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Writing about Data
First Edition ©2025 Joanna Wolfe Formats: E-book
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Authors
-
Joanna Wolfe
Joanna Wolfe (Ph.D., University of Texas at Austin) is Director of the Global Communication Center at Carnegie Mellon University, where she develops new methods for improving communication instruction across the university. She is the author of numerous scholarly articles on teamwork, gender studies, collaborative learning technology , technical writing, and rhetoric Her research on collaborative writing in technical communication classes won the 2006 NCTE award for best article reporting qualitative or quantitative research in technical and scientific communication.
Table of Contents
UNIT 1: INTRODUCTION
Chapter 1: Numbers Do Not Speak for Themselves
Case Study: The Challenger space shuttle
- For Discussion: The Challenger memo
Data reporting involves argument
Data-based arguments depend on purpose, audience, and credibility
Numbers can be manipulated — just like words
Data can be qualitative as well as quantitative
Callout: Is the word data singular or plural?
Is this book for me?
Summary
- Exercise 1.1: Reframing statistics
Chapter 2: Telling a Story with Quantitative Data
Data visualizations and words work together to tell a story
- Exercise 2.1: Telling stories about data
Not all stories are equally credible and ethical
- Avoid breaking common conventions for reporting data
- Do not make small differences seem large
- For Discussion: Adjusted y-axis
- Include important context relevant to understanding the data
- Carefully word claims to avoid exaggeration
Summary
- Exercise 2.2: Rating credibility
UNIT 2: WORKING WITH DATA STORIES
Chapter 3: Visualizing Your Data Story: Part I
The most common visualizations (and the stories they support)
- Callout: Chart, graph, figure? What’s the difference?
Bar graphs
- Line graphs
- Pie graphs
- Tables
The case against pie graphs: Why they should be used sparingly
- For Discussion: Pie graphs
Use captions and labels to complete and reinforce your story
- A checklist for figure and table captions
- Exercise 3.1: Analyzing captions
- Exercise 3.2: Examining visualizations in research articles
Generative AI and data visualization
Summary
- Exercise 3.3: Creating visualizations
Chapter 4: Reinforcing Your Visualization’s Story
Sort to emphasize your story
Group data to foreground one story over another
Reduce non-data ink
Minimize eye movement
Use contrast to emphasize your story
Be consistent and credible in how you display numbers
Summary
- Exercise 4.1: Examining visualizations in research articles
- Exercise 4.2: Revising your data visualizations
- Exercise 4.3: Grouping and arranging data in data visualizations
Chapter 5: Using Basic Math to Shape Your Story
Summarize data to concisely communicate a story
- Exercise 5.1: Summarizing and averaging data
Use weighted data to shape your story
- Weighted data are useful in evaluations
- Weighted data are useful for Likert-scale data
Combine data with other sources to give context
- Exercise 5.2: Combining different types of data
- Case Study: Satisfaction with democracy in the United States, 1997 versus 2022
- For Discussion: Raw numbers versus percentages
- For Discussion: Shaping the story with calculations
Summary
- Exercise 5.3: Turning complex data into a clear story
Chapter 6: Working with Qualitative Data
- Callout: Qualitative and quantitative are not mutually exclusive
Use qualitative data to describe
- For Discussion: Quotations as “data”
Use qualitative data to categorize
Use qualitative data to evaluate
- For Discussion: Qualitative data across disciplines
Report qualitative data ethically
- Exercise 6.1: Ethical interpretation of quotes
Summary
- Exercise 6.2: Analyze a report with qualitative data
UNIT 3: WRITING THE FORMAL DATA REPORT
Chapter 7: Writing a Formal Data Report (IMRD)
IMRD stands for Introduction, Method, Results, and Discussion
IMRD reports have an abstract or executive summary
IMRD sections aid readers by being highly predictable
- Exercise 7.1: Identifying IMRD information
IMRD reports support non-linear reading
- Exercise 7.2: How do you read an IMRD report?
IMRD organization can vary
- Exercise 7.3: Analyze IMRD sections in a sample report
Summary
- Exercise 7.4: Research Posters
- Exercise 7.5: Analysis of good and bad reports
Chapter 8: Writing the Results Section
Write your data story in paragraphs
- For Discussion: How does the paragraph change your understanding?
- Callout: For the mathematically minded...
- Exercise 8.1: Interpreting data
Weave multiple data stories together
Use subheadings to organize into skimmable chunks
A sample Results section: improving instructor ethos through document design
Summary
- Exercise 8.2: Annotating results
Chapter 9: Writing about Methods
Methods readers lie at two extremes of care in reading
Methods are typically organized with subheadings
Methods sections justify choices
- For Discussion: Justifying methodological choices
Methods sections favor past tense and passive voice
- Exercise 9.1: Choosing active or passive voice
Methods themselves are sometimes a major research contribution
Summary
- Exercise 9.2: Comparing methods sections
Chapter 10: Introducing Research Studies
Introductions convey the context and value of your work
Researchers follow a “formula” for introducing research
- Exercise 10.1: Annotating introductions
Introductions can be short and sweet
- For Discussion: Short and sweet introductions
Introductions can be long and encompass multiple sections
Summary
- Exercise 10.2: Annotating Introductions
Chapter 11: Discussions, Conclusions, and Recommendations
IMRD reports conclude by moving from specific to general
- Exercise 11.1: Annotating Discussion sections
What goes in the Results versus the Discussion sections?
- Callout: The combined Results and Discussion
- Exercise 11.2: Choosing Results or Discussion
What goes in the Discussion versus the Conclusion?
A Recommendations section often concludes business and professional reports
Summary
- Exercise 11.3: Annotating concluding sections
Chapter 12: Front Matter: Titles, Abstracts, and Executive Summaries
Titles should clearly and precisely state the main focus
Abstracts and executive summaries are reports in miniature
- Callout: A note of caution
The traditional academic abstract is a single paragraph
The structured academic abstract has labeled sections
An IMRD executive summary is typically one page
The recommendations-first executive summary
Avoid common problems with executive summaries
Summary
- Exercise 12.1: Rating abstracts
APPENDICES
Appendix A: Two Sample IMRD Reports
Version 1: Effect of Verbal Commands in Instructions for Assembly of a Lego Vehicle
Version 2: Effect of Verbal Commands in Instructions for Assembly of a Lego Vehicle
Appendix B: Design or Proof-of-Concept IMRD Reports
Product Updates
Authors
-
Joanna Wolfe
Joanna Wolfe (Ph.D., University of Texas at Austin) is Director of the Global Communication Center at Carnegie Mellon University, where she develops new methods for improving communication instruction across the university. She is the author of numerous scholarly articles on teamwork, gender studies, collaborative learning technology , technical writing, and rhetoric Her research on collaborative writing in technical communication classes won the 2006 NCTE award for best article reporting qualitative or quantitative research in technical and scientific communication.
Table of Contents
UNIT 1: INTRODUCTION
Chapter 1: Numbers Do Not Speak for Themselves
Case Study: The Challenger space shuttle
- For Discussion: The Challenger memo
Data reporting involves argument
Data-based arguments depend on purpose, audience, and credibility
Numbers can be manipulated — just like words
Data can be qualitative as well as quantitative
Callout: Is the word data singular or plural?
Is this book for me?
Summary
- Exercise 1.1: Reframing statistics
Chapter 2: Telling a Story with Quantitative Data
Data visualizations and words work together to tell a story
- Exercise 2.1: Telling stories about data
Not all stories are equally credible and ethical
- Avoid breaking common conventions for reporting data
- Do not make small differences seem large
- For Discussion: Adjusted y-axis
- Include important context relevant to understanding the data
- Carefully word claims to avoid exaggeration
Summary
- Exercise 2.2: Rating credibility
UNIT 2: WORKING WITH DATA STORIES
Chapter 3: Visualizing Your Data Story: Part I
The most common visualizations (and the stories they support)
- Callout: Chart, graph, figure? What’s the difference?
Bar graphs
- Line graphs
- Pie graphs
- Tables
The case against pie graphs: Why they should be used sparingly
- For Discussion: Pie graphs
Use captions and labels to complete and reinforce your story
- A checklist for figure and table captions
- Exercise 3.1: Analyzing captions
- Exercise 3.2: Examining visualizations in research articles
Generative AI and data visualization
Summary
- Exercise 3.3: Creating visualizations
Chapter 4: Reinforcing Your Visualization’s Story
Sort to emphasize your story
Group data to foreground one story over another
Reduce non-data ink
Minimize eye movement
Use contrast to emphasize your story
Be consistent and credible in how you display numbers
Summary
- Exercise 4.1: Examining visualizations in research articles
- Exercise 4.2: Revising your data visualizations
- Exercise 4.3: Grouping and arranging data in data visualizations
Chapter 5: Using Basic Math to Shape Your Story
Summarize data to concisely communicate a story
- Exercise 5.1: Summarizing and averaging data
Use weighted data to shape your story
- Weighted data are useful in evaluations
- Weighted data are useful for Likert-scale data
Combine data with other sources to give context
- Exercise 5.2: Combining different types of data
- Case Study: Satisfaction with democracy in the United States, 1997 versus 2022
- For Discussion: Raw numbers versus percentages
- For Discussion: Shaping the story with calculations
Summary
- Exercise 5.3: Turning complex data into a clear story
Chapter 6: Working with Qualitative Data
- Callout: Qualitative and quantitative are not mutually exclusive
Use qualitative data to describe
- For Discussion: Quotations as “data”
Use qualitative data to categorize
Use qualitative data to evaluate
- For Discussion: Qualitative data across disciplines
Report qualitative data ethically
- Exercise 6.1: Ethical interpretation of quotes
Summary
- Exercise 6.2: Analyze a report with qualitative data
UNIT 3: WRITING THE FORMAL DATA REPORT
Chapter 7: Writing a Formal Data Report (IMRD)
IMRD stands for Introduction, Method, Results, and Discussion
IMRD reports have an abstract or executive summary
IMRD sections aid readers by being highly predictable
- Exercise 7.1: Identifying IMRD information
IMRD reports support non-linear reading
- Exercise 7.2: How do you read an IMRD report?
IMRD organization can vary
- Exercise 7.3: Analyze IMRD sections in a sample report
Summary
- Exercise 7.4: Research Posters
- Exercise 7.5: Analysis of good and bad reports
Chapter 8: Writing the Results Section
Write your data story in paragraphs
- For Discussion: How does the paragraph change your understanding?
- Callout: For the mathematically minded...
- Exercise 8.1: Interpreting data
Weave multiple data stories together
Use subheadings to organize into skimmable chunks
A sample Results section: improving instructor ethos through document design
Summary
- Exercise 8.2: Annotating results
Chapter 9: Writing about Methods
Methods readers lie at two extremes of care in reading
Methods are typically organized with subheadings
Methods sections justify choices
- For Discussion: Justifying methodological choices
Methods sections favor past tense and passive voice
- Exercise 9.1: Choosing active or passive voice
Methods themselves are sometimes a major research contribution
Summary
- Exercise 9.2: Comparing methods sections
Chapter 10: Introducing Research Studies
Introductions convey the context and value of your work
Researchers follow a “formula” for introducing research
- Exercise 10.1: Annotating introductions
Introductions can be short and sweet
- For Discussion: Short and sweet introductions
Introductions can be long and encompass multiple sections
Summary
- Exercise 10.2: Annotating Introductions
Chapter 11: Discussions, Conclusions, and Recommendations
IMRD reports conclude by moving from specific to general
- Exercise 11.1: Annotating Discussion sections
What goes in the Results versus the Discussion sections?
- Callout: The combined Results and Discussion
- Exercise 11.2: Choosing Results or Discussion
What goes in the Discussion versus the Conclusion?
A Recommendations section often concludes business and professional reports
Summary
- Exercise 11.3: Annotating concluding sections
Chapter 12: Front Matter: Titles, Abstracts, and Executive Summaries
Titles should clearly and precisely state the main focus
Abstracts and executive summaries are reports in miniature
- Callout: A note of caution
The traditional academic abstract is a single paragraph
The structured academic abstract has labeled sections
An IMRD executive summary is typically one page
The recommendations-first executive summary
Avoid common problems with executive summaries
Summary
- Exercise 12.1: Rating abstracts
APPENDICES
Appendix A: Two Sample IMRD Reports
Version 1: Effect of Verbal Commands in Instructions for Assembly of a Lego Vehicle
Version 2: Effect of Verbal Commands in Instructions for Assembly of a Lego Vehicle
Appendix B: Design or Proof-of-Concept IMRD Reports
Product Updates
Use data to be a more effective writer
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Are you a campus bookstore looking for ordering information?
MPS Order Search Tool (MOST) is a web-based purchase order tracking program that allows customers to view and track their purchases. No registration or special codes needed! Just enter your BILL-TO ACCT # and your ZIP CODE to track orders.
Canadian Stores: Please use only the first five digits/letters in your zip code on MOST.
Visit MOST, our online ordering system for booksellers: https://tracking.mpsvirginia.com/Login.aspx
Learn more about our Bookstore programs here: https://www.macmillanlearning.com/college/us/contact-us/booksellers
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Our courses currently integrate with Canvas, Blackboard (Learn and Ultra), Brightspace, D2L, and Moodle. Click on the support documentation below to find out more details about the integration with each LMS.
Integrate Macmillan courses with Blackboard
Integrate Macmillan courses with Canvas
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If you’re a verified instructor, you can request a free sample of our courseware, e-book, or print textbook to consider for use in your courses. Only registered and verified instructors can receive free print and digital samples, and they should not be sold to bookstores or book resellers. If you don't yet have an existing account with Macmillan Learning, it can take up to two business days to verify your status as an instructor. You can request a free sample from the right side of this product page by clicking on the "Request Instructor Sample" button or by contacting your rep. Learn more.
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Sometimes also referred to as a spiral-bound or binder-ready textbook, loose-leaf textbooks are available to purchase. This three-hole punched, unbound version of the book costs less than a hardcover or paperback book.
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We can help! Contact your representative to discuss your specific needs for your course. If our off-the-shelf course materials don’t quite hit the mark, we also offer custom solutions made to fit your needs.
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