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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
  • 2. Qualifying quality: issues of presentation and education Archived
    Articles and reports: 11-522-X20010016300
    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.

    The Australian Bureau of Statistics (ABS) produces many statistics that help the government and the wider community make more informed decisions. However, if these decisions are to be truly informed, it is essential that the users are able to understand the limitations of the statistics and how to use the data in an appropriate context. As a result, the ABS has initiated a project entitled Qualifying Quality, which focuses on two key directions: presentation and education. Presentation provides people with information about the quality of the data in order to help them answer the question "Are the data fit for the purpose?"; while education assists those people in appreciating the importance of information on quality and knowing how to use such information. In addressing these two issues, the project also aims to develop and identify processes and technical systems that will support and encourage the appropriate use of data.

    This paper provides an overview of the presentation and education initiatives which have arisen from this project. The paper then explores the different methods of presentation, the systems that support them, and how the education strategies interact with each other. In particular, the paper comments on the importance of supporting education strategies with well developed systems and appropriate presentation methods.

    Release date: 2002-09-12
  • 3. Modelling compositional time series from repeated surveys Archived
    Articles and reports: 12-001-X20010026097
    Description:

    A compositional time series is defined as a multivariate time series in which each of the series has values bounded between zero and one and the sum of the series equals one at each time point. Data with such characteristics are observed in repeated surveys when a survey variable has a multinomial response but interest lies in the proportion of units classified in each of its categories. In this case, the survey estimates are proportions of a whole subject to a unity-sum constraint. In this paper we employ a state space approach for modelling compositional time series from repeated surveys taking into account the sampling errors. The additive logistic transformation is used in order to guarantee predictions and signal estimates bounded between zero and one which satisfy the unity-sum constraint. The method is applied to compositional data from the Brazilian Labour Force Survey. Estimates of the vector of proportions and the unemployment rate are obtained. In addition, the structural components of the signal vector, such as the seasonals and the trends, are produced.

    Release date: 2002-02-28
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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
  • 2. Qualifying quality: issues of presentation and education Archived
    Articles and reports: 11-522-X20010016300
    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.

    The Australian Bureau of Statistics (ABS) produces many statistics that help the government and the wider community make more informed decisions. However, if these decisions are to be truly informed, it is essential that the users are able to understand the limitations of the statistics and how to use the data in an appropriate context. As a result, the ABS has initiated a project entitled Qualifying Quality, which focuses on two key directions: presentation and education. Presentation provides people with information about the quality of the data in order to help them answer the question "Are the data fit for the purpose?"; while education assists those people in appreciating the importance of information on quality and knowing how to use such information. In addressing these two issues, the project also aims to develop and identify processes and technical systems that will support and encourage the appropriate use of data.

    This paper provides an overview of the presentation and education initiatives which have arisen from this project. The paper then explores the different methods of presentation, the systems that support them, and how the education strategies interact with each other. In particular, the paper comments on the importance of supporting education strategies with well developed systems and appropriate presentation methods.

    Release date: 2002-09-12
  • 3. Modelling compositional time series from repeated surveys Archived
    Articles and reports: 12-001-X20010026097
    Description:

    A compositional time series is defined as a multivariate time series in which each of the series has values bounded between zero and one and the sum of the series equals one at each time point. Data with such characteristics are observed in repeated surveys when a survey variable has a multinomial response but interest lies in the proportion of units classified in each of its categories. In this case, the survey estimates are proportions of a whole subject to a unity-sum constraint. In this paper we employ a state space approach for modelling compositional time series from repeated surveys taking into account the sampling errors. The additive logistic transformation is used in order to guarantee predictions and signal estimates bounded between zero and one which satisfy the unity-sum constraint. The method is applied to compositional data from the Brazilian Labour Force Survey. Estimates of the vector of proportions and the unemployment rate are obtained. In addition, the structural components of the signal vector, such as the seasonals and the trends, are produced.

    Release date: 2002-02-28
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