Why they quit: a focused look at teachers who leave for other occupations

Abstract To better understand the factors related to teachers' decisions to leave for jobs outside of education, the study employs a structural equation modeling approach to analyze data from two large national datasets from the National Center for Education Statistics. The focus on occupation switchers is unique, with most studies of teacher attrition failing to differentiate between teachers who leave by their reasons for doing so. Overall, our findings suggest that district- and school-level leaders concerned about keeping good teachers in the classroom can take steps to improve teachers' job satisfaction by enhancing salaries and the conditions in which teachers work. Forced to choose between these levers, administrators may be more successful in boosting satisfaction and reducing the rate by which teachers quit to take a job outside of education by focusing their efforts on improving working conditions. Keywords: teacher attritionschool improvementeducational policystructural equation modeling Notes 1. Urban schools also have difficulty filling vacancies, primarily in mathematics (34.7%), foreign languages (30.3%), and special education (31%) (Strizek et al., 2006). 2. The survey uses a complex sampling framework that includes stratification, clustering, and oversampling of teachers with certain characteristics (e.g., new teachers, bilingual teachers) to ensure that samples of these teachers are large enough to produce reliable estimates. In surveys with complex sample designs, direct estimates of the sampling errors based on the assumption of simple random sampling will typically underestimate the variability in summary statistics and distort tests of statistical significance (Hahs-Vaughn Thomas Hoyle, 1995) as do normed chi-square values of less than 5 (Kline, 2005). Confirmatory factor analysis showed that the fit of the measurement model was satisfactory (χ 2/df = 4.61; RMSEA = .048) with one exception. The significant chi-square statistic 308.94 (df = 67), p < .01 indicates an unsatisfactory model fit. However, the chi-square fit index is highly sensitive to sample size. A model is likely to be rejected when the sample size is large, even though the discrepancy between the sample correlation/covariance matrix and model-predicted correlation/covariance matrix may be small or trivial (Fan, Thompson, Fan Conley, 1991) found that facilitative principal leadership and support that provide teachers opportunities to participate in decisions about policies and practices are positively associated with teachers' job satisfaction and commitment. In addition, teacher authority over instruction and discipline have been found to be related to student behavior wherein fewer student behavioral problems exist in schools where teachers perceive having more control (Evertson Ingersoll, 2003). 9. For the multigroup comparisons, we build a final structural model with four significant constructs (excluding professional development experiences) from the relative weight and mediating models. The equivalence of the measurement model is established, and the structural models for subgroups compared (Byrne, 1998; Jöreskog Kelloway, 1998). The structural paths of interest among the latent variables are compared by examining chi-square and other fit indices (e.g., χ 2 /df and RMSEA) between the fully and partially unconstrained models.