Dates Correspond to the Spring, 2011 Syllabus, but the topics and links are up to date.
The
overarching goal is to develop knowledge of multivariate statistical
techniques, their appropriate application to data derived from different
designs, their limitations, and how to interpret results. Additionally,
I hope you will see connections between various techniques used in this
class. In addition to covering the chisquared test from univariate
statistics, the course focuses on matrix formulations of regression and
applications thereof (i.e., dummy coding of categorical variables for
regression, moderation, and mediation), multivariate analysis of
variance (MANOVA), generalized linear models (including logistic and
Poisson regressions), linear mixed modeling, generalized linear mixed
modeling, generalized estimating equations, principle components
analysis, exploratory factor analysis, and structural equation modeling
(i.e., confirmatory factor analysis, path analysis, and covariance
structure modeling), and canonical correlation. When possible, SPSS will
be used.
PRIOR TO 2014: For exploratory factor analysis, comparisons between SPSS and
the free Comprehensive Exploratory Factor Analysis (CEFA) software will
be made. Structural equation modeling will be conducted using LISREL.
2014 and Beyond: For exploratory factor analysis and structural equation modeling, we will use Mplus. The files associated with it may be found on D2L.
Syllbaus (Winter 20152016)  updates are available through D2L Syllabus (Spring 2015)  updates are available through D2L Syllabus (Spring 2014)  updates are available through D2L Syllabus (Winter 20112012) Syllabus (Spring 2011)
 Week 1
 Day 1, Monday, February 28, 9:00  11:00 (Matrix Algebra and ChiSquared [left over from Stats 1])
 Day 2, Wednesday, March 2, 6:45  8:45 (Matrix Formulations of Multiple Regression)
 Week 2
 Day 1, Monday, March 7, 9:00  11:00 (Moderation and Simple Slopes)
 Day 2, Wednesday, March 9, 6:00  8:00 (Mediation)
 Week 3
 Day
1  Monday, March 14, 9:00  11:00 (Dummy Coding, Moderation with
Continuous and Categorical, and Quadratic/Polynomial Effects)
 Day 2  Wednesday, March 16, 6:00  8:00 (Logistic Regression and Discriminant Function Analysis)
 Week 4
 Day 1  Monday, March 21, 9:00  11:00 (Generalized Linear Models [GzLM])
 Day 2  Wednesday, March 23, 6:00  8:00 (Multivariate Analysis of Variance [MANOVA])
 Week 5
 Day 1  Monday, March 28, 9:00  11:00 (MANOVA and Multivariate Analysis of Covariance [MANCOVA])
 Day
2  Wednesday, March 30, 6:00  8:00 (Repeated Measures MANOVA and
Intro to Multilevel Modeling [Midterm Examination Take Home Portion
Set])
 Week 6
 Day 1  Monday, April 4, 9:00  11:00 (Catch Up and Review Day)
 Midterm Examination In Class  April 6, 6:00  8:00
 Week 7
 Day 1  Monday, April 11, 9:00  11:00 (Linear and Nonlinear Mixed Modeling)
 Readings
 Riesby study for normally distributed outcome data
 Riesby study with binomial data
 Anticonvulsant study with Poisson data
 Day 2  Wednesday, April 13, 6:00  8:00 (Principal Components Analysis)
 Week 8
 Day 1  Monday, April 18, 9:00  11:00 (Exploratory Factor Analysis; EFA)
 Day 2  Wednesday, April 20, 6:00  8:00 (EFA and Factor Rotation)
 Week 9
 Day 1  Monday, April 25, 9:00  11:00 (Confirmatory Factor Analysis; CFA)
 Day 2  Wednesday, April 27, 6:00  8:00 (CFA)
 Week 10
 Day 1  Monday, May 2, 9:00  11:00 (Multivariate Regression and Path Analysis)
 Day 2  Wednesday, May 4, 6:00  8:00 (Path Analysis, Indirect Effects, and MultiGroup Path Analysis)
 Week 11
 Day 1  Monday, May 9, 9:00  11:00 (MultiGroup Path Analysis and Covariance Structure Modeling)
 Day 2 (Covariance Structure Modeling; Final Examination Take Home Portion Sent, Key for HW #9 Sent)
 Breckler (1990)
 Reporting Structural Equation Modeling
 Final Exam
 Data for Final Exam
 Syntax to run Final Exam
 Answer Key for Final Exam  Take Home Section
 Week 12
 Day 1
 Day 2  Final Exam
 Final Exam
 Answer Key for Final Exam  In Class Section
Some Journals Some Sites 
