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El. knyga: Statistics for Marketing and Consumer Research

4.33/5 (6 ratings by Goodreads)
  • Formatas: 432 pages
  • Išleidimo metai: 22-May-2008
  • Leidėjas: SAGE Publications Inc
  • Kalba: eng
  • ISBN-13: 9781446204016
Kitos knygos pagal šią temą:
Statistics for Marketing and Consumer ResearchDidesnis paveikslėlis
  • Formatas: 432 pages
  • Išleidimo metai: 22-May-2008
  • Leidėjas: SAGE Publications Inc
  • Kalba: eng
  • ISBN-13: 9781446204016
Kitos knygos pagal šią temą:

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Balancing simplicity with technical rigour, this practical guide to the statistical techniques essential to research in marketing and related fields, describes each method as well as showing how they are applied.

 

The book is accompanied by two real data sets to replicate examples and with exercises to solve, as well as detailed guidance on the use of appropriate software including:

 

- 750 powerpoint slides with lecture notes and step-by-step guides to run analyses in SPSS (also includes screenshots)

- 136 multiple choice questions for tests

 

This is augmented by in-depth discussion of topics including:

 

- Sampling

- Data management and statistical packages

- Hypothesis testing

- Cluster analysis

- Structural equation modelling
Preface xii
List of Acronyms
xvii
Part I Collecting, Preparing and Checking the Data
1(102)
Measurement, Errors and Data for Consumer Research
2(25)
Measuring the world (of consumers): the problem of measurement
3(3)
Measurement scales and latent dimensions
6(5)
Two sample data-sets
11(7)
Statistical software
18(9)
Summing up
23(1)
Exercises
23(2)
Further readings and web-links
25(1)
Hints for more advanced studies
26(1)
Notes
26(1)
Secondary Consumer Data
27(19)
Primary and secondary data
28(4)
Secondary data sources
32(4)
Household budget surveys
36(4)
Household panels
40(1)
Commercial and scan data
41(5)
Summing up
43(1)
Exercises
44(1)
Further readings and web-links
45(1)
Hints for more advanced studies
45(1)
Notes
45(1)
Primary Data Collection
46(31)
Primary data collection: surveys errors and the research design
47(6)
Administration methods
53(5)
Questionnaire
58(5)
Four types of surveys
63(14)
Summing up
74(1)
Exercises
75(1)
Further readings and web-links
76(1)
Hints for more advanced studies
76(1)
Notes
76(1)
Data Preparation and Descriptive Statistics
77(26)
Data prepartion
78(7)
Data exploration
85(8)
Missing values and outliers detection
93(10)
Summing up
100(1)
Exercises
101(1)
Further readings and web-links
102(1)
Hints for more advanced studies
102(1)
Notes
102(1)
Part II Sampling, Probability and Inference
103(68)
Sampling
104(26)
To sample or not to sample
105(3)
Probability sampling
108(15)
Non-probability sampling
123(7)
Summing up
125(1)
Appendix
126(2)
Exercises
128(1)
Further readings and web-links
128(1)
Hints for more advanced studies
129(1)
Notes
129(1)
Hypothesis Testing
130(20)
Confidence intervals and the principles of hypothesis testing
130(7)
Test on one mean
137(2)
Test on two means
139(3)
Qualitative variables and non-parametric tests
142(3)
Tests on proportions and variances
145(5)
Summing up
146(1)
Exercises
147(1)
Further readings and web-links
147(1)
Hints for more advanced studies
148(1)
Notes
148(2)
Analysis of Variance
150(21)
Comparing more than two means: analysis of variance
151(3)
Further testing issues in one-way ANOVA
154(6)
Multi-way ANOVA, regression and the general linear model (GLM)
160(2)
Starting hints for more complex ANOVA designs
162(9)
Summing up
167(1)
Exercises
167(1)
Further readings and web-links
168(1)
Hints for more advanced studies
169(1)
Notes
169(2)
Part III Relationships Among Variables
171(76)
Correlation and Regression
172(24)
Covariance and correlation measures
173(6)
Linear regression
179(6)
Multiple regression
185(2)
Stepwise regression
187(2)
Extending the regression model
189(7)
Summing up
193(1)
Exercises
193(1)
Further readings and web-links
194(1)
Hints for more advanced studies
195(1)
Notes
195(1)
Association, Log-linear Analysis and Canonical Correlation Analysis
196(22)
Contingency tables and association statistics
196(3)
Log-linear analysis
199(9)
Canonical correlation analysis
208(10)
Summing up
214(1)
Exercises
215(1)
Further readings and web-links
216(1)
Hints for more advanced studies
216(1)
Notes
217(1)
Factor Analysis and Principal Component Analysis
218(29)
Principles and applications of data reduction techniques
219(2)
Factor analysis
221(8)
Principal component analysis
229(6)
Theory into practice
235(12)
Summing up
244(1)
Exercises
245(1)
Further readings and web-links
246(1)
Hints for more advanced studies
246(1)
Notes
246(1)
Part IV Classification and Segmentation Techniques
247(68)
Discriminant Analysis
248(15)
Discriminant analysis and its application to consumer and marketing data
248(2)
Running discriminant analysis
250(4)
Multiple discriminant analysis
254(9)
Summing up
260(1)
Exercises
260(1)
Further readings and web-links
261(1)
Hints for more advanced studies
261(1)
Notes
262(1)
Cluster Analysis
263(20)
Cluster analysis and its application to consumer and marketing data
263(2)
Steps in conducting cluster analysis
265(6)
The application of cluster analysis in SAS and SPSS-empirical issues and solutions
271(12)
Summing up
280(1)
Exercises
280(1)
Further readings and web-links
281(1)
Hints for more advanced studies
281(1)
Notes
282(1)
Multidimensional Scaling
283(17)
Preferences, perceptions and multidimensional scaling
283(3)
Running multidimensional scaling
286(6)
Multidimensional scaling and unfolding using SPSS and SAS
292(8)
Summing up
297(1)
Exercises
297(1)
Further readings and web-links
298(1)
Hints for more advanced studies
299(1)
Notes
299(1)
Correspondence Analysis
300(15)
Principles and applications of correspondence analysis
300(3)
Theory and techniques of correspondence analysis
303(2)
Running correspondence analysis
305(10)
Summing up
311(1)
Exercises
312(1)
Further readings and web-links
312(1)
Hints for more advanced studies
313(1)
Notes
313(2)
Part V Further Methods in Multivariate Analysis
315(44)
Structural Equation Models
316(21)
From exploration to confirmation: structural equation models
316(2)
Structural equation modeling: key concepts and estimation
318(4)
Theory at work: SEM and the Theory of Planned Behavior
322(15)
Summing up
333(1)
Exercises
333(2)
Further readings and web-links
335(1)
Hints for more advanced studies
335(1)
Notes
336(1)
Discrete Choice Models
337(15)
From linear regression to discrete choice models
337(2)
Discrete choice models
339(3)
Discrete choice models in SPSS
342(5)
Choice modeling and conjoint analysis
347(5)
Summing up
349(1)
Exercises
350(1)
Further readings and web-links
351(1)
Hints for more advanced studies
351(1)
The End (and Beyond)
352(7)
Conclusions
353(1)
Data mining
353(1)
The Bayesian comeback
354(5)
Summing up
358(1)
Notes
358(1)
Appendix Fundamentals of Matrix Algebra and Statistics
359(16)
A.1 Getting to know x and y
359(8)
A.2 First steps into statistical grounds
367(8)
Notes
374(1)
Glossary 375(12)
References 387(12)
Index 399
Mario Mazzocchi is Associate Professor in Statistics and Economics at the Department of Statistical Sciences of the University of Bologna. He is also Visiting Research Fellow at the University of Reading, where he has previously served as a lecturer in Applied Economics and Consumer Behaviour. He is currently a consultant to FAO on nutrition policies, and has been appointed by the European Commission as a permanent member of the group of experts on the evaluation of the EU School Fruit Scheme. He is Associate Editor of the journal Food Policy. He has led research teams of the University of Bologna in four EC-funded research projects. He has published two books(Fat Economics and Statistics for Marketing and Consumer Research) and about 40 articles in international refereed papers on a variety of applied economics topics, including consumer research, policy evaluation, health economics, tourism economics, time series econometrics.
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