This chapter critiques the common “static cohort” research design whereby researchers assess turnover causes or predictors on one occasion to determine if they can predict turnover behavior on a later occasion using ordinary least squares regression. While a major methodological advance in twentieth-century research on turnover, methodologists increasingly recommend alternative research designs and statistical techniques to assess the predictive efficacy of turnover predictors and verify turnover models. Specifically, we discuss the advantages of Cox regression analysis and random coefficients models with longitudinal repeated-measures data to improve turnover predictions. For testing models, we review the benefits of using structural equation modeling with repeated-measures data to test cross-lagged panel models and latent growth modeling with such data to assess dynamic relationships among model components implicit in turnover theories.