Publication Trends in the Journal of Educational Measurement: A Bibliometric Network Analysis

Abstract This study examines the intellectual structure and historical development of the Journal of Educational Measurement over its six‐decade history (1964‐2025). Using computational text mining and network analysis, we analyzed 1,678 articles based on a deterministic, expert‐defined taxonomy of 34 psychometric categories to quantify research trends. Results identify item response theory (IRT) as the dominant paradigm and the most connected area in the publication network. Community detection reveals a tripartite structure dividing the discipline into psychometric modeling, applied test operations, and classical measurement foundations. While IRT constitutes the primary theoretical focus, parameter estimation and validity emerge as intermediary concepts linking statistical modeling to practical applications. The analysis also highlights a historical transition from classical test theory to latent trait modeling, with computerized adaptive testing and differential item functioning identified as major contemporary topics. Furthermore, artificial intelligence appears as an emerging area, showing limited activity for many years followed by rapid growth beginning in 2022, primarily driven by automated scoring research. Overall, these findings provide a data‐driven framework for monitoring how the field continues to evolve in response to technological developments.