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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)