—similar to the Rasch model—for the conditional probability of a correct response, while the conditional distribution of response times given the latent variables is assumed to be log-normal. To account for the multilevel structure of the data we assume discrete latent variables at both cluster and individual levels, and we adopt a multinomial logit parameterization to include covariates at both levels. Inference is carried out via a maximum likelihood approach using the Expectation–Maximization algorithm.
We analyze data on dichotomous responses to a mathematical test and related item response times administered to a representative sample of Grade-10 students during the 2017-2018 school year by the Italian National Institute for the Evaluation of the Educational System. Through the proposed model, estimated with covariates at both student and class levels, we identify five distinct ability subpopulations of students characterized by different response times, with a nonlinear association between ability and speed. Low prior mathematics and anxiety emerge as significant covariates among others, being associated with both a lower probability of correct responses and longer response times. Anxiety is particularly influential on the performance of students with average ability.
We also propose a model formulation within a hierarchical Bayesian framework. In this context, estimation is performed via a Markov chain Monte Carlo (MCMC) algorithm based on a data augmentation scheme. We aim to compare the two models and the related estimation methods in terms of computational efficiency, accuracy, and applied results.

The Bradley-Terry model is widely used for the analysis of pairwise comparison data and, in essence, produces a ranking of the items under comparison. We embed the Bradley-Terry model within a stochastic block model, allowing items to cluster. The resulting Bradley-Terry SBM (BT-SBM) ranks clusters so that items within a cluster share the same tied rank. We develop a fully Bayesian specification in which all quantities-the number of blocks, their strengths, and item assignments-are jointly learned via a fast Gibbs sampler derived through a Thurstonian data augmentation. Despite its efficiency, the sampler yields coherent and interpretable posterior summaries for all model components. Our motivating application analyzes men's tennis results from ATP tournaments over the seasons 2000-2022. We find that the top 100 players can be broadly partitioned into three or four tiers in most seasons. Moreover, the size of the strongest tier was small from the mid-2000s to 2018 and has increased since, providing evidence that men's tennis has become more competitive in recent years.

A central task in network analysis is to model social influence, that is, how individual behaviours and outcomes are shaped by their social environment. Classical regression models are not suitable for this purpose, as they frequently rely on independence assumptions that are violated in network data, where individuals' behaviours are inherently interdependent. Although several methods have been proposed to address this problem, existing approaches either treat the network as fixed, rely on multi-step estimation procedures, or are limited to continuous outcome variables.
We introduce the Bayesian logistic actor-attribute latent space model for social influence, a novel approach that jointly models binary actor-level outcomes and the network structure within a unified model framework. The network is represented through a latent social space that provides an interpretable, low-dimensional characterization of the underlying social structure. Our goal is to model a binary actor-level outcome as a function of both observed covariates and latent positions, where the latent social space captures complex network dependencies not explained by covariates alone, but affecting the outcome of interest. Inference is performed within a fully Bayesian framework via a Gibbs sampling algorithm based on Pólya–Gamma data augmentation. This scheme enables principled uncertainty quantification, efficient posterior estimation, and scalability to large networks.