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R Companion to Political Analysis [Pehme köide]

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  • Formaat: Paperback / softback, 224 pages, kõrgus x laius: 279x215 mm, kaal: 540 g
  • Ilmumisaeg: 21-Jan-2014
  • Kirjastus: CQ Press
  • ISBN-10: 1452287317
  • ISBN-13: 9781452287317
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R Companion to Political AnalysisSuurem pilt
  • Formaat: Paperback / softback, 224 pages, kõrgus x laius: 279x215 mm, kaal: 540 g
  • Ilmumisaeg: 21-Jan-2014
  • Kirjastus: CQ Press
  • ISBN-10: 1452287317
  • ISBN-13: 9781452287317
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781452287317.html
Märksõnad:
This guide details the use of the data analysis software R in political science. It explains the use of R for constructing descriptions of variables and performing substantive analysis of political relationships, covering all major topics in data analysis: descriptive statistics, transforming variables, cross-tabulations and mean comparisons, controlled comparisons, statistical inference, chi-square and measures of association, correlation and linear regression, dummy variables and interaction effects, and logistic regression. The first chapter describes how to install, download, and get started with the program, and the last chapter provides ideas for research projects and details how to import data into R and a framework for a research paper. Chapters are presented as tutorials and guide students through examples for performing analysis. Annotation ©2014 Ringgold, Inc., Portland, OR (protoview.com)
List of Boxes and Figures
ix
Preface xi
A Quick Reference Guide to R Companion Functions xv
Chapter 1 Getting Started
1(12)
About R
2(1)
Installing R and Downloading the R Companion Workspace
3(4)
Checklist
7(1)
Running Scripts
7(1)
Managing R Output: Graphics and Text
8(2)
Getting Help
10(1)
Exercises
11(2)
Chapter 2 Descriptive Statistics
13(18)
Interpreting Measures of Central Tendency and Variation
14(1)
Describing Nominal Variables (Unordered Factors)
14(1)
Describing Ordinal Variables (Ordered Factors)
15(2)
Describing Interval Variables (Numerics)
17(5)
Obtaining Case-Level Information
22(2)
Exercises
24(7)
Chapter 3 Transforming Variables
31(10)
Collapsing Numeric Variables
32(3)
Creating Indicator Variables
35(2)
Creating an Additive Index
37(2)
Exercises
39(2)
Chapter 4 Making Comparisons
41(18)
Cross-Tabulations and Mosaic Plots
42(2)
Mean Comparison Analysis
44(1)
Box Plots
45(2)
Line Charts
47(3)
Graphing Indicator Variables
50(2)
Strip Charts
52(1)
Exercises
53(6)
Chapter 5 Making Controlled Comparisons
59(22)
Cross-Tabulation Analysis with a Control Variable
60(3)
Mean Comparison Analysis with a Control Variable
63(1)
Example of an Interaction Relationship
64(1)
Example of an Additive Relationship
65(1)
Graphing Relationships with a Control Variable
66(1)
Graphing a Numeric Dependent Variable
66(4)
Graphing an Indicator Variable
70(2)
Summary
72(1)
Exercises
72(9)
Chapter 6 Making Inferences about Sample Means
81(10)
Finding the 95 Percent Confidence Interval of the Population Mean
82(1)
Testing Hypothetical Claims about the Population Mean
83(2)
Making Inferences about Two Sample Means
85(2)
Exercises
87(4)
Chapter 7 Chi-Square and Measures of Association
91(16)
Svydesign
92(1)
Analyzing an Ordinal-Level Relationship
93(3)
Summary
96(1)
Analyzing an Ordinal-Level Relationship with a Control Variable
96(3)
Analyzing a Nominal-Level Relationship with a Control Variable
99(3)
Exercises
102(5)
Chapter 8 Correlation and Linear Regression
107(20)
Correlation
108(1)
Bivariate Regression
109(2)
Scatterplots
111(2)
Multiple Regression
113(2)
Correlation and Regression with Weighted Data
115(4)
Exercises
119(8)
Chapter 9 Dummy Variables and Interaction Effects
127(14)
Regression with Dummy Variables
127(5)
Interaction Effects in Multiple Regression
132(4)
Exercises
136(5)
Chapter 10 Logistic Regression
141(24)
Logistic Regression with One Independent Variable
143(2)
Logistic Regression with Multiple Independent Variables
145(3)
Working with Predicted Probabilities: Models with One Independent Variable
148(5)
Working with Predicted Probabilities: Models with Multiple Independent Variables
153(1)
The Sample Averages Method
153(2)
The Probability Profile Method
155(2)
An Additional Example of Multivariate Logistic Regression
157(4)
Exercises
161(4)
Chapter 11 Doing Your Own Political Analysis
165(14)
Five Doable Ideas
165(1)
Political Knowledge
166(1)
Economic Performance and Election Outcomes
166(1)
State Courts and Criminal Procedures
167(1)
Electoral Turnout in Comparative Perspective
167(1)
Congress
167(1)
Importing Data
168(1)
SPSS- and Stata-Formatted Datasets
168(2)
Microsoft Excel Datasets
170(4)
HTML Datasets
174(1)
PDF Format or Hand-Coded Data
175(2)
Writing It Up
177(1)
The Research Question
177(1)
Previous Research
178(1)
Data, Hypotheses, and Analysis
178(1)
Conclusions and Implications
178(1)
Appendix 179(1)
Table A-1 179(3)
Table A-2 182(6)
Table A-3 188(3)
Table A-4 191
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.