Includes bibliographical references (p. [387]-398) and index.

Part I. Collecting, Preparing and Checking the Data -- 1. Measurement, Errors and Data for Consumer Research -- 1.1. Measuring the world (of consumers): the problem of measurement -- 1.2. Measurement scales and latent dimensions -- 1.3. Two sample data-sets -- 1.4. Statistical software -- Hints for more advanced studies -- 2. Secondary Consumer Data -- 2.1. Primary and secondary data -- 2.2. Secondary data sources -- 2.3. Household budget surveys -- 2.4. Household panels -- 2.5. Commercial and scan data -- Hints for more advanced studies -- 3. Primary Data Collection -- 3.1. Primary data collection: surveys errors and the research design -- 3.2. Administration methods -- 3.3. Questionnaire -- 3.4. Four types of surveys -- Hints for more advanced studies -- 4. Data Preparation and Descriptive Statistics -- 4.1. Data preparation -- 4.2. Data exploration -- 4.3. Missing values and outliers detection -- Hints for more advanced studies-- Part II. Sampling, Probability and Inference -- 5. Sampling -- 5.1. To sample or not to sample -- 5.2. Probability sampling -- 5.3. Non-probability sampling -- -- Hints for more advanced studies -- 6. Hypothesis Testing -- 6.1. Confidence intervals and the principles of hypothesis testing -- 6.2. Test on one mean -- 6.3. Test on two means -- 6.4. Qualitative variables and non-parametric tests -- 6.5. Tests on proportions and variances -- Hints for more advanced studies -- 7. Analysis of Variance -- 7.1. Comparing more than two means: analysis of variance -- 7.2. Further testing issues in one-way ANOVA -- 7.3. Multi-way ANOVA, regression and the general linear model (GLM) -- 7.4. Starting hints for more complex ANOVA designs -- Hints for more advanced studies -- Part III. Relationships Among Variables -- 8. Correlation and Regression -- 8.1. Covariance and correlation measures -- 8.2. Linear regression -- 8.3. Multiple regression -- 8.4. Stepwise regression -- 8.5. Extending the regression model -- Further reading -- Hints for more advanced studies -- 9. Association, Log-linear Analysis and Canonical Correlation Analysis -- 9.1. Contingency tables and association statistics -- 9.2. Log-linear analysis -- 9.3. Canonical correlation analysis -- Hints for more advanced studies -- 10. Factor Analysis and Principal Component Analysis -- 10.1. Principles and applications of data reduction techniques -- 10.2. Factor analysis -- 10.3. Principal component analysis -- 10.4. Theory into practice -- Hints for more advanced studies --Part IV. Classification and Segmentation Techniques -- 11. Discriminant Analysis -- 11.1. Discriminant analysis and its application to consumer and marketing data -- 11.2. Running discriminant analysis -- 11.3. Multiple discriminant analysis -- Hints for more advanced studies --12. Cluster Analysis -- 2.1. Cluster analysis and its application to consumer and marketing data -- 12.2. Steps in conducting cluster analysis -- 12.3. The application of cluster analysis in SAS and SPSS - empirical issues and solutions -- Hints for more advanced studies -- 13. Multidimensional Scaling -- 13.1. Preferences, perceptions and multidimensional scaling -- 13.2. Running multidimensional scaling -- 13.3. Multidimensional scaling and unfolding using SPSS and SAS -- Hints for more advanced studies -- 14. Correspondence Analysis -- 14.1. Principles and applications of correspondence analysis -- 14.2. Theory and techniques of correspondence analysis -- 14.3. Running correspondence analysis -- Hints for more advanced studies -- Part V. Further Methods in Multivariate Analysis -- 15. Structural Equation Models -- 15.1. From exploration to confirmation: structural equation models -- 15.2. Structural equation modeling: key concepts and estimation -- 15.3. Theory at work: SEM and the Theory of Planned Behavior -- Hints for more advanced studies -- 16. Discrete Choice Models -- 16.1. From linear regression to discrete choice models -- 16.2. Discrete choice models -- 16.3. Discrete choice models in SPSS -- 16.4. Choice modeling and conjoint analysis -- Hints for more advanced studies -- 17. The End (and Beyond) -- 17.1. Conclusions -- 17.2. Data mining -- 17.3. The Bayesian comeback -- Fundamentals of Matrix Algebra and Statistics -- A.1. Getting to know x and y -- A.2. First steps into statistical grounds.