Applied Multivariate Techniques

Preface......Page 5 Contents......Page 9 1.1 Types of Measurement Scales......Page 17 1.2 Classification of Data Analytic Methods......Page 20 1.3 Dependence Methods......Page 21 1.4 Interdependence Methods......Page 26 1.5 Structural Models......Page 29 1.6 Overview of the Book......Page 30 Questions......Page 31 2.1 Cartesian Coordinate System......Page 33 2.2 Vectors......Page 35 2.3 Vectors in a Cartesian Coordinate System......Page 39 2.4 Algebraic Formulae for Vector Operations......Page 41 2.5 Vector Independence and Dimensionality......Page 46 2.6 Change in Basis......Page 47 2.7 Representing Points with Respect to New Axes......Page 48 2.8 Summary......Page 49 Questions......Page 50 3.1 Data Manipulations......Page 52 3.2 Distances......Page 58 3.3 Graphical Representation of Data in Variable Space......Page 61 3.4 Graphical Representation of Data in Observation Space......Page 63 3.5 Generalized Variance......Page 66 3.6 Summary......Page 67 Questions......Page 68 A3.1 Generalized Variance......Page 70 A3.2 Using PROC IML in SAS for Data Manipulations......Page 71 4 Principal Components Analysis......Page 74 4.1 Geometry of Principal Components Analysis......Page 75 4.2 Analytical Approach......Page 82 4.3 How To Perform Principal Components Analysis......Page 83 4.4 Issues Relating to the Use of Principal Components Analysis......Page 87 Questions......Page 97 A4.1 Eigenstructure of the Covariance Matrix......Page 100 A4.2 Singular Value Decomposition......Page 101 A4.3 Spectral Decomposition of a Matrix......Page 102 A4.4 Illustrative Example......Page 103 5.1 Basic Concepts and Terminology of Factor Analysis......Page 106 5.3 Geometric View of Factor Analysis......Page 115 5.4 Factor Analysis Techniques......Page 118 5.5 How to Perform Factor Analysis......Page 125 5.6 Interpretation of SAS Output......Page 126 5.7 An Empirical Illustration......Page 137 5.8 Factor Analysis versus Principal Components Analysis......Page 141 5.9 Exploratory versus Confirmatory Factor Analysis......Page 144 Questions......Page 145 A5.1 One-Factor Model......Page 148 A5.2 Two-Factor Model......Page 149 A5.3 More Than Two Factors......Page 151 A5.4 Factor Indeterminacy......Page 152 A5.5 Factor Rotations......Page 153 A5.6 Factor Extraction Methods......Page 157 A5.7 Factor Scores......Page 158 6.1 Basic Concepts of Confirmatory Factor Analysis......Page 160 6.3 LISREL......Page 164 6.4 Interpretation of the LISREL Output......Page 168 6.5 Multigroup Analysis......Page 186 6.6 Assumptions......Page 189 6.7 An Illustrative Example......Page 190 6.8 Summary......Page 192 Questions......Page 193 Appendix......Page 196 A6.2 Maximum Likelihood Estimation......Page 197 7.1 What Is Cluster Analysis?......Page 201 7.2 Geometrical View of Cluster Analysis......Page 202 7.4 Similarity Measures......Page 203 7.5 Hierarchical Clustering......Page 204 7.6 Hierarchical Clustering Using SAS......Page 211 7.7 Nonhierarchical Clustering......Page 218 7.8 Nonhierarchical Clustering Using SAS......Page 223 7.9 Which Clustering Method Is Best?......Page 227 7.10 Similarity Measures......Page 234 7.12 An Illustrative Example......Page 237 7.13 Summary......Page 248 Questions......Page 249 Appendix......Page 251 8.1 Geometric View of Discriminant Analysis......Page 253 8.2 Analytical Approach to Discriminant Analysis......Page 260 8.3 Discriminant Analysis Using SPSS......Page 261 8.4 Regression Approach to Discriminant Analysis......Page 278 8.5 Assumptions......Page 279 8.6 Stepwise Discriminant Analysis......Page 280 8.7 External Validation of the Discriminant Function......Page 289 8.8 Summary......Page 290 Questions......Page 291 A8.1 Fisher's Linear Discriminant Function......Page 293 A8.2 Classification......Page 294 A8.3 Illustrative Example......Page 300 9.1 Geometrical View of MDA......Page 303 9.2 Analytical Approach......Page 309 9.3 MDA Using SPSS......Page 310 9.4 An Illustrative Example......Page 320 9.5 Summary......Page 324 Questions......Page 325 Appendix......Page 326 A9.1 Classification for More than Two Groups......Page 327 A9.2 Multivariate Normal Distribution......Page 328 10.1 Basic Concepts of Logistic Regression......Page 333 10.2 Logistic Regression with Only One Categorical Variable......Page 337 10.3 Logistic Regression and Contingency Table Analysis......Page 343 10.4 Logistic Regression for Combination of Categorical and Continuous Independent Variables......Page 344 10.5 Comparison of Logistic Regression and Discriminant Analysis......Page 348 10.6 An Illustrative Example......Page 349 10.7 Summary......Page 351 Questions......Page 352 A10.1 Maximum Likelihood Estimation......Page 355 A10.2 Illustrative Example......Page 356 11.1 Geometry of MANOVA......Page 358 11.2 Analytic Computations for Two-Group MANOVA......Page 362 11.3 Two-Group MANOVA......Page 366 11.4 Multiple-Group MANOVA......Page 371 11.5 MANOVA for Two Independent Variables or Factors......Page 382 11.6 Summary......Page 386 Questions......Page 387 12.1 Significance and Power of Test Statistics......Page 390 12.3 Testing Univariate Normality......Page 391 12.4 Testing for Multivariate Normality......Page 396 12.5 Effect of Violating the Equality of Covariance Matrices Assumption......Page 399 12.6 Independence of Observations......Page 403 Questions......Page 404 Appendix......Page 405 13.1 Geometry of Canonical Correlation......Page 407 13.2 Analytical Approach to Canonical Correlation......Page 413 13.3 Canonical Correlation Using SAS......Page 414 13.4 Illustrative Example......Page 422 13.7 Summary......Page 425 Questions......Page 426 Appendix......Page 428 A13.2 Illustrative Example......Page 431 14.1 Structural Models......Page 435 14.2 Structural Models with Observable Constructs......Page 436 14.3 Structural Models with Unobservable Constructs......Page 442 14.4 An Illustrative Example......Page 451 Questions......Page 456 A14.1 Implied Covariance Matrix......Page 460 A14.2 Model Effects......Page 465 Satistical Tables......Page 471 References......Page 485 Tables, Figures, and Exhibits......Page 489 Index......Page 499

finally! Your Students Don’t Have To Be Mathematicians To Learn Multivariate Techniques! Multivariate Techniques Are Fundamental To Research Analysis—but Their Mathematical Derivations Are Often Overwhelming To Students. This Exciting New Book Takes A Gentle Approach That Introduces Students To The Various Multivariate Techniques Used In Business Without Intimidation:

• the Author Uses Geometry To Illustrate Concepts So Students Understand The Various Techniques And Their Application. Matrix Algebra And Proofs Can Also Be Used By Referring To The Appendices Of The Chapters.
• each Technique Is Discussed Analytically Using A Small Data Set And Hand Calculations. This Gives Students A Clearer Understanding Of The Technique.
• computer Outputs From Sas And Spss Are Annotated And Discussed. Students Learn How To Interpret The Results From These Two Popular Statistical Packages.
• the Chapter-ending Pedagogy Includes Exercises With Data Sets That Reinforce Concepts.

instructor’s Manual: 0-471-13061-3 Also Available With This Text Is A Carefully Developed Teaching Tool For Instructors. It Offers Detailed Answers To All End Of Chapter Exercises, Including Computer Output For Questions Which Require Students To Perform Data Analysis. An Additional Set Of Examination Questions With Answers Are Also Provided In The Manual.

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a Graduate Course Text For Students Of Business, And Natural And Behavioral Sciences Who Want To Learn How To Use The Multivariate Data Analytic Techniques Without Getting Bogged Down With Derivations Or Rigorous Proofs, But Would Like A Broader Understanding Than Is Provided By The Simple Cookbook Approaches. Focuses On When To The Use The Various Techniques And How To Interpret The Results, Rather Than The Mathematics. Indeed No Knowledge Of Matrix Algebra Is Assumed Or Required. An Enclosed 3.5 Disk Contains The Data Sets Used In The Text And Exercises. A Teacher's Manual Is Also Available. Annotation C. Book News, Inc., Portland, Or (booknews.com)