A model for understanding teachers' intentions to remain in STEM education
- PMID: 30631663
- PMCID: PMC6310373
- DOI: 10.1186/s40594-017-0061-8
A model for understanding teachers' intentions to remain in STEM education
Abstract
Background: This study examined the relationships of various teacher retention factors with the intentions of math and science teachers to remain in the profession. With data collected from the 2007-08 Schools and Staffing Survey, a sample of 6588 secondary math and science teachers across public schools in the USA was used for structural equation modeling.
Results: Socioeconomic impact, student truancy, and years of experience all showed direct relationships with teacher autonomy, while administrative support, teacher autonomy, and satisfaction with salary were all directly related to these teachers' intentions to remain in the profession. Of these teacher retention factors, satisfaction with salary was found to have the strongest relationship.
Conclusions: By understanding what factors are associated with the intentions of math and science teachers to continue teaching, educational policymakers and practitioners will have practical guidance in helping them make decisions to improve the retention of these teachers in secondary public schools, on whom the fields in STEM are so dependent.
Keywords: Math and science teachers; Satisfaction with salary; Structural equation modeling; Teacher retention.
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References
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- Abbott AD. The system of professions: an essay on the division of expert labor. Chicago, IL: University of Chicago Press; 1988.
-
- Anderson JC, Gerbing DW. Structural equation modeling in practice: a review and recommended two-step approach. Psychological Bulletin. 1988;103:411–423. doi: 10.1037/0033-2909.103.3.411. - DOI
-
- Barnes J, Cote J, Cudeck R, Malthouse E. Factor analysis—checking assumptions of normality before conducting factor analysis. Journal of Consumer Psychology. 2001;10(1,2):79–81.
-
- Baumgartner H, Homburg C. Applications of structural equation modeling in marketing and consumer research: a review. International Journal of Research in Marketing. 1996;13:139–161. doi: 10.1016/0167-8116(95)00038-0. - DOI
-
- Bentler P, Bagozzi RP, Cudeck R, Iacobucci D. Structural equation modeling—SEM using correlation or covariance matrices. Journal of Consumer Psychology. 2001;10(1,2):85–87.
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