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Making Sense of Numbers
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Making Sense of Numbers
Quantitative Reasoning for Social Research



October 2021 | 608 pages | SAGE Publications, Inc

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

Supplements

Instructor Resource Site
edge.sagepub.com/millernumbers1e


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LMS cartridge included with this title for use in Blackboard, Canvas, Brightspace by Desire2Learn (D2L), and Moodle

The LMS cartridge makes it easy to import this title’s instructor resources into your learning management system (LMS). These resources include:

  • Test banks
  • Editable chapter-specific PowerPoint® slides
  • All tables and figures from the textbook 
Don’t use an LMS platform?

You can still access all of the same online resources for this title via the password-protected Instructor Resource Site.

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
Winthrop University

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
Lehman College, CUNY

Making Sense of Numbers is an excellent companion for those learning to navigate the world of quantitative research.

Marc Isaacson
Augsburg University