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Fundamentals of Political Science Research [Kõva köide]

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(Texas A & M University), (Texas A & M University)
  • Formaat: Hardback, 294 pages, kõrgus x laius x paksus: 260x185x25 mm, kaal: 870 g, 38 Tables, unspecified; 2 Halftones, unspecified; 49 Line drawings, unspecified
  • Ilmumisaeg: 15-Dec-2008
  • Kirjastus: Cambridge University Press
  • ISBN-10: 052187517X
  • ISBN-13: 9780521875172
Teised raamatud teemal:
Fundamentals of Political Science ResearchSuurem pilt
  • Formaat: Hardback, 294 pages, kõrgus x laius x paksus: 260x185x25 mm, kaal: 870 g, 38 Tables, unspecified; 2 Halftones, unspecified; 49 Line drawings, unspecified
  • Ilmumisaeg: 15-Dec-2008
  • Kirjastus: Cambridge University Press
  • ISBN-10: 052187517X
  • ISBN-13: 9780521875172
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780521875172.html
Märksõnad:
The Fundamentals of Political Science Research provides an introduction to the scientific study of politics, presenting a series of strategies and developing an integrated approach to research design and empirical analysis that supplies students with the basic tools needed to be both critical consumers and producers of scholarly research in political science.

Paul M. Kellstedt’s and Guy D. Whitten’s The Fundamentals of Political Science Research provides an introduction to the scientific study of politics, supplying students with the basic tools needed to be both critical consumers and producers of scholarly research in political science. The book begins with a discussion of what it means to take a scientific approach to the study of politics. At the core of such an approach is the development of causal theories. Because there is no magic formula by which theories are developed, the authors present a series of strategies and develop an integrated approach to research design and empirical analyses that allows students to determine the plausibility of their causal theories. The text’s accessible presentation of mathematical concepts and regression models with two or more independent variables is a key component to this process, along with the integration of examples from political science and the real world to help students grasp the fundamental concepts.

Arvustused

Many do not fully appreciate that the world is in the midst of a grand march of quantification through areas of human knowledge. As the rapidly improving tools of measurement, inference, and science are adapted to each area, automated statistical algorithms and well designed research vastly outperform casual human judgment. Given the numerous self-proclaimed political experts and pundits vying for media attention, some people may be surprised that science has even come to the study of government and politics. In The Fundamentals of Political Science Research, Kellstedt and Whitten not only set the record straight; they provide an interesting and accessible introduction to this exciting area, so students can better understand claims about the political world and even begin to make discoveries on their own.

- Gary King, Harvard University

Muu info

This textbook introduces the scientific study of politics, supplying students with the basic tools to be critical consumers and producers of scholarly research.
Figures
xiii
Tables
xv
Acknowledgments xvii
The Scientific Study of Politics
1(21)
Overview
1(1)
Political Science?
1(2)
Approaching Politics Scientifically: The Search for Causal Explanations
3(4)
Thinking about the World in Terms of Variables and Causal Explanations
7(7)
Models of Politics
14(1)
Rules of the Road to Scientific Knowledge about Politics
15(3)
Make Your Theories Causal
15(1)
Don't Let Data Alone Drive Your Theories
16(1)
Consider Only Empirical Evidence
17(1)
Avoid Normative Statements
17(1)
Pursue Both Generality and Parsimony
18(1)
A Quick Look Ahead
18(1)
Concepts Introduced in This
Chapter
19(1)
Exercises
20(2)
The Art of Theory Building
22(23)
Overview
22(1)
Good Theories Come from Good Theory-Building Strategies
22(1)
Identifying Interesting Variation
23(3)
Time-Series Example
24(1)
Cross-Sectional Example
25(1)
Learning to Use Your Knowledge
26(2)
Moving from a Specific Event to More General Theories
26(1)
Know Local, Think Global: Can You Drop the Proper Nouns?
27(1)
Examine Previous Research
28(3)
What Did the Previous Researchers Miss?
29(1)
Can Their Theory Be Applied Elsewhere?
29(1)
If We Believe Their Findings, Are There Further Implications?
30(1)
How Might This Theory Work at Different Levels of Aggregation (Micro⇔Macro)?
30(1)
Think Formally about the Causes That Lead to Variation in Your Dependent Variable
31(5)
Utility and Expected Utility
32(2)
The Puzzle of Turnout
34(2)
Think about the Institutions: The Rules Usually Matter
36(3)
Legislative Rules
36(2)
The Rules Matter!
38(1)
Extensions
39(1)
How Do I Know If I Have a ``Good'' Theory?
40(2)
Is Your Theory Causal?
40(1)
Can You Test Your Theory on Data That You Have Not Yet Observed?
41(1)
How General is Your Theory?
41(1)
How Parsimonious Is Your Theory?
41(1)
How New is Your Theory?
41(1)
How Nonobvious is Your Theory?
42(1)
Conclusion
42(1)
Concepts Introduced in This
Chapter
43(1)
Exercises
43(2)
Evaluating Causal Relationships
45(22)
Overview
45(1)
Causality and Everyday Language
45(3)
Four Hurdles along the Route to Establishing Causal Relationships
48(6)
Putting It All Together-Adding Up the Answers to Our Four Questions
50(1)
Identifying Causal Claims is an Essential Thinking Skill
50(3)
What Are the Consequences of Failing to Control for Other Possible Causes?
53(1)
Why is Studying Causality So Important? Three Examples from Political Science
54(7)
Life Satisfaction and Democratic Stability
54(1)
School Choice and Student Achievement
55(2)
Electoral Systems and the Number of Political Parties
57(4)
Why is Studying Causality So Important? Three Examples from Everyday Life
61(4)
Alcohol Consumption and Income
61(1)
Treatment Choice and Breast Cancer Survival
62(1)
Explicit Lyrics and Teen Sexual Behavior
63(2)
Wrapping Up
65(1)
Concepts Introduced in This
Chapter
65(1)
Exercises
65(2)
Research Design
67(19)
Overview
67(1)
Comparison as the Key to Establishing Causal Relationsips
67(1)
Experimental Research Designs
68(9)
``Random Assignment'' versus ``Random Sampling''
74(1)
Are There Drawbacks to Experimental Research Designs?
74(3)
Observational Studies (in Two Flavors)
77(6)
Datum, Data, Data Set
79(2)
Cross-Sectional Observational Studies
81(1)
Time-Series Observational Studies
82(1)
The Major Difficulty with Observational Studies
83(1)
Summary
83(1)
Concepts Introduced in This
Chapter
84(1)
Exercises
84(2)
Measurement
86(18)
Overview
86(1)
Why Measurement Matters
86(2)
Social Science Measurement: The Varying Challenges of Quantifying Humanity
88(3)
Problems in Measuring Concepts of Interest
91(5)
Conceptual Clarity
91(1)
Reliability
92(1)
Measurement Bias and Reliability
93(1)
Validity
94(1)
The Relationship between Validity and Reliability
95(1)
Controversy 1: Measuring Democracy
96(3)
Controversy 2: Measuring Political Tolerance
99(2)
Are There Consequences to Poor Measurement?
101(1)
Conclusions
101(1)
Concepts Introduced in This
Chapter
102(1)
Exercises
102(2)
Descriptive Statistics and Graphs
104(16)
Overview
104(1)
Know Your Data
104(1)
What is the Variable's Measurement Metric?
105(4)
Categorical Variables
106(1)
Ordinal Variables
106(1)
Continuous Variables
107(1)
Variable Types and Statistical Analyses
108(1)
Describing Categorical Variables
109(1)
Describing Continuous Variables
110(8)
Rank Statistics
111(3)
Moments
114(4)
Limitations
118(1)
Concepts Introduced in This
Chapter
118(1)
Exercises
118(2)
Statistical Inference
120(14)
Overview
120(1)
Populations and Samples
120(2)
Learning about the Population from a Sample: The Central Limit Theorem
122(6)
The Normal Distribution
122(6)
Example: Presidential Approval Ratings
128(3)
What Kind of Sample Was That?
129(1)
A Note on the Effects of Sample Size
130(1)
A Look Ahead: Examining Relationships between Variables
131(1)
Concepts Introduced in This
Chapter
132(1)
Exercises
132(2)
Bivariate Hypothesis Testing
134(25)
Overview
134(1)
Bivariate Hypothesis Tests and Establishing Causal Relationships
134(1)
Choosing the Right Bivariate Hypothesis Test
135(1)
All Roads Lead to p
136(3)
The Logic of p-Values
136(1)
The Limitations of p-Values
137(1)
From p-Values to Statistical Significance
138(1)
The Null Hypothesis and p-Values
138(1)
Three Bivariate Hypothesis Tests
139(16)
Tabular Analysis
139(6)
Difference of Means
145(5)
Correlation Coefficient
150(5)
Wrapping Up
155(1)
Concepts Introduced in This
Chapter
156(1)
Exercises
157(2)
Bivariate Regression Models
159(24)
Overview
159(1)
Two-Variable Regression
159(1)
Fitting a Line: Population ⇔ Sample
160(2)
Which Line Fits Best? Estimating the Regression Line
162(3)
Measuring Our Uncertainty about the OLS Regression Line
165(12)
Goodness-of-Fit: Root Mean-Squared Error
167(1)
Goodness-of-Fit: R-Squared Statistic
167(2)
Is That a ``Good'' Goodness-of-Fit?
169(1)
Uncertainty about Individual Components of the Sample Regression Model
169(2)
Confidence Intervals about Parameter Estimates
171(1)
Hypothesis Testing: Overview
172(1)
Two-Tailed Hypothesis Tests
173(2)
The Relationship between Confidence Intervals and Two-Tailed Hypothesis Tests
175(1)
One-Tailed Hypothesis Tests
175(2)
Assumptions, More Assumptions, and Minimal Mathematical Requirements
177(5)
Assumptions about the Population Stochastic Component
177(3)
Assumptions about Our Model Specification
180(1)
Minimal Mathematical Requirements
181(1)
How Can We Make All of These Assumptions?
181(1)
Concepts Introduced in This
Chapter
182(1)
Exercises
182(1)
Multiple Regression Models I: The Basics
183(19)
Overview
183(1)
Modeling Multivariate Reality
183(1)
The Population Regression Function
184(1)
From Two-Variable to Multiple Regression
184(4)
What Happens When We Fail to Control for Z?
188(5)
An Additional Minimal Mathematical Requirement in Multiple Regression
192(1)
Interpreting Multiple Regression
193(3)
Which Effect is ``Biggest''?
196(2)
Statistical and Substantive Significance
198(1)
Implications
199(1)
Concepts Introduced in This
Chapter
200(1)
Exercises
200(2)
Multiple Regression Models II: Crucial Extensions
202(42)
Overview
202(1)
Extensions of OLS
202(1)
Being Smart with Dummy Independent Variables in OLS
203(7)
Using Dummy Variables to Test Hypotheses about a Categorical Independent Variable with Only Two Values
203(4)
Using Dummy Variables to Test Hypotheses about a Categorical Independent Variable with More Than Two Values
207(3)
Testing Interactive Hypotheses with Dummy Variables
210(2)
Dummy Dependent Variables
212(8)
The Linear Probability Model
212(3)
Binomial Logit and Binomial Probit
215(4)
Goodness-of-Fit with Dummy Dependent Variables
219(1)
Outliers and Influential Cases in OLS
220(5)
Identifying Influential Cases
221(3)
Dealing with Influential Cases
224(1)
Multicollinearity
225(8)
How Does Multicollinearity Happen?
226(1)
Detecting Multicollinearity
227(1)
Multicollinearity: A Simulated Example
228(2)
Multicollinearity: A Real-World Example
230(2)
Multicollinearity: What Should I Do?
232(1)
Being Careful with Time Series
233(9)
Time-Series Notation
233(1)
Memory and Lags in Time-Series Analysis
234(2)
Trends and the Spurious Regression Problem
236(3)
The Differenced Dependent Variable
239(2)
The Lagged Dependent Variable
241(1)
Wrapping Up
242(1)
Concepts Introduced in This
Chapter
243(1)
Multiple Regression Models III: Applications
244(11)
Overview
244(1)
Why Controlling for Z Matters
244(1)
The Economy and Presidential Popularity
245(3)
Politics, Economics, and Public Support for Democracy
248(3)
Competing Theories of How Politics Affects International Trade
251(2)
Conclusions
253(1)
Concepts Introduced in This
Chapter
254(1)
Exercises
254(1)
Appendix A. Critical Values of Χ2 255(1)
Appendix B. Critical Values of t 256(1)
Appendix C. The λ Link Function for BNL Models 257(2)
Appendix D. The φ Link Function for BNP Models 259(2)
Bibliography 261(4)
Index 265
Paul M. Kellstedt is Associate Professor of Political Science and Director of the American Politics Program at Texas A&M University. He is the author of The Mass Media and the Dynamics of American Racial Attitudes (2003), which won the Goldsmith Book Prize. Professor Kellstedt is also the author or co-author of articles appearing in scholarly journals such as American Journal of Political Science, British Journal of Political Science, and Political Analysis, as well as several book chapters. He has been an Academic Visitor at Nuffield College, Oxford, and a Harvard University Fellow in the Joan Shorenstein Center on the Press, Politics, and Public Policy in the Kennedy School of Government. Guy D. Whitten is Associate Professor of Political Science and Director of the European Union Center of Excellence at Texas A&M University. He has published a variety of papers in scholarly journals, including the American Journal of Political Science, the British Journal of Political Science, and Electoral Studies. Professor Whitten serves on the editorial board of Electoral Studies and has previously served on the editorial boards of the Journal of Politics and Political Research Quarterly. He has been a visiting researcher at the University of Amsterdam and is a frequent instructor at the Summer School for Social Science Data Analysis and Collection at the University of Essex in the United Kingdom.