For misguided reasons, social scientists have long been reluctant to use gain scores for estimating causal effects. This article develops graphical models and graph-based arguments to show that gain score methods are a viable strategy for identifying causal treatment effects in observational studies. The proposed graphical models reveal that gain…

Suppression effects in multiple linear regression are one of the most elusive phenomena in the educational and psychological measurement literature. The question is, How can including a variable, which is completely unrelated to the criterion variable, in regression models significantly increase the predictive power of the regression models? In…

Randomized controlled trials (RCTs) and quasi-experimental designs like regression discontinuity (RD) designs, instrumental variable (IV) designs, and matching and propensity score (PS) designs are frequently used for inferring causal effects. It is well known that the features of these designs facilitate the identification of a causal estimand…

Experimental and quasi-experimental designs play a central role in estimating cause-effect relationships in education, psychology, and many other fields of the social and behavioral sciences. This paper presents and discusses the causal graphs of experimental and quasi-experimental designs. For quasi-experimental designs the authors demonstrate…

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…