This book is an easily accessible and comprehensive guide which helps make sound statistical decisions, perform analyses, and interpret the results quickly using Stata. It includes advanced coverage of ANOVA, factor, and cluster analyses in Stata, as well as essential regression and descriptive statistics. It is aimed at those wishing to know more about the process, data management, and most commonly used methods in market research using Stata. The book offers readers an overview of the entire market research process from asking market research questions to collecting and analyzing data by means of quantitative methods. It is engaging, hands-on, and includes many practical examples, tips, and suggestions that help readers apply and interpret quantitative methods, such as regression, factor, and cluster analysis. These methods help researchers provide companies with useful insights.
As a Professor of Marketing in a Global MBA program, I have read many, many books on Market Research. While there are many more famous and widely used textbooks for Marketing professors to use, this one is by far the best I have ever read and used.
Even for students with a background in Statistics or Statistical methods, this book provides a very clear and detailed discussion of exactly how each of the basic quantitative methods are derived and applied, including all of the assumptions related to the data that has been collected, the actual methods themselves, and all post-hoc tests.
In addition to these very detailed and easy-to-understand discussions, this book also comes with a companion website that includes excellent data files to be imported into SPSS. Students can then walk through each of the examples provided in the book following a step-by-step process, and gain an understanding of the most basic Market Research analysis tools including Hypothesis testing and ANOVA, Regression Analysis (OLS), Factor Analysis and Cluster Analysis.
For anyone looking for an introduction to SPSS for Market Research, this is an outstanding first step before jumping into the more advanced multivariate techniques outlined in Hair et al's "Multivariate Data Analysis".