Yadira Peralta, PhD

Assistant Professor

Dr. Peralta’s research focuses on developing and applying classical and Bayesian statistical methods to analyze correlated data in the behavioral sciences, specifically to gain insights into the development of cognitive or behavioral processes. In particular, she leverages Bayesian

statistical methods to solve methodological challenges that are intractable with classical methods. Often, cognitive or behavioral processes over a specific span of time do not portray linear or steady change over time (growth). Accordingly, a main part of her research focuses on nonlinear longitudinal models.

Selected Publications

Peralta, Y., Kohli, N., Lock, E. F., & Davison, M. L. (2022). Bayesian modeling of associations in bivariate piecewise linear mixed-effects models. Psychological Methods, 27(1), 44-64. doi: 10.1037/met0000358.

Alarid-Escudero, F., Gracia, V., Luviano, A., Roa, J., Peralta, Y., Reitsma, M. B., Claypool, A. L., Salomon, J. A., Studdert, D. M., Andrews, J. R., Goldhaber-Fiebert, J. D., & Stanford-CIDE Coronavirus Simulation Model (SC-COSMO) Modeling Consortium (2021). Dependence of Covid-19 policies on end-of-year holiday contacts in Mexico City Metropolitan Area: A Modeling Study. MDM Policy & Practice, 6(2), 1-14. doi: 10.1177/23814683211049249.

Kohli, N., Peralta, Y., & Bose, M. (2019). Piecewise random-effects modeling software programs. Structural Equation Modeling: A Multidisciplinary Journal, 26(1), 156-164. doi:10.1080/10705511.2018.1516507

Peralta, Y., Kohli, N., & Wang, C. (2018). A primer on distributional assumptions and model linearity in repeated measures data analysis. The Quantitative Methods for Psychology, 14(3), 199-217. doi:10.20982/tqmp.14.3.p199

Harwell, M., Kohli, N., & Peralta-Torres, Y. (2018). A survey of reporting practices of computer simulation studies in statistical research. The American Statistician, 72(4), 321-327. doi:10.1080/00031305.2017.1342692