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 one of the following files:

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

- How to determine a research design.
- The differences between, and examples of, 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 they are 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 its suitability.
- How to collect primary data.
- How to design a basic questionnaire.
- How to set up basic experiments.
- How to set up 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 IBM SPSS Statistics.

- The logic of hypothesis testing.
- The steps involved in hypothesis testing.
- What test statistics are.
- Types of error in hypothesis testing.
- Common types of t-tests, one-way and two-way ANOVA.
- How to interpret SPSS outputs.

- What regression analysis is and what it can be used for.
- How to specify a regression analysis model.
- How to interpret basic regression analysis results.
- What the issues with, and assumptions of regression analysis are.
- How to validate regression analysis results.
- How to conduct regression analysis in SPSS.
- How to interpret regression analysis output produced by SPSS.

- The principles of exploratory and confirmatory factor analysis.
- The difference between principal components analysis and principal axis factoring.
- Key terms such as Eigenvalues, communality, factor loadings, and factor scores.
- How to determine whether data are suitable for carrying out an exploratory factor analysis.
- How to interpret SPSS factor analysis output.
- The principles of reliability analysis and how to carry it out in SPSS.
- The basic idea behind 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.
- Which elements should be included in a written research report and how these elements can be structured.
- How to communicate the findings in an oral presentation.
- Ethical issues regarding the communication of the report findings to the client.