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149. It should also be noted that coverage in terms of content is particularly weak
with regard to financial data. Although schools report various financial information, such
as quarterly budgets and audited expenses to the district level, it is never consolidated
at higher levels. In most cases, such information is available on paper but it is not
integrated into the EMIS. As a result of this, there is no information on education finance
in the yearbook.
150. Information is needed on non-formal education and on youth with special
needs and disabilities. There is no systematic and centralised entity to collect and
compile the statistics on NFE programmes at the national level. No information and
statistics on disabilities are gathered and published. Furthermore, scant and only very
basic information are available for the vocational, technical and professional education.
151. While emphasis is being placed to improve the quality of teaching staff,
guiding indicators on teachers such as teaching staff by qualification, rank, training,
salary scales, etc. are lacking. This information is available in the secondary education
sector but not yet developed in primary education, though this could be collected
through the school census.
5.4 Analysis of data quality using DQAF (Data Quality Assessment Framework)
152. There are a number of data quality issues at the national level – many of
these are the same or similar to those at the international level. In this section, quality of
education data in Bangladesh is examined through the Data Quality Assessment
Framework (DQAF).
153. DQAF is part of an initiative being lead by IMF to improve overall standard
and comparability of national and international data. It was further developed by the UIS
together with the World Bank for an education-specific DQAF instrument to assess the
overall settings required from education statistics units around the world with a view to
assess the quality of their statistical outputs. One important element of the diagnostic
study is the completion of the DQAF in order to provide a benchmark of the data quality
at the outset of the project and also to help pinpoint weak areas that need to be
addressed.
154. This instrument is based on six main dimensions considered as pertinent for
an evaluation of data quality. These dimensions are:
• Prerequisite conditions to quality
• Integrity
• Methodological soundness
• Accuracy and reliability
• Serviceability
• Accessibility
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