Cover image for Quantitative social science data with R : an introduction
Title:
Quantitative social science data with R : an introduction
Author:
Fogarty, Brian J., author.
ISBN:
9781526411501

9781526411495
Personal Author:
Physical Description:
ix, 312 pages ; 25 cm
Contents:
Introduction -- Introduction to R and R Studio -- Finding Data -- Data Management -- Variables and Manipulation -- Developing Hypotheses -- Univariate and Descriptive Statistics -- Visualising Data -- Hypothesis Testing -- Bivariate Analysis -- Linear Regression and Model Building -- OLS Assumptions and Diagnostic Testing -- Putting it all Together.
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Summary

Summary

" One of the few books that provide an accessible introduction to quantitative data analysis with R. A particular strength of the text is the focus on ′real world′ examples which help students to understand why they are learning these methods." - Dr Roxanne Connelly, University of York

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

Gradually introducing applied statistics and R, it uses examples from across the social sciences to show you how to apply abstract statistical and methodological principles to your own work. At a student-friendly pace, it enables you 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 by incredible digital resources with online tutorials, videos, datasets, and multiple choice questions, this book gives you not only the tools you need to understand statistics, quantitative data, and R software, but also the chance to practice and apply what you have learned. Brian J. Fogarty is a Lecturer in Quantitative Social Science on the Glasgow Q-Step Programme in the School of Social and Political Sciences at the University of Glasgow.


Table of Contents

Prefacep. vi
About the Authorp. x
Online Resourcesp. xi
1 Introductionp. 1
2 Introduction to R and RStudiop. 7
3 Finding Datap. 23
4 Data Managementp. 35
5 Variables and Manipulationp. 55
6 Developing Hypothesesp. 75
7 Univariate and Descriptive Statisticsp. 87
8 Visualising Datap. 113
9 Hypothesis Testingp. 149
10 Bivariate Analysisp. 165
11 Linear Regression and Model Buildingp. 191
12 OLS Assumptions and Diagnostic Testingp. 217
13 Putting It All Togetherp. 249
Bibliographyp. 306
Indexp. 308