Multivariate Analysis, Spring 2016 (KU)

Upcoming Workshops:

Thank you for visiting my course notes. Here are some upcoming opportunities to learn from me and my colleagues in person:

Currently no workshops are planned

Spring 2016 Educational Psychology 905

Course Information

Course InformationInstructor
Name:Jonathan Templin
Office:614 JRP
Office Hours:Tuesdays 2pm-4pm (unless otherwise noted in schedule) or by appointment

Course Materials

Helpful R Links and Resources

Books: R for Data Science (Grolemund and Wickham, 2015)
R for SAS and SPSS Users by Muenchen (2007)
An introduction to R by Venables et al. (2013)
aRrgh: a newcomer's (angry) guide to R
DataCamp Introduction to R
Cookbook for R
Quick References:Reference Card #1 (various authors, noted on card)
Reference Card #2 (various authors, noted on card)

Tentative Schedule of  Course Topics

DateTopicReadingHomework Assignment
20-JanIntroduction and Overview; Review of the General Linear Model; Descriptions of VariabilityMaxwell & Delaney (2004) Appendix BHomework 0 (Due Feb 2 at 11:59pm)
27-Jan(No in-person class) Introduction to R and R StudioVenables et al. (2013)
3-FebSimple, Marginal, and Interaction Effects in GLMsHoffman (2014), Ch. 2
10-FebDistributions and Estimation/Intro to GeneralizedKutner et al. (2005), Ch. 1 and Appendix AHomework 1 (Due Feb 23 at 11:59pm)
17-FebMaximum Likelihood EstimationEnders (2010), Ch. 3
24-FebMatrix Algebra and the Multivariate Normal DistributionJohnson & Wichern (2002), Chs. 2, 3, & 4Homework 2 (Due Mar 8 at 11:59pm)
2-MarML for Multivariate Outcomes
9-MarIntroduction to Path AnalysisKline (2005), Chs. 5 & 6Homework 3 (Due Mar 29 at 11:59pm)
16-MarSpring Break
23-MarPath Analysis and Mediation
30-MarResidual ML Estimation; Multivariate Models in GLM Software Homework 4 (Due Apr 12 at 11:59pm)
6-AprIntroduction to Bayesian Analysis/MCMC EstimationEnders (2010), Ch. 6
13-AprMissing Data/Multiple ImputationEnders (2010), Ch. 4, 7, 8, 9Homework 5 (Due Apr 26 at 11:59pm)
20-AprMixed Models Incorporated RM ANOVA and MANOVAMaxwell & Delaney (2004), Ch. 12-15; Wright (1998)
27-AprPrincipal Components Analysis/Exploratory and Confirmatory Factor AnalysisJohnson & Wichern (2002), Chs. 8 & 9Homework 6 (Due May 10 at 11:59pm)
4-MayAn Introduction to Clustering and Classification MethodsVermunt & Magidson (2002); McCutcheon (2002)



Enders, C. K. (2010). Applied missing data analysis.  New York, NY: Guilford.

Hoffman, L. (2014). Longitudinal analysis: Modeling within-person fluctuation and change. New York, NY: Routledge Academic.

Johnson, R. A. & Wichern, D. W. (2002). Applied multivariate statistical analysis (5th Ed.). Upper Saddle River, N.J.: Prentice-Hall.

Kline, R. B. (2002). Principles and practice of structural equation modeling (2nd Ed.). New York, NY: Guilford.

Kutner, M. H., Nachtsheim, C. J., Neter, J., & Li, W. (2005). Applied linear statistical models (5th Ed.). New York, NY: McGraw-Hill.

Maxwell, S. E., & Delaney, H. D. (2004). Designing experiments and analyzing data. Mahwah, NJ: Erlbaum.

Venables, W. N., Smith, D. M., & the R Core Team (2013). An introduction to R – Notes on R: A programming environment for data analysis and graphics (3.0.2 ed.). R Core Development team. Retrieved from

Articles and Chapters

McCutcheon, A. L. (2002). Basic concepts and procedures in single- and multiple-group latent class analysis. In J. A. Hagenaars & A. L. McCutcheon (Eds.), Applied latent class analysis (pp. 56-88). Cambridge, United Kingdom: Cambridge University Press.

Vermunt, J. K., & Magidson, J. (2002). Latent class cluster analysis. In J. A. Hagenaars & A. L. McCutcheon (Eds.), Applied latent class analysis (pp. 56-88). Cambridge, United Kingdom: Cambridge University Press.

Wright, S. P. (1998). Multivariate analysis using the mixed procedure. Proceedings of the Twenty-Third Annual SAS Users Group International Conference, paper 229. Retrieved from