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Ding, Peng2
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Ding, Peng; Dasgupta, Tirthankar – Grantee Submission, 2017
Fisher randomization tests for Neyman's null hypothesis of no average treatment effects are considered in a finite population setting associated with completely randomized experiments with more than two treatments. The consequences of using the F statistic to conduct such a test are examined both theoretically and computationally, and it is argued…
Descriptors: Statistical Analysis, Statistical Inference, Causal Models, Error Patterns
Lu, Jiannan; Ding, Peng; Dasgupta, Tirthankar – Journal of Educational and Behavioral Statistics, 2018
Assessing the causal effects of interventions on ordinal outcomes is an important objective of many educational and behavioral studies. Under the potential outcomes framework, we can define causal effects as comparisons between the potential outcomes under treatment and control. However, unfortunately, the average causal effect, often the…
Descriptors: Outcomes of Treatment, Mathematical Applications, Probability, Behavioral Science Research