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E-raamat: IBM(R) SPSS(R) Companion to Political Analysis

  • Formaat: EPUB+DRM
  • Ilmumisaeg: 11-Jul-2019
  • Kirjastus: CQ Press
  • Keel: eng
  • ISBN-13: 9781506379647
  • Formaat - EPUB+DRM
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  • Formaat: EPUB+DRM
  • Ilmumisaeg: 11-Jul-2019
  • Kirjastus: CQ Press
  • Keel: eng
  • ISBN-13: 9781506379647

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"[ The text] provides by far the best introduction for students wanting to learn how to use SPSS in conducting statistical analysis. Its clear in-depth examples makes data analysis accessible to even the most numbers-phobic student."  Michael Burch, Eckerd College

In Pollocks trusted IBM SPSS® workbook, students dive headfirst into actual political data and work with a software tool that prepares them not only for future political science research, but the job world as well. Students learn by doing with new guided examples, annotated screenshots, step-by-step instructions, and exercises that reflect current scholarly debates in American political behavior and comparative politics. This Sixth Edition of An IBM SPSS® Companion to Political Analysis features thoroughly revised and updated datasets and is compatible with all post-12 releases of SPSS.

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

"[ The text] provides by far the best introduction for students wanting to learn how to use SPSS in conducting statistical analysis. Its clear in-depth examples makes data analysis accessible to even the most numbers-phobic student." -- Michael Burch "With a focus on both the mechanics of SPSS and interpretation of data, An IBM SPSS Companion to Political Analysis provides a very useful and useable addition to a Political Science Methods course. The chapters have engaging exercises that address many areas of the discipline and are organized to build students understanding of statistical interpretation and skills with SPSS. Students learn to run the statistical procedures, to interpret the results, and to construct arguments with the data." -- Matthew J. Costello

Figures
ix
Preface xiii
Getting Started 1(4)
Downloading the Datasets
2(2)
SPSS Full and Grad Pack Versions: What Is the Difference?
4(1)
Watch Screencasts from SAGE Edge
4(1)
1 Introduction to SPSS
5(16)
The Data Editor
5(3)
Setting Options for Variable Lists
8(1)
A Closer Look: Variables Utility
9(1)
The Viewer
10(1)
A Closer Look: Keyboard Shortcuts
11(2)
Selecting, Printing, and Saving Output
13(1)
How to Format an SPSS Table
14(2)
Saving Commands in Syntax Files
16(1)
Getting Help
17(2)
Chapter 1 Exercises
19(2)
2 Descriptive Statistics
21(24)
How SPSS Stores Information about Variables
21(1)
Interpreting Measures of Central Tendency and Variation
22(1)
Describing Nominal Variables
23(2)
A Closer Look: Weighting the GSS and NES Datasets
25(1)
Describing Ordinal Variables
26(3)
A Closer Look: Analyzing Two Variables at Once
29(1)
Using the Chart Editor to Modify Graphics
29(2)
Describing Interval Variables
31(5)
Obtaining Case-level Information with Case Summaries
36(3)
Chapter 2 Exercises
39(6)
3 Transforming Variables
45(26)
Creating Indicator Variables
46(4)
A Closer Look: Creating Multiple Dummy Variables
50(1)
Working with Variable Labels
51(1)
Recoding Interval-level Variables into Simplified Categories
52(3)
Simplifying an Internal-level Variable with Visual Binning
55(4)
Centering or Standardizing a Numeric Variable
59(3)
Using Compute to Create an Additive Index
62(5)
Chapter 3 Exercises
67(4)
4 Making Comparisons
71(24)
Cross-tabulation Analysis
71(3)
Visualizing Cross-tabulation Analysis with a Bar Chart
74(5)
Mean Comparison Analysis
79(2)
Visualizing Mean Comparison Analysis with a Line Chart
81(2)
Creating a Box Plot to Make Comparisons
83(4)
Chapter 4 Exercises
87(8)
5 Making Controlled Comparisons
95(28)
Cross-tabulation Analysis with a Control Variable
95(6)
Graphing Controlled Comparisons with Categorical Dependent Variables
101(3)
Mean Comparison Analysis with a Control Variable
104(7)
Example of an Interaction Relationship
106(3)
Example of an Additive Relationship
109(2)
Visualizing Controlled Mean Comparisons
111(4)
Chapter 5 Exercises
115(8)
6 Making Inferences about Sample Means
123(22)
Finding the 95% Confidence Interval of a Sample Mean
124(1)
A Closer Look: Treating Census as a Sample
125(3)
Testing a Hypothetical Claim about the Population Mean
128(3)
Inferences about the Difference between Two Sample Means
131(3)
Visualizing Mean Comparisons with Error Bars
134(2)
Making Inferences about Sample Proportions
136(5)
Chapter 6 Exercises
141(4)
7 Chi-square and Measures of Association
145(24)
Analyzing an Ordinal-level Relationship
146(4)
A Closer Look: Reporting and Interpreting Results
150(1)
Analyzing an Ordinal-level Relationship with a Control Variable
151(4)
Analyzing a Nominal-level Relationship
155(3)
Analyzing a Nominal-level Relationship with a Control Variable
158(5)
Chapter 7 Exercises
163(6)
8 Correlation and Linear Regression
169(22)
Correlation Analysis
170(2)
Bivariate Regression
172(3)
A Closer Look: R-Squared and Adjusted R-Squared: What's the Difference?
175(1)
Creating Scatterplots for Bivariate Regression Analysis
176(4)
Multiple Regression
180(2)
A Closer Look: Reporting Regression Results in Tables
182(1)
Visualizing Multiple Regression Analysis with Bubble Plots
183(2)
Chapter 8 Exercises
185(6)
9 Dummy Variables and Interaction Effects
191(22)
Regression with Multiple Dummy Variables
191(6)
A Closer Look: Changing the Reference Category
197(1)
Interaction Effects in Multiple Regression
198(5)
A Closer Look: What Are Standardized Regression Coefficients?
203(1)
Graphing Linear Prediction Lines for Interaction Relationships
204(3)
Chapter 9 Exercises
207(6)
10 Logistic Regression
213(32)
Thinking about Odds, Logged Odds, and Probabilities
213(2)
Estimating Logistic Regression Models
215(6)
Logistic Regression with Multiple Independent Variables
221(3)
Graphing Predicted Probabilities with One Independent Variable
224(4)
Graphing Predicted Probabilities with Multiple Independent Variables
228(11)
Marginal Effects at the Means
229(4)
Marginal Effects at Representative Values
233(6)
Chapter 10 Exercises
239(6)
11 Doing Your Own Political Analysis
245(16)
Seven Doable Ideas
245(4)
Political Knowledge and Interest
246(1)
Self-Interest and Policy Preferences
246(1)
Economic Performance and Election Outcomes
247(1)
Electoral Turnout in Comparative Perspective
247(1)
Interviewer Effects on Public Opinion Surveys
248(1)
Religion and Politics
248(1)
Race and Politics
249(1)
Importing Data into SPSS
249(5)
SPSS Formatted Datasets
249(1)
Other Supported Data Types
250(1)
Microsoft Excel Datasets
250(4)
HTML Table Data
254(1)
Writing It Up
254(5)
The Research Question
257(1)
Previous Research
257(1)
Data, Hypotheses, and Analysis
258(1)
Conclusions and Implications
258(1)
Chapter 11 Exercises
259(2)
Appendix, Table A-1 Variables in the GSS Dataset in Alphabetical Order 261(7)
Appendix, Table A-2 Variables in the NES Dataset in Alphabetical Order 268(4)
Appendix, Table A-3 Variables in the States Dataset by Topic 272(6)
Appendix, Table A-4 Variables in the World Dataset by Topic 278
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.