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Quantitative Research Methods for Political Science, Public Policy and Public Administration (With Applications in R)

Additional Information
Ripberger, Joseph
, author
Minneapolis, MN : Open Textbook Library,.
Place of publication not identified : University of Oklahoma Libraries, 2017.
1 online resource
Open textbook library.
The focus of this book is on using quantitative research methods to test hypotheses and build theory in political science, public policy and public administration. It is designed for advanced undergraduate courses, or introductory and intermediate graduate-level courses. The first part of the book introduces the scientific method, then covers research design, measurement, descriptive statistics, probability, inference, and basic measures of association. The second part of the book covers bivariate and multiple linear regression using the ordinary least squares, the calculus and matrix algebra that are necessary for understanding bivariate and multiple linear regression, the assumptions that underlie these methods, and then provides a short introduction to generalized linear models.The book fully embraces the open access and open source philosophies. The book is freely available in the SHAREOK repository; it is written in R Markdown files that are available in a public GitHub repository; it uses and teaches R and RStudio for data analysis, visualization and data management; and it uses publically available survey data (from the Meso-Scale Integrated Socio-geographic Network) to illustrate important concepts and methods. We encourage students to download the data, replicate the examples, and explore further! We also encourage instructors to download the R Markdown files and modify the text for use in different courses.
I Theory and Empirical Social Science -- 1 Theories and Social Science -- 2 Research Design -- 3 Exploring and Visualizing Data -- 4 Probability -- 5 Inference -- 6 Association of Variables -- II Simple Regression -- 7 The Logic of Ordinary Least Squares Estimation -- 8 Linear Estimation and Minimizing Error -- 9 Bi-Variate Hypothesis Testing and Model Fit -- 10 OLS Assumptions and Simple Regression Diagnostics -- III Multiple Regression -- 11 Introduction to Multiple Regression -- 12 The Logic of Multiple Regression -- 13 Multiple Regression and Model Building -- 14 Topics in Multiple Regression -- 15 The Art of Regression Diagnostic -- IV Generalized Linear Model -- 16 Logit Regression -- V Appendices -- 17 Appendix: Basic
Description based on online resource