Date of this Version
The purpose of this research is to study the movements of teachers’ churn rate in the state of Washington over the past 14 years. The research of teachers’ churn rate is an integrative study, with retrospective part and prospective part. Retrospective study includes the analysis of descriptive statistics (level I), statistical inference (level II) and causal inference (level III) (Berk, R.A. (2016) Statistical Learning from a Regression Perspective. Philadelphia, PA: Springer). Prospective study is mainly about forecasting and statistical inference that generated from the predictions. In this research, we are using longitudinal data analysis. The good point of longitudinal data analysis is that it provides us with the data in the past fifteen years with keeping track of the status of all K-12 teachers in the State of Washington. A Comprehensive meta-analysis research on teacher career trajectories conducted by Borman and Dowling shows that very a few previous studies used long-term longitudinal data to properly track the movement of teachers (Borman & Dowling, 2008). From statistical respect, longitudinal studies always provide better results than other approaches when time is a factor in the analysis. Our research is the holistic contribution from the whole project panel, especially under the advising of Professor Robert Boruch, who is also the director of the whole research project series. Bowen Cai takes the responsibility of retention & churn rate calculating, statistical modeling (including machine learning algorithms and time series forecasting) and statistical analysis from the respects of survey methods and design.
Cai, B., & Boruch, R. (2017). Washington State Public Teachers' Ambient Positional Instability From A Statistical Approach of Retrospective Study & Prospective Study. Retrieved from http://repository.upenn.edu/gse_pubs/418
Applied Statistics Commons, Longitudinal Data Analysis and Time Series Commons, Science and Mathematics Education Commons, Secondary Education and Teaching Commons, Statistical Methodology Commons, Statistical Models Commons, Theory and Algorithms Commons
Date Posted: 14 June 2017