Contemporary solutions for fast data corrections – without cheaters, straight-liners or speeders
Quality is a top priority in market research – and for good reason. Insights are the basis for decisions on important issues regarding products and services. Good data cleaning is part of the standard and is essential for correct results. The accelerating speed at which consumer insights must be accessible for decisions nowadays also applies to data cleaning. Spending time on data correction means slowing down. Direct analyses require fast and quickly corrected results.
Ensure high data quality
Even with fast surveys under time pressure, the collected data should always follow the quality guidelines for proper market research results. In order to be properly informative, expedient results and a good basis for decisions, the data must carry absolutely no risk of data distortion. The final insights should therefore always undergo a data cleaning. It’s imperative that you remove these responses:
1. Straight-liners In matrix questions, straight-liners generally select their responses in a vertical row. Unfortunately, this usually means that the respondent hasn’t really thought about the question.
2. Implausible, self-contradictory answers Respondents with obviously implausible answers should be removed.
An example Question 3 – How do you like this product? Answer: “I don’t like the product at all.” Question 4 – Would you recommend this product to others? Answer: “I would definitely recommend this product to others.”
3. Speeders Speeders are survey participants who answer “too quickly”, for example, faster than 50% of the average response time. Due to the high speed of the answers, there is a risk that they weren’t considered thoroughly. Traditional access to the raw data through SPSS scripts or Excel formulas already takes too much time for the current pace of modern business practice. Insights must be available much more quickly and efficiently.
Automated data cleaning
In today’s age of agile market research, our data cleaning is supported by automated tools that follow a set of parameters and assure the quality of the data with a simple click. The collected data thus meet the required quality criteria and can be used directly for an accurate final report.
Additional benefit:
Another advantage of automated data cleaning – especially for all representative surveys – is the directly controlled filling of quotas. If answers are eliminated during the field period due to low quality, the unfilled target groups automatically continue to be open for participation.
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