I currently teach graduate-level Introduction to Biostatistics for the Biomedical Sciences Ph.D. program at Cedars-Sinai Medical Center.
I spent more than 11 years teaching graduate-level applied statistics courses in the Psychology department at Florida International University. I developed and oversaw the graduate statistics training for the department from 2012 to 2023. I have completed Remote Teach Ready, Online Live, and Hybrid training.
Christopher Clifford (co-mentored with Drs. Leslie Frazier and Timothy Hayes) studied Developmental Science and Quantitative Psychology. His primary research interests involve work that focus on developmental, emotional, and educational quantitative analysis. His dissertation used advanced statistical approaches (i.e., SEM, SAM, factor score regression) to examine how play promotes adaptive outcomes; he also demonstrated how the different statistical approaches performed in real and simulated data.
April Schantz (co-mentored with Dr. Valentina Bruk-Lee) is an assistant professor of Industrial-Organizational Psychology at the University of West Florida. Her dissertation examined the impact of person-environment fit on strain and well-being among first responders using response surface regression. Dissertation
Tyler Stout is a senior data scientist at Zebra / Antuit.ai. His dissertation examined how variability in cluster size impacts parameter estimates and statistical power in mixed models, with an application to stress in the workplace. Dissertation
This course covers topics related to statistical analysis of experimental studies. Topics include using and manipulating datasets, plotting data, probability, estimation and uncertainty, and statistical methods for experimental designs, such as comparing 2 independent or dependent means, comparing 2 independent or dependent proportions, and controlling for multiple comparisons.
This course covers basic techniques of multivariate analysis, emphasizing the rationale and applications to psychological research. Includes matrix algebra, multiple regression, principal component analysis, factor analysis, MANOVA, and mixed models. Topics in this course build on the general linear model (GLM, which includes ANOVA and regression); students are expected to have had graduate-level coursework on ANOVA and regression.
This course covers topics related to statistical analysis of longitudinal data, focusing on methods used in the social sciences and health research. Topics include analysis of covariance (ANCOVA), difference scores, statistical mediation, mixed models (with correlated residuals and/or with random effects), and latent growth modeling. Students will be able to analyze, interpret, and write up results using these methods.
This course covers topics related to statistical analysis of categorical outcome variables, focusing on methods used in the social sciences. Topics include the generalized linear model (GLiM, including logistic regression and Poisson regression) and repeated measures extensions of GLiM (such as GEE and generalized linear mixed models). Students will analyze, interpret, and write up results using these methods.
Statistical graphics play an important role in every step of research - from exploration to analysis to model checking to presentation and dissemination. Creating quality statistical graphics is a critical aspect of research, but it is rarely emphasized or directly taught. This course will cover techniques for creating graphics at each stage of a research project, as well as more general techniques for communicating your research to different audiences (e.g., scientific researchers outside of your area / psychology, teachers, parents, policy makers, lawyers, business owners).