Indiana University Bloomington

Education Y604
Multivariate Analysis In Educational Research

Contact: Ginette Delandshere (
Offered: Spring, Fall, Every Year
Class Time: 11:15 - 12:30
Class Days: Tu, Th
Lab Time: 9:30-10:45
Lab Days: Tu
Capacity: 20-25
Sequence: EDUC-Y502 or other introductory course in statistics as pre-requisite
Pre-Requisites: 1 semesters of graduate coursework in statistics.
Algebra Required: For notation, for understanding concepts and for some homework.
Calculus Required: no.
Contact Person for Authorization: None
Instructor: Ginette Delandshere
Days Per Week Offered: 2 days per week, usually Tue/Thu and one hour/week of computer lab.
Recommended follow-up classes: Depends on research interests ? Y604 is a pre-requisite for Y637, Y639, Y645 and Y655.
Syllabus: No Syllabus Avaliable
Keywords: Multivariate Analysis, General Linear Model and extensions, regression, factor analysis, structural equation models.
Description: This course is based on the premise that the function of statistics is to formulate arguments for explaining comparative differences and relationships or patterns in data. This course focuses on the General Linear Model (GLM) and its extensions, and the various forms it takes in the multivariate context (OLS regression, Manova, Discriminant Function Analysis, Exploratory and Confirmatory Factor Analysis and intro. to SEM). The form of the models will be discussed in relationship to the particular research questions for which they are appropriate. The limitations, assumptions and unresolved issues of the models will be examined.
Books: Tabacknick, B. G. & Fidell, L. S. (2013). Using Multivariate Statistics, Sixth Edition. Upper Saddle River, NJ: Pearson Education, Inc.
Substantive Orientation: Social sciences but other students (e.g., biology, geography) have also found the course useful.
Statistical Orientation: Frequentist.
Applied/Theoretical: This is an applied course (conceptual and critical).
Software: SPSS, R, LISREL, MPlus, SAS
How Software is Used: Computation and data analysis and some programming.
Problem Sets: Yes, with a mix of theoretical/conceptual/analytic problems and applied, data analysis.
Data Analysis: Yes, for assignments.
Presentation: No.
Exams: Yes, there are two take-home exams.