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Katherine Masyn

Associate Professor    

Ph.D., 2003, University of California, LA, Advanced Quantitative Methods in Social Research
M.A., 1999, University California, Berkeley, Biostatistics
B.S.,1995, College of William and Mary, Mathematics


Katherine Masyn is an associate professor of  Epidemiology and Biostatistics for the School of Public Health. Previously she worked as an associate professor at the Harvard Graduate School of Education. She earned her BS in Mathematics from the College of William and Mary, MA in Biostatistics from UC Berkeley, and PhD in Advanced Quantitative Methods in Social Research at UCLA under the mentorship of Bengt Muthén. She worked as a postdoctoral fellow on a NIH Prevention Science training grant through the Johns Hopkins Bloomberg School of Public Health (P.I., Nick Ialongo) before joining the UC Davis Human Development faculty in 2005. Katherine’s research focuses on the development and application of latent variable statistical models related to: survival and event history analysis; multivariate and multi-faceted longitudinal processes, e.g., latent transition growth mixture models; and, more broadly, the characterization and parameterization of both observed and unobserved population heterogeneity in cross-sectional and longitudinal settings, e.g., factor mixture models. Along with her own methodology research, Katherine enjoys close collaborations with colleagues from the fields of Public Health, Prevention Science, Education, and Psychology, and serves as the statistical consultant on multiple federally-funded research grants.


The following is a list of selected recent publications:

Masyn, K. (in press). Measurement invariance and differential item functioning in latent class analysis with stepwise multiple indicator multiple cause modeling. Structural Equation Modeling: A Multidisciplinary Journal.  doi:10.1080/10705511.2016.1254049

Masyn, K. Discrete-time survival analysis in prevention science. (2014). In Z. Sloboda & H. Petras (Eds.), Defining prevention science (pp. 513-535). New York, NY: Springer.  doi:10.1007/978-1-4899-7424-2_22

Masyn, K. (2013). Latent class analysis and finite mixture modeling. In T. D. Little (Ed.), The Oxford handbook of quantitative methods in psychology (Vol. 2, pp. 551-611). New York, NY: Oxford University Press. doi:10.1093/oxfordhb/9780199934898.013.0025

Masyn, K., Henderson, C., & Greenbaum, P. (2010). Exploring the latent structures of psychological constructs in social development using the dimensional-categorical spectrum. Social Development, 19(3), 470-493. doi:10.1111/j.1467-9507.2009.00573.x

Masyn, K. (2009). Discrete-time survival factor mixture analysis for low-frequency recurrent event histories. Research in Human Development, 6(2-3), 165-194.  doi:10.1080/15427600902911270

Calzo, J., Masyn, K., Austin, S., Jun, H., & Corliss, H. (in press). Developmental latent patterns of identification as mostly heterosexual versus lesbian, gay, or bisexual.  Journal of Research on Adolescence.  doi:10.1111/jora.12266

Dunn, E., Masyn, K., Johnston, W., & Subramanian, S. (2015). Modeling contextual effects using individual-level data without aggregation: An illustration of multilevel factor analysis (MLFA) with collective efficacy. Population Health Metrics, 13(12), 1-11. doi:10.1186/s12963-015-0045-1

Musci, R., Masyn, K., Uhl, G., Maher, B., Kellam, S., & Ialongo, N. (in press). Polygenic score x intervention moderation: An application of discrete-time survival analysis to model the timing of first marijuana use among urban youth. Prevention Science.  doi:10.1007/s11121-016-0729-1

Nylund-Gibson, K. & Masyn, K. (2016). Covariates and mixture modeling: Results of a simulation study exploring the impact of misspecified effects on class enumeration.  Structural Equation Modeling: A Multidisciplinary Journal, 23(6), 782-797.  doi:10.1080/10705511.2016.1221313

Talley, B., Masyn, K., Chandora, R., & Vivolo-Kantor, A. (2017). Multilevel analysis of school anti-smoking education and current cigarette use among South African students. Pan African Medical Journal, 26-37.  doi:10.11604/pamj.2017.26.37.7880.