I am a methodologist committed to helping researchers identify effective educational programs and policies.

My research develops methods for efficiently monitoring and analyzing program implementation in field settings using data science techniques, as well as methods for improving the causal validity and replicability of impact estimates. In recent research, I developed scalable methods for collecting local policy data from school district websites and for measuring fidelity in standardized educational interventions. Currently, I am also testing the validity of specification tests in repeated measures designs and training a series of classifiers to identify features of quality collaboration in teacher coaching interventions.

I became interested in evaluation and implementation at my first job out of college teaching middle schoolers in the Mississippi Delta. As a teacher, I wanted to know what programs would work for my students and my circumstances, not the average student in the average circumstances. Today, I address these questions by helping researchers identify causally valid program impacts while paying attention to variation in implementation, ensuring that average outcomes do not hide inequalities.

Selected Publications:

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