Canonical Correlation Analysis
Canonical correlation analysis (CCA) is a multivariate statistical method that analyzes the relationship between two sets of variables, in which each set contains at least two variables. It is the most general type of the general linear model, with multiple regression, multiple analysis of variance, analysis of variance, and discriminant function analysis all being special cases of CCA.
Although the method has been available for more than 70 years, its use has been somewhat limited until fairly recently due to its lack of inclusion in common statistical programs and its rather labor-intensive calculations. Currently, however, many computer programs do include CCA, and thus the method has become somewhat more widely used.
This entry begins by explaining the basic logic of and defining important terms associated with CCA. ...
Looks like you do not have access to this content.
Reader's Guide
Descriptive Statistics
Distributions
Graphical Displays of Data
Hypothesis Testing
Important Publications
Inferential Statistics
Item Response Theory
Mathematical Concepts
Measurement Concepts
Organizations
Publishing
Qualitative Research
Reliability of Scores
Research Design Concepts
Research Designs
Research Ethics
Research Process
Research Validity Issues
Sampling
Scaling
Software Applications
Statistical Assumptions
Statistical Concepts
Statistical Procedures
Statistical Tests
Theories, Laws, and Principles
Types of Variables
Validity of Scores
- All
- A
- B
- C
- D
- E
- F
- G
- H
- I
- J
- K
- L
- M
- N
- O
- P
- Q
- R
- S
- T
- U
- V
- W
- X
- Y
- Z