Managerial applications of multivariate analysis in marketing:
Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Chicago, Ill.
American Marketing Assoc.
2003
|
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=021992227&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | XIX, 395 S. graph. Darst. |
| ISBN: | 0877573018 |
Internformat
MARC
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| 100 | 1 | |a Myers, James H. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Managerial applications of multivariate analysis in marketing |c James H. Myers ; Gary M. Mullet |
| 264 | 1 | |a Chicago, Ill. |b American Marketing Assoc. |c 2003 | |
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| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-021992227 | |
Datensatz im Suchindex
| _version_ | 1819366787896049664 |
|---|---|
| adam_text | Contents
Preface xvii
I Introduction
Chapter 1
Introduction to Multivariate Statistical Analysis 3
Introduction 3
What Is a Variable? 5
What Is Multivariate Statistical Analysis? 5
Dependence Techniques 6
Interdependence Techniques 6
Scope and Coverage 7
Format 7
Level 9
Areas of Application 10
Functional Areas 10
Marketing Topics 10
Multivariate Analysis on Computer 11
Software Packages 12
Chapter Formats 12
Summary 13
Appendix 1 14
References 15
Chapter 2
Some Basic Concepts and Definitions 16
Introduction 16
Multivariate Statistical Techniques 16
What Are Models? 17
Types of Models 18
Deterministic Versus Probabilistic Models 19
Types of Measuring Scales 19
Nominal Scales 20
Ordinal Scales 20
Interval Scales 21
Ratio Scales 22
Importance of Scale Type 23
Interpreting Numbers 23
Statistical Analysis 24
Reliability of Measurement 24
Test-Retest Reliability 25
Split-Half Reliability 27
Coefficient Alpha 30
Validity of Measurement 30
Types of Validity 30
A Caveat 31
References 31
II Dependence Methods
Chapter 3
Basic Regression Analysis: Linear 35
Bivariate Analysis 35
Regression and Correlation 36
Types of Measurement Scales Required 37
Applications of Correlation/Regression Analysis 37
Correlation Analysis 37
Regression Analysis 40
Technical Description 42
Correlation Analysis 42
Regression Analysis 47
Some Problems in Correlation/Regression Analysis 52
Rank Order Correlation 52
Recoding Values 52
Examples of Types of Relationships 53
Appendix 3 53
References 61
Chapter 4
Basic Regression Analysis: Nonlinear 62
Applications of Nonlinear Correlation/Regression 63
Eta Nonlinear Correlation 63
Nonlinear Regression 66
Technical Description 67
Eta Nonlinear Correlation 67
Nonlinear Regression 69
Data Transformations 70
Comment 73
Appendix 4 74
References 75
Chapter 5
Multiple Regression Analysis 76
An Application of Multiple Correlation/Regression Analysis . . .77
Multiple Correlation 77
Multiple Regression 81
Technical Description 82
Graphical Representations 83
Statistical Significance of R 83
Interpretation of Multiple Correlation Coefficients 84
Interpretation of Multiple Regression Coefficients 85
Requirements/Assumptions for Multiple Regression 85
Common Problems in Multiple Correlation/Regression Analysis 86
Missing Values 86
Uniform Ratings 88
Collinearity 89
Dummy Variables 90
Stepwise Regression 92
Chapter 6
Logistic Regression 96
Problem with Ordinary Regression 97
Application of Logit Regression 98
Technical Description 100
Overall Prediction Accuracy 101
Other Aspects of the Logit Model 102
Multinomial Logit Regression 102
Independence of Irrelevant Alternatives 103
Categorical Independent Variables 104
Comment 104
Appendix 6A 104
Appendix 6B 105
References 108
Chapter 7
Discriminant Analysis and Canonical Analysis 110
Comparisons Among Techniques Ill
Applications of Discriminant Analysis Ill
Predicting Segment Membership 112
Visual Representations of Market Structure 115
Technical Description 118
Classification Coefficients (Functions) 118
Visual Representations of Groups 119
Assumptions/Requirements 122
Discriminant Analysis Versus Logistic Regression 122
Canonical Analysis 123
An Application 124
Technical Description 127
Canonical Analysis Versus Discriminant Analysis 128
Appendix 7 128
References 133
Chapter 8
Conjoint Analysis: Full-Profile and Pairwise Trade-Offs . .135
A Family of Related Technologies 136
Preference-Based Conjoint Analysis 137
An Application of Full-Profile Conjoint Analysis 138
Fewer Profiles 139
Results 141
Optimum Combinations of Product Features 142
Relative Importance of Attributes 143
Validating the Conjoint Model 144
Technical Description of Full-Profile Conjoint Analysis 145
Selecting the Reduced Set of Profiles 146
Analyzing the Data for a Single Respondent 147
Analyzing Data for the Total Sample 150
Assumptions/Requirements 152
Pairwise Trade-Off Conjoint Analysis 154
Adaptive Conjoint Analysis 155
Appendix 8 157
References 162
Chapter 9
Conjoint Analysis: Choice Models 163
Choice Models 164
Example of a Choice Model 165
A Hotel Example 169
Technical Description 171
Construction of Product or Service Profiles 171
Construction of Sets of Profiles 172
Estimating the Choice Model 172
Applications 173
Comment 174
Alternative Approaches 175
References 176
Chapter 10
Interaction Detection Methods: AID and CHAID 178
Advantages of Interaction Detection 179
Interaction Detection Methods 180
General Procedure 180
AID 181
Computation Procedure 182
Interpreting the Output 182
Another AID Application 184
Advantages and Limitations 187
CHAID1 188
Advantages of CHAID over AID 189
An Example Using CHAID 190
An Ordinal CHAID 192
Attitude Segmentation 195
CART 196
Procedure 196
Comparison of CART and CHAID 197
Comment 198
References 198
xii Contents III Interdependence Methods
Chapter 11
Factor Analysis 203
Identifying Basic Underlying Ideas 204
Data Reduction 205
Interdependence Versus Dependence Analysis 206
Applications of Factor Analysis 206
Fast-Food Attributes 207
Car Dealer Attributes 210
Technical Description 211
Types of Factors 212
Types of Factor Analysis 213
Steps in a Factor Analysis 214
Table of Intercorrelations 214
Extraction of Common Factors 215
Rotation of Factors 218
How Many Components Should Be Rotated? 222
Interpretation of Factors 226
Factor Scores 230
Appendix 11 231
References 237
Chapter 12
Cluster Analysis: Hierarchical Clustering 238
Clustering Methods 239
Types of Clustering Methods 240
Types of Basis Variables 240
An Example of Hierarchical Cluster Analysis 241
Building Clusters 242
Dendrograms 243
How Many Clusters? 250
Limitations 251
Using Hierarchical Clustering to Segment Business Markets .252
Method 252
Results 253
Comment 255
Technical Description 256
Similarity Measures 256
Contents xiii
Starting Procedure 257
Linkage Rules 257
Evaluation 260
Segmentation Is Probabilistic 261
Overlapping Clusters 262
Appendix 12 263
References 264
Chapter 13
Cluster Analysis: Partition Clustering 265
Points in Space 265
An Example of Partition Clustering 267
Technical Description 271
Starting Procedures 271
k-Means Computation Procedures 274
Comment 289
Implementing Segmentation Results 289
Strategic Level 291
Operating Level 293
Comment 294
Appendix 13A 294
Appendix 13B 299
References 304
Chapter 14
Correspondence Analysis 305
An Application of Correspondence Analysis 306
Method 306
Results 307
Snack Foods 309
Technical Description 313
Stages in the Analytical Process 313
Individual Versus Aggregate Data 317
Caveats 317
Relationships Among Several Techniques 318
References 319
xiv Contents Chapter 15
Structural Equation Models 321
Causal Models 322
Comparison with Factor Analysis 323
Comparison with Regression Analysis 323
An Example of a Structural Equation Model 324
Path Analysis 325
Multiple Construct Measures 326
The Development Process 328
The Final Model 329
Applications 331
Technical Description 332
Comparisons Among Related Technologies 333
Direct Versus Indirect Effects 334
Computations 334
Notation 337
Comment 337
Appendix 15 338
References 342
Chapter 16
Multidimensional Scaling of Similarities Data 343
Metric Scaling 343
Similarities Matrix 344
MDS Technologies 344
Direct Similarities Measures 345
Perceived Similarities 347
An Application of MDS 347
A Word of Caution 351
Using Different Types of Objects 351
Technical Description 354
Similarities Matrix 354
Producing Spatial Maps 356
Assumptions and Limitations 356
Adding Attributes to the MDS Map 358
MDPREF 359
MDS Programs 359
References 362
IV Putting It All Together
Chapter 17
Squeezing More Useful Information Out of Expensive
Consumer Surveys 365
Matching Techniques and Applications 366
Interpreting the Table 367
Dependence Methods 367
Correlation/Regression Analysis 367
AID/CHAID/CART 371
Discriminant Analysis 372
Interdependence Methods 373
Factor Analysis 373
Cluster Analysis 375
Comment 383
References 383
Index 385
|
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| spellingShingle | Myers, James H. Mullet, Gary M. Managerial applications of multivariate analysis in marketing Multivariate Analyse (DE-588)4040708-1 gnd Marketingforschung (DE-588)4200055-5 gnd |
| subject_GND | (DE-588)4040708-1 (DE-588)4200055-5 |
| title | Managerial applications of multivariate analysis in marketing |
| title_auth | Managerial applications of multivariate analysis in marketing |
| title_exact_search | Managerial applications of multivariate analysis in marketing |
| title_full | Managerial applications of multivariate analysis in marketing James H. Myers ; Gary M. Mullet |
| title_fullStr | Managerial applications of multivariate analysis in marketing James H. Myers ; Gary M. Mullet |
| title_full_unstemmed | Managerial applications of multivariate analysis in marketing James H. Myers ; Gary M. Mullet |
| title_short | Managerial applications of multivariate analysis in marketing |
| title_sort | managerial applications of multivariate analysis in marketing |
| topic | Multivariate Analyse (DE-588)4040708-1 gnd Marketingforschung (DE-588)4200055-5 gnd |
| topic_facet | Multivariate Analyse Marketingforschung |
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