Multivariate Methods in Education (ERSH 8350); Fall 2011, University of Georgia

Instructor: Dr. Jonathan Templin    
Email: jtemplin@uga.edu Phone: 706-680-7148
Classroom: 228 Aderhold Hall Office: 570B Aderhold Hall
Time: 4:00-7:00 Wednesdays (3 credits) Office Hours: 1:00-3:00 Tuesdays and by appointment
Downloadable Course Syllabus: Click here to download
Course Facebook Page: Multivariate Statistics (ERSH 8350) Fall 2011
Course Materials Repository: Dropbox

Final Examination:

Final Exam Instructions Data File Audio File of Final Discussion

Tentative Schedule of Course Topics and Assignments:

Week Date Course Materials Readings
1 8/17 Course Introduction - Syllabus [audio file #1]
Introduction to Mplus [audio file #2] and SAS [audio file #3] (Zipped Folder of Files) 		None
Assignment #1: Due August 24 at 4pm
2 8/24 Matrix Algebra
Principal Components Analysis 		[R2.1]
[R2.2]
[R2.3]
Lecture #2 Slides Example Syntax Example Data
[audio file #1] [audio file #2] [audio file #3]
Assignment #2: Due August 31 at 4pm
Link to a good website about SAS PROC IML
3 8/31 Univariate and multivariate statistical distributions [R3.1]
[R3.2]
[R3.3]
Lecture #3 Slides Examples Folder
[audio file #1] [audio file #2]
Assignment #3: Due September 7 at 4pm Assignment Data Set
4 9/7 Univariate Linear Models (with Matrices)
Repeated Measures ANOVA
Multivariate ANOVA 		[R4.1]
[R4.2]
[R4.3]
Lecture #4 Slides SAS Analyses File
[audio file #1] [audio file #2] [audio file #3]
Repeated Measures ANOVA Website PROC GLM SAS User's Guide
Assignment #4: Due September 14 at 4pm Assignment Data Set
5 9/14 Missing Data Methods (Part 1): Multiple Imputation [R5.1-R5.5]
Lecture #5 Slides SAS Analyses File
[audio file #1] [audio file #2] [audio file #3]
Assignment #5: Due September 21 at 4pm Assignment Data Set
6 9/21 Review and Recovery Week - No New Material None
7 9/28 Maximum Likelihood and Bayesian Estimation [R7.1-R7.4]
Lecture #7 Slides SAS Analyses File
[audio file #1] [audio file #2] [audio file #3]
Assignment #6: Due October 5 at 4pm Assignment Data Set
8 10/5 Repeated Measures ANOVA
Multivariate ANOVA in PROC MIXED 		None
Lecture #8 Slides SAS Analyses File
[audio file #1] [audio file #2] [audio file #3]
PROC MIXED SAS User's Guide
Assignment #7: Due October 12 at 4pm Assignment Data Set
9 10/12 An Introduction to Multilevel Models and Random Effects None
Lecture #9 Slides SAS Analyses File Data File
10/19 No Class None
10 10/26 An Introduction to Longitudinal Models None
[audio file #1] [audio file #2]
Lecture #10 Slides SAS Analyses File Data File
11 11/9 Principal Components and Exlporatory Factor Analysis [R11.1] and [R11.2]
Lecture #11 Slides
[audio file #1] [audio file #2] [audio file #3]
PCA SAS Analyses File PCA Data File #1 PCA Data File #2 PCA Data File #3
EFA SAS Analyses File PCA Data File #1
12 11/16 Confirmatory Factor Analysis [R12.1], [R12.2], [R12.3], [R12.4]
Lecture #12 Slides
[audio file #1] [audio file #2] [audio file #3]
Lecture #12 Mplus Example Files
13 11/30 Path Analysis and Structural Equation Modeling [R12.1], [R12.2], [R12.3], [R12.4]
Lecture #13 Slides
[audio file #1] [audio file #2] [audio file #3 -- FINAL DISCUSSION]
Lecture #13 Mplus Example Files

Course Readings:

Week Number Reference
2 R2.1 Chapter 2: Matrix algebra and random vectors. Johnson, R. A. & Wichern, D. W. (2002). Applied Multivariate Statistical Analysis (5th Ed.). Upper Saddle River, N.J., Prentice-Hall.
R2.2 Chapter 8: Principal components. Johnson, R. A. & Wichern, D. W. (2002). Applied Multivariate Statistical Analysis (5th Ed.). Upper Saddle River, N.J., Prentice-Hall.
R2.3 Kramer, J. R., Chan, G. C., Hellelbrock, V. M., Kuperman, S., Bucholz, K. K., Edenberg, H. J., Schuckit, M. A., Nurnberger, J. I., Foroud, T., Dick, D. M., Bierut, L. J., Porjesz, B. (2010). A principal components analysis of the abbreviated desires for alcohol questionnaire (DAQ). Journal of Studies on Alcohol and Drugs, 71, 150-155.
3 R3.1 Chapter 3: Sample geometry and random sampling. Johnson, R. A. & Wichern, D. W. (2002). Applied Multivariate Statistical Analysis (5th Ed.). Upper Saddle River, N.J., Prentice-Hall.
R3.2 Chapter 4: The multivariate normal distribution. Johnson, R. A. & Wichern, D. W. (2002). Applied Multivariate Statistical Analysis (5th Ed.). Upper Saddle River, N.J., Prentice-Hall.
R3.3 Burdenski, T. (2000). Evaluating univariate, bivariate, and multivariate normality using graphical and statistical procedures. Multiple Linear Regression Viewpoints, 26, 15-28.
4 R4.1 Chapter 13: Repeated measures analysis. Stevens, J. P. (2002). Applied Multivariate Statistics for the Social Sciences (4th Ed.). Mahwah, N.J., Erlbaum.
R4.2 Chapter 6: Comparisons of several multivariate means. Johnson, R. A. & Wichern, D. W. (2002). Applied Multivariate Statistical Analysis (5th Ed.). Upper Saddle River, N.J., Prentice-Hall.
R4.3 Lau, S., & Chueng, P. C. (2010). Creativity assessment: Comparability of the electronic and paper-and-pencil versions of the Wallach-Kogan creativity tests. Thinking Skills and Creativity, 5 101-107.
5 R5.1 Chapter 1: An introduction to missing data. Enders, C. K. (2010) Applied Missing Data Analysis. New York: Guildford.
R5.2 Chapter 2: Traditional methods for dealing with missing data. Enders, C. K. (2010) Applied Missing Data Analysis. New York: Guildford.
R5.3 Chapter 7: The imputation phase of multiple imputation. Enders, C. K. (2010) Applied Missing Data Analysis. New York: Guildford.
R5.4 Chapter 8: The analysis and pooling phases of multiple imputation. Enders, C. K. (2010) Applied Missing Data Analysis. New York: Guildford.
R5.5 Chapter 9: Practical issues in multiple imputation. Enders, C. K. (2010) Applied Missing Data Analysis. New York: Guildford.
7 R7.1 Chapter 3: An introduction to maximum likelihood estimation. Enders, C. K. (2010) Applied Missing Data Analysis. New York: Guildford.
R7.2 Chapter 4: Maximum likelihood missing data handling. Enders, C. K. (2010) Applied Missing Data Analysis. New York: Guildford.
R7.3 Chapter 5: Improving the accuracy of maximum likelihood analyses. Enders, C. K. (2010) Applied Missing Data Analysis. New York: Guildford.
R7.4 Chapter 6: An introduction to Bayesian estimation. Enders, C. K. (2010) Applied Missing Data Analysis. New York: Guildford.
11 R11.1 Chapter 8: Principal components. Johnson, R. A. & Wichern, D. W. (2002). Applied Multivariate Statistical Analysis (5th Ed.). Upper Saddle River, N.J., Prentice-Hall.
R11.2 Chapter 9: Factor analysis and inference for structured covariance matrices. Johnson, R. A. & Wichern, D. W. (2002). Applied Multivariate Statistical Analysis (5th Ed.). Upper Saddle River, N.J., Prentice-Hall.
12 R12.1 Chapter 1: Historical foundations of structural equation modeling for continuous and categorical latent variables. Kaplan (2009). Structural Equation Modeling: Foundations and Extensions (2nd Ed.). Thousand Oaks, C.A.: Sage.
R12.2 Chapter 2: Path analysis. Kaplan (2009). Structural Equation Modeling: Foundations and Extensions (2nd Ed.). Thousand Oaks, C.A.: Sage.
R12.3 Chapter 1: Factor analysis. Kaplan (2009). Structural Equation Modeling: Foundations and Extensions (2nd Ed.). Thousand Oaks, C.A.: Sage.
R12.4 Chapter 4: Structural equation models in single and multiple groups. Kaplan (2009). Structural Equation Modeling: Foundations and Extensions (2nd Ed.). Thousand Oaks, C.A.: Sage.

Teaching Links:

Methodological Workshops:

Academic Courses:

Psychometrics

Applied Statistics