Teaching Efficacy: Unravelling Multidimensionality Across Diverse Educational Contexts (Poster 34)

• In Event: AERA Poster Session Thursday 8:00 a.m.
  In Poster Session: Advancements in Educational Data Analysis: Machine Learning, Multilevel Models, and Beyond

Abstract

This study aimed to refine the statistical representation of teaching efficacy by evaluating competing models and examining their relationship with job satisfaction across five countries. Using data from the 2018 Teaching and Learning International Survey (TALIS) involving 19,385 teachers from Brazil, China, Singapore, Slovenia, and the USA, we compared CFA, ESEM, bifactor, and B-ESEM models. The B-ESEM model demonstrated the best fit and was invariant across countries. This model's general factor was significantly associated with job satisfaction. Our findings suggest that the B-ESEM model effectively captures the multidimensional nature of teaching efficacy, providing nuanced insights that can inform targeted interventions and policies in diverse educational contexts.

Authors

• Kendra Lin Wells, University of Alberta

• Lia Daniels, University of Alberta

• Anne C. Frenzel, University of Munich