Brian Keller's main research interests are the statistical analysis of data with missing values, Bayesian statistics, and statistical computing. In addition to his methodological work, he currently develops Blimp statistical software.
Ph.D. in Psychology (Quantitative Methods), University of California, Los Angeles, 2019
M.A. in Psychology (Quantitative Methods), Arizona State University, 2015
B.S. in Psychology, Arizona State University, 2013
Missing data, Bayesian statistics, multilevel modeling, structural equation modeling, general modeling frameworks, statistical computing, and philosophy of science.
Keller, B. & Enders, C. (2022). Blimp Users Guide (Version 3.0). Los Angeles, CA. (View)
Keller, B.T. & Du, H.. (2019). A Fully Conditional Specification Approach to Multilevel Multiple Imputation with Latent Cluster Means. Multivariate Behavioral Research, 54(1), 149–150. doi:10.1080/00273171.2018.1556085.
Enders, C.K., Du, H. & Keller, B.T. (2019). A model-based imputation procedure for multilevel regression models with random coefficients, interaction effects, and nonlinear terms. Psychological Methods. doi:http://dx.doi.org/10.1037/met0000228.
Keller, B.T. & Enders, C.K. (2019). Blimp software manual (Version 2.2). http://www.appliedmissingdata.com/multilevel-imputation.html.
Enders, C.K., Keller, B.T. & Levy, R. (2018). A fully conditional specification approach to multilevel imputation of categorical and continuous variables. Psychological Methods, 23(2), 298–317.
Keller, B. (n.d.). An Introduction to Factored Regression Models with Blimp. Psych, 4(1), 10–37.
Blimp Software development
Institute of Educational Sciences grant (2019-2023)
Model-based multiple imputation for multilevel data.
| Year | Semester | Course |
|---|
| 2021 | Fall | EDP 380C: 16-Hierarchical Linear Modlng |
| 2021 | Fall | EDP 480C: 4-Correlation/Regression Mthds |
| 2021 | Spring | EDP 380C: 23-Missing Data Analysis-Wb |
| 2020 | Fall | EDP 380C: 16-Hierarchical Linear Modg-Wb |
