UsefulLinks
Statistics
Multivariate Analysis
1. Introduction to Multivariate Analysis
2. Foundations in Matrix Algebra and Random Vectors
3. The Multivariate Normal Distribution
4. Data Preparation and Exploration
5. Principal Component Analysis
6. Factor Analysis
7. Multiple Linear Regression
8. Multivariate Analysis of Variance
9. Discriminant Analysis
10. Logistic Regression
11. Cluster Analysis
12. Canonical Correlation Analysis
13. Multidimensional Scaling
14. Advanced Multivariate Methods
8.
Multivariate Analysis of Variance
8.1.
MANOVA Conceptual Framework
8.1.1.
Extension of ANOVA
8.1.2.
Multiple Dependent Variables
8.1.3.
Simultaneous Testing
8.1.4.
Type I Error Control
8.2.
MANOVA vs. Multiple ANOVAs
8.2.1.
Advantages of MANOVA
8.2.2.
Power Considerations
8.2.3.
Interpretation Differences
8.3.
MANOVA Assumptions
8.3.1.
Independence of Observations
8.3.2.
Multivariate Normality
8.3.3.
Homogeneity of Covariance Matrices
8.3.3.1.
Box's M Test
8.3.3.2.
Robustness Considerations
8.3.4.
Adequate Sample Size
8.4.
MANOVA Test Statistics
8.4.1.
Wilks' Lambda
8.4.2.
Pillai's Trace
8.4.3.
Hotelling-Lawley Trace
8.4.4.
Roy's Largest Root
8.4.5.
Choosing Among Test Statistics
8.5.
One-Way MANOVA
8.5.1.
Model Specification
8.5.2.
Hypothesis Formulation
8.5.3.
Test Statistic Calculation
8.5.4.
Effect Size Measures
8.6.
Factorial MANOVA
8.6.1.
Two-Way MANOVA
8.6.2.
Interaction Effects
8.6.3.
Main Effects
8.7.
Follow-Up Analyses
8.7.1.
Univariate ANOVAs
8.7.2.
Bonferroni Correction
8.7.3.
Roy-Bargmann Stepdown Analysis
8.7.4.
Discriminant Analysis
8.8.
Effect Size and Power
8.8.1.
Multivariate Effect Size
8.8.2.
Power Analysis
8.8.3.
Sample Size Determination
Previous
7. Multiple Linear Regression
Go to top
Next
9. Discriminant Analysis