A learning pattern forsurvey
Using ordered response scales
problemSurvey questions often ask for ordered responses, but it can be hard to think of them on the spot
solutionUse these example responses scales for your surveys
created on20:23, 3 June 2016 (UTC)
status:DRAFT
What problem does this solve?
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Survey questions often ask for ordered responses. These are questions that have responses like: strongly agree/strongly disagree, or Very important/Not important. These can be hard to think up when you are doing a survey.
What is the solution?
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Researchers have done a lot of research on this topic and there are responses that are often accepted as established. View the list below to find responses that work for you! Note: If the words do not translate well from English into another language, use number scales instead of words. See this learning pattern. --learning pattern needed-- for more information.
Dichotomous (Forced –choice or anchor points)
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NOTE: Dichotomous pairs are not recommended for most survey questions. These options should only be used for filtering people to the right questions (e.g. did you attend the event? Yes/No), or for voting. You should often include "not sure/don't know" or "not applicable" as an option as well for these lists."
Fair
Unfair
Agree
Disagree
True
False
Yes
No
Pass
Fail
Minimally
Maximally
Not at all
Completely
Three-point scales
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Agree
Neither agree nor disagree
Neutral Disagree
More than I would like
About right
Less than I would like
Too harsh
About right
Too lenient
Too heavy
About right
Too light
Too much
About right
Too little
Not at all
Moderately
Extremely
Too Strict
About right
Too lax
None
Some
A lot
Increased
No Change
Decreased
Improved
Stayed the same
Digressed
Not at all
Occasionally
Frequently
Four-point Scales (Forced -choice)
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Poor
Fair
Good
Excellent
Most of the time
Some of the time
Hardly ever
Very seldom/Never
Strongly agree
Agree
Disagree
Strongly disagree
Exceeded
Met
Nearly met
Missed
Below standard
Approaching standard
At standard
Above standard
Definitely will not
Probably will not
Probably will
Definitely will
Not important
Somewhat important
Very important
Essential
None
Little
Some
Substantial
Not at all
Not very well
Fairly well
Very well
A lot
Quite a bit
A little
None
Excellent
Above average
Average
Below average
Very poor
Much better
Somewhat better
Stayed the same
Somewhat worse
Much worse
Much stronger
Somewhat stronger
No change
Somewhat weaker
Much weaker
Strongly agree
(Somewhat) Agree
Neither agree nor disagree/Neutral
(Somewhat) Disagree
Strongly disagree
Very high
Above average
Average
Below average
Very low
Way too little
Too little
About right
Too much
Way too much
Far too little
Too little
About right
Too much
Far too much
Much higher
Somewhat Higher
About the same
Somewhat lower
Much lower
Not at all
Slightly
Moderately
Mostly
Completely
One of the best
About average
Average
Below average
One of the worst
Completely satisfied
Very satisfied
Fairly well satisfied
Somewhat dissatisfied
Very dissatisfied
Very satisfied
Satisfied
Neither satisfied nor dissatisfied
Dissatisfied
Very dissatisfied
Almost always
Often
Sometimes
Seldom
Never
Always
Very often
Fairly many times
Occasionally
Never
All of the time
Most of the time
Sometimes
Rarely
Never
All
A lot/ An extreme amount
Quite a bit
A little/ Some
None
Very good
Good
Fair
Poor
Very poor
Very often
Regularly
Sometimes
Once or twice
Never
Very important
Quite important
Fairly important
Slightly important
Not at all important
Not like
Somewhat
Moderately
Very much
Totally
Definitely True
True
Do not know
False
Definitely false
Severe
Moderate
Mild
Very mild
None
Six-point Scales (Forced -choice)
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Always
Very often
Fairly often
Sometimes
Almost never
Never
Always
Most of the time
Often
Occasionally
Rarely
Never
Completely satisfied
Very satisfied
Somewhat satisfied
Somewhat dissatisfied
Very dissatisfied
Completely dissatisfied
Seven-point Scales
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Very dissatisfied
Moderately dissatisfied
Slightly dissatisfied
Neutral
Slightly satisfied
Moderately satisfied
Very Satisfied
Far below
Moderately below
Slightly below
Met expectation
Slightly above
Moderately above
Far above
Very poor
Poor
Fair
Good
Very good
Excellent
Exceptional
Words to watch out for
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The following words can bias scale if not careful to balance depending on number of responses:
- Slightly vs. Somewhat
- Very/Extremely
- Completely/Totally
- Excellent vs. Exceptional
Scales for international use
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Translating these scales into another language can be very challenging. There are always slight differences in the meanings of words based on the language and culture. If a survey is being translated or used in multiple countries, it is best to use a number scale and show the extremes only. For example:
- 0 - Very poor
- 1
- 2 - Average
- 3
- 4 - Excellent