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:

• Table of contents
• Preface
• Sample chapter: Chapter 8 on principal component and factor analysis

• 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 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 their 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 Stata.

• 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 Stata 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 in Stata.
• How to interpret regression analysis output produced by Stata.

• Principal component analysis and factor analysis.
  
• The difference between principal component analysis and factor analysis.
  
• Key terms such as communality, eigenvalues, factor loadings, factor scores, and uniqueness.
• What rotation is.
• The principles of exploratory and confirmatory factor analysis.
• How to determine whether data are suitable for carrying out an exploratory 
factor analysis.
• How to interpret Stata principal component and factor analysis output.
• The principles of reliability analysis and its execution in Stata.
  
• 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 Stata 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 to structure these 
elements.

• How to communicate the findings in an oral presentation.
• Ethical issues regarding the communication of the report findings to the client.