How Generalizable Is Your Experiment? An Index for Comparing Experimental Samples and Populations

By Elizabeth Tipton. 2014

151 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a32f61439fc21b7fb428185d0f7fbe91d2e2ebf3

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In Artificial Intelligence

In Statistics and Probability

Citations in the corpus

A national experiment reveals where a growth mindset improves achievement (2019, methodology): “The generalizability index (Tipton, 2014) is a summary measure that provides the degree of distributional similarity between the schools in the analytic sample and the inference population, conditional on a set of covariates.”
Use of Quasi-Experimental Research Designs in Education Research: Growth, Promise, and Challenges (2020, background): “While scholars, practitioners, and funding agencies grapple with this trade-off, methodologists are increasingly working to improve these methods to provide insights for generalizability (Tipton, 2014; Tipton & Olsen, 2018).”
From Local to Global: External Validity in a Fertility Natural Experiment (2015, unknown): “Tipton (2014) used the Bhattacharyya distance between the propensity scores distributions of the selected and unselected sites.”
A Review of Generalizability and Transportability (2021, methodology): “A generalizability index score of <0.5 indicates a study being very challenging to generalize from and a score of >0.9 indicates high generalizability (Tipton, 2014).”
Conformal inference of counterfactuals and individual treatment effects (2020, background): “…to a widely studied problem which goes by the name of generalizability, or transportability, or external validity, and which concerns settings in which there is a distributional shift between the target population and the study population (e.g. Stuart et al., 2011; Tipton, 2014), see Sections 2– 4.”