07752nam a2200097Ia 4500020001900000100008300019245003100102250000600133260001200139650750300151 a978-8131715284 aHAIR JOSEPH , BLACK WILLIAM C, BABIN BARRY J, ANDERSON ROLPH E, TATHAM RONALDL 0aMULTIVARIATE DATA ANALYSIS a6 bPEARSON aPREPARING FOR A MULTI VARIATE ANALYSIS Examining Your Data Factor Analysis DEPENDENCE TECHNIQUES Multiple Regression Analysis Multiple Discriminant Analysis and Logistic Regression Multivariate Analysis of Variance Conjoint Analysis INTERDEPENDENCE TECHNIQUES Cluster Analysis Multidimensional Scaling and Correspondence Analysis MOVING BEYOND THE BASIC TECHNIQUES Structural Equation Modeling: An Introduction SEM: Confirmatory FaWhat Is Multivariate Analysis? Multivariate Analysis in Statistical Terms Some Basic Concepts of Multivariate Analysis The Variate Measurement Scales Measurement Error and Multivariate Measurement Statistical Significance Versus Statistical Power A Classification of Multivariate Techniques Dependence Techniques , Interdependence Techniques , Types of Multivariate Techniques , Principal Components and Common Factor Analysis , Multiple Regression , Multiple Discriminant Analysis and Logistic Regression , Canonical Correlation , Multivariate Analysis of Variance and Covariance , Conjoint Analysis , Cluster Analysis , Perceptual Mapping , Correspondence Analysis , Structural Equation Modeling and Confirmatory Factor Analysis , Guidelines for Multivariate Analyses and Interpretation , Establish Practical Significance as Well as Statistical Significance , Recognize That Sample Size Affects All Results , Know Your Data , Strive for Model Parsimony , Look at Your Errors , Validate Your Results , A Structured Approach to Multivariate Model Building , Stage 1: Define the Research Problem, Objectives, and Multivariate Technique to Be Used Stage 2: Develop the Analysis Plan , Stage 3: Evaluate the Assumptions Underlying the Multivariate Technique , Stage 4: Estimate the Multivariate Model and Assess Overall Model Fit , Stage 5: Interpret the Variate(s) , Stage 6: Validate the Multivariate Model , A Decision Flowchart , Databases , -, , Section III: Interdependence Techniques , Section IV Moving Beyond the Basics , , SECTION I PREPARING FOR A MULTIVARIATE ANALYSIS Examining Your Data , , Graphical Examination of the Data , Univariate Profiling: Examining the Shape of the Distribution , Bivariate Profiling: Examining the Relationship Between Variables , Bivariate Profiling: Examining Group Differences , Multivariate Profiles , , Missing Data , The Impact of Missing Data , A Simple Example of a Missing Data Analysis , A Four-Step Process for Identifying Missing Data and Applying Remedies , An Illustration of Missing Data Diagnosis with the Four-Step Process , Summary , Outliers , Detecting and Handling Outliers , An Illustrative Example of Analyzing Outliers , Testing the Assumptions of Multivariate Analysis , Assessing Individual Variables Versus the Variate , Four Important Statistical Assumptions , Data Transformations , An Illustration of Testing the Assumptions Underlying Multivariate Analysis Incorporating Nonmetric Data with Dummy Variables , Factor Analysis , What Is Factor Analysis? , A Hypothetical Example of Factor Analysis , Factor Analysis Decision Process , Stage 1: Objectives of Factor Analysis , Stage 2: Designing a Factor Analysis , Stage 3: Assumptions in Factor Analysis , Stage 4: Factors and Assessing Overall Fit , Stage 5: Interpreting the Factors , Stage 6: Validation of Factor Analysis , Stage 7: Additional Uses of Factor Analysis Results , An Illustrative Example , Stage I: Objectives of Factor Analysis , Stage 2: Designing a Factor Analysis , Stage 3: Assumptions in Factor Analysis Component Factor Analysis: Stages 4 Through 7 Common Factor Analysis: Stages 4 and 5 A Managerial Overview of the Results SECTION II DEPENDENCE TECHNIQUES Chapter 4 Multiple Regression Analysis What Is Multiple Regression Analysis? An Example of Simple and Multiple Regression Setting a Baseline: Prediction Without an Independent Variable Prediction Using a Single Independent Variable: Simple Regression Prediction Using Several Independent Variables: Multiple Regression A Decision Process for Multiple Regression Analysis Stage 1: Objectives of Multiple Regression Research Problems Appropriate for Multiple Regression Specifying a Statistical Relationship Selection of Dependent and Independent Variables Stage 2: Research Design of a Multiple Regression Analysis Sample Size Creating Additional Variables Fixed Versus Random Effects Predictors Stage 3: Assumptions in Multiple Regression Analysis Assessing Individual Variables Versus the Variate Methods of Diagnosis Linearity of the Phenomenon Constant Variance of the Error Term Independence of the Error Terms Normality of the Error Term Distribution Stage 4: Estimating the Regression Model and Assessing Overall Model Fit Selecting an Estimation Technique Testing the Regression Variate for Meeting the Regression Assumptions Examining the Statistical Significance of Our Model Identifying Influential Observations Stage 5: Interpreting the Regression Variate Using the Regression Coefficients Assessing Multicollinearity Stage 6: Validation of the Results Additional or Split Samples Calculating the PRESS Statistic Comparing Regression Models Forecasting with the Model Illustration of a Regression Analysis Stage 1: Objectives of Multiple Regression Stage 2: Research Design of a Multiple Regression Analysis Stage 3: Assumptions in Multiple Regression Analysis, A Three-Group Illustrative Example, Stage I: Objectives of Discriminant Analysis , Stage 2: Research Design for Discriminant Analysis , Stage 3: Assumptions of Discriminant Analysis , Stage 4: Estimation of the Discriminant Model and Assessing Overall Fit , Stage 5: Interpretation of Three-Group Discriminant Analysis Results , Stage 6: Validation of the Discriminant Results , A Managerial Overview , Logistic Regression: Regression with a Binary Dependent Variable , Representation of the Binary Dependent Variable , Estimating the Logistic Regression Model , Assessing the Goodness-of-Fit of the Estimation Model , Testing for Significance of the Coefficients , Interpreting the Coefficients , , An Illustrative Example of Logistic Regression, Stages I, 2, and 3: Research Objectives, Research Design, and Statistical Assumptions Stage 4: Estimation of the Logistic Regression Model and Assessing Overall Fit, Stage 5: Interpretation of the Results , Stage 6: Validation of the Results , A Managerial Overview , Chapter 6 Multivariate Analysis of Variance , MANOVA: Extending Univariate Methods for Assessing Group Differences Univariate Procedures for Assessing Group Differences , Multivariate Procedures for Assessing Group Differences , A Hypothetical Illustration of MANOVA , Analysis Design 420 Differences from Discriminant Analysis , Forming the Variate and Assessing Differences, A Decision Process for MANOVA , Stage 1: Objectives of MANOVA , When Should We Use MANOVA? , Types of Multivariate Questions Suitable for MANOVA , Selecting the Dependent Measures , Stage 2: Issues in the Research Design of MANOVA , Sample Size Requirements—Overall and by Group , Factorial Designs—Two or More Treatments , Using Covariates—ANCOVA and MANCOVA , MANOVA Counterparts of Other ANOVA Designs , A Special Case of MANOVA: Repeated Measures , Stage 3: Assumptions of ANOVA and MANOVA , Independence , Equality of Variance—Covariance Matrices , Normality,, Linearity and Multicollinearity Among the Dependence Variables , Sensitivity to Outliers , ctor Analysis SEM: Testing a Structural Model ,