The essentials of political analysis

書誌事項

The essentials of political analysis

Philip H. Pollock, III

CQ Press, Sage, c2012

4th ed

  • : pbk

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注記

Includes index

内容説明・目次

内容説明

Political science students need to get comfortable using the tools of political analysis. For stats-phobic undergrads, the research methods course need not need be nerve wracking, especially with Pollock's bestselling text to help them along the way. His brief, accessible guide walks students through the basics - measuring concepts, formulating and testing hypotheses, describing variables - while using key terms, chapter-opening objectives, 80 tables and figures, and class-tested exercises to get them using and applying their new skills. The new Fourth Edition features enhanced treatment of research design and logic-controlled comparison and illustrates complex relationships with more accessible examples. A completely revised rubric further helps students identify these complex relationships in their own research.

目次

Introduction The Definition and Measurement of Concepts Conceptual Definitions Operational Definitions Measuring and Describing Variables Measuring Variables Describing Variables Proposing Explanations, Framing Hypotheses, and Making Comparisons Proposing Explanations Framing Hypotheses Making Comparisons Graphing Relationships and Describing Patterns The "How Else?" Question: Making Controlled Comparisons Experimental Studies Observational Studies Three Scenarios: X Y, Controlling for Z Sampling and Inference Cross-Tabulation Analysis Graphing Controlled Comparisons Foundations of Statistical Inference Population Parameters and Sample Statistics Random Sampling The Standard Error of a Sample Mean Inference Using the Normal Distribution Inference Using the Students' T-Distribution What About Sample Proportions? Tests of Significance and Measures of Association Statistical Significance Measures of Association Correlation and Linear Regression Correlation Bivariate Regression R-Square Dummy Variable Regression Logistic Regression The Logistic Regression Approach Finding the Best Fit: Maximum Likelihood Estimation Logistic Regression with Multiple Independent Variables Working with Probabilities

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