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.