A Concise Guide to Market Research: The Process, Data, and Methods Using IBM SPSS Statistics (Springer Texts in Business and Economics) - Tapa blanda

Acerca del autor

Marko Sarstedt is a Professor of Marketing at the Otto-von-Guericke-University Magdeburg (Germany) and Adjunct Professor at the University of Newcastle (Australia). He holds lectures on market research and multivariate data analysis around the world and his research has been published in top-tier journals such as Journal of the Academy of Marketing Science, International Journal of Research in Marketing, MIS Quarterly, Long Range Planning and Journal of World Business.

Erik Mooi is a Senior Lecturer of Marketing at the University of Melbourne (Australia) and Aston Business School (United Kingdom). He teaches on various topics including statistical methods and market research to graduate students, PhD students and executives. His research has appeared in journals such as the Journal of Marketing and The International Journal of Research in Marketing.

De la contraportada

This accessible, practice-oriented, and compact text provides a hands-on introduction to market research. Using the market research process as a framework, it explains how to collect and describe data and presents the most important and frequently used quantitative analysis techniques, such as ANOVA, regression analysis, factor analysis, and cluster analysis. The book describes the theoretical choices a market researcher has to make with regard to each technique, discusses how these are converted into actions in IBM SPSS version 22, and how to interpret the output. Each chapter concludes with a case study that illustrates the process using real-world data. A comprehensive Web appendix includes additional analysis techniques, datasets, video files, and case studies. Tags in the text allow readers to quickly access Web content with their mobile device.

The new edition features:

• Stronger emphasis on the gathering and analysis of secondary data (e.g., internet and social networking data)
• New material on data description (e.g., outlier detection and missing value analysis)
• Improved use of educational elements such as learning objectives, keywords, self-assessment tests, case studies, and much more
• Streamlined and simplified coverage of the data analysis techniques with more rules-of-thumb
• Uses IBM SPSS version 22