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Vidushi Adlakha; Eric Kuo – Physical Review Physics Education Research, 2023
Recent critiques of physics education research (PER) studies have revoiced the critical issues when drawing causal inferences from observational data where no intervention is present. In response to a call for a "causal reasoning primer" in PER, this paper discusses some of the fundamental issues in statistical causal inference. In…
Descriptors: Physics, Science Education, Statistical Inference, Causal Models
Jo, Booil; Vinokur, Amiram D. – Journal of Educational and Behavioral Statistics, 2011
When identification of causal effects relies on untestable assumptions regarding nonidentified parameters, sensitivity of causal effect estimates is often questioned. For proper interpretation of causal effect estimates in this situation, deriving bounds on causal parameters or exploring the sensitivity of estimates to scientifically plausible…
Descriptors: Statistical Analysis, Statistical Inference, Nonparametric Statistics, Intervention
Coffman, Donna L. – Structural Equation Modeling: A Multidisciplinary Journal, 2011
Mediation is usually assessed by a regression-based or structural equation modeling (SEM) approach that we refer to as the classical approach. This approach relies on the assumption that there are no confounders that influence both the mediator, "M", and the outcome, "Y". This assumption holds if individuals are randomly…
Descriptors: Structural Equation Models, Simulation, Regression (Statistics), Probability
Schochet, Peter Z. – Journal of Educational and Behavioral Statistics, 2010
Pretest-posttest experimental designs often are used in randomized control trials (RCTs) in the education field to improve the precision of the estimated treatment effects. For logistic reasons, however, pretest data often are collected after random assignment, so that including them in the analysis could bias the posttest impact estimates. Thus,…
Descriptors: Pretests Posttests, Scores, Intervention, Scientific Methodology