NotesFAQContact Us
Collection
Advanced
Search Tips
Showing all 6 results Save | Export
Peer reviewed Peer reviewed
Kenneth A. Frank; Qinyun Lin; Spiro J. Maroulis – Grantee Submission, 2024
In the complex world of educational policy, causal inferences will be debated. As we review non-experimental designs in educational policy, we focus on how to clarify and focus the terms of debate. We begin by presenting the potential outcomes/counterfactual framework and then describe approximations to the counterfactual generated from the…
Descriptors: Causal Models, Statistical Inference, Observation, Educational Policy
Peng Ding; Luke W. Miratrix – Grantee Submission, 2019
For binary experimental data, we discuss randomization-based inferential procedures that do not need to invoke any modeling assumptions. We also introduce methods for likelihood and Bayesian inference based solely on the physical randomization without any hypothetical super population assumptions about the potential outcomes. These estimators have…
Descriptors: Causal Models, Statistical Inference, Randomized Controlled Trials, Bayesian Statistics
Timothy Lycurgus; Ben B. Hansen; Mark White – Grantee Submission, 2022
We present an aggregation scheme that increases power in randomized controlled trials and quasi-experiments when the intervention possesses a robust and well-articulated theory of change. Intervention studies using longitudinal data often include multiple observations on individuals, some of which may be more likely to manifest a treatment effect…
Descriptors: Statistical Analysis, Randomized Controlled Trials, Quasiexperimental Design, Intervention
Larry V. Hedges – Grantee Submission, 2017
The scientific rigor of education research has improved dramatically since the year 2000. Much of the credit for this improvement is deserved by Institute of Education Sciences (IES) policies that helped create a demand for rigorous research; increased human capital capacity to carry out such work; provided funding for the work itself; and…
Descriptors: Educational Research, Generalization, Intervention, Human Capital
Peng Ding; Fan Li – Grantee Submission, 2018
Inferring causal effects of treatments is a central goal in many disciplines. The potential outcomes framework is a main statistical approach to causal inference, in which a causal effect is defined as a comparison of the potential outcomes of the same units under different treatment conditions. Because for each unit at most one of the potential…
Descriptors: Attribution Theory, Causal Models, Statistical Inference, Research Problems
Fan Yang; Peng Ding – Grantee Submission, 2018
In some randomized clinical trials, patients may die before the measurements of their outcomes. Even though randomization generates comparable treatment and control groups, the remaining survivors often differ significantly in background variables that are prognostic to the outcomes. This is called the truncation by death problem. Under the…
Descriptors: Randomized Controlled Trials, Medical Research, Patients, Death