 Research Design and Methods: A Process Approach, 5/e Kenneth S. Bordens,
Indiana University/Purdue University - Fort Wayne
Bruce Barrington Abbott,
Indiana University/Purdue University
Using Multivariate Design and Analysis
Chapter OutlineExperimental and Correlational Multivariate Designs
Correlational Multivariate Design
Experimental Multivariate Design
Multivariate Statistical Tests
Advantages of the Experimental Multivariate Strategy
Advantages of the Correlational Multivariate Strategy
Causal Inference
Assumptions and Requirements of Multivariate Statistics
Linearity
Outliers
Identifying Outliers
Dealing with Outliers
Normality and Homoscedasticity
Multicollinearity
Error of Measurement
Sample Size
Multivariate Statistical Tests
Factor Analysis
Factor Loadings
Rotation of Factors
Principal-Components and Principal-Factors Analysis
An Example of Factor Analysis
Partial and Part Correlations
Partial Correlation
Part Correlation
Multiple Regression
The Multiple Regression Equation
Types of Regression Analysis
An Example of Multiple Regression
Multiple R and R-Square
Regression Weights
Interpretation of Regression Weights
Discriminant Analysis
An Example of Discriminant Analysis
Canonical Correlation
Multivariate Analysis of Variance
An Example of MANOVA
Using MANOVA for Within-Subjects Designs
Loglinear Analysis
Applications of Loglinear Analysis
How Loglinear Analysis Works
Path Analysis
Causal Relationships
Types of Variables and Causal Models
Estimating the Degree of Causality
Interpreting Path Analysis
Multivariate Analysis: A Cautionary Note
Summary
Key Terms |
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2002 McGraw-Hill Higher Education