"Pollock and Edwards explain the nuts-and-bolts of research design and data analysis in a clear and concise style. The Essential of Political Analysis is an intuitive introduction to complex material, replete with examples from the political science literature that add relevance to statistical concepts. This text offers students an excellent balance between the technical and the practical." —Francis Neely, San Francisco State University Gain the skills you need to conduct political analysis and critically assess statistical research. In this Sixth Edition of The Essentials of Political Science, bestselling authors Philip H. Pollock III and Barry C. Edwards build students’ analytic abilities and develop their statistical reasoning with new data, fresh exercises, and accessible examples. This brief, accessible guide walks students through the essentials—measuring concepts, formulating and testing hypotheses, describing variables—while using key terms, chapter-opening objectives, over 80 tables and figures, and practical exercises to get them using and applying their new skills. Using SPSS, STATA or R? Discounted package deals available with Philip H. Pollock’s companion workbooks. . Give your students the SAGE edge! SAGE edge offers a robust online environment featuring an impressive array of free tools and resources for review, study, and further exploration, keeping both instructors and students on the cutting edge of teaching and learning.
Popular for its speed, flexibility, and attractive graphics, Stata is a powerful tool for political science students. With Philip Pollock's Fourth Edition of A Stata® Companion to Political Analysis, students quickly learn Stata via step-by-step instruction, more than 50 exercises, customized datasets, annotated screen shots, boxes that highlight Stata's special capabilities, and guidance on using Stata to read raw data. This attractive and value-priced workbook, an ideal complement to Pollock’s Essentials of Political Analysis, is a must-have for any political science student working with Stata.
With this Third Edition, students quickly learn Stata with step-by-step instructions, more than 50 exercises, customized data sets, annotated screen shots, boxes that highlight Stata's capabilities, and guidance on using Stata to read raw data.
Tables and Figures ix Preface xiii Getting Started 1 Copying the Data Files to Floppy Disk or Other Portable Media 2 How to Handle the Datasets 2 Contents of the Datasets 4 Notes 4 Introduction to Stata 5 Commands Covered describe, codebook, set more off, search, help Changing General Preferences 5 Obtaining Information about a Dataset 6 Obtaining Information about Variables 9 Creating and Viewing a Log File 10 A Closer Look Obtaining Specific Variable Information with Define Notes 11 Printing Results 13 Getting Help 13 Exercises 17 Notes 17 Descriptive Statistics 19 Commands Covered tab1, summarize, sktest, histogram, Submit button Interpreting Measures of Central Tendency and Variation 19 Describing Nominal Variables 20 Describing Ordinal Variables 21 Describing Interval-level Variables 23 Obtaining Bar Charts and Histograms 27 Creating Bar Charts 27 Creating Histograms 32 A Closer Look The Submit Button and the OK Button: What's the Difference? 33 Exercises 35 Notes 40 Transforming Variables 43 Commands Covered recode, generate; generate, recode; xtile, nquantiles; generate; tabulate, generate; label variable; label define; label values; drop; aorder Transforming Categorical Variables 44 Transforming Interval-level Variables 46 A Closer Look The xtile Command 48 The label define and label values Commands 51 Creating an Additive Index 51 Creating Indicator Variables 53 Exercises 55 Notes 58 Making Comparisons 59 Commands Covered tabulate, column; tabulate, summarize; graph bar; format; replace Cross-tabulation Analysis 59 Mean Comparison Analysis 60 A Closer Look The format Command 62 Visualizing Relationships with Bar Charts 62 Graphing an Interval-level Dependent Variable 63 Graphing a Categorical Dependent Variable 67 A Closer Look The replace Command 70 Exercises 73 Notes 83 Making Controlled Comparisons 85 Commands Covered (prefix) bysort:, tabulate, col; tabulate, summarize; graph bar; if Cross-tabulation Analysis with a Control Variable 86 A Closer Look The if Qualifier 88 Bar Charts for Controlled Comparisons with a Categorical Dependent Variable 89 Mean Comparison Analysis with a Control Variable 91 Bar Charts for Controlled Comparisons with an Interval-level Dependent Variable 96 Exercises 98 Notes 105 Making Inferences about Sample Means 107 Commands Covered ttest; ttest, by; robvar, by Describing a Sample Mean 108 Testing the Difference between Two Sample Means 111 Exercises 116 Notes 118 Chi-square and Measures of Association 121 Commands Covered (tabulate option) chi2; (tabulate option) taub; (tabulate option) V; somersd; lambda; which; ssc install Installing lambda and somersd 122 Analyzing Ordinal-level Relationships 122 Analyzing Nominal-level Relationships 127 A Problem with Lambda 129 Exercises 130 Notes 136 Correlation and Linear Regression 137 Commands Covered correlate; regress; graph twoway (scatter); graph twoway (scatter)(lfit) The correlate Command and the regress Command 137 Creating a Scatterplot 139 A Closer Look R-squared and Adjusted R-squared: What's the Difference? 140 Exploring Multivariate Relationships with Regression 147 Exercises 150 Notes 156 Dummy Variables and Interaction Effects 159 Commands Covered xi: regress; char [omit]; test Regression with Dummy Variables A Closer Look The test Command 162 Interaction Effects in Multiple Regression 164 Graphing Linear Prediction Lines for Interaction Relationships 167 Exercises 173 Notes 178 Logistic Regression 181 Commands Covered logit; logistic; estimates store; Irtest, force; predict; adjust, by pr gen; quietly:; tabstat, by The logit Command and the logistic Command 182 Logistic Regression with Multiple Independent Variables 185 A Closer Look The estimates Command and the Irtest Command 187 Working with Predicted Probabilities: Models with One Independent Variable 188 Working with Predicted Probabilities: Models with Multiple Independent Variables 191 Exercises 196 Notes 201 Doing Your Own Political Analysis 203 Commands Covered insheet Five Doable Ideas 203 Political Knowledge 204 Economic Performance and Election Outcomes 204 State Courts and Criminal Procedure 204 Electoral Turnout in Comparative Perspective 205 Congress 205 Doing Research on the U.S. Senate 205 Finding Raw Data 206 How to Code Raw Data 206 A Closer Look The Stata Data Editor 208 Two Possible Coding Shortcuts 210 Importing Text Data 212 Writing It Up 215 The Research Question 215 Previous Research 216 Data, Hypotheses, and Analysis 216 Conclusions and Implications 216 Notes 217.
Teach your students to conduct political research using R, the open source programming language and software environment for statistical computing and graphics. An R Companion to Political Analysis offers the same easy-to-use and effective style as the best-selling SPSS and Stata Companions. The all-new Second Edition includes new and revised exercises and datasets showing students how to analyze research-quality data to learn descriptive statistics, data transformations, bivariate analysis (cross-tabulations and mean comparisons), controlled comparisons, statistical inference, linear correlation and regression, dummy variables and interaction effects, and logistic regression. The clear explanation and instruction is accompanied by annotated and labeled screen shots and end-of-chapter exercises to help students apply what they have learned. "Students will love this book, as will their teachers." – Courtney Brown, Emory University
This is a shrink-wrapped, discounted package containing Philip Pollock's popular, student-friendly political science methods text and new student workbook using Stata. Workbook contains a data CD.
This textbook introduces the scientific study of politics, supplying students with the basic tools to be critical consumers and producers of scholarly research.
Popular for its speed, flexibility, and attractive graphics, Stata is a powerful tool for political science students. With Pollock's second edition, students quickly learn Stata with step-by-step instruction, more than 50 exercises, customized datasets, annotated screen shots, boxes that highlight Stata's special capabilities, and guidance on using Stata to read raw data. Updates to this edition include: Comprehensive coverage of the Stata Graph Editor; Enhanced discussion of graph types, with brand-new coverage of group means and the twoway command; New and updated datasets, including GSS and NES (all datasets include additional variables for use on research papers, exams, or assignments); New end-of-chapter exercises, which gradually increase in difficulty to reinforce and build on key concepts; and Improved guidance on using Internet-available data, from downloading to inputting multiple formats- Excel, HTML, and PDF.
In politics, you begin by asking theoretically interesting questions. Sometimes statistics can help answer those questions. When it comes to applied statistics, students shouldn’t just learn a vast array of formula—they need to learn the basic concepts of statistics as solutions to particular problems. Peter Galderisi demonstrates that statistics are a summary of how to answer the problem: learn the math but only after learning the concepts and methodological considerations that give it context. With this as a starting point, Understanding Political Science Statistics asks students to consider how to address a research problem conceptually before being led to the appropriate formula. Throughout, Galderisi looks at problems through a lens of "observations and expectations," which can be applied to myriad statistical techniques, both descriptive and inferential. This approach links the answers researchers get from their individual data analysis to the research designs and questions from which these analyses are derived. By emphasizing the underlying logic of statistical analysis for greater understanding and drawing on applications and examples from political science (including law), the book illustrates how students can apply statistical concepts and techniques in their own research, in future coursework, and simply as an informed consumer of numbers in public discourse. The following features help students master the material: Legal and Methodological sidebars highlight key concepts and provide applied examples on law, politics, and methodology; End-of-chapter exercises allow students to test their mastery of the basic concepts and techniques along the way; A Sample Solutions Guide provides worked-out answers for odd-numbered exercises, with all answers available in the Instructor’s Manual; Key Terms are helpfully called out in both Marginal Definitions and a Glossary; A Companion Website (www.routledge.com/cw/galderisi) with further resources for both students and instructors; A diverse array of data sets include subsets of the ANES and Eurobarometer surveys; CCES; US Congressional district data; and a cross-national dataset with political, economic, and demographic variables; and Companion guides to SPSS and Stata walk students through the procedures for analysis and provide exercises that go hand-in-hand with online data sets.
Straightforward, clear, and applied, this book will give you the theoretical and practical basis you need to apply data analysis techniques to real data. Combining key statistical concepts with detailed technical advice, it addresses common themes and problems presented by real research, and shows you how to adjust your techniques and apply your statistical knowledge to a range of datasets. It also embeds code and software output throughout and is supported by online resources to enable practice and safe experimentation. The book includes: · Original case studies and data sets · Practical exercises and lists of commands for each chapter · Downloadable Stata programmes created to work alongside chapters · A wide range of detailed applications using Stata · Step-by-step guidance on writing the relevant code. This is the perfect text for anyone doing statistical research in the social sciences getting started using Stata for data analysis.
