Multivariate data analysis with readings:
Gespeichert in:
| Format: | Buch |
|---|---|
| Sprache: | English |
| Veröffentlicht: |
New York
Macmillan u.a.
1992
|
| Ausgabe: | 3. ed. |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XV, 544 S. graph. Darst. |
| ISBN: | 002348750X |
Internformat
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Datensatz im Suchindex
| _version_ | 1804123528277000192 |
|---|---|
| adam_text | Contents
Preface v
CHAPTER ONE
Introduction 1
CHAPTER PREVIEW 1 KEY TERMS 1
What Is Multivariate Analysis? 2
Impact of the Computer Revolution 3
Multivariate Analysis Defined 4
Measurement Scales 5
Types of Multivariate Techniques 7
Multiple Regression 7
Multiple Discriminant Analysis 7
Multivariate Analysis of Variance 8
Canonical Correlation 8 Linear Probability Models 8
Conjoint Analysis 9 Structural Equation Modeling 9
Factor Analysis 10 Cluster Analysis 10
Multidimensional Scaling 10 Correspondence Analysis 11
A Classification of Multivariate Techniques 11
Database 15
Importance of Selected Benefits Sought 16
Customer Characteristics 16
SUMMARY 17 QUESTIONS 17 REFERENCES 17
CHAPTER TWO
Multiple Regression Analysis 19
CHAPTER PREVIEW 19 KEY TERMS 20
What Is Multiple Regression Analysis? 24
How Is Regression Analysis Used? 25
Relating Predictor to Criterion Variables
with Regression 26
Prediction Using a Single Measure—The Average 26
Prediction Using Two Measures—Simple Regression 27
Major Assumptions 31
Fixed Versus Random Predictors 33
Prediction Using Several Measures: Multiple Regression Analysis 34
What New Assumptions Have We Made? 35
Evaluating the Multiple Regression Model
and Results 38
Determining the Appropriateness of Our Predictive Model 38
Examining the Statistical Significance of Out Model 44
Examining the Strength of Association Among the Variables 46
Identifying Influential Observations 49
Transforming and Creating Variables for Regression 51
Data Transformations 52 Creating Additional Variables 53
A Model Building Approach to Regression Analysis 56
General Approaches to Variable Selection 56
Comparing Regression Models 58 Model Validation 58
Regression with a Binary Dependent Variable 60
Unique Characteristics of Logit Analysis 60
Illustration of a Regression Analysis 62
A Single Variable (UnivariateJ Regression Model 63
The Two Variable fMultivariate) Model—Adding X3 67
The Three Variable Model—Variable X6 Added 68
Testing the Assumptions of Regression Analysis 69
Measuring the Degree and Impact of Multicollinearity 73
Identifying Outliers as Influential Observations 74
SUMMARY 76 QUESTIONS 77 REFERENCES 77
READING:
Factors Affecting the Performance of Individual Chain
Store Units: An Empirical Analysis 78
CHAPTER THREE
Multiple Discriminant Analysis 87
CHAPTER PREVIEW 87 KEY TERMS 88
What Is Discriminant Analysis? 89
Analogy with Regression and ANOVA 91
Assumptions of Discriminant Analysis 92
Hypothetical Example of Discriminant Analysis 92
Objectives of Discriminant Analysis 94
Geometric Representation 95
Application of Discriminant Analysis 96
Stage One: Derivation 96 Stage Two: Validation 100
Stage Three: Interpretation 106
A Two Group Illustrative Example 110
Stage One: Derivation 111 Stage Two: Validation 113
Stage Three: Interpretation 119
A Three Group Illustrative Example 120
Stage One: Derivation 120
Stage Two: Validation of Discriminant Functions 124
Stage Three: Interpretation of Three Group Discriminant Analysis
Results 127
SUMMARY 135 QUESTIONS 136 REFERENCES 136
READING:
Private Physicians or Walk In Clinics: Do the Patients
Differ? 138
CHAPTER FOUR
Multivariate Analysis of Variance 153
CHAPTER PREVIEW 153 KEY TERMS 154
What Is Multivariate Analysis of Variance? 155
When Do We Use MANOVA? 156
Statistical Benefits from Using MANOVA 157
Conceptual Questions Appropriately Addressed by MANOVA 157
Assumptions of ANOVA and MANOVA 159
Independence 159 Equality of Variance Covahance Matrices 159
Normality 160
Issues in the Application of MANOVA 160
Increased Sample Sizes 160 Sensitivity to Outliers 160
Linearity Among the Dependent Variables 161
Criteria for Significance Testing 161 Summary 162
Case 1: Difference Between Two Independent
Groups 162
L/nivariate Approach: The t test 162
Example of the L/nivariate Approach 163
Multivariate Approach: HotelJing s T2 163
Post Hoc Tests 166 Example of the Multivariate Approach 167
Case 2: Difference Between k Independent Groups 169
Univariate Approach: ANOVA 169 Post Hoc Tests 170
Example of the Univariate Approach: k Groups ANOVA 171
Example of Post Hoc Testing 173
Multivariate Approach: MANOVA 173 Post Hoc Tests 174
Example of the MuItiVariafe Approach: k Groups 175
Post Hoc Testing 175
Factorial Designs 177
MANOVA Counterparts of Other ANOVA Designs 178
ANCOVA and MANCOVA 178
A Special Case of MANOVA: Repeated Measures 179
SUMMARY 180 QUESTIONS 180 REFERENCES 181
READING:
The American Export Trading Company: Designing a
New International Marketing Institution 182
CHAPTER FIVE
Canonical Correlation Analysis 193
CHAPTER PREVIEW 193 KEY TERMS 194
What Is Canonical Correlation? 194
Hypothetical Example of Canonical Correlation 195
Objectives of Canonical Analysis 196
Application of Canonical Correlation 196
Deriving the Canonical Functions 196
Information Available from Canonical Analysis 197
An Illustrative Example 198
Which Canonical Functions Should Be Interpreted? 199
Interpretation Methods for Canonical Functions 203
How to Interpret Canonical Functions 206
Limitations of Canonical Analysis 207
SUMMARY 207 QUESTIONS 208 REFERENCES 208
READING:
The Impact of Channel Leadership Behavior on
Intrachannel Conflict 209
CHAPTER SIX
Factor Analysis 223
CHAPTER PREVIEW 223 KEY TERMS 224
What Is Factor Analysis? 225
Purposes of Factor Analysis 225
Factor Analysis Decision Diagram 226
Approaches for Deriving the Correlation Matrix 229
Common Factor Analysis and Component Analysis 230
The Rotation of Factors 231
Criteria /or the Number of Factors to Be Extracted 236
Criteria for the Significance of Factor Loadings 239
Interpreting a Factor Matrix 240
An Illustrative Example 241
Component Analysis 242 Common Factor Analysis 246
Naming of Factors 249
How to Select Surrogate Variables for Subsequent
Analysis 250
How to Use Factor Scores 251
SUMMARY 252 QUESTIONS 252 REFERENCES 253
READING:
The Organizational Context of Market Research
Use 254
CHAPTER SEVEN
Cluster Analysis 265
CHAPTER PREVIEW 265 KEY TERMS 265
What Is Cluster Analysis? 267
How Does Cluster Analysis Work? 268
Application of Cluster Analysis 269
Stage One: Partitioning 269 Stage Two: Interpretation 279
Stage Three: Validation and Profiling 280
An Illustrative Example 280
Stage One: Partitioning 281 Stage Two: Interpretation 284
Stage Three: Validation and Profiling 289
SUMMARY 289 QUESTIONS 290 REFERENCES 290
READING:
A Typology of Consumer Dissatisfaction Response
Styles 292
CHAPTER EIGHT
Multidimensional Scaling 317
CHAPTER PREVIEW 317 KEY TERMS 317
What Is Multidimensional Scaling? 320
A Decision Framework for Perceptual Mapping 321
Step One: Identification of All Relevant Firms, Products/Services,
Ideas, or Other Objects to Be Evaluated 322
Step Two: Selection of Either a Decompositional or Compositional
Approach 322
Step Three: Selection of the Appropriate Technique Based on the
Approach Selected 323
Step Four: Interpretation of Results 324
Assumptions of Multidimensional Scaling 324
A Simplified Look at How Multidimensional Scaling
Works 325
A Generalized Approach to Multidimensional
Scaling 327
Stage One: Determining an Object s Position in the Perceptual
Map 327
Stage Two: Selecting the Dimensionality of the Perceptual Map 328
Stage Three: Identifying the Dimensions 329
Other Issues in Multidimensional Scaling 331
Objects: Their Number and Selection 331
Similarities Versus Preference Data 332
Nonmetric Versus Metric Methods 334
Aggregate Versus Disaggregate Analysis 334
Incorporating Preferences into Multidimensional
Scaling 335
Collecting Preference Data 335 Ideal Points 336
Internal Versus External Analysis of Preference Data 338
Vector Versus Point Representations 339 Summary 340
Correspondence Analysis 340
Data Requirements 342 Analytical Method 342
Interpretation of Results 342
Advantages and Disadvantages of Correspondence Analysis 343
Conclusion 344
Illustration of Multidimensional Scaling and
Correspondence Analysis 344
The HATCO Image Study Database 344
Developing and Interpreting Perceptual Maps 345
Incorporating Preferences in the Perceptual Map 352
SUMMARY 354 QUESTIONS 355 REFERENCES 355
READINGS:
Product Positioning: An Application of
Multidimensional Scaling 357
A Reduced Space Approach to the Clustering of
Categorical Data in Market Segmentation 368
CHAPTER NINE
Conjoint Analysis 378
CHAPTER PREVIEW 378 KEY TERMS 379
What Is Conjoint Analysis? 382
The Managerial Uses of Conjoint Analysis 384
Comparing Conjoint Analysis with Other Multivariate
Methods 384
Compositional Versus Decompositional Techniques 384
Separate Models for Each Individual 385
Types of Relationships 385
Designing a Conjoint Analysis Experiment 386
The Research Problem 386
Phase One: Designing Stimuli 387
Phase Two: Data Collection 394
Phase Three: Estimating Part Worths 396
Conjoint Analysis with a Large Number of Factors 399
An Illustration of Conjoint Analysis 400
Design of the Experiment and Stimuli 400
Estimation and Interpretation of the Part Worths 402
Application of a Choice Simulator 402
SUMMARY 406
APPENDIX 9A
An Illustration of Conjoint Analysis 407
PREVIEW 407
Assuming an Additive Model 407
When Is an Interactive Composition Rule
Appropriate? 410
Checking for Interactions 411
SUMMARY 411 QUESTIONS 412 REFERENCES 412
READING:
Strategic Marketing Applications of Conjoint Analysis:
An HMO Perspective 414
CHAPTER TEN
Structural Equation Modeling 426
CHAPTER PREVIEW 426 KEY TERMS 427
What Is Structural Equation Modeling? 432
Accommodating Multiple Interrelated Dependence
Relationships 432
Incorporating Variables That We Do Not Measure Directly 433
The Role of Theory in Structural Equation Modeling 434
Steps in Structural Equation Modeling 435
Step One: Developing a Theoretically Based Model 435
Step Two: Constructing a Path Diagram of Causal
Relationships 437
Step Three: Converting the Path Diagram into a Set of Structural
Equations and Specifying the Measurement Model 439
Step Four: Choosing the Input Matrix Type and Estimating the
Proposed Model 442
Step Five: Assessing the Identification of the Structural Model 445
Step Six: Evaluating Goodness of Fit Criteria 446
Step Seven: Model Interpretation and Modifications 451
A Recap of the Seven Step Process 452
Two Illustrations of Structural Equation Modeling 452
A Confirmatory Factor Analysis 453
Step One: Developing a Theoretically Based Model 453
Step Two: Constructing a Path Diagram of Causal
Relationships 454
Step Three: Converting the Path Diagram into a Set of Structural
Equations and Specifying the Measurement Model 454
Step Four: Choosing Input Matrix Type and Obtaining Model
Estimates 455
Step Five: Assessing the Identification of the Structural Model 456
Step Six: Evaluating Goodness Of Fit Criteria 456
Step Seven: Interpreting and Modifying the Model 461
Summary 461
Estimating a Path Model with Structural Equation
Modeling 462
Step One: Developing a Theoretically Based Model 463
Step Two: Constructing a Path Diagram of the Causal
Relationships 464
Step Three: Converting the Path Diagram into Structural Equations
and Specifying the Measurement Model 465
Step Four: Choosing Input Matrix Type and Estimating Model 466
Step Five: Assessing the Identification of the Structural Model 466
Step Six: Evaluating Goodness of Fit Criteria 466
Step Seven: Interpreting and Modifying the Model 474
Review of the Structural Equation Modeling Process 474
SUMMARY 474 QUESTIONS 477
APPENDIX 10A
A Mathematical Representation in LISREL
Notation 478
LISREL Notation 478
Structural Model 479 Measurement Models 480
Structural Equation Correlations 480
Measurement Model (Indicator) Correlations 480
From a Path Diagram to LISREL Notation 481
Constructing Structural Equations from the Path Diagram 481
Denoting the Correspondence of Indicators and Constructs 481
Specifying the LISREL Structural and Measurement Model
Equations 482
Specifying the Structural Equation Correlations 482
Measurement Model (Indicator) Correlations 484
SUMMARY 485
APPENDIX 10B
Path Analysis: A Method of Computing Structural
Coefficients 487
APPENDIX IOC
Overall Goodness of Fit Measures for Structural
Equation Modeling 489
Measures of Absolute Fit 489
Likelihood Ratio Chi Square Statistic 489
Goodness of Fit Index 490 Root Mean Square Residual 490
Incremental Fit Measures 491
Tucker Lewis Index 491 Normed Fit Index 491
Parsimonious Fit Measures 491
Adjusted Goodness of Fit Index 492
Normed Chi Square 492 Parsimonious Fit Index 492
Akaike Information Criterion 492
SUMMARY 494 REFERENCES 494
READING:
Linking Theory Construction and Theory Testing:
Models with Multiple Indicators of Latent
Variables 497
APPENDIX A
Applications of Multivariate Data Analysis 509
Appendix Preview 509
Software Used and Presented 510
SPSS Control Commands 511
Annotated SPSS Control Commands 515
SAS Control Commands 521
Annotated SAS Control Commands 523
Annotated PC MDS Control Commands 526
LISREL VI Control Commands 530
Annotated LISREL VI Control Commands 533
HATCO Datasets 536
Index 539
|
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| isbn | 002348750X |
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| spelling | Multivariate data analysis with readings Joseph F. Hair ... 3. ed. New York Macmillan u.a. 1992 XV, 544 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Análisis estadístico multivariable Multivariate analyse gtt Multivariate analysis Datenanalyse (DE-588)4123037-1 gnd rswk-swf Multivariate Analyse (DE-588)4040708-1 gnd rswk-swf 1\p (DE-588)4123623-3 Lehrbuch gnd-content Multivariate Analyse (DE-588)4040708-1 s Datenanalyse (DE-588)4123037-1 s 2\p DE-604 Hair, Joseph F. 1944- Sonstige (DE-588)135615828 oth HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=006041450&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Multivariate data analysis with readings Análisis estadístico multivariable Multivariate analyse gtt Multivariate analysis Datenanalyse (DE-588)4123037-1 gnd Multivariate Analyse (DE-588)4040708-1 gnd |
| subject_GND | (DE-588)4123037-1 (DE-588)4040708-1 (DE-588)4123623-3 |
| title | Multivariate data analysis with readings |
| title_auth | Multivariate data analysis with readings |
| title_exact_search | Multivariate data analysis with readings |
| title_full | Multivariate data analysis with readings Joseph F. Hair ... |
| title_fullStr | Multivariate data analysis with readings Joseph F. Hair ... |
| title_full_unstemmed | Multivariate data analysis with readings Joseph F. Hair ... |
| title_short | Multivariate data analysis with readings |
| title_sort | multivariate data analysis with readings |
| topic | Análisis estadístico multivariable Multivariate analyse gtt Multivariate analysis Datenanalyse (DE-588)4123037-1 gnd Multivariate Analyse (DE-588)4040708-1 gnd |
| topic_facet | Análisis estadístico multivariable Multivariate analyse Multivariate analysis Datenanalyse Multivariate Analyse Lehrbuch |
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