An Introduction to Quantitative Research Methods for Marketing: Tools and Techniques Using SPSS and R

by Ahmad Daryanto

Overview

This introductory text covers the foundational concepts and statistical applications of quantitative research techniques using SPSS and R.

Using step-by-step examples throughout, the book is broken down into six core sections: Part 1 covers an introduction to quantitative research methods and how to get started with SPSS and R; Part 2 covers basic concepts in measurement, data descriptions, and distributions; Part 3 discusses hypothesis testing, and basic statistical tests; Part 4 covers regression analysis; Part 5 discusses advanced topics in regression analysis and analysis of variance; and finally Part 6 covers advanced statistical methods. Each chapter contains learning objectives and summaries to structure learning, while breakout boxes provide tips and draw students’ attention to dos and don’ts in statistical research. SPSS and R Action Boxes present step-by-step instructions on how to perform statistical tests and procedures with SPSS and R. Review questions prompt self-reflection on concepts taught in each chapter and are complemented by exercises that allow students to put their learning into practice.

A very applied text designed to make this complex subject accessible to students with no background in quantitative methods, this book is valuable recommended and core reading for advanced undergraduate and postgraduate students studying business and marketing research methods, business analytics, marketing analytics, statistical skills and quantitative methods.

Online supplementary resources include data sets and programming files.

Product Details

ISBN-13:	9781032525662
Publisher:	Taylor & Francis
Publication date:	06/03/2025
Pages:	490
Product dimensions:	6.88(w) x 9.69(h) x (d)

About the Author

Ahmad Daryanto is a Professor of Marketing at Lancaster University Management School in the United Kingdom. He received his BSc degree in Environmental Engineering from Bandung Institute of Technology in Indonesia, a MSc degree in Engineering Mathematics from Twente University in the Netherlands, and a PhD degree in Marketing from Maastricht University in the Netherlands. He has published in top international journals such as the Journal of Consumer Psychology, Journal of the Academy of Marketing Science, Journal of Service Research, Industrial Marketing Management, Personality and Individual Differences, Cities, Journal of Business Research among others. Some of his research has been funded by the Fundamental Research Funds for the Central Universities in China. Anto currently acts as an associate editor of an Elsevier journal, Sustainable Futures, and has acted as a co-chair session on marketing analytics and big data at the prestigious AMA winter conference. Anto held a visiting professor position at Beijing Foreign Studies University under the scheme of High-End Foreign Expert Program of Government of China, and has acted as an external grant reviewer for UK Research Innovation (UKRI) future leadership and a lead reviewer for the British Academy small research grants. He is currently an external examiner at two universities: International Business School of Xi’an-Jiaotong Liverpool University for its Marketing degree programs, and Regents’ University of London for its new MSc program in Data Science and Business. He was an external examiner at Imperial College Business School, London, for its MSc in Strategic Marketing program. Anto is a statistical programmer who has written several SPSS macros. He is the inventor of EndoS, which is a software that can be used to address an endogeneity problem discussed in this book; his HeteroskedasticityV3 software has been used by graduate students and researchers worldwide. Anto is a keen chess player and runs a chess club at the Lancaster public library in the UK.

Table of Contents

Part 1: Getting Started  1. An Introduction  2. Getting Started with SPSS  3. Getting Started with R  Part 2: Basic Concepts in Measurement, Data Descriptions and Distributions  4. Construct and Measurement  5. Describing Quantitative Data  6. Normal Distribution  7. Distributions Derived from Normal Distribution  8. Sampling Distribution  Part 3: Hypothesis Testing and Basic Statistical Tests  9. Hypothesis Testing and Significance  10. Testing Proportions  11. Correlation  Part 4: Regression Analysis  12. Simple Linear Regression  13. Multiple Regression  14. Regression Diagnostics  Part 5: Advanced Topics in Regression and ANOVA  15. Mediation  16. Moderation  17. Endogeneity  18. Analysis of Variance  19. Experiment  Part 6: Advanced Statistical methods  20. Cluster Analysis  21. Principal Component Analysis  22. Factor Analysis  23. Binary Logistic Regression