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 (in preparation)
  • Preface (in preparation)
  • Sample chapter: Chapter 9 on cluster analysis (in preparation)

Chapter 1: Introduction to Market Research

  • 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.

chapter 2: The market research process

  • 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.

Chapter 3: Data

  • 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.

Chapter 4: Getting data

  • 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.

Chapter 5: Descriptive statistics

  • 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.

Chapter 6: Hypthesis testing & ANOVA

  • 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.

Chapter 7: Regression analysis

  • 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.

Chapter 8: Principal Component and Factor analysis

  • 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.

Chapter 9: Cluster analysis

  • 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.

Chapter 10: Communicating the Results

  • 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.