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Quantitative Social Science Data with R

An Introduction
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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, it uses 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 by incredible 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.
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 Glasgow's 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 And Manipulation Developing Hypotheses Univariate And Descriptive Statistics Data Visualisation Hypothesis Testing Bivariate Analysis Linear Regression And Model Building OLS Assumptions And Diagnostic Testing Generalised Linear Models Count Models Putting It All Together
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