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Cover: Writing about Data, 1st Edition by Joanna Wolfe
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Writing about Data

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First  Edition|©2025  Joanna Wolfe

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ISBN:9781319339784

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About

Using real-world examples, this guide explains how data can be used effectively to support your written documents and addresses the key strategies for working with data, including best practices for creating charts and graphs.

Digital Options

E-book

Read online (or offline) with all the highlighting and notetaking tools you need to be successful in this course.

Learn More

Contents

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

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.


Use data to be a more effective writer

Using real-world examples, this guide explains how data can be used effectively to support your written documents and addresses the key strategies for working with data, including best practices for creating charts and graphs.

E-book

Read online (or offline) with all the highlighting and notetaking tools you need to be successful in this course.

Learn More

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

Headshot of Joanna Wolfe

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.


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