Multivariate Statistics (HPSC511 – Psychological Statistics 2)

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 chi-squared 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 2017-2018) - updates will be available through D2L
Syllbaus (Winter 2016-2017) - updates are available through D2L
Syllbaus (Winter 2015-2016) - updates are available through D2L
Syllabus (Spring 2015) - updates are available through D2L
Syllabus (Spring 2014) - updates are available through D2L
Syllabus (Spring 2013)
Syllabus (Winter 2011-2012)
Syllabus (Spring 2011)

Some Journals
Some Sites