Quality is as quality does to paraphrase the line from the movie Forrest Gump. Whether we are measuring quality, customer loyalty, or brand awareness, we need to have an eye out for data quality. When we are in questionnaire design mode, we have to balance the information needs of our clients with the customer experience our participants will have. Careful survey construction will create a more concise instrument that can balance both of these needs.
Respondent fatigue is one of the key issues facing longer surveys. There is an inverse relationship between data quality and respondent fatigue. When respondents grow tired of the survey experience there is an increased chance that they will skip questions, provide nonsensical responses, or abandon the survey altogether. Either of these results leads to data quality issues through missing data, meaningless data or reduced completion rates. Consumer behavior is often a very complex process that cannot always be measured by the short surveys being touted by some technology vendors.
So what’s a researcher to do?
Try putting a momentary detour into your survey experience or call it a bit of mental palette cleansing. This can be achieved in a few ways. Some researchers will insert a palette cleanser like the following in the middle of the survey:
Thinking about your summer vacation which of the following would you like to visit the most?
- National Parks
- Theme-Park (e.g. Disneyland, Marine World, etc.)
Another option is to use a specific response question. Here the respondent is told specifically which response to choose, such as ‘Mountains’ in the previous example. If the wrong option is chosen the participant can be terminated from the survey. This carries some risk to completion rate, but can weed out those who are not completely committed to providing thoughtful and correct data.
Working to maintain respondent engagement through thoughtful question wording, use of audio and visual components, momentary detours and attentive sample selection will go a long way toward increasing the quality of your data. Needless to say this has a positive impact upon the conclusions you can draw from the data.