Quantitative Social Science Data with R
eBook - ePub

Quantitative Social Science Data with R

An Introduction

  1. 408 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Quantitative Social Science Data with R

An Introduction

About this book

Relevant, engaging, and packed with student-focused learning features, this book provides the basic step-by-step introduction to quantitative research and data every student needs.

Gradually introducing applied statistics and the language and functionality of R and R Studio software, ituses examples from across the social sciences to show students how to apply abstract statistical and methodological principles to their own work. Maintaining a student-friendly pace, it goes beyond a normal introductory statistics book and shows students where data originates and how to:

-Understand and use quantitative data to answer questions

-Approach surrounding ethical issues

-Collect quantitative data

-Manage, write about, and share the data effectively

Supported byincredible digital resources with online tutorials, videos, datasets, and multiple choice questions, this book gives students not only the tools they need to understand statistics, quantitative data, and R software, but also the chance to practice and apply what they have learned.

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Yes, you can access Quantitative Social Science Data with R by Brian J. Fogarty in PDF and/or ePUB format, as well as other popular books in Social Sciences & Social Science Research & Methodology. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Half Title
  3. Publisher Note
  4. Title Page
  5. Copyright Page
  6. Table of Contents
  7. Preface
  8. About the Author
  9. Online Resources
  10. 1 Introduction
  11. 2 Introduction to R and RStudio
  12. 3 Finding Data
  13. 4 Data Management
  14. 5 Variables and Manipulation
  15. 6 Developing Hypotheses
  16. 7 Univariate and Descriptive Statistics
  17. 8 Data Visualisation
  18. 9 Hypothesis Testing
  19. 10 Bivariate Analysis
  20. 11 Linear Regression and Model Building
  21. 12 OLS Assumptions and Diagnostic Testing
  22. 13 Generalised Linear Models
  23. 14 Count Models
  24. 15 Putting It All Together
  25. References
  26. Index