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Stata® Companion to Political Analysis 4th Revised edition [Pehme köide]

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  • Formaat: Paperback / softback, 288 pages, kõrgus x laius: 279x215 mm, kaal: 740 g
  • Ilmumisaeg: 08-Feb-2019
  • Kirjastus: CQ Press
  • ISBN-10: 1506379702
  • ISBN-13: 9781506379708
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Stata® Companion to Political Analysis 4th Revised editionSuurem pilt
  • Formaat: Paperback / softback, 288 pages, kõrgus x laius: 279x215 mm, kaal: 740 g
  • Ilmumisaeg: 08-Feb-2019
  • Kirjastus: CQ Press
  • ISBN-10: 1506379702
  • ISBN-13: 9781506379708
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781506379708.html
Märksõnad:
"This textbook is a great resource for teaching students how to conduct basic quantitative analysis using Stata. It provides intuitive examples from real data sets. I think it is a great resource for teaching students how to carry their own research projects." Sabri Ciftci, Kansas State University

Popular for its speed, flexibility, and attractive graphics, Stata is a powerful tool for political science students. With Philip Pollocks 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 Statas special capabilities, and guidance on using Stata to read raw data. This attractive and value-priced workbook, an ideal complement to Pollocks Essentials of Political Analysis, is a must-have for any political science student working with Stata.

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.

Arvustused

"An excellent companion for statistical computing using Stata that is a must-use for those instructors that assign the Pollock text and use Stata in their course." -- Donald Gooch "This textbook is a great resource for teaching students how to conduct basic quantitative analysis using Stata. It provides intuitive examples from real data sets. I think it is a great resource for teaching students how to carry their own research projects." -- Sabri Ciftci "This is a great workbook to teach Stata to students who are also learning the basics of statistical analysis. It comes with four datasets that can be used to run analyses. Its exercises are very useful and the instructor tools are great." -- Tijen Demirel-Pegg "For teaching Stata to undergraduates, this book provides the friendliest approach I have found. Over six straight semesters of teaching the same course, I have found it to make both my teaching experience and the students learning experience far more interesting and interactive than a typical "Research Methods" course. It provides exceptional instructional assistance, and presents information to students in an easily digestible way." -- Lilliana Mason, Rutgers

Figures and Tables
ix
Preface xi
Introduction: Getting Started xv
About Companion Datasets xvi
Chapter 1 Introduction to Stata
1(16)
Information About a Dataset
2(1)
Information About Variables
3(1)
General Syntax of Stata Commands
4(1)
Do-files
5(3)
Printing Results and Copying Output
8(2)
Log Files
10(1)
Getting Help
10(3)
Customizing Your Display
13(1)
Exercises
14(3)
Chapter 2 Descriptive Statistics
17(28)
Interpreting Measures of Central Tendency and Variation
18(1)
Describing Nominal Variables
18(2)
A CLOSER LOOK: Weighting the GSS and NES Datasets
20(1)
Describing Ordinal Variables
20(1)
Describing Interval Variables
21(5)
Bar Charts for Nominal and Ordinal Variables
26(2)
A CLOSER LOOK: Stata's Graphics Editor
28(2)
Histograms for Interval Variables
30(2)
Obtaining Case-Level Information With sort and list
32(2)
Exercises
34(11)
Chapter 3 Transforming Variables
45(20)
Creating Indicator Variables
46(4)
Working With Variable Labels
50(2)
Collapsing Variables Into Simplified Categories
52(4)
Centering or Standardizing a Numeric Variable
56(1)
Creating an Additive Index
57(3)
Exercises
60(5)
Chapter 4 Making Comparisons
65(26)
Cross-Tabulation Analysis
65(2)
Visualizing Comparisons With Nominal or Ordinal Dependent Variables
67(3)
A CLOSER LOOK: The replace Command
70(1)
Mean Comparison Analysis
71(2)
A CLOSER LOOK: The format Command
73(1)
Visualizing Comparisons With Interval-Level Dependent Variables
73(4)
Strip Charts: Graphs for Small-N Datasets
77(2)
Exercises
79(12)
Chapter 5 Making Controlled Comparisons
91(24)
Cross-Tabulation Analysis With a Control Variable
92(3)
A CLOSER LOOK: The "If" Qualifier
95(2)
Visualizing Controlled Comparisons With Categorical Dependent Variables
97(1)
Mean Comparison Analysis With a Control Variable
98(3)
An Example of Interaction
99(1)
An Example of an Additive Relationship
100(1)
Visualizing Controlled Mean Comparisons
101(2)
Exercises
103(12)
Chapter 6 Making Inferences About Sample Means
115(20)
Finding the 95 Percent Confidence Interval of a Sample Mean
116(2)
Testing a Hypothetical Claim About the Population Mean
118(2)
Testing the Difference Between Two Sample Means
120(2)
A CLOSER LOOK: Inferences About Means With Unweighted Data
122(1)
Extending the mean and lincom Commands to Other Situations
123(3)
Making Inferences About Sample Proportions
126(1)
A CLOSER LOOK: Inferences About Proportions With Unweighted Data
126(5)
Exercises
131(4)
Chapter 7 Chi-Square and Measures of Association
135(20)
Analyzing Ordinal-Level Relationships
136(1)
A CLOSER LOOK: Analyzing Unweighted Data With The tabulate Command
137(5)
Summary: Reporting and Interpreting Results
141(1)
Analyzing an Ordinal-Level Relationship With a Control Variable
142(3)
Analyzing Nominal-Level Relationships
145(3)
Exercises
148(7)
Chapter 8 Correlation and Linear Regression
155(20)
Correlation Analysis
155(2)
Regression Analysis
157(1)
A CLOSER LOOK: Treating Census as a Sample
158(1)
A CLOSER LOOK: R-Squared and Adjusted R-Squared: What's the Difference?
159(1)
Creating a Scatterplot With a Linear Prediction Line
160(1)
Multiple Regression
161(2)
A CLOSER LOOK: Bubble Plots
163(1)
Correlation and Regression Analysis With Weighted Data
163(2)
Exercises
165(10)
Chapter 9 Dummy Variables and Interaction Effects
175(16)
Regression With Multiple Dummy Variables
175(3)
Interaction Effects in Multiple Regression
178(3)
Graphing Linear Prediction Lines for Interaction Relationships
181(1)
Changing the Reference Category
181(3)
Exercises
184(7)
Chapter 10 Logistic Regression
191(30)
Thinking About Odds, Logged Odds, and Probabilities
191(2)
Estimating Logistic Regression Models
193(4)
Logistic Regression With Multiple Independent Variables
197(3)
A CLOSER LOOK: Comparing Logistic Regression Models With the estimates and lrtest Commands
200(1)
Graphing Predicted Probabilities With One Independent Variable
201(4)
Graphing Predicted Probabilities With Multiple Independent Variables
205(8)
The margins Command With the atmeans Option
206(2)
The margins Command With the over Option
208(2)
Combining atmeans and over Options
210(3)
Exercises
213(8)
Chapter 11 Doing Your Own Political Analysis
221(14)
Seven Doable Ideas
221(4)
Political Knowledge and Interest
222(1)
Self-Interest and Policy Preferences
222(1)
Economic Performance and Election Outcomes
223(1)
Electoral Turnout in Comparative Perspective
223(1)
Interviewer Effects on Public Opinion Surveys
223(1)
Religion and Politics
224(1)
Race and Politics
224(1)
Importing Data Into Stata
225(7)
Stata-Formatted Datasets
225(1)
Microsoft Excel Datasets
225(6)
HTML Table Data
231(1)
Writing It Up
232(3)
The Research Question
233(1)
Previous Research
234(1)
Data, Hypotheses, and Analysis
234(1)
Conclusions and Implications
234(1)
Appendix
Table A-1 Variables in the GSS Dataset in Alphabetical Order
235(10)
Table A-2 Variables in the NES Dataset in Alphabetical Order
245(6)
Table A-3 Variables in the States Dataset by Topic
251(8)
Table A-4 Variables in the World Dataset by Topic
259
Philip H. Pollock III is a professor of political science at the University of Central Florida. He has taught courses in research methods at the undergraduate and graduate levels for more than thirty years. His main research interests are American public opinion, voting behavior, techniques of quantitative analysis, and the scholarship of teaching and learning. His recent research has been on the effectiveness of Internet-based instruction. Pollocks research has appeared in the American Journal of Political Science, Social Science Quarterly, and the British Journal of Political Science. Recent scholarly publications include articles in Political Research Quarterly, the Journal of Political Science Education, and PS: Political Science and Politics. Barry C. Edwards writes textbooks and works for Fair Trial Analysis, LLC, a company that conducts research on juries and jurors for civil and criminal litigation. He received his B.A. from Stanford University, a J.D. from New York University, and a Ph.D. from the University of Georgia. He taught survey design and analysis, research methods, and prelaw courses at the University of Central Florida and continues to teach occasional courses for the University of Georgia. His political science interests include American politics, public law, and research methods. He founded the Political Science Data Group and created the PoliSciData.com website. His research has been published in American Politics Research, Congress & the Presidency, Election Law Journal, Emory Law Journal, Georgia Bar Journal, Harvard Negotiation Law Review, Journal of Politics, NYU Journal of Legislation and Public Policy, Political Research Quarterly, Presidential Studies Quarterly, Public Management Review, State Politics and Policy Quarterly, and UCLA Criminal Justice Law Review.