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viii, 359 pages : illustrations ; 22 cm
" A short and entertaining book on the modern art of writing well by New York Times bestselling author Steven Pinker Why is so much writing so bad, and how can we make it better? Is the English language being corrupted by texting and social media? Do the kids today even care about good writing? Why should any of us care? In The Sense of Style, the bestselling linguist and cognitive scientist Steven Pinker answers these questions and more. Rethinking the usage guide for the 21st century, Pinker doesn't carp about the decline of language or recycle pet peeves from the rulebooks of a century ago. Instead, he applies insights from the sciences of language and mind to the challenge of crafting clear, coherent, and stylish prose. In this short, cheerful, and eminently practical book, Pinker shows how writing depends on imagination, empathy, coherence, grammatical knowhow, and an ability to savor and reverse-engineer the good prose of others. He replaces dogma about usage with reason and evidence, allowing writers and editors to apply the guidelines judiciously, rather than robotically, being mindful of what they are designed to accomplish. Filled with examples of great and gruesome prose, Pinker shows us how the art of writing can be a form of pleasurable mastery and a fascinating intellectual topic in its own right"-- Provided by publisher.
"Pinker has a lot of ideas and sometimes controversial opinions about writing and in this entertaining and instructive book he rethinks the usage guide for the 21st century. Don't blame the internet, he says, good writing has always been hard. It requires imagination, taking pleasure in reading, overcoming the difficulty we all have in imagining what it's like to not know something we do know"-- Provided by publisher.
Green Library
x, 314 p. : ill. ; 24 cm.
  • Research design and the accumulation of knowledge
  • Big questions, little answers : how the questions you choose affect the answers you get
  • How the cases you choose affect the answers you get : selection bias and related issues
  • How the evidence you use affects the answers you get : rigorous use of the evidence contained in case studies
  • How the approach you choose affects the answers you get : the uses and limitations of rational choice comparative politics.
Green Library
197 p. : ill. (some col.), maps (some col.) ; 28 cm.
  • I: Graphical practice
  • Graphical excellence
  • Graphical integrity
  • Sources of graphical integrity and sophistication
  • II: ry of data graphics
  • Data-ink and graphical redesign
  • Chartjunk: vibrations, grids, and ducks
  • Data-ink maximization and graphical design
  • Multifunctioning graphical elements
  • Data density and small multiples
  • Aesthetics and technique in data graphical design
  • Epilogue: Designs for the display of information.
Green Library
xi, 245 p. : ill. ; 24 cm.
  • Preface ix 1 The Science in Social Science 3 1.1 Introduction 3 1.1.1 Two Styles of Research, One Logic of Inference 3 1.1.2 Defining Scientific Research in Social Sciences 7 1.1.3 Science and Complexity 9 1.2 Major Components of Research Design 12 1.2.1 Improving Research Questions 14 1.2.2 Improving Theory 19 1.2.3 Improving Data Quality 23 1.2.4 Improving the Use of Existing Data 27 1.3 Themes of This Volume 28 1.3.1 Using Observable Implications to Connect Theory and Data 28 1.3.2 Maximizing Leverage 29 1.3.3 Reporting Uncertainty 31 1.3.4 Thinking like a Social Scientist: Skepticism and Rival Hypotheses 32 2 Descriptive Inference 34 2.1 General Knowledge and Particular Facts 35 2.1.1 "Interpretation" and Inference 36 2.1.2 "Uniqueness, " Complexity, and Simplification 42 2.1.3 Comparative Case Studies 43 2.2 Inference: the Scientific Purpose of Data Collection 46 2.3 Formal Models of Qualitative Research 49 2.4 A Formal Model of Data Collection 51 2.5 Summarizing Historical Detail 53 2.6 Descriptive Inference 55 2.7 Criteria for Judging Descriptive Inferences 63 2.7.1 Unbiased Inferences 63 2.7.2 Efficiency 66 3 Causality and Causal Inference 75 3.1 Defining Causality 76 3.1.1 The Definition and a Quantitative Example 76 3.1.2 A Qualitative Example 82 3.2 Clarifying Alternative Definitions of Causality 85 3.2.1 "Causal Mechanisms" 85 3.2.2 "Multiple Causality" 87 3.2.3 "Symmetric" and "Asymmetric" Causality 89 3.3 Assumptions Required for Estimating Causal Effects 91 3.3.1 Unit Homogeneity 91 3.3.2 Conditional Independence 94 3.4 Criteria for Judging Causal Inferences 97 3.5 Rules for Constructing Causal Theories 99 3.5.1 Rule 1: Construct Falsifiable Theories 100 3.5.2 Rule 2: Build Theories That Are Internally Consistent 105 3.5.3 Rule 3: Select Dependent Variables Carefully 107 3.5.4 Rule 4: Maximize Concreteness 109 3.5.5 Rule 5: State Theories in as Encompassing Ways as Feasible 113 4 Determining What to Observe 115 4.1 Indeterminate Research Designs 118 4.1.1 More Inferences than Observations 119 4.1.2 Multicollinearity 122 4.2 The Limits of Random Selection 124 4.3 Selection Bias 128 4.3.1 Selection on the Dependent Variable 129 4.3.2 Selection on an Explanatory Variable 137 4.3.3 Other Types of Selection Bias 138 4.4 Intentional Selection of Observations 139 4.4.1 Selecting Observations on the Explanatory Variable 140 4.4.2 Selecting a Range of Values of the Dependent Variable 141 4.4.3 Selecting Observations on Both Explanatory and Dependent Variables 142 4.4.4 Selecting Observations So the Key Causal Variable Is Constant 146 4.4.5 Selecting Observations So the Dependent Variable Is Constant 147 4.5 Concluding Remarks 149 5 Understanding What to Avoid 150 5.1 Measurement Error 151 5.1.1 Systematic Measurement Error 155 5.1.2 Nonsystematic Measurement Error 157 5.2 Excluding Relevant Variables: Bias 168 5.2.1 Gauging the Bias from Omitted Variables 168 5.2.2 Examples of Omitted Variable Bias 176 5.3 Including Irrelevant Variables: Inefficiency 182 5.4 Endogeneity 185 5.4.1 Correcting Biased Inferences 187 5.4.2 Parsing the Dependent Variable 188 5.4.3 Transforming Endogeneity into an Omitted Variable Problem 189 5.4.4 Selecting Observations to Avoid Endogeneity 191 5.4.5 Parsing the Explanatory Variable 193 5.5 Assigning Values of the Explanatory Variable 196 5.6 Controlling the Research Situation 199 5.7 Concluding Remarks 206 6 Increasing the Number of Observations 208 6.1 Single-Observation Designs for Causal Inference 209 6.1.1 "Crucial" Case Studies 209 6.1.2 Reasoning by Analogy 212 6.2 How Many Observations Are Enough? 213 6.3 Making Many Observations from Few 217 6.3.1 Same Measures, New Units 219 6.3.2 Same Units, New Measures 223 6.3.3 New Measures, New Units 224 6.4 Concluding Remarks 229 References 231 Index 239.
  • (source: Nielsen Book Data)9780691034713 20160528
While heated arguments between practitioners of qualitative and quantitative research have begun to test the very integrity of the social sciences, Gary King, Robert Keohane, and Sidney Verba have produced a farsighted and timely book that promises to sharpen and strengthen a wide range of research performed in this field. These leading scholars, each representing diverse academic traditions, have developed a unified approach to valid descriptive and causal inference in qualitative research, where numerical measurement is either impossible or undesirable. Their book demonstrates that the same logic of inference underlies both good quantitative and good qualitative research designs, and their approach applies equally to each. Providing precepts intended to stimulate and discipline thought, the authors explore issues related to framing research questions, measuring the accuracy of data and uncertainty of empirical inferences, discovering causal effects, and generally improving qualitative research. Among the specific topics they address are interpretation and inference, comparative case studies, constructing causal theories, dependent and explanatory variables, the limits of random selection, selection bias, and errors in measurement. Mathematical notation is occasionally used to clarify concepts, but no prior knowledge of mathematics or statistics is assumed. The unified logic of inference that this book explicates will be enormously useful to qualitative researchers of all traditions and substantive fields.
(source: Nielsen Book Data)9780691034713 20160528
Green Library