Job Candidate Kylie Anglin
Kylie L. Anglin is a Ph.D. student in Education Policy at the University of Virginia and a 2020 NAEd/Spencer Dissertation Fellow. Her research focuses on developing and using data science approaches to examine variations in policy and intervention implementation, as well as the impact of intervention heterogeneity on student outcomes. In an article published in the Journal of Research on Educational Effectiveness, she proposed a framework for using web scraping and natural language processing techniques to collect data on district responses to state policies from online policy documents. In that article, Kylie illustrates this framework in the context of statewide education deregulation under the Texas District of Innovation statute. In her dissertation, she builds on her descriptive work surrounding the District of Innovation statute to evaluate the impact of the policy, and heterogeneous district responses, on student outcomes.
Kylie is also developing methods for using natural language processing techniques to assess treatment fidelity and replicability in intervention evaluations that take place in educational settings. She has published on methods related to causal inference, replication, and open science in Evaluation Review, Oxford Bibliography in Education, and Zeitschrift für Psychologie, and she is a regular presenter at APPAM, AEFP, and SREE.
Prior to coming to University of Virginia, Kylie earned a B.A. in Political Science from Southwestern University, a Post-Baccalaureate in Mathematics from Northwestern University, and a Masters in Public Policy from the University of Virginia. Kylie has worked as a 7th grade English teacher and as an evaluator for an after-school program.
Faculty Advisor: Vivian Wong
Anglin, K. L., Krishnamachari, A., & Wong, V. (2020). Methodological Approaches for Impact Evaluation in Educational Settings. https://doi.org/10.1093/obo/9780199756810-0244
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
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