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Applied statistics using R : a guide for the social sciences / Mehmet Mehmetoglu, Matthias Mittner.

By: Contributor(s): Material type: TextTextLanguage: English Publisher: London ; Thousand Oaks, Calfornia : SAGE Publications Ltd, [2022]Copyright date: ©2022Description: xxii, 449 pages : illustrations (some color) ; 25 cmISBN:
  • 9781526476227
  • 9781526476234
Subject(s): DDC classification:
  • 300.285/5133 23
LOC classification:
  • H61.3 .M44 2022
Online resources:
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
Summary: 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.
Holdings
Item type Current library Call number Status Date due Barcode
Book TBS Barcelona QA276.4 MEH (Browse shelf(Opens below)) Available B04125

Includes bibliographical references (pages 437-442) and index.

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

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.

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