This book includes presentations given at the 88th annual meeting of the Psychometric Society, held in Maryland, USA on July 24-28, 2023.

The proceeding covers a diverse set of psychometric topics. The topics include, but are not limited to item response theory, cognitive diagnostic models, Bayesian estimation, validity and reliability issues, and several applications within different fields. The authors are from all over the world, they work in different psychometrics areas, as well as having diverse professional and academic experiences.

Autorentext

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.

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 psychology and 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.

Hao Wu is an associate professor of Quantitative Methods in Department of Psychology and Human Development of Vanderbilt University. His research focuses on the evaluation of statistical models used in psychology and education, especially structural equation models. This includes identifiability, the quantification of various sources of uncertainty, model fit and effect size. His research interest also includes robust and nonparametric methods.

Tracy Sweet is Associate Professor in the Measurement, Statistics and Evaluation program in the Department of Human Development and Quantitative Methodology. Her research focuses on methods for social network analysis with particular focus on multilevel social network models. Recent projects include network interference, measurement error, and missing data.