Surveys are an extremely effective way to understand your customers. With powerful research technology platforms, such as our own FocusVision Decipher, you can quickly run ‘just in time’ surveys to get answers to pressing questions or conduct complex, multi-dimensional surveys to delve into meaty topics.
While research technology helps streamline many aspects of the process, there are still many considerations at every step of the journey. Creating surveys that work for participants and deliver high-quality insight is no mean feat. Ultimately each decision made at each stage in the process impacts your data quality.
We often get asked what can be done to improve data quality. As a rule of thumb, I think about three areas:
- Reducing Response Burden.
- Include Data Quality Questions.
- Clean the Survey Data.
Let’s take a brief look at each. (If you’d like more detail, take a look at our downloadable white paper: A 3 Step Guide for Better Research Data Quality)
1. Response Burden
We most often think about response burden in terms of the overall LOI – length of interview. This certainly plays a role, but there are other areas to think about. Per Norman Bradburn (1978), four factors can increase burden: the length of interview, the amount of effort required of the participant, the amount of stress on the participant, and the frequency with which the participant is interviewed. We can reduce burden by reducing LOI as well as the amount of effort and stress by eliminating or reducing challenging elements within the questionnaire such as repetitive questioning, limiting grids, and open ends, while making the survey as easy and enjoyable to take as possible.
2. Include Data Quality Questions
It is possible to measure a participant’s attentiveness by adding in specific data quality questions such as attention checks, red herrings, and duplicate questions. These questions provide a tangible way to check the data that you receive. However, a word of caution, use these questions minimally and with due care, so they aren’t harmful to genuine participants.
3. Clean Survey Data
Once you have the survey data, you can run various checks to identify those inattentive and/or bogus participants. The most common checks are straightlining (where participants answer in a pattern to move through the survey), speeding (completing the survey unduly quickly), and gibberish responses within open ends. Tip – FocusVision Decipher has features that make data cleaning a relatively painless process.
Paying attention to these three areas can make a notable difference to your data quality. But you may wish to go further. For a deeper dive, view our latest on-demand webinar, where guest speaker Caroline Jarrett, the forms and survey specialist, introduced the Survey Octopus, an easy-to-follow framework covering the considerations at each step of the survey process and how this can impact your outcomes. I’d also encourage you to look out for her forthcoming book: Surveys that Work: A Practical Guide for Designing Better Surveys.