A critical evaluation of alignment optimization for improving cross- national comparability in international large-scale assessments

This study critically examines the use of alignment optimization to improve cross-national comparability of teacher and principal scales from the Teaching and Learning International Survey (TALIS) 2018. By investigating key psychometric properties, including dimensionality, reliability, and measurement invariance, the study highlights critical challenges in international large-scale assessments. While unidimensionality and high internal consistency were established for all scales, traditional multiple-group confirmatory factor analysis (MGCFA) suggested that scalar invariance could not be fully established for most scales, raising concerns about the robustness of cross-national comparisons under strict invariance assumptions. In contrast, alignment optimization emerged as a flexible and robust method, significantly enhancing the comparability of principal scales, all of which met alignment criteria. However, persistent challenges were identified for many teacher scales, which fell below alignment thresholds, emphasizing unresolved methodological complexities. This study demonstrates the transformative potential of alignment optimization for advancing psychometric rigor in global educational research and underscores the need for innovative approaches to address lingering comparability issues in international assessments.