Remarks From the Editor-in-Chief

Dear Psychometrika Readers, Hope you are having a nice summer or winter, depending on which hemisphere of the earth you are located at.Many of you are probably preparing for the annual meeting of the Psychometric Society in Prague.I anticipate meeting with many of you in Prague.The June 2024 issue of Psychometrika begins with an "Applications Review and Case Studies" (ARCS) section article on semiparametric factor analysis of item responses and response time (written by Yang Liu and Weimeng Wang).The issue then includes twelve "Theory and Methods" section articles that were accepted by the previous Editor-in-Chief of the journal.These articles are on (i) exploratory generalized structured component analysis (Naoto Yamashita), (ii) use of external information for more precise inferences in general regression models (Martin Jann and Martin Spiess), (iii) Bayesian semiparametric longitudinal inverse-Probit mixed models for category learning (Minerva Mukhopadhyay, Jacie McHaney, Bharath Chandrasekaran, and Abhra Sarkar), (iv) identifiability of 3-and 4-parameter IRT models (Stefano Noventa, Sangbeak Ye, Augustin Kelava & Andrea Spoto), (v) measures of agreement with multiple raters (Jonas Moss), (vi) post-selection inference in multiverse analysis (Paolo Girardi, Anna Vesely, Daniël Lakens, Gianmarco Altoè, Massimiliano Pastore, Antonio Calcagnì, and Livio Finos), (vii) efficient corrections for standardized person-fit statistics (Kylie Gorney, Sandip Sinharay, and Carol Eckerly), (viii) restricted latent class models for nominal response data (Ying Liu and Steven Culpepper), (ix) a spectral method for identifiable grade of membership analysis with binary responses (Ling Chen and Yuqi Gu), (x) learning Bayesian networks (Federico Castelletti), (xi) A model implied instrumental variable approach to exploratory factor analysis (Kenneth Bollen, Kathleen M. Gates, and Lan Luo), and (xii) identifiability of DINA models with polytomous responses (Mengqi Lin and Gongjun Xu).