Multivariate Methods in Education, Fall 2011 (UGA)


Upcoming Workshops:

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

WorkshopDatesLocationInstructor
Diagnostic MeasurementMay 22-25, 2017Omni Hilton Head Oceanfront Resort, Hilton Head Island, South CarolinaJonathan Templin
Introduction to Longitudinal Multilevel ModelsMay 30-June 2, 2017Omni Hilton Head Oceanfront Resort, Hilton Head Island, South CarolinaLesa Hoffman

Multivariate Methods in Education (ERSH 8350)
Fall, 2011: University of Georgia
Course Documents
Materials
Downloadable Course Syllabus
Course Facebook Page
Final Examination
Final Exam InstructionsData FileAudio File of Final Discussion
Lecture Slides and Example Files
WeekDateCourse MaterialsReadings
18/17Course 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
28/24Matrix 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
38/31Univariate 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
49/7Univariate 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
59/14Missing 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
69/21Review and Recovery Week - No New Material
None
79/28Maximum 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
810/5Repeated 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
910/12An Introduction to Multilevel Models and Random Effects

None

Lecture #9 Slides



SAS Analyses File



Data File

10/19No Class None
1010/26An Introduction to Longitudinal Models

None
audio file #1
audio file #2


Lecture #10 Slides



SAS Analyses File



Data File

1111/9Principal 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

1211/16Confirmatory 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

1311/30Path Analysis and Structural Equation ModelingR12.1, 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

References
WeekNumberReference
2R2.1Chapter 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.
R2.2Chapter 8: Principal components. Johnson, R. A. & Wichern, D. W. (2002). Applied Multivariate Statistical Analysis (5th Ed.). Upper Saddle River, N.J., Prentice-Hall.
R2.3Kramer, 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.
3R3.1Chapter 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.
R3.2Chapter 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.
R3.3Burdenski, T. (2000). Evaluating univariate, bivariate, and multivariate normality using graphical and statistical procedures. Multiple Linear Regression Viewpoints, 26, 15-28.
4R4.1Chapter 13: Repeated measures analysis. Stevens, J. P. (2002). Applied Multivariate Statistics for the Social Sciences (4th Ed.). Mahwah, N.J., Erlbaum.
R4.2Chapter 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.
R4.3Lau, 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.
5R5.1Chapter 1: An introduction to missing data. Enders, C. K. (2010) Applied Missing Data Analysis. New York: Guildford.
R5.2Chapter 2: Traditional methods for dealing with missing data. Enders, C. K. (2010) Applied Missing Data Analysis. New York: Guildford.
R5.3Chapter 7: The imputation phase of multiple imputation. Enders, C. K. (2010) Applied Missing Data Analysis. New York: Guildford.
R5.4Chapter 8: The analysis and pooling phases of multiple imputation. Enders, C. K. (2010) Applied Missing Data Analysis.New York: Guildford.
R5.5Chapter 9: Practical issues in multiple imputation. Enders, C. K. (2010) Applied Missing Data Analysis. New York: Guildford.
7R7.1Chapter 3: An introduction to maximum likelihood estimation. Enders, C. K. (2010) Applied Missing Data Analysis. New York: Guildford.
R7.2Chapter 4: Maximum likelihood missing data handling. Enders, C. K. (2010) Applied Missing Data Analysis. New York: Guildford.
R7.3Chapter 5: Improving the accuracy of maximum likelihood analyses. Enders, C. K. (2010) Applied Missing Data Analysis.New York: Guildford.
R7.4Chapter 6: An introduction to Bayesian estimation. Enders, C. K. (2010) Applied Missing Data Analysis. New York: Guildford.
11R11.1Chapter 8: Principal components. Johnson, R. A. & Wichern, D. W. (2002). Applied Multivariate Statistical Analysis (5th Ed.). Upper Saddle River, N.J., Prentice-Hall.
R11.2Chapter 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.
12R12.1Chapter 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.
R12.2Chapter 2: Path analysis. Kaplan (2009). Structural Equation Modeling: Foundations and Extensions (2nd Ed.). Thousand Oaks, C.A.: Sage.
R12.3Chapter 1: Factor analysis. Kaplan (2009). Structural Equation Modeling: Foundations and Extensions (2nd Ed.).Thousand Oaks, C.A.: Sage.
R12.4Chapter 4: Structural equation models in single and multiple groups. Kaplan (2009). Structural Equation Modeling: Foundations and Extensions (2nd Ed.). Thousand Oaks, C.A.: Sage.