McJames, Nathan and Parnell, Andrew and O’Shea, Ann (2023) Factors affecting teacher job satisfaction: a causal inference machine learning approach using data from TALIS 2018. Educational Review. pp. 1-25. ISSN 0013-1911

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Abstract

Teacher shortages and attrition are problems of international concern. One of the most frequent reasons for teachers leaving the profession is a lack of job satisfaction. Accordingly, in this study we have adopted a causal inference machine learning approach to identify practical interventions for improving overall levels of job satisfaction. We apply our methodology to the English subset of the data from TALIS 2018. Of the treatments we investigate, participation in continual professional development and induction activities are found to have the most positive effect. The negative impact of part-time contracts is also demonstrated

Item Type:	Article
Keywords:	Teacher job satisfaction; teacher retention; causal inference; machine learning; TALIS;
Academic Unit:	Faculty of Science and Engineering > Mathematics and Statistics Faculty of Science and Engineering > Research Institutes > Hamilton Institute Faculty of Social Sciences > Research Institutes > Irish Climate Analysis and Research Units, ICARUS
Item ID:	18990
Identification Number:	https://doi.org/10.1080/00131911.2023.2200594
Depositing User:	Andrew Parnell
Date Deposited:	08 Oct 2024 15:34
Journal or Publication Title:	Educational Review
Publisher:	Taylor and Francis Group
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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