Trent Buskirk

Trent

Trent Buskirk

  • Position: Professor
  • Phone: 419-372-2363
  • Email: buskirk@bgsu.edu
  • Address: 241C Maurer Center

Trent D. Buskirk, PhD is the Novak Family Professor of Data Science at Bowling Green State University.  His research interests are varied and include Mobile and Smartphone Survey Designs, methods for calibrating and weighting nonprobability samples, and in the use of machine learning methods for social and survey science design and analysis.  Prior to his post at BGSU, Trent served as the Director for the Center for Survey Research at UMASS Boston and prior to that Trent was the Vice President for Statistics and Methodology at the Marketing Systems Group (MSG) and was tenured in the department of Biostatistics in the School of Public Health at Saint Louis University.  Dr. Buskirk’s research has been published in leading survey, statistics and health related journals such as Field Methods, Journal of Royal Statistical Society, Social Science Computer Review, Journal of Official Statistics, Preventative Medicine, Cancer, Journal of Clinical Epidemiology, Survey Methods: Insights from the Field and Methods, Data and Analysis, Public Opinion Quarterly and the Journal of Survey Statistics and Methodology.  Recently, Trent served as the President of the Midwest Association for Public Opinion Research and was the past Conference Chair for AAPOR.  In 2017 Trent was named Fellow of the American Statistical Association.   When Trent is not working or thinking about surveys or machine learning, you can find him playing resident prince to his two princesses or playing an action packed game of pickleball!

EDUCATION

Fellow of the American Statistical Association, 2017 with citation: "For contributions to survey statistics and methodology, including methods for analysis of nonprobability samples, nonresponse weighting adjustments, mobile data collection, and applications of data science to survey methodology; for enthusiastic contributions to continuing education forums; and for dedicated service to the profession."

Ph.D. in Statistics, 1999, Department of Mathematics, Arizona State University, Tempe, AZ (GPA: 4.0/4.0), Graduate College Fellow, Preparing Future Faculty Program. Dissertation: Using Nonparametric Methods for Density Estimation with Complex Survey Data. Advisor: Dr. Sharon Lohr, Full Professor, Department of Mathematics, ASU.

M.S. in Mathematics, 1994, Department of Mathematics/Statistics, University of South Alabama, Mobile, AL (GPA: 4.0/4.0).

B.S. in Mathematics, 1992, Presbyterian College, Clinton, SC (GPA: 3.95/4.00).

Post-Doctoral Course Work, 2000, School of Public Health, The University of Michigan, Graduate Summer Session in Epidemiology and 2007, Claremont Graduate University Program in Evaluation
Courses included:

  • Clinical Epidemiology
  • Design and Conduct of Clinical Trials
  • Advanced Computing Methods for use in analyzing Survey Data such as NHANES III
  • Computer Options for Variance Estimation using Survey Data
  • Policy Analysis and Program Evaluation Methods in Health
CURRENT POSITION AND AFFILIATIONS

Novak Family Distinguished Professor of Data Science and Chair, Department of Applied Statistics and Operations Research (with Tenure) – College of Business, Bowling Green State University (01/2019 – Present).

Affiliate Faculty, Center for Family and Demographic Research, Bowling Green State University (February, 2019 – Present)

Adjunct Research Professor (March, 2020 – Present) Survey Research Center, Survey Methods Program, University of Michigan

Member of Faculty of International Program in Survey and Data Sciences (January, 2018 – Present), German Federal Ministry of Education and Research (BMBF)

Affiliate Faculty (January 2021 – Present) Social Data Science Center, University of Maryland

CURRENT SPECIAL INDUSTRY/GOVERNMENT/FUNDED PROJECTS
  • Principal Investigator (Summer, 2022 – Summer 2025) "Why Did Judy go to Juvie? Using Explainable AI to Increase Equity and Transparency in the Juvenile Justice Systems Use of Risk Scores." Submitted to the NSF Program on Fairness in Artificial Intelligence in Collaboration with Amazon. FUNDED, to begin Summer 2022.
  • Consultant (6/1/2021 – Present) Exploring Opinions and Perceptions of Privacy, Special Project funded by Facebook, Inc.
  • Member, Technical Working Group (6/1/2021 – Present) Modernizing and Evaluating Imputation Strategies for Agricultural Surveys: National Agricultural Statistical Service (NASS) for the United States Department of Agriculture.
  • Consultant (1/1/2020 – Present) Stanford Medicine's Community Alliance to Test Coronavirus at Home (CATCH) Study. Statistical Survey Design and Recruitment consultant. Project funded by the Chan-Zuckerburg Biohub.
PRIOR PROFESSIONAL EXPERIENCE

Professor of Management Information Systems (Data Science and Methodology) and Director of the Center for Survey Research – University of Massachusetts-Boston, 08/2016 – 01/2019. Direct responsibility for the Center for Survey Research’s productivity, staffing, research oversight and operations and supervision of a staff of 10 academic professionals and faculty.

Vice President of Statistics and Methodology – Marketing Systems Group, 06/2013 – 08/2016. Lead a small team responsible for design, evaluation and weighting of statistical sampling plans and development of new technologies and offerings for sampling design and analysis. Total direct reports include 1 full time analyst as well as a total of 3 indirect reports.

Research Director, Advanced Methods – The Nielsen Company Center of Innovation, 01/2012 – 06/2013. Lead and provided thought leadership and advanced statistical analysis for various mobile and sampling design projects within the Measurement Science Division. Had no direct reports but various indirect reports through different projects.

Associate Professor of Biostatistics (with Tenure) – Saint Louis University School of Public Health, 07/2009 – 01/2012. Led teams of research analysts in the design, collection and analysis of health related data on various grant funded projects. Direct reports varied from 1 to three depending on the teams and projects.

Assistant Professor of Biostatistics – Saint Louis University School of Public Health, 08/2006 – 06/2009.

Assistant Research Professor of Statistics – Center for Research on Education in Science, Mathematics, Engineering and Technology (CRESMET), Arizona State University, 09/2005 – 07/2006.

Assistant Professor of Statistics – Graduate Program in Public Health and Epidemiology and Biostatistics Core, Eastern Virginia Medical School, Norfolk, VA, 11/2003 – 08/2005.

Director of Sampling and Statistics – Behavioral Research Department, American Cancer Society, National Home Office, Atlanta, GA (on leave from University of Nebraska), 08/2002 – 10/2003. Lead a small team (myself and one direct report) of sampling statisticians supporting design and analysis aspects of two large national quality of life surveys.

Assistant Professor of Statistics – Department of Mathematics and Statistics and Survey Research and Methodology Program, University of Nebraska Lincoln, 08/1999 – 05/2002.

  • Survey Data Science
  • Survey Methodology
  • Data Collection Methods
  • Machine Learning Applications to Survey and Social Sciences
  • Fairness in AI
  • Explainable ML
  • Big Data
  • Data Quality

Buskirk, T. D., Blakely, B. P., Eck, A., McGrath, R., Singh, R., & Yu, Y. (2022). Sweet tweets! Evaluating a new approach for probability-based sampling of Twitter. EPJ Data Science11(1), 9.

Brenner, P. S., & Buskirk, T. D. (2022). Scratch the Scratch-off: Testing Prepaid and Conditional Incentives With Postcard and Letter Invitations in a Web-push Design With an Address-based Sample. Field Methods, 1525822X211069640.

Dutwin, D., & Buskirk, T. D. (2021). Telephone sample surveys: dearly beloved or nearly departed? Trends in survey errors in the era of declining response rates. Journal of Survey Statistics and Methodology9(3), 353-380.

Oakley-Girvan, I., Hancock, J., O'Brien, D., Buskirk, T., Gore-Felton, C., Palesh, O., ... & Nelson, L. M. (2020, October). Can mHealth provide a path to shrink health disparities?. In APHA's 2020 VIRTUAL Annual Meeting and Expo (Oct. 24-28). APHA.

Nelson, L. M., Miller, Y., O'Brien, D., Buskirk, T., McGuire, V., Gore-Felton, C., ... & Oakley-Girvan, I. (2020, October). Using a smartphone survey app to collect population health surveillance: Comparing social media advertising recruitment with probability-based sampling. In APHA's 2020 VIRTUAL Annual Meeting and Expo (Oct. 24-28). APHA.

Buskirk, T. D., & Kirchner, A. (2020). Why machines matter for survey and social science researchers: Exploring applications of machine learning methods for design, data collection, and analysis. Big Data Meets Survey Science: A Collection of Innovative Methods, 9-62.

Hill, C. A., Biemer, P. P., Buskirk, T. D., Japec, L., Kirchner, A., Kolenikov, S., & Lyberg, L. E. (Eds.). (2020). Big Data meets survey science: A collection of innovative methods. John Wiley & Sons.

Weaver, N. L., Buskirk, T. D., Jupka, K., & Williams, J. (2019). Organizational factors related to the adoption of an injury prevention program by US children’s hospitals. Translational behavioral medicine9(4), 768-776.

Fowler Jr, F. J., Brenner, P. S., Buskirk, T. D., & Roman, A. (2019). Telephone health survey estimates: Effects of nonresponse and sample limitations. Health services research54(3), 700-706.

Hill, C. A., Biemer, P. P., Buskirk, T. D., Callegaro, M., Cordova Cazar, A. L., Eck, A., ... & Sturgis, P. (2019, April). Exploring new statistical frontiers at the intersection of survey science and Big Data: Convergence at “Bigsurv18.”. In Survey Research Methods (Vol. 13, No. 1).

English, N., Kennel, T., Buskirk, T., & Harter, R. (2019). The construction, maintenance, and enhancement of address-based sampling frames. Journal of Survey Statistics and Methodology7(1), 66-92.

Hill, C., Biemer, P., Buskirk, T., Callegaro, M., Cordova Cazar, A. L., Eck, A., ... & Sturgis, P. (2019). Exploring New Statistical Frontiers at the Intersection of Survey Science and Big Data: A Report from BigSurv18.

Buskirk, T. D., Kirchner, A., Eck, A., & Signorino, C. S. (2018). An introduction to machine learning methods for survey researchers. Survey Practice11(1), 1-10.

Buskirk, T. D. (2018). Surveying the forests and sampling the trees: An overview of classification and regression trees and random forests with applications in survey research. Survey Practice11(1), 1-13.

Keusch, F., Buskirk, T. D., & Gerdon, F. (2018). Getting persnickety about pair-wise Wikis: Investigating the relationship between initial settings for pair-wise Wiki Surveys and respondent engagement using a randomized experiment.

Dutwin, D., & Buskirk, T. D. (2017). Apples to oranges or gala versus golden delicious? Comparing data quality of nonprobability internet samples to low response rate probability samples. Public Opinion Quarterly81(S1), 213-239.

Updated: 05/17/2022 04:11PM