Applied Multivariate Analysis
Like most academic authors, my views are a joint product of my teaching and my research. Needless to say, my views reflect the biases that I have acquired. One way to articulate the rationale (and limitations) of my biases is through the preface of a truly great text of a previous era, Cooley and Lo...
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Format: | eBook |
Language: | English |
Published: |
New York, NY
Springer New York
1988, 1988
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Edition: | 1st ed. 1988 |
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Online Access: | |
Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- Oblique Multiple Groups Tests of Weak Structure
- LISREL Tests of Weak Substantive Models
- LISREL Tests of Strong Substantive Models
- Causal Models and Path Analysis
- Causal Models and LISREL
- Addendum: A Program to Obtain Oblique Multiple Groups Solutions
- 8 Classification Methods—Part 1. Forming Discriminant Axes
- Overview
- Discriminant Analysis with Two Groups and Two Predictors
- Discriminant Analysis with Two Predictors and Three Groups
- Discriminant Analysis—The General Case
- 9 Classification Methods—Part 2. Methods of Assignment
- Overview
- The Equal Variance Gaussian Model
- The Unequal Variance Gaussian Model
- Other Signal Detection Models
- Strategies for Individual Classification
- Alternative Strategies—An Overview
- A Numerical Example
- Classification Based on Salient Variables
- Discriminant Functions and Classification
- Classification Based on Distance Measures
- A Summary of Strategic Considerations in Classification
- Example 2—Imperfect Prediction plus a Look at Residuals
- Example 3—Real Personality Assessment Data
- Alternative Approaches to Data Aggregation
- 5 Multiple Regression and Correlation—Part 2. Advanced Applications
- Overview
- Nonquantitative Variables
- The Simple Analysis of Variance (ANOVA)
- Multiple Comparisons
- Evaluation of Quantitative Relations
- The Two-Way ANOVA
- The Analysis of Covariance (ANCOVA)
- Repeated Measures, Blocked and Matched Designs
- Higher-Order Designs
- 6 Exploratory Factor Analysis
- Overview
- The Basic Factor Analytic Model
- Common Uses of Factor Analysis
- An Overview of the Exploratory Factoring Process
- Principal Components
- Factor Definition and Rotation
- The Common Factor Model
- An Example of the Common Factor Model
- Factor Scores
- Addendum: Constructing Correlation Matrices with a Desired FactorStructure
- 7 Confirmatory Factor Analysis
- Overview
- Comparing Factor Structures
- 10 Classification Methods—Part 3. Inferential Considerations in the Manova
- Overview
- The Two-Group MANOVA and Hotelling’s T2
- Tests of Vector Means with Multiple Groups
- The Simple MANOVA with Multiple Groups
- The Multivariate MANOVA
- The MANCOVA
- 11 Profile and Canonical Analysis
- Overview
- Profile Similarity
- Simple and Hierarchical Clustering
- Canonical Analysis
- 12 Analysis of Scales
- Overview
- Properties of Individual Items
- Test Reliability
- Numerical Example I: A Unifactor Scale
- Numerical Example II: A Two-Factor Scale
- Test Validity
- Appendix A—Tables of the Normal Curve
- Appendix D—Tables of Orthogonal Polynomial Coefficients
- Problems
- References
- Author Index
- 1 Introduction and Preview
- Overview
- Multivariate Analysis: A Broad Definition
- Multivariate Analysis: A Narrow Definition
- Some Important Themes
- The Role of Computers in Multivariate Analysis
- Choosing a Computer Package
- Problems in the Use of Computer Packages
- 2 Some Basic Statistical Concepts
- Overview
- Univariate Data Analysis
- Bivariate Data Analysis
- Statistical Control: A First Look at Multivariate Relations
- 3 Some Matrix Concepts
- Overview
- Basic Definitions
- Basic Matrix Operations
- An Application of Matrix Algebra
- More about Linear Combinations
- Eigenvalues and Eigenvectors
- 4 Multiple Regression and Correlation—Part 1. Basic concepts
- Overview
- Assumptions Underlying Multiple Regression
- Basic Goals of Regression Analysis
- The Case of Two Predictors
- The Case of More Than Two Predictors
- Inferential Tests
- Evaluating Alternative Equations
- Example 1—Perfect Prediction