This course provides an overview of factor analysis. Factor analysis is one of the most frequently used statistical methods to uncover underlying structure in data. In this course, we will mainly talk about why/when we need factor analytic methods, what their underlying assumptions are, how they work, and how their results should be interpreted. This course emphasizes on developing a balance of theoretical and applied understanding of the factor analysis. The course will begin with an introduction to matrix notation. Then we will discuss principal components analysis (PCA), exploratory factor analysis (EFA), and confirmatory factor analysis (CFA). Additional topics related to multivariate analysis may also be covered (if time allows).
The course will employ a combination of lectures, class discussion, and computer labs. Students are expected to have a solid knowledge of basic statistics such as variance, correlation, and regression analysis. Familiarity with statistical programming languages (e.g., R) is desirable.
Gopher Grades is maintained by Social Coding with data from Summer 2017 to Summer 2025 provided by the University in response to a public records request