Infographic: 3 Steps to Better Data Quality

Better Research Data Quality

Overview

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?


Better Research Data Quality

Improving data quality can be approached in three 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:

1

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

2

The amount of effort required of the participant,

3

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

Brandburn (1978) Respondent Burden.

What to do:

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

The length of the interview

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.

The length of the interview

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

The length of the interview

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


Better Research Data Quality

Improving data quality can be approached in three 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:

What to do:

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

The length of the interview

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.

The length of the interview

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.

The length of the interview

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.


Better Research Data Quality

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

What to do:

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

The length of the interview

Straightlining

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

The length of the interview

Speeders

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

The length of the interview

Gibberish Responses

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

Concluding Thought

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.

For more detail on these steps, see the accompanying white paper.

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