This course is the second in a two-part scale development course series on scale development. Part one of the series focused on the general theories and methods used in the scale development process. This second portion focuses on the analytic methods used to statistically test, refine, and validate scale data. These methods include Cronbach’s alpha, exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and basic structural equation models testing for predictive validity.
This course will teach participants how to use the programming language Julia to load, clean, plot, and analyze social-science data. Julia is a newer programming language with a focus on high-performance scientific computing, and allows efficient manipulation of large datasets. The course will cover the basics of loading tabular data; cleaning, filtering, and joining that data; calculating descriptive statistics; and estimating statistical models.