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3 Steps guide for better research data quality

Good insight relies on good data. Yet we aren’t always convinced we’ve collected good quality data. In particular sample quality is an on-going concern, with nearly half of researchers citing it as a primary frustration in our annual Research Trends study. So, what can be done to ensure the integrity of your data?

1. Reduce Response Burden

Improving data quality can be approached in 3 ways:

Response burden is generally defined and measured as the time it takes a participant to complete a questionnaire. However, there are other elements at play:

The length of the interview (or questionnaire length for self-administered surveys).

Brandburn (1978) Respondent Burden.

The amount of effort required of the participant.

The amount of stress on the participant, and the frequency with which the participant is interviewed.


Pay close attention to reducing response burden by designing short, mobile-first questionnaires.

If possible, approach your research questions iteratively, building upon the body of knowledge about your (or your client’s) brand, product, universe, one short study at a time.

Additionally, wherever possible, make use of existing sample profiling data to avoid asking known information.

For help creating your survey, see The Definitive Guide to Effective Online Surveys and the accompanying 10 Tips to Creating Great Survey Questions infographic.

2. Include Data Quality Questions

With concerns of rogue respondents running high coupled with increasing responsibilities of survey ‘bots’ negatively impacting data, adding specific quality questions is standard practice.


Sprinkle a few data quality questions throughout the questionnaire
(however, do so with caution!)

Attention Checks
Attention check questions are an overt way of checking whether the participant is paying attention to the questions and instructions. For example, “What color is the sky?” The instruction tells participants to select an incorrect response such as yellow.

Red Herrings
A more subtle approach is to add red herrings to a response list. For example, include one or two fake brands or products within the response list.

Duplicate Questions
Another option could be to ask the same question at different places in the questionnaire or ask for the same information in different ways to check for consistency.

3. Clean Survey Data

Once you have your survey data, you can undertake various checks to identify inattentive and bogus respondents to remove them from your dataset.


Review the data set to identify respondents who exhibit the following inattentive signs:

Straightlining is when participants select the same or patterned response for a set of questions.

Speeders are participants that move through the questionnaire at a rapid pace.

Gibberish Responses
A third data check is to seek gibberish responses to open-end questions.

As we’ve seen, you can take several steps to improve survey data quality. We strongly recommended that all of these steps are employed simultaneously to address quality holistically.


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