Your data is only as good as the survey behind it – here, Qualtrics’ Dave Vanette talks best practice for designing surveys and how to manage them to get the best possible data back.

1. #QualtricsConverg
e
Best Practices for Survey
Design
Dave Vanette, Principal Research Scientist, Qualtrics

2. #QualtricsConverg
e
1. Use best practices from the extensive survey methodology
literature!
1. Pre-test your survey!
2 TECHNIQUES TO AVOID MOST
QUESTIONNAIRE PROBLEMS

3. #QualtricsConverg
e
A set of strategies that respondents use in
order to avoid engaging with survey
questions.
Satisficing

4. #QualtricsConverg
e
Selecting the first reasonable response
Agreeing with assertions
Straightlining
Saying “don’t know”
Responding completely at random
Forms of satisficing behavior

5. #QualtricsConverg
e
There are two primary components that we can
influence as researchers to reduce satisficing:
Task difficulty
• Make questions as easy as possible
• Minimize distractions
• Keep the duration short
Combating satisficing

6. #QualtricsConverg
e
There are two primary components that we can
influence as researchers to reduce satisficing:
Task difficulty
• Make questions as easy as possible
• Minimize distractions
• Keep the duration short
Respondent motivation
• Leverage survey importance
• Keep the duration short
• Use incentives and encouragement to increase engagement
Combating satisficing

7. #QualtricsConverg
e
Open vs. closed questions
Ranking vs. rating
Number of scale points
Construct-specific scales
Labels on scale points
Response options

8. #QualtricsConverg
e
Ask open questions whenever you cannot be certain of the universe
of possible answers to a categorical question.
• “Other – specify” does NOT work
• The only way to be sure you know the universe of possible
answers is to pre-test the question extensively
• Ask open questions whenever eliciting a number
Responses to open questions are often more reliable and more
valid.
Open questions

9. #QualtricsConverg
e
Costs:
• They take more time for respondents
• Respondents often don’t provide much of use in web
surveys
• You have to code the responses
• Variance and/or bias
• More work for you
Open questions

10. #QualtricsConverg
e
Evaluating relative performance, importance, preference, etc.
“Rank the following political parties in order of most preferred to least preferred”
Republican
Democrat
Independent
Ranking

11. #QualtricsConverg
e
Methods of ranking
• Full ranking of all objects
• Partial ranking: e.g., 3 most important, most and least
important
• Minimal ranking: e.g., most important
• Number of items to be ranked needs to be small or
need to rank items only at the ends of the distribution
Ranking

12. #QualtricsConverg
e
Benefits of ranking:
• Allows/forces absolute comparisons
• Non-differentiation isn’t a problem
• Reliability is high
Ranking

13. #QualtricsConverg
e
Benefits of ranking:
• Allows/forces absolute comparisons
• Non-differentiation isn’t a problem
• Reliability is high
•Costs of ranking
• Difficult cognitive task, especially if all of the items are
quite different or all very desirable or undesirable
• Can be time-consuming
• Analysis is more complicated
Ranking

14. #QualtricsConverg
e
“How much did you learn from the survey design
breakout?”
Learned a great deal
Learned a lot
Learned a moderate amount
Learned a little
Learned nothing at all
Rating

15. #QualtricsConverg
e
Benefits of rating:
• Easier for respondents
• Easier to analyze the data
• Preferred by respondents
Rating

16. #QualtricsConverg
e
Benefits of rating:
• Easier for respondents
• Easier to analyze the data
• Preferred by respondents
Costs of rating:
• Less effort may lead to lower data quality
• Responses are less reliable over time
• Susceptible to response style
• Avoiding ends of scales, acquiescence, etc.
• May lead to correlated response patterns
Rating

17. #QualtricsConverg
e
What to do?
• When life forces choices, use ranking
• Otherwise use ratings
(be aware of straightlining risk)
Ranking vs. Rating

18. #QualtricsConverg
e
Goals:
• Differentiate between meaningful levels of a construct
• Avoid ambiguity between scale points
• Maximize reliability
Number of scale points

19. #QualtricsConverg
e
Use 7-point scales for bipolar constructs
• (e.g. Extremely good-Extremely bad)
• Use bipolar scales for bipolar constructs
Number of scale points

20. #QualtricsConverg
e
Number of scale points
Use 7-point scales for bipolar constructs
• (e.g., Extremely good-Extremely bad)
Use 5-point unipolar scales for unipolar constructs
• (e.g., Instructor cared a great deal-Instructor didn’t care at
all)

21. #QualtricsConverg
e
Use middle alternatives, especially with bipolar scales
Use branching to get more detailed bipolar measures
“Generally speaking, do you consider yourself to be a
Democrat, Republican, Independent, or what?”
• Would you say you are a strong (X) or weak (X)?
• Would you say you lean toward one party or the other?
(for Independents)
Number of scale points

22. #QualtricsConverg
e
Generic Likert (avoid):
“Qualtrics cares about the success of their clients”
Strongly Agree
Agree
Neither agree nor disagree
Disagree
Strongly disagree
Use construct-specific response scales
whenever possible

23. #QualtricsConverg
e
“How much does Qualtrics care about the success of
their clients?”
Cares a great deal
Cares a lot
Cares a moderate amount
Cares a little
Does not care at all
Use construct-specific response
scales whenever possible

24. #QualtricsConverg
e
Goals:
• Respondents should find it easy to interpret the meanings of the scale points
• Respondents should believe the meaning of each scale point to be clear
• All respondents should interpret the meanings of the scale points identically
• The labels should differentiate respondents from one another validly as
much as possible
• The resulting scale includes points that correspond to all points on the
underlying construct’s continuum
Labeling scale points

25. #QualtricsConverg
e
• Numbers alone are ambiguous – generally best to omit
them
• Label all scale points – labels may attract people if only
some points have them
• Respondents presume equal spacing of scale points and
the underlying construct continuum – reinforce this with
labels
Labeling scale points

26. #QualtricsConverg
e
Goals:
• Univocality
• Only mention the construct that you
want to measure
• Avoid double-barreled questions
• Meaning uniformity
• Each question should mean the same
thing to all respondents
• Economy of words
• Use as many words as are needed to
convey the idea clearly to all
respondents…and no more
Question wording

27. #QualtricsConverg
e
Word selection guidelines:
• Select words with one meaning (dictionary)
• Simple words (few syllables)
• Simple sentences (few words)
• Readability scores
• Homonyms (fare/fair)
• Heteronyms (lead/lead)
Question wording

28. #QualtricsConverg
e
In general, questions should be worded to:
• Be simple, direct, comprehensible
• Not use jargon
• Be specific and concrete (rather than general and abstract)
• Avoid ambiguous words
• Avoid double-barreled questions
• Avoid negations
• Avoid leading questions
• Include filter questions
• Be sure questions read smoothly aloud
• Avoid emotionally-charged words
• Allow for all possible responses
Question wording

29. #QualtricsConverg
e
Send it out for review
• Collaborators, colleagues, friends, experts, etc. can all
help catch problems that you didn’t notice
Do a few cognitive interviews
• “How did you get to that response?”
Pre-test
• Always pre-test a new questionnaire on non-experts
even if it’s only been edited
You’ve just built or edited a
questionnaire…now what?

30. #QualtricsConverg
e
1. Any pre-testing is better than none
▪ Friends, colleagues, people in this room,
non-experts
▪ A small sample of respondents
2. At very least you’ll catch glaring errors
▪ Typos, broken skip logic, question/response
option mismatch, etc.
3. But hopefully you’ll get some qualitative
feedback, too
▪ What was confusing? None of this
▪ What was difficult? Less of this
▪ What was easy? More of this
WHAT
DOES
PRE-
TESTING
LOOK LIKE?

31. #QualtricsConverg
e
1. Satisficing is a big threat – don’t enable
it with your questionnaires
2. Choose the right response format for
your research
• Open text
• Ranking
• Rating
• Use the right number of scale
points
• Use construct–specific response
options
• Verbally label your scale points
3. Question wording matters a lot, be
deliberate
Review

32. #QualtricsConverg
e
1.“Survey Research” by Krosnick (Ann. Rev. Psych, 1999)
2.“The Psychology of Survey Response” by Tourangeau, Rips, & Rasinski
(2000)
3.“The Science of Asking Questions“ by Schaeffer & Presser (Ann. Rev. Soc,
2003)
4.“Thinking About Answers” by Sudman & Bradburn (1996)
5.“Question and Questionnaire Design” by Krosnick & Presser (in the
Handbook of Survey Research, 2010)
6.“Answering Questions: A Comparison of Survey Satisficing and
Mindlessness” by Vannette & Krosnick (The Wiley-Blackwell Handbook of
Mindfulness, 2014)
7.“The Palgrave Handbook of Survey Research” by Vannette & Krosnick
(forthcoming from Palgrave this year)
Further reading

33. #QualtricsConverg
e
THANKS!