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Improve your value at work: Using evidence to influence decision making

This talk—part of a keynote to the 2019 Summit of the Society for Technical Communication—argued that communicators need to become more critical consumers of evidence and empirical research. It showed how bringing data to the table (rather than opinion) can have a powerful influence on how others view your value at work. Learning how to use evidence can develop your personal expertise and enhance your credibility on the job—whether you focus on editing, content strategy, technical illustration, plain language, or information design.

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Improve your value at work:
Using evidence to influence decision making
Karen Schriver
KSA Communication Design & Research
@firstwren
08 March 2019
People in organizations who are not part of our field
may have trouble understanding what we do
@firstwren #stc19
Document features
of products or services
From blog of Sarah Maddox, technical writer (Google Australia)
https://ffeathers.wordpress.com/2019/04/01/illustrating-the-
multifaceted-role-of-a-tech-writer/#comment-53004
What they think we do
@firstwren #stc19
Sarah Maddox, “Illustrating the multifaceted role of a tech writer” (2019).
https://ffeathers.wordpress.com/?s=a+tech+writer
What we may actually do
@firstwren #stc19
Relevant research is interdisciplinary
Artificial IntelligenceUser Experience Design
Inclusive Design
@firstwren #stc19
Critique
Claim
A practical explanation or reason
for why a claim has merit.
An evaluation of a claim by citing
quantitative or qualitative evidence
that supports (or refutes) it.
A conclusion or argument a
speaker is trying to persuade you
to accept, the merits of which
need to be convincing.
Rationale
Does the evidence support the claim?
Questioning arguments
@firstwren #stc19

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Improve your value at work: Using evidence to influence decision making

  • 1. Improve your value at work: Using evidence to influence decision making Karen Schriver KSA Communication Design & Research @firstwren 08 March 2019
  • 2. People in organizations who are not part of our field may have trouble understanding what we do @firstwren #stc19
  • 3. Document features of products or services From blog of Sarah Maddox, technical writer (Google Australia) https://ffeathers.wordpress.com/2019/04/01/illustrating-the- multifaceted-role-of-a-tech-writer/#comment-53004 What they think we do @firstwren #stc19
  • 4. Sarah Maddox, “Illustrating the multifaceted role of a tech writer” (2019). https://ffeathers.wordpress.com/?s=a+tech+writer What we may actually do @firstwren #stc19
  • 5. Relevant research is interdisciplinary Artificial IntelligenceUser Experience Design Inclusive Design @firstwren #stc19
  • 6. Critique Claim A practical explanation or reason for why a claim has merit. An evaluation of a claim by citing quantitative or qualitative evidence that supports (or refutes) it. A conclusion or argument a speaker is trying to persuade you to accept, the merits of which need to be convincing. Rationale Does the evidence support the claim? Questioning arguments @firstwren #stc19
  • 7. Critique Claim Texts using simple language in English will be easier to translate and localize. Texts using simple language benefit both expert and novice users—helping everyone. Simpler texts are usually easier to translate and localize. Texts should be written using simple language because weak readers will understand them faster. Rationale Does the evidence support the claim? Questioning arguments @firstwren #stc19
  • 8. Take a rival hypothesis stance § What is the claim? § Does the rationale for the claim have merit? § What is the evidence for the claim? § Is the claim supported by evidence? § Who were the participants? § Might the evidence tell a different story? @firstwren #stc19
  • 9. Ways of drawing faulty conclusions about data §Exaggerate the evidence §Fail to qualify the evidence §Authorize the evidence: “experts say or do X” §Misapply the evidence §Hold different values for different participant groups §Assume data apply across contexts §Cherry pick from the evidence §Assume correlation means causation §Overgeneralize from a single population @firstwren #stc19
  • 10. Consider these claims §Since pictures can be more effective than words, images are always a better choice than text because they are visual. §Madcap Flare is technical communication experts’ favorite tool. §Using contractions in texts makes content friendlier and adds a more personal tone. §Writers who use AI-based language analysis software produce better content. @firstwren #stc19
  • 11. Since pictures can be more effective than words, images are always a better choice than text because they are visual. Claim @firstwren #stc19
  • 13. What kind of picture? For whom? Hey, show me the data! @firstwren #stc19
  • 14. Research says § Images are powerful and can be more effective than words § Images can be more memorable and enjoyable than words BUT § Whether an image is the best choice depends on – Type of image and clarity of signals – Quality of image – Relatedness of image to context – Knowledge of viewer @firstwren #stc19
  • 18. Critique Claim A picture is worth a thousand words. Images can be more effective than words, BUT comprehension depends on – Type of image, quality, relatedness – Knowledge of user Since pictures can be more effective than words, images are always a better choice than text because they are visual. Rationale Critical consumer’s conclusion? § Claim needs to be qualified § Claim exaggerates the evidence @firstwren #stc19
  • 19. Madcap Flare is technical communication experts’ favorite tool. . Claim @firstwren #stc19
  • 21. Rationale for claim Ferry Vermeulen of Berlin-based INSTRKTIV asked 83 technical communication experts to nominate their top 3 favorite technical writing tools and found... @firstwren #stc19
  • 22. Experts’ top 10 technical writing tools 1. MadCap Flare 2. DITA 3. oXygen XML Editor 4. Adobe FrameMaker 5. NotePad ++ 6. Camtasia Studio 7. GitHub 8. Snagit 9. Acrolinx 10. Adobe Captivate See Vermeulen (2019) https://instrktiv.com/en/technical-writing-tools/ @firstwren #stc19
  • 24. Critique Claim Madcap Flare is expert technical communicators’ favorite tool. Participants may not represent expert technical communicators’ preferences more generally. An interview study showed participants preferred Madcap Flare over other software. Rationale Critical consumer’s conclusion? § Claim overgeneralizes the evidence § Results may not apply beyond the study’s participants @firstwren #stc19
  • 25. Using contractions in texts makes content friendlier and adds a more personal tone. Claim @firstwren #stc19
  • 26. Hey, show me the data! All contractions? Friendlier for everyone? @firstwren #stc19
  • 27. Types of contractions Positive – You’ve – You’re – We’ll – We’d Negative – Don’t – Won’t – Can’t – Shouldn’t Modal – Should’ve – Could’ve – Would’ve – Might’ve @firstwren #stc19
  • 28. What research says Contractions make the text friendlier and add a more personal tone for native speakers of English who are also fluent readers. @firstwren #stc19
  • 29. What research says Language experience in English matters § Experience in listening to and reading English plays a role in whether people will have trouble § Contractions can be hard for those just learning to read English @firstwren #stc19
  • 30. What research says Literacy matters § People who are poor readers in their native language are also likely to have challenges in L2 § Native English speakers with low literacy skills may find contractions that express a modal verb harder to understand – should’ve – could’ve – shouldn’t – couldn’t – mightn’t – mustn’t @firstwren #stc19
  • 31. What research, best practices say Some contractions cause problems § Negative contractions may place heavier cognitive demands on some L2 readers § Some people rely on reading the NOT – ChangePeople (2016) @firstwren #stc19
  • 32. The UK’s Government Digital Service found that negative contractions led some users to make errors that spelling it out did not. – Lord & Lanman (2016) @firstwren #stc19
  • 33. What research, best practices say Not all contractions cause problems § Positive contractions are quite effective – You’ve, you’re, you’ve, you’ll, you’d – We’ve, we’re, we’ll, we’d @firstwren #stc19
  • 34. Critique Claim Using contractions in texts makes content friendlier and adds a more personal tone. Contractions can make the text friendlier, BUT some types may lead to problems for people with low literacy or who are non-native speakers. Since contractions make the text more friendly, it’s a good idea to use them. Rationale Critical consumer’s conclusion? § Claim is supported but needs to be qualified – Neglects evidence from different populations @firstwren #stc19
  • 35. Growing body of empirical evidence Plain Language and Information Design Discourse Level Sentence Level Typographic Features Writing Visual Design Layout Features Whole-text Graphic Features Word Level @firstwren #stc19
  • 36. Noun strings Word length Word frequency Word difficulty Word concreteness Nominalizations Word-level features @firstwren #stc19
  • 40. Line length Columns White space Use of grid Layout features Proximity Chunking @firstwren #stc19
  • 41. Tone Visual impression Hierarchy Grouping Use of lists Density Whole-text graphic features Repetition Consistency Images Contrast @firstwren #stc19
  • 42. Stay tuned… Karen A. Schriver @firstwren #stc19
  • 43. Writers who use AI-based language analysis software produce better content. Claim @firstwren #stc19
  • 44. Better content than what? For all audiences? @firstwren #stc19
  • 45. Study 1: Hypothesis Using Acrolinx will enable editors to produce better plain language summaries than using guidelines. Key Question Would automated feedback on style lead medical editors to develop better plain language summaries? Alessandra Rossetti (2018a, 2018b) Dublin City University @firstwren #stc19
  • 46. Research context Example: 4 million Spanish summaries searched between 2012 and 2014 Goal: Timely health information for English and non-English speakers @firstwren #stc19
  • 47. Methods: Study 1 Test for editors 12 experienced Cochrane editors created §a set of summaries using Cochrane guidelines §another set after using Acrolinx – Editors rated satisfaction with both Test for readers §59 native English speakers & 23 non-native speakers – Recall test about the content for the two sets @firstwren #stc19
  • 49. Results: Study 1 What editors thought §No significant preference for Acrolinx over guidelines §Preference for using both for authoring support §Complaints – Long lists of Cochrane guidelines were cumbersome – Acrolinx flagged non-problems (e.g., using “very”) @firstwren #stc19
  • 50. Results: Study 1 What readers remembered § NO difference in recall of main ideas between two sets of summaries – Acrolinx did not improve native or non-native speakers recall of the content more than using paper-based guidelines @firstwren #stc19
  • 51. Hypothesis: Study 2 Using Acrolinx will improve the machine translation (MT) quality of plain language summaries from English to Spanish better than not using it. Key Questions Would using Acrolinx’s controlled language checker to revise § Improve abstracts in English? § Lead to more accurate and fluent MT of Spanish? Rossetti (2018a, 2018b) @firstwren #stc19
  • 52. Methods: Study 2 Stage 1: Revise and translate 12 health professionals used Acrolinx to check for translation, content, and style issues § 12 “Before” texts written in English with Spanish MT outputs § 12 “After” texts (revised using Acrolinx) in English with Spanish MT outputs @firstwren #stc19
  • 53. Methods: Study 2 (cont.) Stage 2: Rate the translations § Compare “before” and “after” Spanish translations § 41 native Spanish speakers (also health domain experts) randomly assigned to score the translations sentence by sentence – Accuracy: Maintains source content (1 = none of it; 4 = all of it) – Fluency: Grammatical and in fluent Spanish (1 = incorrect and disfluent; 4 correct and fluent) @firstwren #stc19
  • 54. Results: Study 2 Revising with Acrolinx did not lead to a significant difference in translation quality Translation Quality Pre-Acrolinx Post-Acrolinx Adequacy (1–4) 3.72 3.78 Fluency (1–4) 3.27 3.33 @firstwren #stc19
  • 55. Critique Claim Critical consumer’s conclusion? § Claim is not supported by current evidence § Rationale is unrelated to data about content quality § Claim is an unproven overstatement Writers who use AI-based language analysis software produce better content. Using AI-based software did not – produce significantly better content – lead to significantly better translations. Since AI-based software can score content with significant economies of speed and scale, writers will produce better content using it. Rationale @firstwren #stc19
  • 56. Faulty conclusions about evidence §Overstate the evidence §Fail to qualify the evidence §Authorize the evidence: “experts say or do X” §Exaggerate the implications of evidence §Misapply the evidence §Hold different values for different participant groups §Assume data apply across contexts §Cherry pick from the evidence §Assume correlation means causation §Overgeneralize from a single population @firstwren #stc19
  • 57. Conclusion: Become a critical consumer of evidence §Be skeptical of claims and rationales §You don’t have to be a researcher to ask good questions §Don’t be bamboozled by not knowing the research §If a claim sounds too good to be true… §Things are not always what they seem: Don’t settle for first impressions @firstwren #stc19
  • 58. Thank you! Karen Schriver, PhD KSA Communication Design & Research 412.828.8791 kschriver@earthlink.net karenschriverassociates.com [relaunch coming] linkedin.com/in/karenschriver ©Karen Schriver (2019) @firstwren #stc19
  • 59. References Change People. (2016). How to make information accessible: A guide to producing easy read documents. 1-52. Retrieved from https://www.changepeople.org/Change/media/Change- Media-Library/Free%20Resources/How-to-make-info-accessible-guide-2016-Final.pdf Chavan, A. L. (2012). User experience research and market research. Retrieved from http://www.humanfactors.com/whitepapers/user_experience_research_and_market_rese arch.asp Lord, A., & Lanman, J. (2016, Mar. 31). GOV.UK Verify and the government design standards. Retrieved from https://identityassurance.blog.gov.uk/2016/03/31/gov-uk- verify-and-the-government-design-standards/ Richards, S. (2018). Content Design. London: Content Design London. Rossetti, A. (2018a). Cochrane plain language summaries: A study of authors' satisfaction and users' comprehension. Paper presented at the EARLI Special Interest Group on Writing, Antwerp, Belgium. https://www.academia.edu/37333783/Cochrane_plain_language_summaries_A_study_of _authors_satisfaction_and_users_comprehension @firstwren #stc19
  • 60. Rossetti, A. (2018b). Spanish translations of Cochrane plain language summaries: Assessing the impact of a controlled language checker on machine translation quality. Retrieved from https://dcu.academia.edu/AlessandraRossetti/Conference-Presentations Rossetti, A., Vázquez, S. R., & O’Brien, S. (2017). Interaction of Cochrane authors with a non-automated and automated editing approach for the production of Plain Language Summaries. Retrieved from https://www.academia.edu/34464622/_Report_Interaction_of_Cochrane_auth ors_with_a_nonautomated_and_automated_editing_approach_for_the_produc tion_of_Plain_Language_Summaries Maguire, L. K., & Clarke, M. (2014, Nov.). How much do you need: A randomized experiment of whether readers can understand the key messages from summaries of Cochrane Reviews without reading the full review. Journal of the Royal Society of Medicine, 107(11), 444–449. doi:10.1177/0141076814546710 Redish, J. C. (2012). Letting go of the words: Writing web content that works (2nd ed.). San Francisco, CA: Morgan Kaufmann/Elsevier. Schriver, K. A. (1997). Dynamics in document design: Creating texts for readers. New York, NY: John Wiley & Sons. @firstwren #stc19
  • 61. Schriver, K. A. (2012). What we know about expertise in professional communication. In V. W. Berninger (Ed.), Past, present, and future contributions of cognitive writing research to cognitive psychology (pp. 275- 312). New York, NY: Psychology Press. Schriver, K. A. (2013). What do technical communicators need to know about information design? In J. Johnson-Eilola & S. Selber (Eds.), Solving problems in technical communication (pp. 386-427). Chicago, IL: Univ. of Chicago Press. Schriver, K. A. (2017, Dec.). Plain language in the United States gains momentum: 1940-2015. IEEE Transactions on Professional Communication, 60(4), 343–383. Schriver, K. A. (Speaker). (2018, Jan. 18). On plain language [Audio podcast]. Retrieved from http://sites.ieee.org/pcs/podcast-karen-a-schriver/ Schriver, K. A. (in preparation). Reaching busy readers: Evidence-based plain language and information design. @firstwren #stc19