Publications
Anderson, C.J., Kim, J.-S., Keller, B.S. (2013). Multilevel modeling of categorical response variables. In L. Rutkowski, M. von Davier, and D. Rutkowski (Eds.), A handbook of international large-scale assessment: Background, technical issues, and methods of data analysis. London: Chapman Hall/CRC Press, 481–519.
Hall, Courtney E., Peter M. Steiner & Jee-Seon Kim (2015). Doubly robust estimation of treatment effects from observational multilevel data. In van der Ark, L.A., Bolt, D.M., Wang, W.-C., Douglas, J.A., Chow, S.-M. (Eds.), Quantitative Psychology Research, The 79th Annual Meeting of the Psychometric Society, Madison, Wisconsin, 2014. New York: Springer.
Keller Bryan, Jee-Seon Kim & Peter M. Steiner (2013). Abstract: Data Mining Alternatives to Logistic Regression for Propensity Score Estimation: Neural Networks and Support Vector Machines. Multivariate Behavioral Research, 48, 164.
Keller Bryan, Jee-Seon Kim & Peter M. Steiner (2015). Neural networks for propensity score estimation: Simulation results and recommendations. In van der Ark, L.A., Bolt, D.M., Wang, W.-C., Douglas, J.A., Chow, S.-M. (Eds.), Quantitative Psychology Research, The 79th Annual Meeting of the Psychometric Society, Madison, Wisconsin, 2014. New York: Springer.
Kim, J.-S., Anderson, C.J., & Keller, B.S. (2013). Multilevel analysis of assessment data. In L. Rutkowski, M. von Davier, and D. Rutkowski (Eds.), A handbook of international large-scale assessment: Background, technical issues, and methods of data analysis. London: Chapman Hall/CRC Press, 389-424.
Kim, Jee-Seon & Peter M. Steiner (2015). Multilevel propensity score methods for estimating causal effects: A latent class modeling strategy. In van der Ark, L.A., Bolt, D.M., Wang, W.-C., Douglas, J.A., Chow, S.-M. (Eds.), Quantitative Psychology Research, The 79th Annual Meeting of the Psychometric Society, Madison, Wisconsin, 2014. New York: Springer.
Kim, Jee-Seon, Steiner, Peter M. & Lim, Wen-Chiang (2016). Mixture modeling strategies for causal inference with multilevel data. In J. R. Harring, L. M. Stapleton, & S. Natasha Beretvas (Ed.), Advances in Multilevel Modeling for Educational Research: Addressing Practical Issues Found in Real-World Applications. Charlotte, NC: IAP - Information Age Publishing, Inc.
Steiner, Peter M. (2016). Design-based and model-based analysis of propensity score designs. In A. von Eye & W. Wiedermann, Statistics and Causality: Methods for Applied Empirical Research, Hoboken, NJ: John Wiley & Sons.
Selected Conference Presentations & Talks
Steiner, Peter M. (2016). Multilevel Matching Strategies for Observational Studies. Invited talk at the Department of Education and Psychology, Freie Universität Berlin, Germany, June 2, 2016.
Thoemmes, Felix (2016). An applied introduction to propensity score methods. Invited workshop at the University of Tübingen, Germany, June.
Steiner, Peter M., & Vivian Wong (2016). Analyzing Empirical Evaluations of Non-Experimental Methods in Field Settings. Spring Conference of the Society for Research on Educational Effectiveness, Washington D.C., March 2-5, 2016.
Steiner, Peter M. (2015). Observational Studies in Education: Multilevel Matching Strategies. Invited talk at the Interdisciplinary Training Seminar in Education Sciences, University of Wisconsin-Madison, October 30, 2015.
Kim, Jee-Seon, Peter M. Steiner, & Wen-Chiang Lim (2015). Causal inference with observational multilevel data: Challenges & Strategies. Invited State of Art Talk at International Meeting of Psychometric Society, Beijing, China, July 11-16, 2015.
Steiner, Peter M., & Jee-Seon Kim (2015). Estimating Treatment Effects via Multilevel Matching within Homogenous Groups of Clusters. Spring Conference of the Society for Research on Educational Effectiveness, Washington D.C., March 5-7, 2015.
Wong, Vivian, & Peter M. Steiner (2015). Evaluation of Non-Experimental Methods Using Within-Study Comparison Designs. Spring Conference of the Society for Research on Educational Effectiveness, Washington D.C., March 5-7, 2015.
Kim, Jee-Seon, Peter M. Steiner & Wen-Chiang Lim (2014). Mixture Modeling Strategies for Causal Inference with Multilevel Data. Conference on Advances in Multilevel Modeling for Educational Research: Addressing Practical Issues Found in Real-World Applications, Maryland University, College Park, MD, November 14-15, 2014.
Steiner, Peter M. (2014). Propensity Score Designs for Causal Inference: Challenges in Practice. Invited State of the Art Talk at the International Meeting of the Psychometric Society, Madison, WI, July 21-25, 2014.
Kim, Jee-Seon & Peter M. Steiner (2014). Contemporary Issues in Estimating Causal Effects Using Propensity Score Methods. Talk at the International Meeting of the Psychometric Society, Madison, WI, July 21-25, 2014.
Hall, Courtney, Jee-Seon Kim & Peter M. Steiner (2014). Matching Strategies for Observational Data with Multilevel Structures. Annual Meeting of the American Educational Research Association (AERA), Philadelphia, April 3-7, 2014.
Steiner, Peter M. & Yongnam Kim (2014). On the Bias-Amplifying Effect of Near Instruments in Observational Studies. Spring Conference of the Society for Research on Educational Effectiveness, Washington D.C., March 6-8, 2014.
Thoemmes, Felix, & W. Liao (2013). Propensity Score Matching (with multilevel data) Using SPSS and R. Modern Modeling Conference, Storrs, Connecticut, May.
Steiner, Peter M. (2013). Covariate Selection for Causal Inference with Observational Data. Invited talk at the Vienna PhD School of Informatics at the Technical University of Vienna, June 24, 2013.
Steiner, P. M., Kim, J.-S. & Hall, C. (2013). Matching Designs for Observational Studies with Multilevel Data. Invited talk at Q-Center Colloquium of the Institute for Policy Research, Northwestern University, Evanston, May 21, 2013.
Kim, J.-S., Steiner, P.-M., Hall, C., & Thoemmes, F. (2013). Within-Cluster and Across-Cluster Matching with Observational Multilevel Data. Spring Conference of the Society for Research on Educational Effectiveness, Washington D.C., March 7-9, 2013.
Keller, B., Kim, J.S., & Steiner, P. M. (2013). Propensity Score Estimation with Data Mining Techniques: Alternatives to Logistic Regression. Spring Conference of the Society for Research on Educational Effectiveness, Washington D.C., March 7-9, 2013.
Steiner, P. M., Kim, J.-S., & Thoemmes, F.(2012). Matching Strategies for Observational Multilevel Data. Joint Statistical Meeting, San Diego, July 28 – August 2.
Steiner, P. M. (2011). Matching Strategies for Observational Data with Multilevel Structure. Spring Conference of the Society for Research on Educational Effectiveness, Washington D.C., March 3-5, 2011.
This joint project is housed within the Wisconsin Center for Education Research at the School of Education, University of Wisconsin-Madison. Copyright ©2013, The Board of Regents of the University of Wisconsin System.