Quantitative Research using R

Smruti Bulsari, Kiran Pandya

• Publisher: Routledge
• Year: 2026
• eBook ISBN: 9781040856109
• Topic: Social Sciences
• Subtopic: Statistics for Business & Economics
• Index: Social Sciences

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 analyses 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 science 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 for using R for quantitative research.

Table of contents

• Cover
• Half-Title
• Endorsements
• Title
• Copyright
• Dedication
• Contents
• List of Figures
• List of Tables
• 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
• Index