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  • 1. Korea's experiences in statistical quality assessment Archived
    Articles and reports: 11-522-X20010016250
    Description:

    This paper discusses in detail issues dealing with the technical aspects of designing and conducting surveys. It is intended for an audience of survey methodologists.

    This paper describes the Korea National Statistics Office's (KNSO) experiences in data quality assessment and introduces the strategies of institutionalizing the assessment procedure. This paper starts by briefly describing the definition of quality assessment, quality dimensions and indicators at the national level. It introduces the current situation of the quality assessment process in KNSO and lists the six dimensions of quality that have been identified: relevance, accuracy, timeliness, accessibility, comparability and efficiency. Based on the lessons learned from these experiences, this paper points out three essential elements required in an advanced system of data quality assessment: an objective and independent planning system, a set of appropriate indicators and competent personnel specialized in data quality assessment.

    Release date: 2002-09-12
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Articles and reports (1)

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  • 1. Korea's experiences in statistical quality assessment Archived
    Articles and reports: 11-522-X20010016250
    Description:

    This paper discusses in detail issues dealing with the technical aspects of designing and conducting surveys. It is intended for an audience of survey methodologists.

    This paper describes the Korea National Statistics Office's (KNSO) experiences in data quality assessment and introduces the strategies of institutionalizing the assessment procedure. This paper starts by briefly describing the definition of quality assessment, quality dimensions and indicators at the national level. It introduces the current situation of the quality assessment process in KNSO and lists the six dimensions of quality that have been identified: relevance, accuracy, timeliness, accessibility, comparability and efficiency. Based on the lessons learned from these experiences, this paper points out three essential elements required in an advanced system of data quality assessment: an objective and independent planning system, a set of appropriate indicators and competent personnel specialized in data quality assessment.

    Release date: 2002-09-12
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