Quantitative Psychology: The 87th Annual Meeting of the Psychometric Society, Bologna, Italy, 2022

The volume represents presentations given at the 87th annual meeting of the Psychometric Society, held in Bologna, Italy at July 11-15, 2022. The proceedings cover a diverse set of psychometric topics, including item response theory, Bayesian models, reliability, latent variable models, causal inference, and cognitive diagnostic models.

Marie Wiberg is professor in Statistics with specialty in psychometrics at Umeå University in Sweden. Her research interests include test equating, applied statistics, parametric and nonparametric item response theory, large-scale assessments and educational measurement and psychometrics in general.

Dylan Molenaar is an assistant professor at the department of psychology, University of Amsterdam. His research interests include item response theory, factor analysis, response time modeling, and modeling of intelligence test scores.

Jorge González is an associate professor at the Department of Statistics, Faculty of Mathematics, Pontificia Universidad Católica de Chile, and an associate researcher at the Millennium Nucleus on Intergenerational Mobility: From Modelling to Policy (MOVI). His research is focused on the statistical modeling of data arising from the social sciences, particularly on the fields of test theory, educational measurement and psychometrics. He has conducted research on item response theory (IRT) models, standard settings procedures, structural equation models, value-added models, test equating, identifiability in IRT models, and methods of statistical inference under both the classical and the Bayesian approach.

Jee-Seon Kim is a professor in the Department of Educational Psychology at the University of Wisconsin-Madison. Her research interests are concerned with the development and application of quantitative methods in the social and behavioral sciences, focusing on causal inference, heterogeneous treatment effects, omitted variable bias, multilevel models and clustered data analysis, latent variable and mixture modeling, and causal machine learning methods.

Heungsun Hwang is Professor of Quantitative Psychology at McGill University in Canada. His research is devoted to the development of quantitative analytics tools for examining complex relationships of various data from psychologyand other disciplines toward a better understanding of human behaviour and cognition. Methodologically, he is interested in a wide array of statistical methods in multivariate statistics, structural equation modeling, machine learning, functional data analysis, and genetic and neuroimaging data analysis.