Dear Psychometrika Readers,

Welcome to the third Psychometrika issue of 2026. Some of you must be preparing to attend IMPS2026 to be held in Seoul, South Korea, in mid-July. I look forward to meeting many of you during the meeting.

This issue begins with 10 “Application and Case Studies” articles that constitute a special section on integrating and analyzing complex high-dimensional data in social and behavioral sciences research. The first of the 10 articles is the introduction to the special issue by Eric Lock and Katrijn Van Deun who were the guest editors for the special section. The introduction article provides brief descriptions of the 10 special section articles.

This Psychometrika issue then includes one more “Application and Case Studies” section article, by Jing Lu, Chun Wang, Jiwei Zhang, and Zefeng Liu, who propose three approaches based on change-point analysis methods to detect test speededness in time-limit tests.

This Psychometrika issue then includes three “Theory and Methods” section articles. In the first, Xinyu Zhang, Xiangbin Meng, Wei Gao, and Gongjun Xu propose a joint modeling framework for graded item responses and response times. The second, by Stefano Noventa, Andrea Spoto, Jurgen Heller, and Augustin Kelava, attempts a systematization and generalization of some approaches to local dependence. The third, by Jean-Paul Fox, suggests a new Bayesian modeling approach for differential item functioning.

In the next article, which belongs to the “Review” section of the journal, Rutger van Oest and Jonas Moss review interpretations and connections of chance-corrected agreement coefficients with quadratic weights in applications where raters classify objects or subjects on an ordinal scale.

This issue ends with two articles belonging to the “Theory and Methods” section. The first, by Dexin Shi, Wolfgang Wiedermann, Amanda Fairchild, and Bo Zhang, develops statistical methods for bivariate causal learning using higher-order moment information from two-wave longitudinal data. The second, by Jiguang Li, Robert Gibbons, and Veronika Ročková, introduces deep computerized adaptive testing (CAT), which is a novel CAT system that builds on recent advances in Bayesian multivariate item response theory.

Hope you enjoy the issue.