Dr. David Atkins taught the following courses between 2003-2008, while he was on faculty in the Department of Clinical Psychology, Fuller Graduate School of Psychology. At present, he is not teaching them (and hence, have not been updating materials).
The first course in the Research Methods sequence briefly covers elementary statistics but primarily focuses on multiple regression analyses. Regression analysis is at the heart of statistics, and a sound knowledge of regression methods will serve you well as you design and conduct master’s and dissertation research projects. We begin with simple regression and then consider extensions such as multiple predictors, nonlinear effects, categorical predictors, and interactions. Students will also learn how to use SPSS or R to conduct regression analyses, and students will begin reading articles in their area of interest to facilitate exploring possible research topics for their master’s project.
[Syllabus] [Monograph] [Lecture Slides]
This course covers the theory and application of multilevel statistical models. Research data in the social sciences are often grouped in ways that impact our statistical analyses (e.g., in marital studies, spouses are more similar to one another than other study participants) and lead to interesting and substantive hypotheses (e.g., how do qualities of the relationship interact with an individual’s personality?). We will focus on why these types of data are problematic for classical statistics and the advantages of multilevel approaches. The seminar will heavily emphasize the practical application of multilevel models and rely on examples to demonstrate their need and application. We will cover the general aspects of multilevel models as well as their extension to longitudinal and multivariate data.
Note that the R code below is a modified version of the R code posted at UCLA’s ATS website for Singer and Willett’s Applied Longitudinal Data Analysis book: http://www.ats.ucla.edu/stat/R/examples/alda/
