Trent D. Buskirk


  • Position: Novak Family Professor of Data Science and Chair of Applied Statistics and Operations
  • Phone: 419-372-2363
  • Email:

Curriculum Vitae

Ph.D., Arizona State University, 1999

CFDR Primary Research Area: Social Relationships and Well-Being

Dr. Buskirk's research involves applications of machine learning and data science methodologies to the design, evaluation and analysis of data from health, social science and survey research.  He also works to design, develop and evaluate new data collection methodologies that leverage technology and para data to reduce respondent burden and improve measurement of survey outcomes.  Finally, he investigates methods for improving inferences that can be derived from big data, social media and other forms of nonprobability based data. 

Recent Publications:

English, N. Kennel, T., Buskirk, T. D., Harter, R. (2019). “The Construction, Maintenance and Enhancements for Address-Based Sampling Frames." Journal of Survey Statistics and Methodology, 7(1), 66-92. doi: 10.1093/jssam/smy003

Harter, R. M., McMichael, J. P., Brown, D. S., Amaya, A., Buskirk, T. D., & Malarek, D. (2018). Telephone Appends for Address-based Samples - An Introduction. RTI Press No. OP-0050-1802. Research Triangle Park, NC: RTI Press. doi:10.3768/rtipress.2018.op.0050.1802

Dutwin, D., & Buskirk, T. D. (2017). "Apples to Oranges or Gala versus Golden Delicious? Comparing Data Quality of Non-Probability Internet Samples to Low Response Rate Probability Samples." Public Opinion Quarterly, 81(S1), 213-239. doi: org/10.1093/poq/nfw061

Battaglia, M. P., Dillman, D. A., Frankel, M. R., Harter, R., Buskirk, T. D., McPhee, C. B., DeMatteis, J. M., & Yancey, T. (2016). “Sampling, Data Collection, and Weighting Procedures for Address-Based Sample Surveys.” Journal of Survey Statistics and Methodology, 4(4), 476-500. doi: 10.1093/jssam/smw025

Buskirk, T. D., Saunders, T., & Michaud, J. (2015). "Are Sliders Too Slick for Surveys: An Experiment comparing slider scales for survey data collection using Computers, Tablets and Smartphones." Methods and Data Analysis, 9(2), 229-260. doi: 10.12758/mda.2015.013

Buskirk, T. D., & Kolenikov S. (2015). “Finding Respondents in the Forest: A Comparison of Logistic Regression and Random Forest Models for Response Propensity Weighting and Stratification. Survey Insights: Methods from the Field, Weighting: Practical Issues and ‘How to’ Approach. doi:10.13094/SMIF-2015-00003

Updated: 08/03/2021 02:14PM