Treatment effect estimates from a "regression discontinuity design" (RDD) have high internal validity. However, the arguments that support the design apply to a subpopulation that is narrower and usually different from the population of substantive interest in evaluation research. The disconnect between RDD population and the…

When randomized experiments are infeasible, quasi-experimental designs can be exploited to evaluate causal treatment effects. The strongest quasi-experimental designs for causal inference are regression discontinuity designs, instrumental variable designs, matching and propensity score designs, and comparative interrupted time series designs. This…

Regression discontinuity design (RD) has been widely used to produce reliable causal estimates. Researchers have validated the accuracy of RD design using within study comparisons (Cook, Shadish & Wong, 2008; Cook & Steiner, 2010; Shadish et al, 2011). Within study comparisons examines the validity of a quasi-experiment by comparing its…

In Shadish (2010) and West and Thoemmes (2010), the authors contrasted 2 approaches to causality. The first originated in the psychology literature and is associated with work by Campbell (e.g., Shadish, Cook, & Campbell, 2002), and the second has its roots in the statistics literature and is associated with work by Rubin (e.g., Rubin, 2006). In…

This report summarizes the complex research literature on quantitative methods for assessing how impacts of educational interventions on instructional practices and student learning differ across students, educators, and schools. It also provides technical guidance about the use and interpretation of these methods. The research topics addressed…

Defines and illustrates basic concepts of dichotomous logistic regression (DLR) and presents strategies for teaching these concepts. Strategies include using analogies between ordinary least squares regression and logistic regression; illustrating concepts with contingency tables; and linking logistic regression concepts to interpretation of…

Rectifies the unsatisfactory textbook treatment of the finite-sample proprieties of estimators of regression models with a lagged dependent variable and autocorrelated disturbances. Maintains that the bias of the ordinary least squares estimator is determined by the dynamic and correlation effects. (MJP)