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Indicators for Managing and Indicators for Managing and Improving the Data Collection Improving the Data Collection ProcessProcess
Sindre Børke and Jonas Dahl, Division for Data Collection Methods,
Statistics Norway
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Looking around
• Eurostat focus on quality issues through several years
• Statistics Norway– Quality Issues at Statistics Norway– Systematic Quality Work in official Statistics – Theory and Practice– FOSS; A Standardisation Programme
Development of standardised working prosesses, methods and systems A system for systematic quality measurements and control Organisation and human resource development supporting this
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Defining a Project
• Information for Different Levels of Management– Continuous Quality Improvement
• Part of the Statistical Value Chain
• Questionnaire-based Data Collection, and Editing on Micro Level
• Limited Number of Indicators
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Indicators
• ”Specific and measurable elements of statistical practice”
• Defined by parameters
• Representative for the component it indicates
• Easy to interpret
• (Easy to collect data)
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Indicators definitions
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Groups of Indicators
• Illuminating stages in the value chain– choice of instrument(s) and designing questionnaires – respondent “behaviour” (incl use of support-telephone and e-mail) – progress in data collection (effect from reminders etc)– non-response – the merging of data in multi-mode design surveys– controls and corrections
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Sick leave questionnaire – respondents’ telephone calls
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Groups of Questionnaires
• Not all indicators are relevant on all questionnaires
• Use of indicators means comparing
• Clustering of questionnaires to create meaningful comparisons (benchmarking)
Year, Quarter or Month Business or Huseholds/Persons Interview/selfadministration ….
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Response rates – development over time
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Recurrent surveys
• Comparisons/development over time– Expected results of changes in questionnaire or process
• Stability in processes
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Hotel statistics questionnaire – Electronic Data Delivery
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Summing up
• No standards set, but observing level in each indicator
• Following recurrent surveys over time, identifying changes
• Benchmarking comparable surveys
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Experiences so far
• Split up is necessary to establish relevance– Small steps on the value chain– Groups of comparable questionnaires
• Criterias for clustering are not established
• Some interesting indicators will be difficult (expensive) to establish
• Project challenge to keep focus on definitions, leaving the interpretation and action to others
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The near future in the project
• Reformulating targets and ambitions
• Some more results in demo-version
• Defining indicators
• Clusering questionnaires
• Data collection design for defined parameters (implementing project results)