Bryan Keller

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Bryan Keller

Associate Professor of Practice in Applied Statistics
212-678-3277

Office Location:

453E GDodge

TC Affiliations:

Educational Background

Ph.D., Educational Psychology, Quantitative Methods, University of Wisconsin-Madison, 2013
M.S., Educational Psychology, Quantitative Methods, University of Wisconsin-Madison, 2010
M.A., Mathematics, Binghamton University, 2002
B.S., Mathematics, Binghamton University, 2000

Scholarly Interests

My research is related to the application, development, and assessment of quantitative methods in the social and behavioral sciences. I am particularly interested in methods for causal inferences and estimation of treatment effects. In the quasi-experimental setting I have worked on propensity score methods for the analysis of non-equivalent control group designs, detection of treatment effect heterogeneity, and developed methods for variable selection. In the experimental setting I am interested in design-replication studies and permutation-based statistical tests. I find computationally intensive methods such as regression trees and resampling and reordering methods to be very useful tools in my work.

Selected Publications

Methods:

Keller, B., Wong, V. C., Park, S., Zhang, J., Sheehan, P., & Steiner, P. M. (2024). A new four-arm within-study comparison: Design, implementation, and data. OSF Preprints. https://osf.io/preprints/osf/2gur9

Keller, B. & Branson, Z. (2024). Defining, identifying, and estimating causal effects with the potential outcomes framework: a review for education research. Asia Pacific Educ. Rev. 25, 575–594. (link) (arxiv)

Keller, B. & Marchev, D. (2022). Analysis of Covariance: Univariate and Multivariate Applications. In Tierney, R., Rizvi, F. & Ercikan, K. (Eds.), International Encyclopedia of Education, 4th Edition, vol. 14, 536-547. Elsevier. (link) (pdf)

Keller, B. (2020). Variable Selection for Causal Effect Estimation: Conditional Random Forest Variable Importance Under Permutation. Journal of Educational and Behavioral Statistics, 45: 119-142 (link) (pdf) (R_package) (R_code)

Keller, B., Chen, J., & Zhang, T. (2019) Heterogeneous Subgroup Identification with Observational Data: A Case Study Based on the National Study of Learning Mindsets. Observational Studies, 5: 93-104. (link) (pdf)

Chen, J. & Keller, B. (2019). Heterogeneous Subgroup Identification in Observational Studies. Journal of Research on Educational Effectiveness. (link) (pdf) (R_code)

Bazaldua, D. A. L., Lee, Y.-S., Keller, B., & Fellers, L. (2017). Assessing the Performance of Classical Test Theory Item Discrimination Estimators in Monte Carlo Simulations. Asia Pacific Education Review, 18: 585–598. (link)

Keller, B. & Tipton, E. (2016). Propensity score analysis in R: A software review. Journal of Educational and Behavioral Statistics, 41: 326–348. (link) (pdf) 

Keller, B., Kim, J.-S., & Steiner, P. M. (2015). Neural networks for propensity score estimation: Simulation results and recommendations. In L. A. van der Ark, D. M. Bolt, S.-M. Chow, J. A. Douglas, & W.-C. Wang (Eds.), Quantitative psychology research. New York, NY: Springer. (pdf) (R_package) (R_code)

Kim, J.-S., Anderson, C. J., & Keller, B. (2014). Multilevel analysis of large-scale assessment data. In L. Rutkowski, M. von Davier, & D. Rutkowski (Eds.), A handbook of international large-scale assessment: Background, technical issues, and methods of data analysis. London: Chapman Hall/CRC Press. (pdf)

Anderson, C. J., Kim, J.-S., & Keller, B. (2014). Modeling multilevel categorical response variables. In L. Rutkowski, M. von Davier, & D. Rutkowski (Eds.), A handbook of international large-scale assessment: Background, technical issues, and methods of data analysis. London: Chapman Hall/CRC Press. (pdf)

Keller, B., Kim, J.-S., & Steiner, P. M. (2013). Data mining alternatives to logistic regression for propensity score estimation: Neural networks and support vector machines. Multivariate Behavioral Research, 48, 164 (Abstract). (pdf) 

Keller, B. (2012). Detecting treatment effects with small samples: The power of some tests under the randomization model. Psychometrika, 77, 324-338. (link) (pdf) (R_code)

Kaplan, D. & Keller, B. (2011). A note on cluster effects in latent class analysis. Structural Equation Modeling, 18, 525-536. (link)

Applications:

Du, X., Lyublinskaya, I. & Keller, B. (2023). Longitudinal study of pre-service teachers’ Technological Pedagogical Content Knowledge and Stage of Adoption of technology during an online educational technology course. Journal of Technology and Teacher Education. 31(3), 271-298. (link)

Moya-Galé, G., Keller, B., Escorial, S. & Levy, E. S. (2021). Speech treatment effects on narrative intelligibility in French-speaking children with dysarthria. Journal of Speech, Language, and Hearing Research. (link)

Schwinn, T. M., Schinke, S. P., Keller, B., Hopkins, J. E. (2019). Two- and Three-Year Follow-Up from a Gender-Specific, Web-Based Drug Abuse Prevention Program for Adolescent Girls. Addictive Behaviors, 93: 86-92. (link)

Yang, J., Clarke-Midura, J., Keller, B., Baker, R. S., Paquette, L., & Ocumpaugh, J. (2018) Note-Taking and Science Inquiry in an Open-ended Learning Environment. Journal of Contemporary Educational Psychology, 55: 12–29. (link)

McCullough, A. K., Keller, B., Qiud, S., & Ewing Garber, C. (2018). Analysis of accelerometer-derived interpersonal spatial proximities: A calibration, simulation, and validation study. Measurement in Physical Education and Exercise Science, 22: 275–286. (link)

Schwinn, T. M., Schinke, S. P., Hopkins, J. E., Keller, B., & Liu, X. (2018). An online drug abuse prevention program for adolescent girls: Posttest and 1-year outcomes. Journal of Youth and Adolescence, 47: 490–500. (link)

Weishaar, T., Rajan, S., Keller, B. (2016). Probability of vitamin D deficiency by body weight and race-ethnicity. Journal of the American Board of Family Medicine, 29: 226–232. (link)

Rajan, S., Weishaar, T., Keller, B. (2016). Weight and skin color as predictors of vitamin D status: Results of an epidemiological investigation using nationally representative data. Public Health Nutrition, 12: 1–8. (link)

  • HUDM 4125 - Statistical Inference; 2014 - 2017
  • HUDM 5026 - Introduction to Data Analysis and Graphics in R; 2014 - present
  • HUDM 5123 - Linear Models and Experimental Design; 2014 - present
  • HUDM 5133 - Causal Inference and Program Evaluation; 2016 - present
  • HUDM 5150 - Capstone, Careers, and Communication; 2018 - present
  • HUDM 6122 - Multivariate Analysis; 2017
  • HUDM 6026 - Computational Statistics; 2014 - 2016, 2023

Links for the M.S. Program in Applied Statistics

Journal Reviewing

  • Advances in Methods and Practices in Psychological Science
  • Evaluation Review
  • Journal of Causal Inference
  • Journal of Educational and Behavioral Statistics
  • Journal of Educational Data Mining
  • Journal of Research on Educational Effectiveness
  • Measurement in Physical Education and Exercise Science
  • Multivariate Behavioral Research
  • Pharmaceutical Statistics
  • PLOS One
  • Psychological Methods
  • Research Synthesis Methods
  • Statistics in Medicine
  • Structural Equation Modeling: A Multidisciplinary Journal

Grant Reviewing

  • 2020 Member, IES Scientific Review Panel
  • 2020 Member, NSF Review Panel
  • 2019 Reviewer, Spencer Foundation
  • 2018 Reviewer, Flanders Research Foundation, Belgium (FWO)

See here for a call for paper submissions for a special issue of Behaviormetrika on the intersection of statistics and data mining in education research.

Attachments

Keller_CV

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