Factors affecting teacher job satisfaction - a causal inference machine learning approach using data from TALIS 2018 - study findings

Aug 4, 2026

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Teacher shortages and attrition pose major international challenges, and job dissatisfaction is repeatedly identified as a common reason teachers leave. This study uses a causal inference machine learning framework to find practical levers for increasing overall job satisfaction, applying the approach to the English subset of TALIS 2018. Results indicate that participation in continual professional development and induction activities has the strongest positive impact, while part-time contracts show a measurable negative effect.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages26

Reading Time1 h 6 min

AccessFree to download

Introduction

Teacher supply, demand, and attrition context

Methodology and causal inference approach

Findings on interventions and contract effects

Implications for improving job satisfaction

Educational Review ISSN: (Print) (Online) Journal [homepage: ](homepage: www.tandfonline.com/journals/cedr20)[www.tandfonline.com/journals/cedr20](homepage: www.tandfonline.com/journals/cedr20) Factors affecting teacher job satisfaction: a causal inference machine learning approach using data from TALIS 2018 Nathan McJames, Andrew Parnell & Ann O’Shea To cite this article: Nathan McJames, Andrew Parnell & Ann O’Shea (12 May 2023): Factors affecting teacher job satisfaction: a causal inference machine learning approach using data from TALIS 2018, Educational Review, DOI: 10. 1080/00131911 .2023.2200594 To link to this article: [https://doi.org/10.1080/0013191](https://doi.org/10.1080/0013191) 1.2023.2200594 © 2023 The Author(s) . Published by Informa UK Limited, trading as Taylor & Francis Group | Published online: 12 May 2023. | | | --- | --- | | Submit your article to this journal | | | Article views: 3148 | | | | View related articles | | | View Crossmark data | | Citing articles: 2 View citing articles | | Full Terms & Conditions of access and use can be found at [https://www.tandfonline.com/action/journalInformation?journalCode=cedr20](https://www.tandfonline.com/action/journalInformation?journalCode=cedr20) EDUCATIONAL REVIEW [https://doi.org/10.1080/0013191](https://doi.org/10.1080/0013191)1.2023.2200594 Factors aﬀecting teacher job satisfaction: a causal inference machine learning approach using data from TALIS 2018 Nathan McJames a,b, Andrew Parnella,b and Ann O’Sheab aHamilton Institute, Maynooth University, Maynooth, Ireland; bDepartment of Mathematics and Statistics, Maynooth University, Maynooth, Ireland 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. ARTICLE HISTORY Received 8 April 2022 Accepted 3 April 2023 KEYWORDS Teacher job satisfaction; teacher retention; causal inference; machine learning; TALIS Introduction Background Teacher supply and demand is an important challenge faced by many countries around the world (UNESCO, 2015) . The scale of this problem is partly reﬂected in the teacher shortages currently facing many countries including England (Hilton, 2017), Ireland (O’Doherty & Harford, 2018), the United States (Wiggan et al., 2021), and many others. The scale of the challenge currently facing England is made clear by a recent House of Commons report which reveals that the 2019 ﬁve-year retention rate was at its lowest level since 1997, with 32.6% of teachers entering the profession in 2014 no longer teaching in classrooms ﬁve years later (Long & Danechi, 2021) . These sustained high levels of attrition have led to a situation where the total number of all qualiﬁed teachers in England working outside of the state funded sector in 2019 (350,000) was nearly as high as the number of teachers working inside it (454,000) . This comes at a time when secondary school pupil numbers are expected to rise by 7% in England between 2020 and 2026, thus placing increasing pressure on already diﬃcult recruitment and retention targets. Teacher shortages are often more pronounced in Science, Technology, CONTACT Nathan McJames [nathan.mcjames.2016@mumail.ie](nathan.mcjames.2016@mumail.ie) Hamilton Institute, Maynooth University, Maynooth, Co. Kildare, Ireland Department of Mathematics and Statistics, Maynooth University, Maynooth, Co. Kildare, Ireland © 2023 The Author(

FAQs

Why does the study focus on teacher job satisfaction?

Teacher shortages and attrition are widespread, and a lack of job satisfaction is one of the most frequent reasons teachers leave. Improving satisfaction is therefore linked to better retention and recruitment.

What data and research approach are used?

The study applies a causal inference machine learning approach to the English subset of TALIS 2018 data to identify interventions affecting job satisfaction.

Which factors have the strongest positive and negative effects?

Participation in continual professional development and induction activities shows the most positive effect on job satisfaction. The negative impact of part-time contracts is also demonstrated.

Aug 4, 2026

Upload by Violet