Elizabeth Tipton
Professor of Statistics and Data Science
Co-Director of the STEPP Center | IPR Fellow - On leave 2024–25
PhD, Statistics, Certificate in Education Science, Northwestern University, 2011
Statistician Elizabeth Tipton’s research focuses on the design and analysis of field experiments, particularly on their external validity and on how to make causal generalizations from them. She is developing methods and tools to improve the generalizability of large randomized trials, especially in education and psychology. Her research in meta-analysis—the combination of results across many smaller trials—examines modeling and adjusting for dependence between effect sizes. Additionally, in a new line of research she has begun studying ways to improve the mobilization of statistical information in decision making, e.g., through improved data visualizations and data summaries.
Tipton’s research has been published in the Journal of Educational and Behavioral Statistics, Statistics in Medicine, Psychological Methods, Evaluation Review, and the Journal of Research on Educational Effectiveness, among others. Her work has been supported by the National Science Foundation, the Institute for Education Sciences, the Spencer Foundation, and the Raikes Foundation. Tipton is an elected Fellow of both the American Statistical Association and the American Educational Research Association.
Current Research
The Generalizer. In a previous grant from the Spencer Foundation, Tipton developed The Generalizer, a free webtool for developing sampling and recruitment plans for randomized trials in K-12 education and for assessing the generalizability of results of a study to different policy relevant populations. In a recent research study funded by the Institute of Education Sciences (IES), Tipton and Jessaca Spybrook of Western Michigan University updated The Generalizer to include modules on power-analysis, thus creating a one-stop-shop for designing randomized trials in education. At the same time, in another research study funded by IES, Tipton and Michael Weiss of MDRC extended The Generalizer to include data on postsecondary schools. Tipton and her students have also developed an R package (‘generalizeR’) that replicates The Generalizer with user-specified data, thus extending its use beyond education research.
Meta-Analysis. With former IPR graduate research assistant James Pustejovsky (now at the University of Wisconsin–Madison), Tipton is continuing the development of small-sample methods for improved hypothesis testing using Cluster Robust Variance Estimation (CRVE). CRVE is used when there are multiple effect sizes per study in a meta-analysis, and these recent innovations include new working models for implementation. Additionally, Tipton is co-teaching a one-week workshop on advanced meta-analysis methods (MATI) that is funded by IES, as well as a one-week workshop on introductory meta-analysis methods (MMARI), supported by the National Science Foundation.
Generalization and Heterogeneity. For over a decade, Tipton has focused a line of research on improved causal research designs that account for variation in treatment effects. Recently, this has included calls for improved understandings of heterogeneity in randomized trials and in meta-analysis, as well as the development of methods for improved trial designs.
Knowledge Mobilization. With her former student, Katie Fitzgerald (now at Azusa Pacific University), Tipton has been developing and testing new approaches to conveying statistical and research findings to education decision makers. This includes the development of a knowledge mobilization framework, as well as new data visualizations. These data visualizations will be further tested and refined as part of a new grant from the Institute of Education Sciences.
Selected Publications
Fitzgerald, K. and E. Tipton. 2024. A knowledge mobilization framework: Toward evidence-based statistical communication practices in education research. Journal of Research on Educational Effectiveness 17(3): 540–60.
Hou, Z. and E. Tipton. 2024. Enhancing recall in automated record screening: A resampling algorithm. Research synthesis methods 15(3): 372–83.
Pustejovsky, J. and E. Tipton. 2022. Meta-analysis with robust variance estimation: Expanding the range of working models. Prevention Science 23: 425–38.
Tipton, E. 2022. Sample selection in randomized trials with multiple target populations. American Journal of Evaluation 43(1): 70–89.
Bryan, C., E. Tipton, and D. Yeager. 2021. Behavioral science is unlikely to change the world without a heterogeneity revolution. Nature Human Behavior 5: 980–9.
Tipton, E. 2021. Beyond the ATE: Designing randomized trials to understand treatment effect heterogeneity. Journal of the Royal Statistics Society: Series A 184(2): 504–21.
Tipton, E., J. Spybrook, K. Fitzgerald, Q. Wang, and C. Davidson. 2021. Towards a system of evidence for all: Current practices and future opportunities in 37 randomized trials. Educational Researcher 50(3): 145–56.
Tipton, E., D. Yeager, B. Schneider, and R. Iachan. 2019. Designing probability samples to identify sources of treatment effect heterogeneity. In Experimental Methods in Survey Research: Techniques that Combine Random Sampling with Random Assignment, 435–56, ed. P.J. Lavrakas (New York: Wiley).
Yeager, D., P. Hanselman, G. Walton, J. Murray, R. Crosnoe, C. Muller, E. Tipton, [...] and C. Dweck. 2019. A national experiment reveals where a growth mindset improves achievement. Nature 573: 364–69.
Tipton, E. and R. Olsen. 2018. A review of statistical methods for generalizing from evaluations of educational interventions. Educational Researcher 47(8): 516–24.
Pustejovsky, J. and E. Tipton. 2018. Small sample methods for cluster-robust variance estimation and hypothesis testing in fixed effects models. Journal of Business and Economic Statistics 36(4): 672–83.
