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Quality of data

Findings
171. There exists a data validation mechanism at the Upazila level for primary
education. However, due to a lack of skills and human resources, rigorous measures
cannot be undertaken. At the secondary and tertiary levels, data validation is done
centrally by BANBEIS. The centralised data processing makes it more difficult and there
is little local participation in data validation as well as utilisation of data. All these factors
in turn affect the reliability, consistency and timeliness of data.

172. After schools have completed the questionnaires, these forms are submitted
to the sub-district (Upazila/Thana) education officers. They are the first education
authorities to validate the data submitted from schools. They are the officials who
actually monitor and know the situation of the individual school in his/her authority.
However, most of the capacity building activities and training only reached the district
level and rarely touch further levels.

173. During the study, it was found that the EMIS software used in both ministries
have few or no automated built-in data validation functions. Error and missing data
reporting facilities are very limited.

174. The findings show that there remain serious concerns yet to be fully
addressed about the accuracy and quality of the EMIS data, especially enrolment data
by age. The quality of current data by age is so weak that it is difficult to do any age-
related data analysis.

Recommendations

175. The instructional manual and checklist on validation of the data on school
census forms should be prepared and distributed. Follow-up training is to be organized
for the staff at local level, especially at Upazila/Thana level. Necessary enforcement and
procedures are to be established in order to set up and sustain this practice. This would
greatly improve the quality of data submitted from schools.

176. Design the data entry software to include various data validation rules and
techniques, as well as comprehensive error generation report modules which would
save significant manpower, energy and enhance the accuracy of data.

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