The UIS Education DQAF

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The DQAF is organized in a cascading structure that progresses from the abstract/general to the more concrete/specific. The first-digit level defines the six dimensions presented above. The first-digit level is sub-divided by sub-dimensions (two-digit level) and indicators (three-digit level). At the next level, practices describe quality features that may be considered in assessing the indicators (see DQAF Manual / Presentation of the UIS DQAF).

This chapter presents in details all elements of the framework. Each dimension is developed in a separated page where sub-dimensions, indicators and practices are listed and commented when needed.

In addition, the Good practices section provides examples and justifications of scoring that have been given in DQAF conducted in selected countries.

0 - Pre-requisites of quality

Data quality is regulated by a framework of statistical laws, policies, standards and practices, and technical and human resources. This framework cannot exist in a vacuum. Pre-requisites of quality, as one of the dimensions of data quality, do not comprise a qualitative dimension, but refer to the evaluation and understanding of the institutional context in which the statistical processes exist and which is essential to the other dimensions. This dimension presents the integrated nature in which available statistical laws, as well as essential human and technical resources, impact on other quality dimensions.

Read More... Pre-requisites of quality

1 - Integrity

This dimension captures the notion that statistical systems should be based on adherence to the principle of objectivity in the collection, compilation, and dissemination of statistics. The dimension encompasses institutional arrangements that ensure professionalism in statistical policies and practices, transparency, and ethical standards. The three elements for this dimension of quality are:

  • Professionalism
  • Transparency
  • Ethical standards

Read More... Integrity

2 - Methodological soundness

This dimension covers the idea that the methodological basis for the production of statistics should be sound and that this can be attained by following internationally accepted standards, guidelines, or good practices. This dimension is necessarily dataset-specific, reflecting different methodologies for different datasets. This dimension has four elements, namely

  • concepts and definitions
  • scope
  • classification/sectorization
  • basis for recording

Read More... Methodological soundness

3 - Accuracy and reliability

This dimension of quality is based on the principle that data produced give an adequate picture of the reality of the education sector. Therefore, this dimension is specific for each data set and reflects the specificity of its sources and treatments. The elements of this dimension cover: • source data • statistical techniques • assessment and validation of source data • assessment and validation of intermediate data and statistical outputs, and revision studies

Read More... Accuracy and reliability

4 - Serviceability

The quality dimension of serviceability looks at the extent to which statistics are useful for planning or policy purposes. It refers, mainly, to periodicity and timeliness, and consistency. Data is timely when it is current or up-to-date as defined by the owner of the data. Data must be on time and available when it is required, otherwise the credibility of the information system diminishes. Given that data is actually accurate, it looks at the extent to which they reflect a reality either of the moment or of the past.

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5 - Accessibility

This dimension is based on the principle that data and metadata should be presented in a clear and understandable way and should be easily available to users. Metadata should also be relevant and regularly updated. In addition, assistance to users should be available, efficient and performed in a reasonable time frame.

Read More... Accessibility

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