Effects of school climate and teacher self-efficacy on job satisfaction of mostly STEM teachers: a structural multigroup invariance approach

Abstract

Background: Identification and retention of effective teachers in STEM education play cardinal roles in teacher recruitment exercises worldwide. Studies on factors that characterize effective teachers have therefore gained popularity in recent times. Teacher self-efficacy, job satisfaction and school climate are among other factors that have attracted global attention. Thus, proper understanding of the relations between these factors is equally important. The purpose of this study is to validate and cross-validate a model of direct/indirect effects of school climate and teacher self-efficacy on job satisfaction. Results: The data used for the current study are extracted from a publicly available data of Teaching and Learning International Survey (TALIS) 2018 survey. Structural equation modeling approach was used in the analyses coupled with robust maximum likelihood to ensure accurate estimations in the models. The results of the validated models show a strong direct impact of school climate on job satisfaction, a direct impact of teacher self-efficacy on job satisfaction and a mediating effect of teacher self-efficacy between school climate and job satisfaction. This model exhibits structural invariance in factor loadings, intercepts and regression weights across two independent samples from a population of 3951 lower secondary school teachers in Norway. Conclusion: The findings of this study do provide empirical evidence for the relations between teacher self-efficacy, job satisfaction and school climate among Norwegian lower secondary school teachers. The cross-validation of these relations was also established using an independent sample to enhance generalization of the findings. Two methodological observations concerning recoding of some items as well as an addition of item cross-loading in the measurement model of the job satisfaction scales are raised and addressed. It is therefore recommended that researchers who will be using TALIS 2018 data should take note of these observations.