Teaching Philosophy:
My approach to teaching and mentorship is simple: create a training experience that I would value myself. As a student or trainee I have specific demands of my instructor. I want structured guidance as I navigate through challenging topics. I want assurances that the pedagogical techniques my teacher or mentor use are engaging and efficient. I want the lessons and training to be useful for professions in that domain. I want my thoughts and ideas to be valued. If these are my demands as a student and trainee, then they are my duty as an instructor and mentor.
This course covers advanced topics in statistics and experimental design necessary for applied research in modern psychology, including information design, exploratory data analysis, data visualization, nonparametric statistics, data and inference errors (multicollinearity, overfitting, Simpson's and Robinson's paradox), sanitization (data anonymization, de-identification), and linear models (including conditional process models). Students get hands on experience with simulating, analyzing, and visualizing data in the R statistical environment.
This is a hands-on laboratory course is designed to foster broad and basic skills in the empirical approaches used in cognitive neuroscience research. Students will learn how to evaluate which cognitive neuroscience method is best suited to a research question, basic experimental design practices, and how to perform and draw inferences from analyses of neuroscientific data. The course will focus on functional MRI, but also covers structural MRI (diffusion imaging), EEG, and touch on various other methods. Students work with actual datasets using modern software and explore the use of contemporary analytic techniques used by cognitive neuroscience researchers.