Statistical Surveys

  • Traditionally, census data, administrative data, and survey data were the dominant data sources in the statistical business process lifecycle. Nowadays, big data constitute an emerging data source, which provides data on a specific process (business data, social network data, and other processes). The big question is if survey sampling and design are still necessary in our days. The answer to this question is not only positive but it seems that these methods are more necessary than ever for the preservation of data quality.
  • Survey design and data sampling focus on the collection of data from a subset of the target population which may or may not be known. In the case of big data, the information is derived from the actual process, whereas in survey collected data specific criteria are set for the relevance and accuracy of the collected data, though the application of survey planning.
  • Our strong statistical background in combination with our long-time experience in statistical surveys in the field of official statistics and other domains of business life constitute our competitive advantage. Our methodological approach on the implementation of statistical surveys is composed of the following processes:

METHODS

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Define sampling frame
  • Define target population:
  • Target population refers to the entire set of individuals about which findings of a survey refer to the total number of population units can be known or unknown
  • Define sampling technique:
  • Simple random sampling, systematic sampling, stratified sampling, cluster, multistage sampling
  • Sample size
  • Sampling frame (attributes of selected sample)
  • Representativeness
Define sampling frame
  • Define target population:
  • Target population refers to the entire set of individuals about which findings of a survey refer to the total number of population units can be known or unknown
  • Define sampling technique:
  • Simple random sampling, systematic sampling, stratified sampling, cluster, multistage sampling
  • Sample size
  • Sampling frame (attributes of selected sample)
  • Representativeness
Design data collection process
  • Define data collection method:
  • Face to face interview, computer assisted interview
  • Phone survey
  • Electronic questionnaire (e-mail, online)
  • Build infrastructure for data collection
  • Integrate data quality rules
Questionnaire design
  • Question content
  • Question-wording
  • Define open/closed questions
  • Pilot questionnaire
  • Compilation of final questionnaire
Survey management
  • Interviewer training
  • Supervisor training
  • Data management infrastructure monitoring
  • Data collection process
  • Reminders
Quality assurance management
  • Response rate/ errors
  • Data entry errors
  • Privacy and confidentiality
  • Assurance
  • Asses data completeness and accuracy
Weighting, calibration and imputation
  • Sampling weights are adjusted to reproduce known population totals
  • Calibration techniques refer to the use of auxiliary information from administrative or other sources in the calculation of final weights
  • Imputation techniques are used for the estimation and fill in of missing data

APPLICATIONS

  • Customer satisfaction
  • Customer segmentation
  • Market awareness
  • Purchase awareness
  • Demand estimation
  • Market Shares
  • Completion Analysis
  • Clinical trials
  • Household surveys
  • Retail
  • Education
  • Business surveys
  • Tourism
  • Research and development
  • Employment surveys
  • Ad-hoc surveys

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