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This text demonstrates that there is a politics model that unifies the discipline and structures its relationship to the other social sciences. It shows how this model underlies important works of applied research in all the main political science subfields.

In this concise but wide-ranging text, Alan Zuckerman introduces the reader to the various approaches to political explanation. He shows how researchers espousing different theoretical assumptions, levels of explanation, variables, and data come to offer conflicting accounts of the phenomena to be studied. He then introduces five paradigms of polit

There have been a number of attempts in recent years to define the subject-matter of political science and to provide a theoretical framework within which the discipline may be expected to develop. Among these, the work of David Easton occupies a leading place.1 This article discusses how successful Easton has been in adumbrating a general theory embracing the discipline. It then offers a rather looser and less ambitious framework within which the theories collectively called 'political science' may be placed and their interrelationships perceived.

Intro -- Experimental Political Science -- Contents -- List of Figures -- List of Tables -- Preface and Acknowledgments -- Notes on the Contributors -- Chapter 1 Introduction: Experimental Political Science in Perspective -- Part I Overview -- Chapter 2 Voting Behavior and Political Institutions: An Overview of Challenging Questions in Theory and Experimental Research -- Chapter 3 Laboratory Tests of Formal Theory and Behavioral Inference -- Chapter 4 Voting Mechanism Design: Modeling Institutions in Experiments -- Part II Experimental Designs -- Chapter 5 Strategic Voting in the Laboratory -- Chapter 6 Survey Experiments: Partisan Cues in Multi-party Systems -- Chapter 7 Experimental Triangulation of Coalition Signals: Varying Designs, Converging Results -- Part III Exploring and Analyzing Experimental Data -- Chapter 8 Statistical Analysis of Experimental Data -- Chapter 9 Experimental Chats: Opening the Black Box of Group Experiments -- Part IV Challenges to Inferences from Experiments -- Chapter 10 On the Validity of Laboratory Research in the Political and Social Sciences: The Example of Crime and Punishment -- Chapter 11 Gathering Counter-Factual Evidence: An Experimental Study on Voters' Responses to Pre-Electoral Coalitions -- Chapter 12 Using Time in the Laboratory -- Part V Conclusion -- 13 Conclusion: Ways Ahead in Experimental Political Science -- Appendix: Resources for Experimental Research in the Social Sciences -- Index.

R for Political Data Science: A Practical Guide is a handbook for political scientists new to R who want to learn the most useful and common ways to interpret and analyze political data. It was written by political scientists, thinking about the many real-world problems faced in their work. The book has 16 chapters and is organized in three sections. The first, on the use of R, is for those users who are learning R or are migrating from another software. The second section, on econometric models, covers OLS, binary and survival models, panel data, and causal inference. The third section is a data science toolbox of some the most useful tools in the discipline: data imputation, fuzzy merge of large datasets, web mining, quantitative text analysis, network analysis, mapping, spatial cluster analysis, and principal component analysis. Key features: Each chapter has the most up-to-date and simple option available for each task, assuming minimal prerequisites and no previous experience in R Makes extensive use of the Tidyverse, the group of packages that has revolutionized the use of R Provides a step-by-step guide that you can replicate using your own data Includes exercises in every chapter for course use or self-study Focuses on practical-based approaches to statistical inference rather than mathematical formulae Supplemented by an R package, including all data As the title suggests, this book is highly applied in nature, and is designed as a toolbox for the reader. It can be used in methods and data science courses, at both the undergraduate and graduate levels. It will be equally useful for a university student pursuing a PhD, political consultants, or a public official, all of whom need to transform their datasets into substantive and easily interpretable conclusions.