Ph.D., Education, University of Chicago, 1991
Teaching & Research Interests
- Hierarchical models
- Methods for Bayesian analysis
- Methodology for multi-site evaluations
- Longitudinal analysis
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.