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McJames, Nathan, 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: | 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 |
| Related URLs: | |
| URI: | https://mu.eprints-hosting.org/id/eprint/18990 |
| 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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