Making Sense of Numbers
Quantitative Reasoning for Social Research
1st Edition
Published on 8. November 2021
Book
Paperback/Softback
608 pages
978-1-5443-5559-7 (ISBN)
Description
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, editable PowerPoint slides, and tables and figures from the book.
An instructor website for the book includes a test bank, editable PowerPoint slides, and tables and figures from the book.
Reviews / Votes
This text invites students to develop an in-depth understanding of core concepts in research methods, clearly guides them through real-life examples, and offers tools needed for the development of strong analytical skills highly valued in the labor market. -- Maria Aysa-Lastra This an incredibly useful textbook, showing students how to interpret others' quantitative research, think about quantitative research of their own, and communicate the findings of that research. I learned several great tips myself on writing effectively about quantitative research findings! -- Susan A. Dumais Making Sense of Numbers is an excellent companion for those learning to navigate the world of quantitative research. -- Marc IsaacsonMore details
Language
English
Place of publication
Thousand Oaks
United States
Target group
College/higher education
Dimensions
Height: 232 mm
Width: 187 mm
Weight
908 gr
ISBN-13
978-1-5443-5559-7 (9781544355597)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Person
Jane E. Miller is a Professor at the Edward J. Bloustein School of Planning and Public Policy at Rutgers University, where she is Lead Instructor for the undergraduate Research Methods course and instructor for the undergraduate Honors Research Program. She also teaches graduate courses on data visualization and quantitative research. She was previously Faculty Director of Project L/EARN - an intensive social science research training program for undergraduates from historically under-represented groups.
Dr. Miller has written two other books: The Chicago Guide to Writing about Numbers and The Chicago Guide to Writing about Multivariate Analysis (University of Chicago Press) - both in their second editions, and also available in Chinese translation (Xinhua Publishing). She has also authored a series of articles in teaching and research journals on how to communicate about quantitative research. Dr. Miller's research interests include relationships between poverty, child health, health insurance, and access to health care. She earned her bachelor's degree in Economics from Williams College and her M.A. and PhD in Demography from the University of Pennsylvania.
Dr. Miller has written two other books: The Chicago Guide to Writing about Numbers and The Chicago Guide to Writing about Multivariate Analysis (University of Chicago Press) - both in their second editions, and also available in Chinese translation (Xinhua Publishing). She has also authored a series of articles in teaching and research journals on how to communicate about quantitative research. Dr. Miller's research interests include relationships between poverty, child health, health insurance, and access to health care. She earned her bachelor's degree in Economics from Williams College and her M.A. and PhD in Demography from the University of Pennsylvania.
Content
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
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