The Essentials of Political Analysis

Name: The Essentials of Political Analysis
Price: 8990 ISK
Availability: InStock
Author: Philip H. Pollock III; Barry C. Edwards
ISBN: 9781071861530

Höfundar: Philip H. Pollock III; Barry C. Edwards (Útgáfa: 7)

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Equip students with the skills and confidence they need to conduct political analyses and critically assess statistical research. In the Seventh Edition of The Essentials of Political Science, bestselling authors Philip H. Pollock III and Barry C. Edwards build students’ analytic abilities and develop their statistical reasoning with new data, fresh exercises, and clear examples. This brief and reader-friendly guide walks students through the essentials— defining measurement, formulating and testing hypotheses, measuring variables—while using key terms, chapter-opening objectives, over 80 tables and figures, and practical exercises to get them using and applying their new skills.

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Útgefandi: SAGE Publications, Inc. (US)

ISBN: 9781071861530

Print ISBN: 9781071967638

Format: ePub

Útgáfa: 7

Höfundar: Philip H. Pollock III; Barry C. Edwards

Tungumál: English

Útgefið: 2024-12-26

Prent takmörkun á líftíma: 10

Prent takmörkun: 2

Afritunar takmörkun: 2

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Cover
Contents
List of Tables
List of Figures
List of Boxes
Preface
Acknowledgments
About the Authors
Introduction
Chapter VM Vantage Module: Information Literacy, Academic Integrity, and Responsible AI Use: Essential Skills for Learning
Information Literacy and Critical Thinking
Transparency and Verification
Misinformation, Hallucination, and Disinformation
Misinformation
Hallucination
Disinformation
Steps to Evaluate Sources and Information
SIFT: Stop
SIFT: Investigate the Source
SIFT: Find Better Coverage
SIFT: Trace Claims, Quotes, and Media to Their Original Context
SIFT Summary
Key Takeaways: Why Evaluating Information Matters
Exercising Academic Integrity and Intellectual Honesty
Integrity in Practice
Academic Integrity
Integrity in the Workplace
Responsible Collaboration
How to Paraphrase and Cite Sources
Paraphrasing
Citing
Cheating
Consequences of Cheating
Key Takeaways: Why Academic Integrity Matters
Understanding AI: Types, Capabilities, and Limitations
How AI Works
Types of AI
What AI Can Do Well
AI in College
AI in the Workplace
Where AI Falls Short
AI (In)accuracy and Biases
AI Overreliance
AI Tensions
Key Takeaways: Why Understanding AI Matters
Using AI Responsibly
How to Build Good AI Habits
Use AI as a Helper—Not a Thinker
Think Critically About AI Data
How to Prompt AI Effectively
Prompting Approaches
Prompting Framework
Prompting Process
Revising Outputs
How to Put AI Guidelines Into Practice
Purpose
Transparency
Accuracy
Ethics and Policy
Key Takeaways: Why Responsible AI Use Matters
Chapter Summary
Closing Reflection
Glossary
Review Questions
Think Critically
Put the Chapter to Work
Case Study: Fact or Fiction? When AI Gets It Wrong
Disclosure Statement
Endnotes
Chapter 1 The Definition and Measurement of Concepts
1.1 Conceptual Definitions
1.1.1 Clarifying a Concept
1.1.2 A Template for Writing a Conceptual Definition
1.1.3 Why It’s Important to Identify the Unit of Analysis
1.2 Operational Definitions
1.3 Measurement Error
1.3.1 Systematic Measurement Error
1.3.2 Random Measurement Error
1.4 Reliability and Validity
1.4.1 Evaluating Reliability
1.4.2 Evaluating Validity
1.5 Working With Datasets, Codebooks, and Software
Summary
Key Terms
Exercises
Chapter 2 Measuring and Describing Variables
2.1 Essential Features
2.2 Levels of Measurement
2.2.1 Nominal-Level Variables
2.2.2 Ordinal-Level Variables
2.2.3 Interval-Level Variables
2.2.4 Which Level of Measurement Is Best?
2.3 Central Tendency and Dispersion of Variables
2.4 Describing Nominal-Level Variables
2.5 Describing Ordinal-Level Variables
2.6 Describing Interval-Level Variables
2.6.1 Describing Distribution of Values With Tables and Graphs
2.6.2 Measures of Central Tendency
2.6.3 Measures of Dispersion: Range, Standard Deviation, and Variance
2.6.4 Skewness and Kurtosis
2.6.5 Using Box Plots to Compare Dispersions
Summary
Key Terms
Exercises
Chapter 3 Creating and Transforming Variables
3.1 Transforming Interval-Level Variables With Math Functions
3.2 Sometimes, Less Is More: Simplifying Variables
3.2.1 Dummy Variables
3.2.2 Interval- to Ordinal-Level Transformations
3.3 Managing Data and Metadata
3.4 Additive Indexes and Measurement Scales
3.5 Advanced Data Transformation Methods
Summary
Key Terms
Exercises
Chapter 4 Proposing Explanations, Framing Hypotheses, and Making Comparisons
4.1 “All Models Are Wrong, but Some Are Useful”
4.1.1 Causal Diagrams
4.1.2 Probabilistic Explanations
4.2 Proposing Explanations
4.2.1 Generating Plausible Explanations
4.2.2 Explaining Varying Support for Social Security
4.2.3 Explaining Influence of a Social Connector
4.2.4 Effects of Declining Civic Engagement
4.2.5 Translating Explanations to Causal Diagrams
4.3 Framing Hypotheses
4.3.1 Template for Writing a Research Hypothesis
4.3.2 Common Mistakes in Hypothesis Writing
4.3.3 The Null Hypothesis
4.4 Making Comparisons
4.4.1 Cross-Tabulations
4.4.2 Mean Comparisons
Summary
Key Terms
Exercises
Chapter 5 Graphing Relationships and Describing Patterns
5.1 Historic Examples of Data Visualization
5.2 Levels of Measurement and Choice of Graph Types
5.3 Visualizing Relationships With Categorical Variables
5.4 Describing Patterns
5.5 Graphing Relationship Between Interval-Level Variables
5.6 Challenges of Visualizing Data
Summary
Key Terms
Exercises
Chapter 6 Research Design, Research Ethics, and Evidence of Causation
6.1 Establishing Causation
6.2 Experimental Designs
6.2.1 Random Assignment
6.2.2 Pretreatment Measurements
6.2.3 Laboratory Experiments
6.2.4 Field Experiments
6.3 Selecting Cases for Analysis
6.3.1 Random Sampling
6.3.2 Weighting Sample Observations
6.3.3 Nonrandom Sampling and Qualitative Research Designs
6.4 Conducting Research Ethically
6.4.1 Experiments Involving Human Participants
6.4.2 Ethical Responsibilities to the Academic Community
Summary
Key Terms
Exercises
Chapter 7 Making Controlled Comparisons
7.1 The Logic of Controlled Comparisons
7.2 Essential Terms and Concepts
7.3 Effect of Partisanship on Gun Control Vote, Controlling for Gender: An Illustrative Example
7.3.1 A Spurious Relationship
7.3.2 Additive Relationships
7.3.3 Interactive Relationships
7.3.4 Many Faces of Interaction
7.4 Controlled Mean Comparisons
7.4.1 Example of an Additive Relationship
7.4.2 Example of an Interactive Relationship
7.5 Identifying Patterns
7.6 Advanced Methods of Making Controlled Comparisons
Summary
Key Terms
Exercises
Chapter 8 Foundations of Statistical Inference
8.1 Population Parameters and Sample Statistics
8.2 The Central Limit Theorem and the Normal Distribution
8.3 Quantifying Standard Errors
8.3.1 Standard Error of a Sample Mean
8.3.2 Standard Error of a Sample Proportion
8.4 Confidence Intervals
8.5 Sample Size and the Margin of Error of a Poll
8.6 Inferences With Small Batches: The Student’s t-Distribution
Summary
Key Terms
Exercises
Chapter 9 Hypothesis Tests With One or Two Samples
9.1 Statistical Significance and Null Hypothesis Testing
9.1.1 Specifying Research Hypothesis and Null Hypothesis
9.1.2 Setting Threshold for Statistical Significance
9.1.3 Estimating Parameters With Sample Data
9.1.4P-Value and Confidence Interval Approaches
9.1.5 Drawing Conclusions
9.2 One-Sample Significance Tests
9.2.1 Testing Hypothesis With Sample Proportion
9.2.2 Testing Hypotheses With One Sample Mean
9.3 Two-Sample Significance Tests
9.3.1 Difference of Proportions Test
9.3.2 Difference of Means Test
9.3.3 Variants of the Difference of Means Test
9.4 Criticisms of Null Hypothesis Testing
Summary
Key Terms
Exercises
Chapter 10 Chi-Square Test and Analysis of Variance
10.1 Null Hypothesis Tests With More than Two Groups
10.2 The Chi-Square Test of Independence
10.2.1 Conducting a Chi-Square Test
10.2.2 Evaluating Chi-Square Test Statistics
10.2.3 Chi-Square Test With Control Variable
10.3 Measures of Association
10.3.1 Lambda
10.3.2 Somers’ d
10.3.3 Cramer’s V
10.4 Analysis of Variance (ANOVA)
10.4.1 Steps of Hypothesis Testing With ANOVA
10.4.2 Example of Single-Factor ANOVA
10.4.3 Calculating the F-Statistic
10.4.4 Evaluating F-Statistics
10.4.5 Two-Factor ANOVA
Summary
Key Terms
Exercises
Chapter 11 Correlation and Bivariate Regression
11.1 Correlation
11.1.1 Calculating the Correlation Between Interval Variables
11.1.2 Correlation Analysis With Categorical Variables
11.2 Bivariate Regression
11.2 Educational Attainment and Voter Turnout in States Example
11.4R-Square and Adjusted R-Square
11.5 All Models Are Still Wrong, but Some Are Useful
Summary
Key Terms
Exercises
Chapter 12 Multiple Regression
12.1 Multiple Regression Equation
12.2 Educational Attainment and Voter Turnout in States Revisited
12.3 Regression With Multiple Dummy Variables
12.4 Interaction Effects in Multiple Regression
12.4.1 Visualizing Multiple Regression
12.5 Some Practical Issues in Multiple Regression Analysis
12.5.1 Multicollinearity
12.5.2 Parsimony and Variable Selection
12.5.3 Missing Data
Summary
Key Terms
Exercises
Chapter 13 Analyzing Regression Residuals
13.1 What Are Regression Residuals?
13.2 Assumptions About Regression Residuals
13.3 Diagnostic Graphs of Regression Residuals
13.4 Testing Assumptions About Regression Residuals
13.4.1 Normality Tests
13.4.2 Constant Variance Tests
13.4.3 Autocorrelation Tests
13.4.4 Outlier and Influence Tests
13.5 What If Assumptions Are Violated?
Summary
Key Terms
Exercises
Chapter 14 Logistic Regression
14.1 The Logistic Regression Approach
14.2 Logistic Regression Analysis of Vote Choice in the 2020 Presidential Election
14.3 Finding the Best Fit: Maximum Likelihood Estimation
14.3.1 Quantifying Model Fit With Logged Likelihood
14.3.2 Maximizing Likelihood With the Right Coefficients
14.3.3 Measuring Model Fit Using Change in Likelihood
14.4 Logistic Regression With Multiple Independent Variables
14.5 Graphing Predicted Probabilities With Multiple Independent Variables
14.5.1 Marginal Effects at the Means
14.5.2 Marginal Effects at Representative Values
Summary
Key Terms
Exercises
Chapter 15 Conducting Your Own Political Analysis
15.1 Picking a Good Topic
15.2 Getting Focused and Staying Motivated
15.2.1 Defining Your Goals With a Research Plan
15.2.2 Organizing Ideas With an Outline
15.3 Reviewing Prior Literature
15.4 Collecting Data
15.4.1 Political Science Data Archives
15.4.2 Making Observations
15.4.3 Automated Data Collection
15.4.4 Survey Research
15.4.5 Interviews
15.5 Writing It Up
15.5.1 General Suggestions About Tone, Citations, and Text Bots
15.5.2 Suggestions for Paper Organization
15.6 Maintain a Scientific Mindset
Summary
Key Terms
Exercises
Glossary
Endnotes
Index