did-you-know? rent-now

Amazon no longer offers textbook rentals. We do!

did-you-know? rent-now

Amazon no longer offers textbook rentals. We do!

We're the #1 textbook rental company. Let us show you why.

9780877573012

Managerial Applications of Multivariate Analysis in Marketing

by ;
  • ISBN13:

    9780877573012

  • ISBN10:

    0877573018

  • Edition: 1st
  • Format: Hardcover
  • Copyright: 2003-03-01
  • Publisher: Cengage Learning
  • View Upgraded Edition
  • Purchase Benefits
  • Free Shipping Icon Free Shipping On Orders Over $35!
    Your order must be $35 or more to qualify for free economy shipping. Bulk sales, PO's, Marketplace items, eBooks and apparel do not qualify for this offer.
  • eCampus.com Logo Get Rewarded for Ordering Your Textbooks! Enroll Now
  • Write a Review Icon Write a Review
List Price: $50.95

Summary

Multivariate statistical analysis techniques are now an integral part of most large-scale strategic market studies, so marketing practitioners must learn what these techniques can do and how to apply them.However, most marketers have little or no formal training in complex analytical methods, and many have neither the time nor the interest in acquiring this knowledge. If you are one of them, this book is for you. Managerial Applications of Multivariate Analysis in Marketing is written for marketing research practitioners-even those who don't have time to read it cover to cover. Each chapter is as self-contained as possible so that researchers and decision makers can more quickly understand the fundamentals of any one statistical technique. It is a reference book, not a textbook, so it does not focus on the statistical techniques themselves and leave you to wonder how they apply to marketing. Most of the calculations in this book can be done by a personal computer, so the authors only cover the math you need while focusing on the marketing implications.

Author Biography

Gary M. Mullet is the principal of Gary Mullet Associates, Inc.

Table of Contents

Preface xvii
I Introduction
Introduction to Multivariate Statistical Analysis
3(13)
Introduction
3(2)
What Is a Variable?
5(1)
What Is Multivariate Statistical Analysis?
5(2)
Dependence Techniques
6(1)
Interdependence Techniques
6(1)
Scope and Coverage
7(3)
Format
7(2)
Level
9(1)
Areas of Application
10(1)
Functional Areas
10(1)
Marketing Topics
10(1)
Multivariate Analysis on Computer
11(1)
Software Packages
12(1)
Chapter Formats
12(1)
Summary
13(1)
Appendix 1
14(1)
References
15(1)
Some Basic Concepts and Definitions
16(19)
Introduction
16(1)
Multivariate Statistical Techniques
16(2)
What Are Models?
17(1)
Types of Models
18(1)
Deterministic Versus Probabilistic Models
19(1)
Types of Measuring Scales
19(4)
Nominal Scales
20(1)
Ordinal Scales
20(1)
Interval Scales
21(1)
Ratio Scales
22(1)
Importance of Scale Type
23(1)
Interpreting Numbers
23(1)
Statistical Analysis
24(1)
Reliability of Measurement
24(6)
Test--Retest Reliability
25(2)
Split-Half Reliability
27(3)
Coefficient Alpha
30(1)
Validity of Measurement
30(1)
Types of Validity
30(1)
A Caveat
31(1)
References
31(4)
II Dependence Methods
Basic Regression Analysis: Linear
35(27)
Bivariate Analysis
35(1)
Regression and Correlation
36(1)
Types of Measurement Scales Required
37(1)
Applications of Correlation/Regression Analysis
37(5)
Correlation Analysis
37(3)
Regression Analysis
40(2)
Technical Description
42(10)
Correlation Analysis
42(5)
Regression Analysis
47(5)
Some Problems in Correlation/Regression Analysis
52(1)
Rank Order Correlation
52(1)
Recoding Values
52(1)
Examples of Types of Relationships
53(1)
Appendix 3
53(8)
References
61(1)
Basic Regression Analysis: Nonlinear
62(14)
Applications of Nonlinear Correlation/Regression
63(4)
Eta Nonlinear Correlation
63(3)
Nonlinear Regression
66(1)
Technical Description
67(6)
Eta Nonlinear Correlation
67(2)
Nonlinear Regression
69(1)
Data Transformations
70(3)
Comment
73(1)
Appendix 4
74(1)
References
75(1)
Multiple Regression Analysis
76(20)
An Application of Multiple Correlation/Regression Analysis
77(5)
Multiple Correlation
77(4)
Multiple Regression
81(1)
Technical Description
82(4)
Graphical Representations
83(1)
Statistical Significance of R
83(1)
Interpretation of Multiple Correlation Coefficients
84(1)
Interpretation of Multiple Regression Coefficients
85(1)
Requirements/Assumptions for Multiple Regression
85(1)
Common Problems in Multiple Correlation/Regression Analysis
86(6)
Missing Values
86(2)
Uniform Ratings
88(1)
Collinearity
89(1)
Dummy Variables
90(2)
Stepwise Regression
92(4)
Logistic Regression
96(14)
Problem with Ordinary Regression
97(1)
Application of Logit Regression
98(2)
Technical Description
100(2)
Overall Prediction Accuracy
101(1)
Other Aspects of the Logit Model
102(2)
Multinomial Logit Regression
102(1)
Independence of Irrelevant Alternatives
103(1)
Categorical Independent Variables
104(1)
Comment
104(1)
Appendix 6A
104(1)
Appendix 6B
105(3)
References
108(2)
Discriminant Analysis and Canonical Analysis
110(25)
Comparisons Among Techniques
111(1)
Applications of Discriminant Analysis
111(7)
Predicting Segment Membership
112(3)
Visual Representations of Market Structure
115(3)
Technical Description
118(5)
Classification Coefficients (Functions)
118(1)
Visual Representations of Groups
119(3)
Assumptions/Requirements
122(1)
Discriminant Analysis Versus Logistic Regression
122(1)
Canonical Analysis
123(4)
An Application
124(3)
Technical Description
127(1)
Canonical Analysis Versus Discriminant Analysis
128(1)
Appendix 7
128(5)
References
133(2)
Conjoint Analysis: Full-Profile and Pairwise Trade-Offs
135(28)
A Family of Related Technologies
136(1)
Preference-Based Conjoint Analysis
137(1)
An Application of Full-Profile Conjoint Analysis
138(7)
Fewer Profiles
139(2)
Results
141(1)
Optimum Combinations of Product Features
142(1)
Relative Importance of Attributes
143(1)
Validating the Conjoint Model
144(1)
Technical Description of Full-Profile Conjoint Analysis
145(9)
Selecting the Reduced Set of Profiles
146(1)
Analyzing the Data for a Single Respondent
147(3)
Analyzing Data for the Total Sample
150(2)
Assumptions/Requirements
152(2)
Pairwise Trade-Off Conjoint Analysis
154(3)
Adaptive Conjoint Analysis
155(2)
Appendix 8
157(5)
References
162(1)
Conjoint Analysis: Choice Models
163(15)
Choice Models
164(1)
Example of a Choice Model
165(6)
A Hotel Example
169(2)
Technical Description
171(3)
Construction of Product or Service Profiles
171(1)
Construction of Sets of Profiles
172(1)
Estimating the Choice Model
172(1)
Applications
173(1)
Comment
174(1)
Alternative Approaches
175(1)
References
176(2)
Interaction Detection Methods: AID and CHAID
178(25)
Advantages of Interaction Detection
179(1)
Interaction Detection Methods
180(1)
General Procedure
180(1)
AID
181(7)
Computation Procedure
182(1)
Interpreting the Output
182(2)
Another AID Application
184(3)
Advantages and Limitations
187(1)
CHAID1
188(8)
Advantages of CHAID over AID
189(1)
An Example Using CHAID
190(2)
An Ordinal CHAID
192(3)
Attitude Segmentation
195(1)
CART
196(2)
Procedure
196(1)
Comparison of CART and CHAID
197(1)
Comment
198(1)
References
198(5)
III Interdependence Methods
Factor Analysis
203(35)
Identifying Basic Underlying Ideas
204(1)
Data Reduction
205(1)
Interdependence Versus Dependence Analysis
206(1)
Applications of Factor Analysis
206(5)
Fast-Food Attributes
207(3)
Car Dealer Attributes
210(1)
Technical Description
211(3)
Types of Factors
212(1)
Types of Factor Analysis
213(1)
Steps in a Factor Analysis
214(17)
Table of Intercorrelations
214(1)
Extraction of Common Factors
215(3)
Rotation of Factors
218(4)
How Many Components Should Be Rotated?
222(4)
Interpretation of Factors
226(4)
Factor Scores
230(1)
Appendix 11
231(6)
References
237(1)
Cluster Analysis: Hierarchical Clustering
238(27)
Clustering Methods
239(2)
Types of Clustering Methods
240(1)
Types of Basis Variables
240(1)
An Example of Hierarchical Cluster Analysis
241(11)
Building Clusters
242(1)
Dendrograms
243(7)
How Many Clusters?
250(1)
Limitations
251(1)
Using Hierarchical Clustering to Segment Business Markets
252(4)
Method
252(1)
Results
253(2)
Comment
255(1)
Technical Description
256(5)
Similarity Measures
256(1)
Starting Procedure
257(1)
Linkage Rules
257(3)
Evaluation
260(1)
Segmentation Is Probabilistic
261(1)
Overlapping Clusters
262(1)
Appendix 12
263(1)
References
264(1)
Cluster Analysis: Partition Clustering
265(40)
Points in Space
265(2)
An Example of Partition Clustering
267(4)
Technical Description
271(18)
Starting Procedures
271(3)
k-Means Computation Procedures
274(15)
Comment
289(1)
Implementing Segmentation Results
289(5)
Strategic Level
291(2)
Operating Level
293(1)
Comment
294(1)
Appendix 13A
294(5)
Appendix 13B
299(5)
References
304(1)
Correspondence Analysis
305(16)
An Application of Correspondence Analysis
306(7)
Method
306(1)
Results
307(2)
Snack Foods
309(4)
Technical Description
313(5)
Stages in the Analytical Process
313(4)
Individual Versus Aggregate Data
317(1)
Caveats
317(1)
Relationships Among Several Techniques
318(1)
References
319(2)
Structural Equation Models
321(22)
Causal Models
322(1)
Comparison with Factor Analysis
323(1)
Comparison with Regression Analysis
323(1)
An Example of a Structural Equation Model
324(8)
Path Analysis
325(1)
Multiple Construct Measures
326(2)
The Development Process
328(1)
The Final Model
329(2)
Applications
331(1)
Technical Description
332(6)
Comparisons Among Related Technologies
333(1)
Direct Versus Indirect Effects
334(1)
Computations
334(3)
Notation
337(1)
Comment
337(1)
Appendix 15
338(4)
References
342(1)
Multidimensional Scaling of Similarities Data
343(22)
Metric Scaling
343(1)
Similarities Matrix
344(1)
MDS Technologies
344(3)
Direct Similarities Measures
345(2)
Perceived Similarities
347(1)
An Application of MDS
347(7)
A Word of Caution
351(1)
Using Different Types of Objects
351(3)
Technical Description
354(5)
Similarities Matrix
354(2)
Producing Spatial Maps
356(1)
Assumptions and Limitations
356(2)
Adding Attributes to the MDS Map
358(1)
MDPREF
359(1)
MDS Programs
359(3)
References
362(3)
IV Putting It All Together
Squeezing More Useful Information Out of Expensive Consumer Surveys
365(20)
Matching Techniques and Applications
366(1)
Interpreting the Table
367(1)
Dependence Methods
367(6)
Correlation/Regression Analysis
367(4)
AID/CHAID/CART
371(1)
Discriminant Analysis
372(1)
Interdependence Methods
373(10)
Factor Analysis
373(2)
Cluster Analysis
375(8)
Comment
383(1)
References
383(2)
Index 385

Supplemental Materials

What is included with this book?

The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.

The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.

Rewards Program