Quantitative Research using R
Routledge (Verlag)
978-1-032-82787-2 (ISBN)
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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.
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.
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
| Erscheint lt. Verlag | 25.3.2026 |
|---|---|
| Zusatzinfo | 24 Tables, black and white; 78 Halftones, black and white; 78 Illustrations, black and white |
| Verlagsort | London |
| Sprache | englisch |
| Maße | 174 x 246 mm |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Statistik |
| Sozialwissenschaften ► Soziologie ► Empirische Sozialforschung | |
| Wirtschaft ► Volkswirtschaftslehre ► Ökonometrie | |
| ISBN-10 | 1-032-82787-4 / 1032827874 |
| ISBN-13 | 978-1-032-82787-2 / 9781032827872 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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