The book is a unique blend of quantitative research and statistical analysis using R. Lucidly written, it covers a range of statistical techniques applicable to cross-sectional data in the backdrop of quantitative research and survey research. In addition to the basic concepts, this book also explores advanced multivariate statistics topics like principal components analysis, cluster analysis, multidimensional scaling and more.
This volume begins with an introduction to R, RStudio and gives a step-by-step approach to installation and usage. The chapters on quantitative data and sampling build the background for understanding quantitative and survey research. It gradually builds the foundations into descriptive and inferential statistics, while simultaneously providing and describing the R code as well as the interpretation of the output generated by executing that R code. This gives the reader clarity in both the techniques as well as the R code. Many examples relevant to different statistical analysis make the book interesting to readers across different disciplines.
The book will be useful to the students, researchers and teachers of Economics, Psychology, Management, Data Science, Education, and other social sciences disciplines. Students at undergraduate and graduate-level, doctoral, post-doctoral and professional researchers, as well as teachers of research methodology and quantitative techniques will find this book a handy resource to using R for quantitative research.
Table of Contents:
Foreword Preface 1. Introduction to R and RStudio 2. Understanding Quantitative Data 3. Sample Size and Sample Selection Bias 4. Data Structures and Wrangling 5. Data Visualisation 6. Hypothesis Testing 7. Descriptive Statistics 8. t-Tests: One-Sample, Independent Samples and Paired Samples 9. Analysis of Variance 10. Correlation Analysis 11. Regression Analysis 12. Analysis of Covariance (ANCOVA) 13. Logistic Regression (Logit) and Probit Models 14. Discriminant Analysis 15. Non-Parametric Tests 16. Factor Analysis: Principal Components Method 17. Cluster Analysis 18. Multidimensional Scaling 19. Sensitivity Analysis 20. Survival Analysis 21. Multiresponse Analysis Appendix – A: Description of Datasets used in this book
About the Author :
Smruti Bulsari is currently a Senior Research Officer at the University of Essex. She holds a PhD in Economics and Master’s degrees in Economics and Data Science. She has a wide experience of working with large datasets and different statistical software. She makes extensive use of R for quantitative analysis as well as developing simulation models in health economics. She has a wide experience of teaching and research; she has worked on research projects funded by NIHR, ESRC, British Academy, ICSSR and Government of Gujarat. She holds a MSc in Data Science and PhD in economics.
Kiran Pandya is currently the Provost of Sarvajanik University. He has more than 40 years of teaching, research and administrative experience, out of which he was the Professor and Head in the Department of Human Resource Development, Veer Narmad South Gujarat University, Surat (Gujarat, India) for close to 16 years. He has conducted a large number of training programmes and workshops in quantitative techniques using different softwares, for researchers and academics. Kiran holds the degree of Doctor of Philosophy in Economics from the University of Sussex, for which he was awarded the Academic Staff Scholarship by the Commonwealth Commission in the UK.
Review :
This book is an excellent resource for anyone working with R. It combines clear explanations with practical examples, making even complex concepts easy to grasp. Whether you are new to R or looking to deepen your skills, you will find this book both accessible and highly valuable. I strongly recommend it to students, researchers, and professionals who want to make the most of R in their work.
Magdalena Wallbaum, Research Fellow, Care Policy and Evaluation Centre (CPEC), London School of Economics and Political Science, United Kingdom
This book offers a clear and comprehensive overview to quantitative methods using R, beginning with an introduction to the programming language and data handling, to then progress to statistical analysis. The book successfully complements the theory with practical implementations and their respective analyses. This balance makes it a great resource for graduate students, academics who might consider it as a textbook, as well as practitioners such as data scientists and quantitative researchers.
Dr Felipe Maldonado, Assistant Professor, School of Mathematics, Statistics and Actuarial Science, University of Essex, United Kingdom
Even in the age of AI-generated codes, the research community needs a trusted and experienced guide. Also, 90% of research involves doing the basics right. Dr Pandya and Dr Bulsari's new book on R analysis offers such guide on the basics and much more. Health economists among the readers would find chapter 20 on survival analysis and chapter 16 on factor analysis particularly useful for decision modelling and multidimensional health outcomes research.
Joseph Kwon, Senior Researcher in Health Economics, NIHR ARC Dementia Research Fellow, Mental Health Mission NICE Lead, Nuffield Department of Primary Care and Health Services, University of Oxford, United Kingdom
An excellent and comprehensive text that demystifies quantitative methods using R. With its wide coverage from foundational concepts to sophisticated statistical techniques it is a must-have for anyone engaged in research.
Pallavi Banerjee, Assistant Professor, Faculty of Education, University of Cambridge, United Kingdom
This new book on R for Quantitative Research is a must-read for both beginners and advanced researchers in the social sciences. It offers a clear and accessible introduction to R, seamlessly guiding readers into the world of statistical analysis. Covering essential topics with depth and clarity, it equips users with the tools and knowledge needed to tackle data analysis confidently, making it an invaluable resource for anyone looking to master statistics with R.
Gaurang Rami, Professor in Economics, and Head, Department of Economics, Veer Narmad South Gujarat University, Surat, India
R for Quantitative Researchers offers an extremely comprehensive walk through of all aspects of R and R Studio for researchers new to quantitative software as well as those much more established. The use of appropriate examples offers the reader opportunities to interactively engage with the book and advance their knowledge and understanding. It has been incredibly helpful in my professional development as a quantitative researcher.
Ben Gould, Senior Research Officer, Institute of Public Health and Wellbeing, University of Essex, United Kingdom
This book is an essential guide for researchers in economics and the social sciences, offering an ideal mix of statistical theory with practical R applications. The chapters on multivariate analysis and non-parametric statistics are particularly valuable, providing the tools to analyse complex, real-world social data that often challenges traditional methods.
Samar Ajeeb, Assistant Professor in Economics and Project Management, Taif University, Saudi Arabia
