Polish the Gold: Calibrating Intervention Effect Estimates for Small-Sample Randomized Controlled Trials

• In Event: AERA Roundtable Session Friday 3:45 pm - Gold 4
  In Roundtable Session: RT5: Advances and Implications for Causal Inference and Power in Randomized Treatment Designs

Abstract

Randomized control trials (RCTs), as the most common type of experimental research design in education, are widely regarded as the most robust method for establishing causal effects. A fundamental reason why RCTs can provide valid estimates of intervention effects is that random assignment to treatment conditions theoretically minimizes or eliminates the influence of confounding variables by balancing outcome-related covariates (e.g., students’ baseline performance) across groups. In large sample studies, balance can be usually reached, as the law of large numbers ensures covariate balance. In contrast, with smaller sample sizes, chance imbalance may arise, potentially biasing estimates of the intervention’s effect. The current study provides practical guidelines for researchers to systematically address the practical challenge of confounder imbalance in small-sample RCTs.

Authors

• Yixiao Dong, University of California - Santa Barbara

• Garrett J. Roberts, University of Denver