- Häftad (Paperback)
- Antal sidor
- SAGE Publications Ltd
- Fogarty, Brian (ed.)
- Color illustrations
- 244 x 185 x 18 mm
- Antal komponenter
- 4450:Standard Color 7.44 x 9.69 in or 246 x 189 mm (Crown 4vo) Perfect Bound on Standard 70 White w/
- 726 g
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Bob Woodward, Robert CostaInbunden
Quantitative Social Science Data with R
"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."
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- 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.
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A great, action-oriented book for novice data analysts. If you have no background in quantitative social science, Fogarty's book gives you a toolbox for starting statistical analysis and developing your skills.
The chapters are well organised and guide the student through the background using worked examples – this is vital to gain the necessary proficiency in the R interface. I would recommend this book to anyone wanting to develop not only software competence, but a wider appreciation of the background to data analysis techniques in the social sciences.
This landmark book provides an engaging multidisciplinary introduction to quantitative social sciences using R, increasingly the preferred programme for statistical analysis across disciplines. Fogarty’s book is a perfect companion for the novice and it is likely to become the standard reference in quantitative methods teaching across the social sciences.
As Fogarty notes, there is little need for another statistics book. What there is a need for is a comprehensive text that provides the necessary tools for quantitative research in the social sciences, which is what this book does. Students need books that will guide them through finding, cleaning, analyzing, and presenting data using statistical programs like R, making this book an important text for any higher-level data analysis, statistics or research course in the social sciences.
Are you new to stats? Do you also need help writing your first lines of R code? Then this book is your guide. Fogarty's Quantitative Social Science Data with R is a great start for all those who want to get their head around how to implement a quantitative research project.
Brian Fogarty is Director of and Associate Professor of the Practice at the Center for Social Science Research, within the Center for Research Computing, at the University of Notre Dame, US. He is also concurrent research assistant professor in the Department of Political Science. As director of the CSSR, he works with social science researchers to support their project research design, data, and quantitative analysis needs. His current research focuses on the news media as a strategic actor in politics and understanding perceptions of voter and electoral fraud.
Before joining Notre Dame, he was a lecturer in quantitative social science at the University of Glasgows Q-Step Centre. Prior to joining Glasgow, he was an associate professor of political science at the University of Missouri St. Louis. He received his Ph.D. in political science from the University of North Carolina Chapel Hill.
Introduction Introduction to R and R Studio Finding Data Data Management Variables & Manipulation Developing Hypotheses Univariate & Descriptive Statistics Visualising Data Hypothesis Testing Bivariate Analysis Linear Regression & Model Building OLS Assumptions & Diagnostic Testing Putting it all Together