Kylie L. Anglin

Assistant Professor

Expertise: Evaluation

Department of Educational Psychology

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Title:

Assistant Professor
Research Methods, Measurement, and Evaluation (RMME)

Academic Degrees:

Ph.D., Education Policy, University of Virginia, 2021

MPP, Leadership and Public Policy, University of Virginia, 2018

BA, Political Science, Southwestern University, 2013

Biography:

Dr. Anglin teaches graduate courses in research methods, data science, and text analytics. Her research develops methods for efficiently monitoring program implementation in impact evaluations using natural language processing techniques, as well as methods for improving the causal validity and replicability of impact estimates. Her work has appeared in journals such as the Journal of Research on Educational Effectiveness, Prevention Science, and AERA Open, and Evaluation Review. She received her Ph.D. from the University of Virginia, where she participated in the Institute for Education Sciences (IES) Pre-doctoral Training Program and received an NAEd/Spencer dissertation fellowship.

**I am currently accepting applications for PhD students. If you are interested in education policy and/or educational data science, please email me or apply here.**

Selected Publications: 

Anglin, K. L., (2023) The Role of State Education Regulation: Evidence From the Texas Districts of Innovation Statute. Educational Evaluation and Policy Analysis. 0(0) https://doi.org/10.3102/01623737231176509.

Anglin, K. L., Wong, V. C., & Boguslav, A. (2021). A Natural Language Processing Approach to Measuring Treatment Adherence and Consistency Using Semantic Similarity. AERA Open, 7. https://doi.org/10.1177/23328584211028615

Anglin, K. L. (2019). Gather-Narrow-Extract: A Framework for Studying Local Policy Variation Using Web-Scraping and Natural Language Processing. Journal of Research on Educational Effectiveness, 12(4), 685–706. https://doi.org/10.1080/19345747.2019.1654576

Wong, V. C., Anglin, K., & Steiner, P. M. (2021). Design-Based Approaches to Causal Replication Studies. Prevention Science. https://doi.org/10.1007/s11121-021-01234-7

Steiner, P. M., Wong, V. C., & Anglin, K. L. (2019). A Causal Replication Framework for Designing and Assessing Replication Efforts. Zeitschrift Für Psychologie / Journal of Psychology, 227(4), 280–292. https://doi.org/10.1027/2151-2604/a000385

Wong, V. C., Steiner, P. M., & Anglin, K. L. (2018). What Can Be Learned From Empirical Evaluations of Nonexperimental Methods? Evaluation Review, 42(2), 147–175. https://doi.org/10.1177/0193841X18776870