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
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.
Methods:
Keller, B. & Branson, Z. (2023). Defining, Identifying, and Estimating Effects with the Rubin Causal Model: A Review for Education Research. PsyArXiv. https://osf.io/preprints/psyarxiv/58qmp
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. (Accepted). 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.
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)
Links for the M.S. Program in Applied Statistics
Journal Reviewing
Grant Reviewing
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.