Here you will find an overview of each chapter's learning objectives that will give you a good idea of what this book is all about. Also check out the sample chapters on principal component and factor analysis (Chapter 8) and cluster analysis (Chapter 9).

- What market and marketing research are and how they differ.
- How practitioner and academic market(ing) research differ.
- When market research should be conducted.
- Who provides market research and the importance of the market research industry.

- How to determine an appropriate research design.
- The differences between exploratory research, descriptive research, and causal research.
- What causality is.
- The market research process.

- How to explain what kind of data you use.
- The differences between primary and secondary data.
- The differences between quantitative and qualitative data.
- What the unit of analysis is.
- When observations are independent and when dependent.
- The difference between dependent and independent variables.
- Different measurement scales and equidistance.
- Validity and reliability from a conceptual viewpoint.
- How to set up different sampling designs.
- How to determine acceptable sample sizes.

- How to find secondary data and decide on their suitability
- How to collect primary data
- How to design a basic questionnaire
- How to design basic experiments
- How to design basic qualitative research

- The workflow involved in a market research study.
- Univariate and bivariate descriptive graphs and statistics.
- How to deal with missing values.
- How to transform data (z-transformation, log transformation, creating dummies, aggregating variables).
- How to identify and deal with outliers.
- What a codebook is.
- The basics of using SPSS.

- The logic of hypothesis testing.
- The steps involved in hypothesis testing.
- What a test statistic is.
- Types of error in hypothesis testing.
- Common types of t-tests, one-way ANOVA.
- How to interpret SPSS output.

- The basic concept of regression analysis.
- How regression analysis works.
- The requirements and assumptions of regression analysis.
- How to specify a regression analysis model.
- How to interpret regression analysis results.
- How to predict and validate regression analysis results.
- How to conduct regression analysis with SPSS.
- How to interpret regression analysis output produced by SPSS.

- The basics of principal component and factor analysis.
- The principles of exploratory and confirmatory factor analysis.
- Key terms, such as communality, eigenvalues, factor loadings, and factor scores.
- What factor rotation is.
- How to determine whether data are suitable for carrying out an exploratory factor analysis.
- How to interpret SPSS principal component output.
- The principles of reliability analysis and its execution in SPSS.
- The concept of structural equation modeling.

- The basic concepts of cluster analysis.
- How basic cluster algorithms work.
- How to compute simple clustering results manually.
- The different types of clustering procedures.
- The SPSS clustering outputs.

- Why communicating the results is a crucial element of every market research study.
- The elements that should be included in a written research report and how to structure these elements.
- How to communicate the findings in an oral presentation.
- The ethical issues concerning communicating the report findings to the client.