School of Information Studies
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Course Description

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.

Credit(s)

3.0

Professor of Record

Jeffrey Stanton

Audience

Learning Objectives

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.

Course Syllabus

IST 777 Spring 2021 Syllabus - Jeffrey Stanton


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