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The Craft of Political Research immerses readers not only in how political scientists work but also in how ideas produce research questions and guide the selection of research methods.Emphasizing the internal logic of research methods and the collaborative nature of the research process, this slender text explores the design behind interesting questions, problems in measurement and analysis, and key statistical methods. Brief and inexpensive to include in any course, The Craft of Political Research's elegant explanations inspire a big picture understanding of how political scientists explain political reality and encourage students to create their own inventive, original, and bold research work.

Features include:

Focuses on the big picture of how good research leads to good theories instead of just what research method to use.

Provides concise and accessible coverage of key topics, including the nature of research, research design, sampling, statistical analysis, ethics, and more.

Includes detailed examples of classic and contemporary political science research to give students models for their own original research.

Tabla de contenidos provista por Syndetics

• Preface(p. xi)
• 1 Doing Research(p. 1)
• Social Research(p. 2)
• Types of political research(p. 4)
• The research mix(p. 8)
• Evaluating different types of research(p. 10)
• Ethics of Political Research(p. 11)
• 2 Political Theories and Research Topics(p. 13)
• Causality and Political Theory(p. 14)
• What Does Good Theory Look Like?(p. 15)
• Example of elegant research: Robert Putnam(p. 17)
• To quantify or not?(p. 18)
• Choosing a Topic(p. 19)
• Engineering research(p. 20)
• Theory-oriented research(p. 20)
• Setting Out a Research Design(p. 21)
• Observations, puzzles, and the construction of theories(p. 22)
• Machiavellian Guide to Developing Research Topics(p. 26)
• Further Discussion(p. 29)
• 3 The Importance of Dimensional Thinking(p. 30)
• English as a Language for Research(p. 31)
• Ordinary language(p. 32)
• The Proper Use of Multidimensional Words(p. 35)
• Example of dimensional analysis(p. 36)
• Further Discussion(p. 38)
• 4 Problems of Measurement: Accuracy(p. 39)
• Reliability(p. 43)
• Reliability as a characteristic of concepts(p. 44)
• Testing the reliability of a measure(p. 45)
• Validity(p. 46)
• Some examples(p. 47)
• Checking for validity(p. 49)
• The Impact of Random and Nonrandom Errors(p. 50)
• The Importance of Accuracy(p. 52)
• Further Discussion(p. 53)
• 5 Problems of Measurement: Precision(p. 55)
• Precision in Measures(p. 56)
• Precision in Measurement(p. 59)
• The innate nature of levels of measurement(p. 61)
• The sin of wasting information(p. 62)
• Enriching the level of precision in measurement(p. 64)
• Examples of enrichment(p. 65)
• Quantifiers and nonquantifiers again(p. 69)
• Further Discussion(p. 70)
• 6 Causal Thinking and the Design of Research(p. 72)
• The Subjective Nature of Causation(p. 73)
• Eliminating Alternative Causal Interpretations(p. 74)
• Summary(p. 75)
• A Few Basics of Research Design(p. 76)
• Designs without a control group(p. 77)
• Use of a control group(p. 79)
• The true experiment(p. 81)
• Designs for Political Research(p. 82)
• Special design problem for policy analysis: regression to the mean(p. 86)
• The Use of Varied Designs and Measures(p. 89)
• Example of Varied Designs and Measures(p. 90)
• Conclusion(p. 91)
• Holding a Variable Constant(p. 91)
• Further Discussion(p. 93)
• 7 Introduction to Statistics: Measuring Relationships for Interval Data(p. 95)
• Statistics(p. 96)
• The Importance of Levels of Measurement(p. 96)
• Working with Interval Data(p. 97)
• Regression analysis(p. 97)
• Correlation analysis(p. 106)
• Correlation and regression compared(p. 111)
• The problem of measurement error(p. 114)
• Further Discussion(p. 115)
• 8 Introduction to Statistics: Further Topics on Measurement of Relationships(p. 117)
• Measures of Relationship for Ordinal Data(p. 117)
• Measures of Relationship for Nominal Data(p. 120)
• Dichotomies and regression analysis(p. 121)
• Logit and Probit Analysis(p. 124)
• Multivariate Analysis(p. 126)
• Conclusion(p. 131)
• 9 Introduction to Statistics: Inference, or How to Gamble on Your Research(p. 133)
• The Logic of Measuring Significance(p. 134)
• Example of Statistical Inference(p. 135)
• Hypothesis Testing(p. 136)
• The null hypothesis(p. 137)
• Example: x[superscript 2](p. 138)
• Sampling distributions(p. 140)
• The importance of N(p. 143)
• The problem of independent observations(p. 145)
• Is a Significance Test Always Necessary?(p. 146)
• Polling and Significance Tests(p. 147)
• Uses and Limitations of Statistical Tests(p. 148)
• Conclusion(p. 149)
• Further Discussion(p. 149)
• 10 Where Do Theories Come From?(p. 150)
• Selected Bibliography(p. 154)
• Index(p. 159)