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 donts 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.
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
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