Applied statistics using R : a guide for the social sciences

Author(s)

    • Mehmetoglu, Mehmet
    • Mittner, Matthias

Bibliographic Information

Applied statistics using R : a guide for the social sciences

Mehmet Mehmetoglu, Matthias Mittner

SAGE, c2022

  • : pbk

Available at  / 5 libraries

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Description and Table of Contents

Description

If you want to learn to use R for data analysis but aren't sure how to get started, this practical book will help you find the right path through your data. Drawing on real-world data to show you how to use different techniques in practice, it helps you progress your programming and statistics knowledge so you can apply the most appropriate tools in your research. It starts with descriptive statistics and moves through regression to advanced techniques such as structural equation modelling and Bayesian statistics, all with digestible mathematical detail for beginner researchers. The book: Shows you how to use R packages and apply functions, adjusting them to suit different datasets. Gives you the tools to try new statistical techniques and empowers you to become confident using them. Encourages you to learn by doing when running and adapting the authors' own code. Equips you with solutions to overcome the potential challenges of working with real data that may be messy or imperfect. Accompanied by online resources including screencast tutorials of R that give you step by step guidance and R scripts and datasets for you to practice with, this book is a perfect companion for any student of applied statistics or quantitative research methods courses.

Table of Contents

Chapter 1: Introduction to R Chapter 2: Importing and working with data in R Chapter 3: How does R work? Chapter 4: Data management Chapter 5: Data visualisation with ggplot2 Chapter 6: Descriptive statistics Chapter 7: Simple (bivariate) regression Chapter 8: Multiple linear regression Chapter 9: Dummy-variable regression Chapter 10: Moderation/interaction analysis using regression Chapter 11: Logistic regression Chapter 12: Multilevel and longitudinal analysis Chapter 13: Factor analysis Chapter 14: Structural equation modelling Chapter 15: Bayesian statistics

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