{"id":117279,"date":"2021-02-24T08:07:32","date_gmt":"2021-02-24T16:07:32","guid":{"rendered":"https:\/\/www.questionpro.com\/blog\/?p=117279"},"modified":"2021-02-24T08:07:32","modified_gmt":"2021-02-24T16:07:32","slug":"boost-survey-data-quality-with-multi-level-quality-filters","status":"publish","type":"post","link":"https:\/\/www.questionpro.com\/blog\/boost-survey-data-quality-with-multi-level-quality-filters\/","title":{"rendered":"Boost your survey data quality with multi-level response quality filters"},"content":{"rendered":"

Data cleansing is a vital activity before drawing conclusions from any survey responses and certainly before decision-making. Clean survey data boosts data analysis and helps you uncover relevant information – information that you can base your decisions on. Clean data will also help you save time and cost, leaving you with time to make appropriate decisions.<\/span><\/p>\n

What is data cleansing? And, how do you deal with unclean data? QuestionPro Audience helps customers identify and eliminate poor quality responses or responses that aren’t collected from the right target audience. We control the data quality at ideally three stages – I’ll explain them further. Despite being one of the most critical research steps, researchers overlook data quality and cleaning. This is due to time-consuming data collection processes, lack of budgets, time constraints, and limited data cleansing knowledge.<\/span><\/p>\n

Select your respondents<\/strong><\/a><\/p>\n

Three-level survey data quality control system<\/b><\/h2>\n

Being an established panel services provider, over the years, QuestionPro has harnessed technology and machine learning to help researchers collect hi-quality research data – data that matters:<\/span><\/p>\n

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  1. Strict screening at recruitment\u00a0<\/span><\/li>\n
  2. Double-opt in surveys\u00a0<\/span><\/li>\n
  3. Response health check\u00a0<\/span><\/li>\n<\/ol>\n

    A combination of these measures is crucial for clean, useable, decision-worthy data. Let’s take a closer look at each of these.\u00a0<\/span><\/p>\n