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Stata Companion to Political Analysis 2nd Revised edition [Mīkstie vāki]

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  • Formāts: Paperback / softback, 256 pages, height x width: 279x215 mm, weight: 1360 g
  • Izdošanas datums: 26-Jan-2011
  • Izdevniecība: CQ Press
  • ISBN-10: 1608716716
  • ISBN-13: 9781608716715
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Stata Companion to Political Analysis 2nd Revised editionPalielināt
  • Formāts: Paperback / softback, 256 pages, height x width: 279x215 mm, weight: 1360 g
  • Izdošanas datums: 26-Jan-2011
  • Izdevniecība: CQ Press
  • ISBN-10: 1608716716
  • ISBN-13: 9781608716715
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781608716715.html
Keywords:
Popular for its speed, flexibility, and attractive graphics, Stata is a powerful tool for political science students. With Pollocks second edition, students quickly learn Stata with 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.





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.
Figures x
Preface xiii
Getting Started 1(4)
Dataset CD
2(1)
Notes
3(2)
Chapter 1 Introduction to Stata 5(10)
Commands Covered: describe; codebook varname; set more off; File->Log->Begin; search keyword; help command_name; which package_name; ssc install package_name
Obtaining Information about a Dataset
5(2)
Obtaining Information about Variables
7(1)
Creating and Viewing a Log File
8(1)
Printing Results and Copying Output
9(1)
Getting Help
10(2)
Installing Statistical Modules
12(1)
Exercises
13(1)
Notes
13(2)
Chapter 2 Descriptive Statistics 15(28)
Commands Covered: tabulate varname; summarize varname, detail; sktest varname; histogram varname, d percent; histogram varname, percent; sort varname; list varname; The Graph Editor Recorder
Interpreting Measures of Central Tendency and Variation
15(1)
Describing Nominal Variables
16(1)
Describing Ordinal Variables
17(2)
Describing Interval Variables
19(3)
Obtaining Bar Charts and Histograms
22(1)
Creating Bar Charts
23(6)
A Closer Look: Graph Editor Recorder
28(1)
Creating Histograms
29(3)
Obtaining Case-level Information with sort and list
32(1)
Exercises
33(9)
Notes
42(1)
Chapter 3 Transforming Variables 43(16)
Commands Covered: recode, generate ( ); generate, recode( ); xtile, nquantiles ( ); generate; tabulate, generate ( ); label variable; label define; label values; drop; aorder
A Workbook Convention: Weighting the nes2008 Dataset
44(1)
Transforming Categorical Variables
44(2)
Transforming Interval Variables
46(4)
A Closer Look: The xtile Command
48(2)
The label define and label values Commands
50(1)
Creating an Additive Index
51(2)
Creating Indicator Variables
53(1)
Exercises
54(4)
Notes
58(1)
Chapter 4 Making Comparisons 59(34)
Commands Covered: tabulate dep_var indep_var, column; tabulate indep_var; summarize (dep_var); format; _gwtmean, dep_var_mean=dep_var [ if], by (indep_var); if; twoway (line dep_var indep_var; sort); replace; graph bar (mean) dep_var, over (indep_var)
Cross-tabulation Analysis
59(2)
Mean Comparison Analysis
61(1)
Visualizing Relationships with Line Charts and Bar Charts
62(1)
A Closer Look: The format Command
63(1)
Graphing an Interval-level Dependent Variable
63(7)
A Closer Look: The if Qualifier
65(5)
Graphing a Categorical Dependent Variable
70(3)
A Closer Look: The replace Command
71(2)
Exercises
73(17)
Notes
90(3)
Chapter 5 Making Controlled Comparisons 93(28)
Commands Covered: bysort cntrl_var: tabulate dep_var indep_var; col; tabulate cntrl_var indep_var, summarize (dep_var); graph bar dep_var; over (cntrl_var) over (indep_var); twoway (line dep_var indep_var if cntrl_var==value1, sort) (line dep_var indep_var if cntrl_var==value2, sort)
Cross-tabulation Analysis with a Control Variable
94(3)
Bar Charts for Controlled Comparisons with a Categorical Dependent Variable
97(3)
Mean Comparison Analysis with a Control Variable
100(3)
Line Charts for Controlled Comparisons with an Interval-level Dependent Variable
103(3)
Exercises
106(13)
Notes
119(2)
Chapter 6 Making Inferences about Sample Means 121(16)
Commands Covered: ttest varname = testvalue; ttest varname, by(group_var); robvar varname, by(group_var)
Describing a Sample Mean
122(5)
Testing the Difference between Two Sample Means
127(5)
Exercises
132(3)
Notes
135(2)
Chapter 7 Chi-square and Measures of Association 137(20)
Commands Covered: (tabulate option) chi2; (tabulate option) V; somersd indep_var dep_var; lambda dep_var indep_var
Analyzing Ordinal-level Relationships
138(5)
Analyzing Nominal-level Relationships
143(2)
A Problem with Lambda
145(1)
Exercises
146(9)
Notes
155(2)
Chapter 8 Correlation and Linear Regression 157(18)
Commands Covered: correlate varlist; regress dep_var indep_var(s); twoway (scatter dep_var indep_var) dep_var indep_var)
The correlate Command and the regress Command
157(3)
A Closer Look: R-squared and Adjusted R-squared: What's the Difference?
159(1)
Creating a Scatterplot with a Linear Prediction Line
160(3)
Exploring Multivariate Relationships with Regression
163(2)
Exercises
165(8)
Notes
173(2)
Chapter 9 Dummy Variables and Interaction Effects 175(18)
Commands Covered: xi: regression dep_var; i.indep_var; char varname [ omit] #; test varname1 = varname2; predict newvar
Regression with Dummy Variables
175(5)
A Closer Look: The test Command
178(2)
Interaction Effects in Multiple Regression
180(3)
Graphing Linear Prediction Lines for Interaction Relationships
183(3)
Exercises
186(6)
Notes
192(1)
Chapter 10 Logistic Regression 193(22)
Commands Covered: logit dep_var indep_var(s); logistic dep_var indep_var(s), [ -coef]; estimates store name; Irtest name, force; adjust indep_var1, by(indep_var2) pr gen(newvar); quietly; tabstat dep_var1 dep_var 2, by(indep_var)
The logit Command and the logistic Command
194(3)
Logistic Regression with Multiple Independent Variables
197(3)
A Closer Look: The estimates Command and the lrtest Command
199(1)
Working with Predicted Probabilities: Models with One Independent Variable
200(3)
Working with Predicted Probabilities: Models with Multiple Independent Variables
203(5)
Exercises
208(5)
Notes
213(2)
Chapter 11 Doing Your Own Political Analysis 215(12)
Five Doable Ideas
215(2)
Political Knowledge
216(1)
Economic Performance and Election Outcomes
216(1)
State Courts and Criminal Procedure
216(1)
Electoral Turnout in Comparative Perspective
217(1)
Congress
217(1)
Inputting Data
217(6)
Stata-formatted Datasets
217(1)
Microsoft Excel Datasets
218(3)
PDF Format or Hand-coded Data
221(2)
Writing It Up
223(3)
The Research Question
225(1)
Previous Research
225(1)
Data, Hypotheses, and Analysis
225(1)
Conclusions and Implications
226(1)
Notes
226(1)
Appendix
Table A-1 Descriptions of Constructed Variables in gss2006
227(3)
Table A-2 Descriptions of Variables in the states Dataset
230(5)
Table A-3 Descriptions of Variables in the world Dataset
235
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.