Experienced Teachers’ Stated Preferences Regarding Transferring From Well-Performing to Low-Performing Schools: A Discrete Choice Experiment
Doctor of Education (EdD)
Year of Completion
There is an enormous educational disparity among schools in the United States. One reason for this disparity is the teachers employed by well-performing schools and low-performing schools. This study reports the factors and financial tradeoffs that would influence well-qualified teachers to work in low-performing schools. Teacher employment is viewed as a set of discrete choices made over time and based on a finite group of factors. This study uses a multinomial discrete choice experiment to determine how the school-related factors (alternative-specific variables) and teacher-related factors (case-specific variables) influence the willingness of experienced teachers in well-performing schools to transfer to low-performing schools. A discrete choice experiment (DCE) using an optimal, fractional factorial, experimental design (D-efficiency = 96.5 and A-efficiency = 92.9) with an adequate sample (n =111) was employed. The data are analyzed using alternative-specific conditional logistic regression, nested logistic regression, and latent class conditional logistic regression. The latent class conditional logistic regression with 3-classes was deemed the best fit and its results were interpreted. The first class has high job satisfaction and generally stays in their current school. The second class is most likely female and does not value salary, but rather better student behavior and school climate. The third and largest class has similar values with Latent Class 2, but fiscal incentives could impact their decision. This study shows that teachers are willing to work in low-performing schools, but school- and teacher-related factors impact the overall attractiveness to well-qualified teachers.
Chagares, Adam, "Experienced Teachers’ Stated Preferences Regarding Transferring From Well-Performing to Low-Performing Schools: A Discrete Choice Experiment" (2016). Selected Dissertation Abstracts, 2012-2017. 3.