Demonstrated knowledge of advanced quantitative methods commonly used in education, prevention, and intervention research (e.g., generalized linear models, mixed-effects and multilevel models, growth models, structural equation models, latent variable and mixture modeling approaches, psychometric analyses, item response theory, and missing data handling techniques).Experience managing, cleaning, documenting, and analyzing complex datasets with nested and longitudinal data structures. The successful candidate should have demonstrated experience applying advanced quantitative methods commonly used in education and prevention/intervention research, including generalized linear models, mixed-effects and multilevel modeling, growth modeling, structural equation modeling, latent variable and mixture modeling approaches, psychometric analyses, and missing data methods.