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<p>The retention of rural teachers is crucial not only for enhancing the quality of education in rural areas but also for the cultivation of talent and broader social development. This article conducts a detailed analysis of rural teachers’ willingness to remain in their positions by constructing and comparing various statistical models, including multi-level linear models and logistic regression models. Based on the results of model fitting, it specifically derives a logistic regression model that identifies the factors influencing rural teachers’ intentions to stay at the conclusion of their terms. Finally, 250 rural teachers as a survey research object, using the research method of this paper to analyze and explore. After the study, it is concluded that social support will have a significant positive impact on the willingness to stay when B = 0.121, 95% confidence interval is [0.298, 0.434], β = 0.532, t = 18.326, p &lt; 0.001. Work pressure and working hours negatively predicted rural teachers’ willingness to stay in the profession, while social support positively predicted rural teachers’ desire to remain in the profession. The higher the level of social support, the higher the degree of recognition of rural teachers’ profession in the family, school, and society.</p>
</abstract>

The retention of rural teachers is crucial not only for enhancing the quality of education in rural areas but also for the cultivation of talent and broader social development. This article conducts a detailed analysis of rural teachers’ willingness to remain in their positions by constructing and comparing various statistical models, including multi-level linear models and logistic regression models. Based on the results of model fitting, it specifically derives a logistic regression model that identifies the factors influencing rural teachers’ intentions to stay at the conclusion of their terms. Finally, 250 rural teachers as a survey research object, using the research method of this paper to analyze and explore. After the study, it is concluded that social support will have a significant positive impact on the willingness to stay when B = 0.121, 95% confidence interval is [0.298, 0.434], β = 0.532, t = 18.326, p < 0.001. Work pressure and working hours negatively predicted rural teachers’ willingness to stay in the profession, while social support positively predicted rural teachers’ desire to remain in the profession. The higher the level of social support, the higher the degree of recognition of rural teachers’ profession in the family, school, and society.

Regression analysis of the effect of social support on rural teachers’ willingness to stay in the profession

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The retention of rural teachers is crucial not only for enhancing the quality of education in rural areas but also for the cultivation of talent and broader...

Regression analysis of the effect of social support on rural teachers’ willingness to stay in the profession

Online veröffentlicht: 03. Juni 2024

Seitenbereich: -

Eingereicht: 19. Feb. 2024

Akzeptiert: 22. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1369

Schlüsselwörter
Social support, Rural teachers, Logistic regression model, Linear model

© 2024 Chenglin Du et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Regression analysis of the effect of social support on rural teachers’ willingness to stay in the profession

Online veröffentlicht: 03. Juni 2024

Seitenbereich: -

Eingereicht: 19. Feb. 2024

Akzeptiert: 22. Apr. 2024

DOI: https://doi.org/10.2478/amns-2024-1369

SchlüsselwörterSocial support, Rural teachers, Logistic regression model, Linear model

© 2024 Chenglin Du et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Social support, Rural teachers, Logistic regression model, Linear model