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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Bibliographic Details
Main Author: Bernstein, Ira H.
Format: eBook
Language:English
Published: New York, NY Springer New York 1988, 1988
Edition:1st ed. 1988
Subjects:
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