INSEAD Library

Doriot: for 2012-2013 courses

Includes bibliographical references and index

Statistical Analysis of Management Data provides a comprehensive approach to multivariate statistical analyses that are important for researchers in all fields of management, including finance, production, accounting, marketing, strategy, technology, and human resources. This book is especially designed to provide doctoral students with a theoretical knowledge of the concepts underlying the most important multivariate techniques and an overview of actual applications. It offers a clear, succinct exposition of each technique with emphasis on when each technique is appropriate and how to use it. This second edition, fully revised, updated, and expanded, reflects the most current evolution in the methods for data analysis in management and the social sciences. In particular, it places a greater emphasis on measurement models, and includes new chapters and sections.

Digitized

Statistical Analysis of Management Data Contents 1 Introduction ........................................................................................................ 1 1.1 Overview .............................................................................................. 1 1.2 Objectives ............................................................................................. 2 1.2.1 Develop the Student's Knowledge of the Technical Details of Various Techniques for Analyzing Data ....................................................................... 2 1.2.2 Expose Students to Applications and "Hand-On" Use of Various Computer Programs for Carrying Out Statistical Analyses of Data ........................ 2 1.3 Types of Scales .................................................................................... 3 1.3.1 Definition of Different Types of Scales ................................. 4 1.3.2 The Impact of the Type of Scale on Statistical Analysis .................................................................................. 4 1.4 Topics Covered .................................................................................... 5 1.5 Pedagogy .............................................................................................. 6 Bibliography .................................................................................................... 8 2 Multivariate Normal Distribution ................................................................... 9 2.1 Univariate Normal Distribution ........................................................... 9 2.2 Bivariate Normal Distribution ............................................................. 9 2.3 Generalization to Multivariate Case ................................................... 11 2.4 Tests About Means ............................................................................. 12 2.4.1 Sampling Distribution of Sample Centroids ........................ 12 2.4.2 Significance Test: One-Sample Problem ............................. 13 2.4.3 Significance Test: Two-Sample Problem ............................ 15 2.4.4 Significance Test: K-Sample Problem ................................. 17 2.5 Examples Using SAS ......................................................................... 19 2.5.1 Test of the Difference Between Two Mean Vectors ­ One-Sample Problem ........................................... 19 2.5.2 Test of the Difference Between Several Mean Vectors ­ K-Sample Problem ............................................... 21 2.6 Assignment ......................................................................................... 27 Bibliography .................................................................................................. 28 Basic Technical Readings ................................................................... 28 Application Readings........................................................................... 28 3 Reliability Alpha, Principle Component Analysis, and Exploratory Factor Analysis ................................................................ 29 3.1 Notions of Measurement Theory ....................................................... 29 3.1.1 Definition of a Measure ....................................................... 29 3.1.2 Parallel Measurements ......................................................... 30 3.1.3 Reliability ............................................................................. 30 3.1.4 Composite Scales ................................................................. 31 3.2 Exploratory Factor Analysis .............................................................. 34 3.2.1 Axis Rotation ........................................................................ 34 3.2.2 Variance-Maximizing Rotations (Eigenvalues and Eigenvectors) ........................................... 35 3.2.3 Principal Component Analysis (PCA) ................................. 39 3.2.4 Exploratory Factor Analysis (EFA) ..................................... 41 3.3 Application Examples Using SAS ........................................................... 47 3.4 Assignment ......................................................................................... 53 Bibliography ................................................................................................... 56 Basic Technical Readings ................................................................... 56 Application Readings .......................................................................... 57 4 Confirmatory Factor Analysis ....................................................................... 59 4.1 Confirmatory Factor Analysis: A Strong Measurement Model ........................................................................... 59 4.2 Estimation ........................................................................................... 61 4.2.1 Model Fit .............................................................................. 62 4.2.2 Test of Significance of Model Parameters ........................... 65 4.3 Summary Procedure for Scale Construction ...................................... 65 4.3.1 Exploratory Factor Analysis ................................................ 65 4.3.2 Confirmatory Factor Analysis .................................................... 66 4.3.3 Reliability Coefficient a ....................................................... 66 4.3.4 Discriminant Validity ........................................................... 66 4.3.5 Convergent Validity ............................................................. 66 4.4 Second-Order Confirmatory Factor Analysis .......................................... 67 4.5 Multi-group Confirmatory Factor Analysis ....................................... 69 4.6 Application Examples Using LISREL ..................................................... 72 4.6.1 Example of Confirmatory Factor Analysis .......................... 72 4.6.2 Example of Model to Test Discriminant Validity Between Two Constructs ....................................................... 73 4.6.3 Example of Model to Assess the Convergent Validity of a Construct ........................................................... 78 4.6.4 Example of Second-Order Factor Model ................................... 98 4.6.5 Example of Multi-group Factor Analysis ........................... 114 4.7 Assignment ....................................................................................... 120 Bibliography ................................................................................................. 121 Basic Technical Readings ................................................................. 121 Application Readings ........................................................................ 121 5 Multiple Regression with a Single Dependent Variable ................................. 123 5.1 Statistical Inference: Least Squares and Maximum Likelihood ......................................................................................... 123 5.1.1 The Linear Statistical Model ............................................... 123 5.1.2 Point Estimation .................................................................. 125 5.1.3 Maximum Likelihood Estimation ....................................... 127 5.1.4 Properties of Estimator ........................................................ 129 5.2 R-Squared as a Measure of Fit ............................................ 5.1.5 Pooling Issues ................................................................................... 5.2.1 Linear Restrictions .............................................................. 5.2.2 Pooling Tests and Dummy Variable Models ........................... 130 135 135 138 5.2.3 Strategy for Pooling Tests ................................................... 141 5.3 Examples of Linear Model Estimation with SAS ............................. 141 5.4 Assignment ....................................................................................... 147 Bibliography ................................................................................................. 147 Basic Technical Readings ................................................................. 147 Application Readings ........................................................................ 147 6 System of Equations ........................................................................................ 151 6.1 Seemingly Unrelated Regression (SUR) .......................................... 151 6.1.1 Set of Equations with Contemporaneously 6.1.2 6.1.3 6.2 Correlated Disturbances........................................................ 151 Estimation ............................................................................ 153 Special Cases ....................................................................... 155 A System of Simultaneous Equations ............................................... 155 6.2.1 The Problem ........................................................................ 155 6.2.2 Two-Stage Least Squares: 2SLS .............................................. 159 6.2.3 Three-Stage Least Squares: 3SLS ....................................... 160 6.3 Simultaneity and Identification ......................................................... 160 6.3.1 The Problem ........................................................................ 160 6.3.2 Order and Rank Conditions ................................................. 161 6.4 Summary ................................................................................................. 163 6.4.1 Structure of I' Matrix ........................................................... 6.4.2 Structure of E Matrix .......................................................... 6.4.3 Test of Covariance Matrix .................................................. 6.4.4 3SLS Versus 2SLS ................................................................... 6.5 Examples Using SAS .............................................................................. 6.5.1 163 163 164 165 165 Seemingly Unrelated Regression Example ......................... 165 6.5.2 Two-Stage Least Squares Example .......................................... 176 6.6 6.5.3 Three-Stage Least Squares Example .................................. 176 Assignment ....................................................................................... 180 Bibliography ................................................................................................. 184 Basic Technical Readings .................................................................. 184 Application Readings.......................................................................... 184 7 Canonical Correlation Analysis ................................................................... 187 7.1 The Method ....................................................................................... 187 7.1.1 Canonical Loadings ............................................................. 190 7.1.2 Canonical Redundancy Analysis ......................................... 190 7.2 Testing the Significance of the Canonical Correlations ................... 190 7.3 Multiple Regression as a Special Case of Canonical Correlation Analysis ........................................................................... 192 7.4 Examples Using SAS ........................................................................ 193 7.5 Assignment ....................................................................................... 198 Bibliography ................................................................................................. 198 Application Readings.......................................................................... 198 8 Categorical Dependent Variables ................................................................ 199 8.1 Discriminant Analysis ....................................................................... 199 8.1.1 The Discriminant Criterion ................................................. 199 8.1.2 Discriminant Function ......................................................... 202 8.1.3 Classification and Fit ........................................................... 204 8.2 Quantal Choice Models........................................................................... 208 8.2.1 The Difficulties of the Standard Regression Model with Categorical Dependent Variables ..................... 208 8.2.2 Transformational Logit ....................................................... 209 8.2.3 Conditional Logit Model ..................................................... 212 8.2.4 Fit Measures ........................................................................ 215 8.3 Examples ........................................................................................... 217 8.3.1 Example of Discriminant Analysis Using SAS .................. 217 8.3.2 Example of Multinomial Logit ­ Case 1 Analysis Using LIMDEP ...................................................... 223 8.3.3 Example of Multinomial Logit ­ Case 2 Analysis Using LIMDEP ...................................................... 225 8.4 Assignment ....................................................................................... 227 Bibliography .................................................................................................. 227 Basic Technical Readings .................................................................. 227 Application Readings.......................................................................... 228 9 Rank-Ordered Data ...................................................................................... 231 9.1 Conjoint Analysis ­ MONANOVA.......................................................... 231 9.1.1 Effect Coding Versus Dummy Variable Coding . . . .......... 231 9.1.2 Design Programs ................................................................. 238 9.1.3 Estimation of Part-Worth Coefficients ............................... 238 9.2 Ordered Probit .................................................................................. 239 9.3 Examples ........................................................................................... 243 9.3.1 Example of MONANOVA Using PC-MDS.............................. 243 9.3.2 Example of Conjoint Analysis Using SAS ........................ 244 9.3.3 Example of Ordered Probit Analysis Using LIMDEP ....... 246 9.4 Assignment ....................................................................................... 248 Bibliography ................................................................................................ 250 Basic Technical Readings ................................................................ 250 Application Readings ....................................................................... 250 10 Error in Variables -- Analysis of Covariance Structure ........................ 253 10.1 The Impact of Imperfect Measures ..................................................... 253 10.1.1 Effect of Errors-in-Variables ................................................ 253 10.1.2 Reversed Regression ............................................................. 255 10.1.3 Case with Multiple Independent Variables .......................... 256 10.2 Analysis of Covariance Structures ...................................................... 257 10.2.1 Description of Model ............................................................ 257 10.2.2 Estimation ............................................................................. 259 10.2.3 Model Fit ............................................................................... 262 10.2.4 Test of Significance of Model Parameters ........................... 263 10.2.5 Simultaneous Estimation of Measurement Model Parameters with Structural Relationship Parameters Versus Sequential Estimation .......................... 263 10.2.6 Identification ......................................................................... 263 10.2.7 Special Cases of Analysis of Covariance Structure . . ......... 264 10.3 Analysis of Covariance Structure with Means..................................... 266 10.4 Examples of Structural Model with Measurement Models Using LISREL .................................................................... 267 10.5 Assignment .......................................................................................... 268 Bibliography ................................................................................................ 291 Basic Technical Readings ................................................................ 291 Application Readings........................................................................ 291 11 Cluster Analysis ........................................................................................... 295 11.1 The Clustering Methods ...................................................................... 295 11.1.1 Similarity Measures............................................................ 296 11.1.2 The Centroid Method ............................................................ 296 11.1.3 Ward's Method ...................................................................... 300 11.1.4 Nonhierarchical Clustering: K-Means Method (FASTCLUS) ...................................................................... 305 11.2 Examples Using SAS 306 11.2.1 Example of Clustering with the Centroid Method . . . 306 11.2.2 Example of Clustering with Ward's Method ........................ 310 11.2.3 Example of FASTCLUS ....................................................... 310 11.3 Evaluation and Interpretation of Clustering Results ........................... 312 11.3.1 Determining the Number of Clusters .................................... 312 11.3.2 Size, Density, and Separation of Clusters............................. 320 11.3.3 Tests of Significance on Other Variables than Those Used to Create Clusters..................................... 320 11.3.4 Stability of Results ................................................................ 320 11.4 Assignment .......................................................................................... Bibliography................................................................................................. Basic Technical Readings................................................................. Application Readings........................................................................ 321 321 321 321 12 Analysis of Similarity and Preference Data............................................... 323 12.1 Proximity Matrices............................................................................... 323 12.1.1 Metric Versus Nonmetric Data ............................................. 323 12.1.2 Unconditional Versus Conditional Data................................ 324 12.1.3 Derived Measures of Proximity ............................................ 324 12.1.4 Alternative Proximity Matrices.............................................. 324 12.2 Problem Definition .............................................................................. 325 12.2.1 Objective Function ................................................................ 326 12.2.2 Stress as an Index of Fit ........................................................ 326 12.2.3 Metric..................................................................................... 327 12.2.4 Minimum Number of Stimuli ................................................ 328 12.2.5 Dimensionality ...................................................................... 328 12.2.6 Interpretation of MDS Solution............................................. 328 12.2.7 The KYST Algorithm............................................................ 329 12.3 Individual Differences in Similarity Judgments................................... 330 12.4 Analysis of Preference Data................................................................. 331 12.4.1 Vector Model of Preferences ................................................ 331 12.4.2 Ideal Point Model of Preferences .......................................... 33I 12.5 Examples Using PC-MDS.................................................................... 332 12.5.1 Example of KYST.................................................................. 332 12.5.2 Example of INDSCAL .......................................................... 335 12.5.3 Example of PROFIT (Property Fitting) Analysis.................. 341 12.5.4 Example of MDPREF............................................................ 350 12.5.5 Example of PREFMAP ......................................................... 356 12.6 Assignment .......................................................................................... 358 Bibliography................................................................................................. 368 Basic Technical Readings................................................................. 368 Application Readings........................................................................ 368 13 Appendices..................................................................................................... 369 Appendix A: Rules in Matrix Algebra ........................................................ 369 Vector and Matrix Differentiation.................................................... 369 Kronecker Products........................................................................... 369 Determinants .................................................................................... 369 Trace ................................................................................................. 369 Appendix B: Statistical Tables..................................................................... 370 Cumulative Normal Distribution ..................................................... 370 Chi-Squared Distribution ................................................................. 370 F Distribution.................................................................................... 371 Appendix C: Description of Data Sets ........................................................ 372 The MARKSTRAT® Environment....................................... 373 Marketing Mix Decisions.................................................... 375 Survey................................................................................ 376 Indup ................................................................................................ 381 Panel.................................................................................................. 381 Scan................................................................................................... 382 Bibliography............................................................................................... 384 About the Author............................................................................................. 385 Index .................................................................................................................. 387