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Factors affecting teacher job satisfaction and retention: A causal inference machine learning approach using data from TALIS 2018 [post]

Nathan McJames, Andrew Parnell, Ann O'Shea
2022 unpublished

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Teacher shortages and attrition are problems of international concern. Studies investigating this problem often identify important correlates of these two outcomes, but fail to produce easily implementable recommendations. Accordingly, in this study we have adopted a causal inference machine learning approach to identify practical interventions for improving job satisfaction/retention. We apply our methodology to TALIS 2018 data from England. Our results indicate that participation in continual
more » ... professional development and induction activities have the most positive effect on both of these outcomes. Out-of-field teaching and part-time contracts are shown to have a negative effect on retention and job satisfaction respectively.
doi:10.35542/osf.io/nasq9 fatcat:amyosu4rkfdxbj3fbbbrrdn2da
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Nathan McJames, Andrew Parnell, Ann O'Shea. "Factors affecting teacher job satisfaction and retention: A causal inference machine learning approach using data from TALIS 2018." (2022)