Classical statistical procedures used in information transfer research. Emphasis on underlying rationale for each procedure and on criteria for selecting procedures in a given research situation.
Students who successfully complete the course can expect the following outcomes:
The ability to design a study that supports causal inference.
The ability to develop an analysis plan that supports causal inference: including exploratory factor analysis, confirmatory factor analysis, multiple regression, path analysis, and structural equation modeling.
Improved familiarity with R, R-Studio, and the ecosystem of add-on packages on offer, leading to the capability of independently undertaking causal analysis on future research projects.
Essential knowledge of how to diagnose, repair, and interpret causal analytical models with manifest and latent variables.
Practice with conducting analyses of and writing about analytical results for these various kinds of data.
IST 777 Spring 2021 Syllabus - Jeffrey Stanton