图书简介
Making Sense of Numbers teaches students the skills they need to be both consumers and producers of quantitative research: able to read about, collect, calculate, and communicate numeric information for both everyday tasks and school or work assignments. The text teaches how to avoid making common errors of reasoning, calculation, or interpretation by introducing a systematic approach to working with numbers, showing students how to figure out what a particular number means. The text also demonstrates why it is important to apply a healthy dose of skepticism to the numbers we all encounter, so that we can understand how those numbers can (and cannot) be interpreted in their real-world context. Jane E. Miller uses annotated examples on a wide variety of topics to illustrate how to use new terms, concepts, and approaches to working with numbers. End-of-chapter engagement activities designed based on Miller’s three decades of teaching experience can be used in class or as homework assignments, with some for students to do individually and others intended for group discussion. The book is ideally suited for a range of courses, including quantitative reasoning, research methods, basic statistics, data analysis, and communicating quantitative information. An instructor website for the book includes a test bank and editable PowerPoint slides.
List of Figures \\ List of Tables \\ Preface \\ Acknowledgments \\ About the Author \\ PART I: INTRODUCTION \\ Chapter 1: Introduction to Making Sense of Numbers \\ The Many Uses of Numbers \\ Common Tasks Involving Numbers \\ Plausibility of Numeric Values \\ Challenges in Making Sense of Numbers \\ How We Learn to Make Sense of Numbers \\ Chapter 2: Foundational Concepts for Quantitative Research \\ Terminology for Quantitative Research \\ The Research Circle \\ Goals Of Quantitative Research \\ The W’s \\ Report and Interpret Numbers \\ Specify Direction and Magnitude \\ PART II: HOW TOPIC, MEASUREMENT, AND CONTEXT HELP MAKE SENSE OF NUMBERS \\ Chapter 3: Topic and Conceptualization \\ Conceptualization \\ Scope of a Definition \\ How Topic and Scope Affect Plausibility \\ How Topic and Perspective Affect Optimal Values \\ Chapter 4: Measurement \\ Measurement \\ Factors Affecting Operationalization \\ Levels of Measurement \\ Units \\ Data Collection and Level of Measurement \\ How Measurement Affects Plausibility \\ Reliability and Validity of Numeric Measures \\ Chapter 5: Context \\ What Is Context? \\ How Context Affects Plausibility \\ How Context Affects Measurement \\ Population Versus Study Sample \\ Representativeness \\ Generalization \\ Level of Analysis and Fallacy of Level \\ PART III: EXHIBITS FOR COMMUNICATING NUMERIC INFORMATION \\ Chapter 6: Working With Tables \\ Criteria for Effective Tables \\ Anatomy of a Table \\ Organizing Data in Tables and Charts \\ Reading Data From Tables \\ Considerations for Creating Tables \\ Chapter 7: Working With Charts and Visualizations \\ Criteria for Effective Charts and Visualizations \\ Visual Perception Principles \\ Anatomy of a Chart or Visualization \\ Charts and Visualizations for Specific Tasks \\ Design Issues \\ Common Errors in Chart Creation \\ PART IV: MAKING SENSE OF NUMBERS FROM MATHEMATICAL AND STATISTICAL METHODS \\ Chapter 8: Comparison Values, Contrast Sizes, and Standards \\ Reference Groups and Comparison Values \\ Standards, Thresholds, and Target Values \\ Contrast Sizes for Quantitative Variables \\ Considerations for Comparability \\ Chapter 9: Numbers, Comparisons, and Calculations \\ Numeric Measures of Level \\ Plausibility Criteria for Measures of Level \\ Measures of Position in a Ranked List \\ Plausibility Criteria for Measures of Position \\ Mathematical Calculations \\ Plausibility Criteria for Results of Calculations \\ How Level of Measurement Affects Valid Types of Comparison \\ Choosing Types of Comparisons \\ Chapter 10: Distributions and Associations \\ Distributions of Single Variables \\ Plausibility Criteria for Univariate Statistics \\ Tables and Charts for Presenting Distributions \\ Associations Between Two or More Variables \\ Three-Way Associations \\ Plausibility Criteria for Bivariate and Three-Way Statistics \\ Comparisons by Level of Measurement, Revisited \\ PART V: ASSESSING THE QUALITY OF NUMERIC ESTIMATES \\ Chapter 11: Bias \\ What Is Bias? \\ Time Structure of Study Designs \\ Sampling Methods \\ Study Nonresponse \\ Item Nonresponse \\ Measurement Bias \\ Data Sources \\ Chapter 12: Causality \\ Causality Defined \\ Criteria for Assessing Causality \\ Experimental Studies \\ Observational Studies \\ Research Strategies for Assessing Confounding \\ Random Sampling vs. Random Assignment \\ Implications of Causality for Quantitative Research \\ Chapter 13: Uncertainty of Numeric Estimates \\ What Is Statistical Uncertainty? \\ Inferential Statistics \\ Measures of Uncertainty \\ Uncertainty vs. Bias \\ Basics of Hypothesis Testing \\ Drawbacks of Traditional Hypothesis Testing \\ Interpreting Inferential Statistics for Bivariate and Three-Way Procedures \\ PART VI: PULLING IT ALL TOGETHER \\ Chapter 14: Communicating Quantitative Research \\ Tools for Presenting Quantitative Research \\ Expository Writing Techniques \\ Writing About Numbers in Particular \\ Conveying the Type of Measure or Calculation \\ Writing About Distributions \\ Writing About Associations \\ Writing About Complex Patterns \\ Content and Structure of Research Formats \\ Chapter 15: The Role of Research Methods in Making Sense of Numbers \\ The W’s Revisited \\ Practical Importance \\ Importance of a Numeric Finding: The Big Picture \\ How Study Design, Measurement, and Sample Size Affect “Importance” \\ Making Sense of Numbers in Quantitative Research Tasks \\ APPENDIXES \\ Appendix A: Why and How to Create New Variables \\ Why New Variables Might Be Needed \\ Transformations of Numbers \\ Indexes and Scales \\ New Continuous Variables \\ New Categorical Variables \\ Appendix B: Sampling Weights \\ The Purpose of Sampling Weights \\ Sampling Weights for Disproportionate Sampling \\ Communicating Use of Sampling Weights \\ Appendix C: Brief Technical Background on Inferential Statistics \\ Standard Error and Sample Size \\ Margin of Error \\ Confidence Interval \\ Criteria for Making Sense of Measures of Uncertainty \\ Hypothesis Testing \\ Errors in Hypothesis Testing \\ Plausibility Criteria for Inferential Test Statistics \\ References \\ Index
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