Daniel McGeeney has been performing statistical analysis and modeling for the past five years. He joined ERG in 2022 and has conducted survey design and analysis for a range of federal and state clients, including the U.S. Food and Drug Administration, the Office of the Assistant Secretary for Planning and Evaluation at the Department of Health and Human Services, the U.S. Environmental Protection Agency, and the National Oceanic and Atmospheric Administration. In the survey planning phase, he has applied complex sampling methods such as multistage clustering and stratified sampling to maximize client resources while achieving the precision needed to answer their research questions. During the analysis phase, he has used a range of advanced techniques to ensure that the survey findings are representative, including weights calculations, poststratification, nonresponse bias analysis, iterative proportional fitting, and weights trimming. He is also skilled in qualitative and thematic analysis and is skilled at developing reports that convey key findings to broad audiences.

Daniel has performed survey analysis and statistical modeling across a wide range of topics, including perinatal outcomes, silica-linked diseases among miners, invasive organisms in ballast water, pharmaceutical supply chains, cosmetics manufacturers, and web accessibility, to name a few. He enjoys combining datasets to broaden the impact of the analysis and extend the value of the collected data, ensuring that clients get the most out of their survey or other data collection efforts. He has developed expertise in many types of statistical models, machine learning models, and data mining techniques, and he has worked with numerous large, nationally representative surveys, data sources, and registries.

Daniel holds a B.A. in physics from Amherst College and an M.S. in biostatistics from the University of Louisville. In his free time, he enjoys live music, cooking, and taking his senior dog for slow walks through the park.