Indiana University Bloomington

Education Y645
Covariance Structure Analysis

Offered: Fall, Every Year
Capacity: 20
Sequence: Not explicitly; however, I ask that students have 2 semesters of graduate level course work in statistics.
Pre-Requisites: Intermediate and multivariate statistics. Students should be comfortable with regression, hypothesis testing, and simple matrix manipulations.
Algebra Required: For notation, proofs and homework.
Calculus Required: Used for concepts and some (very limited) derivations. No homework that uses calculus.
Contact Person for Authorization: None
Instructor: Julie Lorah
Days Per Week Offered: 2 days/week, usually Tue/Thu. Yes, there is a lab component, as needed.
Recommended follow-up classes: Depends on research interests
Syllabus: No Syllabus Avaliable
Keywords: Multivariate, latent variable modeling, structural equation modeling, path analysis
Description: This course focuses on the study of latent variables and structural equation modeling. Extensions of the regression model and factor analysis model are used to introduce path analysis, confirmatory factor analysis, and structural models. We examine how these models are jointly used in the study of structural linear relationships. Students will be introduced to the theory and practice of structural equation modeling. Examples for the course will come primarily from the field of education; however, the methods are presented in general and examples are easily extended to many fields. Students will learn to develop, implement, interpret, and report research involving these models. Further, students are expected to gain proficiency in Mplus as it pertains to the models developed in this class.
Books: Bollen (1989). Structural equations with latent variables. The closest book would be Kaplan (2009). Structural equation modeling: Foundations and extensions.
Substantive Orientation: Generally, social sciences; however, there are many applications to the natural/physical sciences.
Statistical Orientation: Frequentist perspective
Applied/Theoretical: This is a theoretical course with applications.
Software: R, MPlus
How Software is Used: For data analysis.
Problem Sets: Yes, with a mix of theoretical/conceptual/analytic problems and applied, data analysis.
Data Analysis: Yes, for the homework assignments and the final project.
Presentation: Yes, a research paper is required for the final project. Can include methodological or applied perspectives.
Exams: None at present; however, that could change.