Michael Seltzer
Moore Hall 2019C
405 Hilgard Avenue
Los Angeles, CA 90095-1521

P: (310) 825-5191
E: mseltzer@ucla.edu

Michael Seltzer

Professor

Education

Ph.D., Education, University of Chicago, 1991

Teaching & Research Interests

  • Hierarchical models
  • Methods for Bayesian analysis
  • Methodology for multi-site evaluations
  • Longitudinal analysis

Select Publications

Seltzer, M. & Rickles, J. (2017). Multilevel analysis.  In R. Coe, M. Waring, L. Hedges &  J. Arthur, (Eds.), Research Methods and Methodologies in Education, (2nd ed.) (pp. 326 – 338). London: Sage Publications.

Yang, J. & Seltzer, M. (2015). Handling measurement error in predictors using a multilevel latent variable plausible-values approach.  In J. Harring, L. Stapleton & N.  Beretvas (Eds.), Advances in Multilevel Modeling for Educational Research. Charlotte, NC: Information Age Publishing.

Rickles, J. & Seltzer, M. (2014). A two-stage propensity score matching strategy for treatment effect estimation in a multisite observational study.  Journal of Educational and Behavioral Statistics, 39, 612-636.

Seltzer, M. (2012). Comments on Statistical analysis for multi-site trials using instrumental variables with random coefficients, by S. Raudenbush, S. Reardon & T. Nomi.  Journal of Research on Educational Effectiveness, 5, 338-341.

Seltzer, M. & Rose, M. (2011).  Constructing analyses:  The development of thoughtfulness in working with quantitative methods.  In C. Conrad & R. Serlin (Eds.),  Handbook for Research in Education: Engaging Ideas and Enriching Inquiry, (2nd ed.) (pp. 245-262).  Thousand Oaks, CA:  Sage Publications.

Denson, N. & Seltzer, M. (2011).  Meta-Analysis in Higher Education:   An illustrative example using HLM.  Research in Higher Education, 52,215-244.

Kim, J.O. & Seltzer, M. (2011). Examining heterogeneity in residual variance to detect differential response to treatments.  Psychological Methods, 16, 192-208.

Choi, K. & Seltzer, M. (2010).  Modeling heterogeneity in relationships between initial status and rates of change: Treating latent variable regression coefficients as random coefficients in a three-level hierarchical model.  Journal of Educational and Behavioral Statistics, 35, 54-91.

 

Earlier Work on Treatment Effect Variation:

Seltzer, M. (2004).  The use of hierarchical models in analyzing data from experiments and quasi-experiments conducted in field settings.  In D. Kaplan (Ed.), The Handbook of Quantitative Methods for the Social Sciences (pp. 259-280).   Thousand Oaks, CA:  Sage Publications.

Seltzer, M. (1994).  Studying variation in program success:  A multilevel modeling approach.  Evaluation Review, 18, 342-361.


Earlier work on the use of MCMC in Bayesian Analysis of Multilevel Data:

Seltzer, M. & Choi, K. (2002).  Model checking and sensitivity analysis for multilevel models.  In S. Reise & N. Duan (Eds.), Multilevel modeling:  Methodological advances, issues, and applications.  Hillsdale, NJ:  Lawrence Earlbaum.

Seltzer, M., Novak, J. Choi, K. & Lim, N. (2002).  Sensitivity analysis for hierarchical models employing t level-1 assumptions.  Journal of Educational and Behavioral Statistics, 27, 181-222.

Seltzer, M., Wong, W. & Bryk, A. (1996).  Bayesian analysis in applications of hierarchical models:  Issues and methods.  Journal of Educational and Behavioral Statistics, 21, 131-167.

Seltzer, M. (1993).  Sensitivity analysis for fixed effects in the hierarchical model:  A Gibbs sampling approach.  Journal of Educational Statistics, 18, 207-235.