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Resources for Applied Researchers


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In 2017, Katie Miller and I developed and launched The Generalizer, a free web tool to help education researchers develop sampling and recruitment plans for evaluations conducted in schools. 

Over time, TG has expanded to include:

  • K12 (CCD) and higher education (IPEDS) data

  • Sampling plans 

  • Assessments of similarity between a sample and population

  • Power analysis (for the ATE)

The team has expanded as well. See here for updates. 


generalizeR package

In collaboration with Ben Ackerman, Nicole Nixon, Tim Ruel, Katie Coburn, Beatrice Chao, and several other former students, I developed an R package that replicates and extends the functionality of The Generalizer to include:

  • The ability to use your own data, extending outside of education or to include your own variables

  • Weighting estimators for the ATE or other estimands

​The package is innovative in that in addition to the typical package coding, it also includes an interactive mode, making it easy to use for R novices. 

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RVE in Meta-Analysis

In 2014, in collaborations with Zach Fisher and Zhipeng Hou, I created the robumeta R package for Robust Variance Estimation. ​


More recently, James Pustejovsky has created the clubSandwich R package, which also implements RVE but with a wider range of working models and weights. ​


Even more recently, RVE is now integrated into the metafor R package, thanks to Wolfang Viechtbauer.


​In Stata, RVE is implemented in the robumeta and the reg_sandwich macros.


Generalization Guides

If you have questions about how to address and improve the generalizability of your study results:

  1. For those interested in the statistical details:

    • Tipton, E. & Hartman, E. Generalizability and Transportability. Chapter in Handbook of Multivariate Matching and Weighting (Edited by Stuart, E., Rosenbaum, P., Small, D., & Zubizarreta, J.).

  2. For those planning or conducting analyses from RCTs in education:

    • Tipton, E., & Olsen, R. B. Enhancing the Generalizability of Impact Studies in Education. (NCEE 2022-003). Washington, DC: U.S. Department of Education, Institute of Education Sciences, National Center for Education Evaluation and Regional Assistance. Retrieved from 

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Meta-Analysis Guides

Over time, I've written several tutorials on how to use Robust Variance Estimation in meta-analysis. A couple in particular may help you get started:

  1. For an intro to robumeta, see here:

    • ​​Tanner-Smith, E. E., Tipton, E., & Polanin, J. R. (2016). Handling complex meta-analytic data structures using robust variance estimates: A tutorial in R. Journal of Developmental and Life-Course Criminology, 2, 85-112.

  2. For an intro to clubSandwich and the idea of combining multilevel models with RVE, see here:

    • ​Pustejovsky, J. E., & Tipton, E. (2022). Meta-analysis with robust variance estimation: Expanding the range of working models. Prevention Science, 23(3), 425-438.

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