Full Description
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.
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
