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Susan Athey; Raj Chetty; Guido Imbens – National Bureau of Economic Research, 2025
Researchers increasingly have access to two types of data: (i) large observational datasets where treatment (e.g., class size) is not randomized but several primary outcomes (e.g., graduation rates) and secondary outcomes (e.g., test scores) are observed and (ii) experimental data in which treatment is randomized but only secondary outcomes are…
Descriptors: Observation, Research Problems, Bias, Data Science
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Hairui Yu; Suzanne E. Perumean-Chaney; Kathryn A. Kaiser – Journal of Statistics and Data Science Education, 2024
Missing data can significantly influence results of epidemiological studies. The National Health and Nutrition Examination Survey (NHANES) is a popular epidemiological dataset. We examined recent practices related to the prevalence and the reporting of the amount of missing data, the underlying mechanisms, and the methods used for handling missing…
Descriptors: Statistics Education, Data Science, Data Use, Research Problems