Course Description
In-depth coverage of multivariate analysis of variance; multivariate analysis of covariance; binary and multinomial logistic regression; factor analysis; discriminant function analysis; path analysis; and structural equation modeling. Using SPSS and “real-world” datasets, students in the public health, behavioral, and social sciences will apply techniques to “real-world” datasets.
Athena Title
Multivariate Statistics
Equivalent Courses
Not open to students with credit in STAT 4250, STAT 6250, STAT 8210
Prerequisite
Permission of department
Semester Course Offered
Offered spring
Grading System
A - F (Traditional)
Course Objectives
At the conclusion of this course, students should be able to: · Conduct exploratory analyses of "canned" and "real-world" datasets to ensure that data satisfy assumptions germane to the multivariate technique being conducted; · Demonstrate mastery of fundamental statistical techniques (e.g., t-tests, ANOVA, simple linear regression), preparing them to conduct more sophisticated multivariate techniques; · Conduct multivariate tests that assess mean differences (e.g., MANOVA, MANCOVA); · Conduct analyses that model dichotomous or categorical outcomes (e.g., binary logistic regression and multinomial regression); · Conduct analyses used to build or test theoretical models and frameworks (e.g., path analysis, structural equation modeling); · Conduct data reduction analyses, such as factor analysis/principal components analysis; · Assess the psychometric properties of multi-item measures (e.g., reliability analyses); · Demonstrate a thorough understanding of SPSS; and · Understand how to write up results from multivariate analyses for publication in peer-reviewed scientific publications. Note: Students will be encouraged to apply statistical techniques covered in this course to a dataset of their own (e.g., dissertation project) or from a laboratory in which they work to gain an in-depth understanding of the challenges of applying multivariate techniques to real-world datasets.
Topical Outline
· Data Screening/Exploratory Data Analyses · Significance of Group Differences: · T-Test and One-Way ANOVA · One-Way ANCOVA · Factorial ANOVA · Factorial ANCOVA · ONE-WAY MANOVA · ONE-WAY MANCOVA · FACTORIAL MANOVA · FACTORIAL MANCOVA · Predicting Group Membership · One-Way Discriminant Function Analysis (DFA) · Logistic Regression · Factorial DFA · Structure/Data Reduction · Principal Components Analysis · Factor Analysis · Structural Equation Modeling · Time-Series Analysis · Psychometric Theory
Syllabus
Public CV