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Accuracy and reliability

Contact info

Population and Education, Social Statistics
Annemarie Schriver
+45 40 18 43 54

rie@dst.dk

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Childcare institutions

The accuracy is considered to be high, as it is used by municipalities for their financial administration, and updates in the institution registry undergo extensive error-checking.

Overall accuracy

The count covers all 98 muncipalities in Denmark and therefore all institutions on the day care facilities for children in the age of 0 to 17.

Their might be uncertainty in the way Muncipalities allocate unique keys to the institutions. There is examples of institutions which have more than one unique key. The number of institutions might therefore be overestimated in the count.

However, the overall accuracy is generally high, as many resources are used on controlling for errors in the data and manually assess whether new institutions should be added to the count.

Sampling error

Not relevant for these statistics.

Non-sampling error

In this count institutions established under the Day Care Act, the Primary Education Act, Act on the Private, Independent Schools, Youth School Act and the Peoples Information Act are counted. Errors have been identified where institutions incorrectly reported which legal act they where established under. This is especially a problem for institutions under the Youth school act. Statistics Denmark is aware of these mistakes and are continuously correcting for these errors. Furthermore, institutions are individually examined before added to the count.

Errors have also been observed in the ID allocated to the institutions by Muncipalities. This key is supposed to be unique for all institutions, but there is departments within one institutions which have given separate ID’s. Statistics Denmark try to correct for these errors, but it can not be guaranteed that all errors are corrected in the validating process. Institutions might therefore be overrepresented in the count.

Quality management

Statistics Denmark follows the recommendations on organisation and management of quality given in the Code of Practice for European Statistics (CoP) and the implementation guidelines given in the Quality Assurance Framework of the European Statistical System (QAF). A Working Group on Quality and a central quality assurance function have been established to continuously carry through control of products and processes.

Quality assurance

Statistics Denmark follows the principles in the Code of Practice for European Statistics (CoP) and uses the Quality Assurance Framework of the European Statistical System (QAF) for the implementation of the principles. This involves continuous decentralized and central control of products and processes based on documentation following international standards. The central quality assurance function reports to the Working Group on Quality. Reports include suggestions for improvement that are assessed, decided and subsequently implemented.

Quality assessment

The quality of the information in this count is estimated to be high, as many ressources are used to validate data from data suppliers. At the same time adding or deleting insititutions in the count is done manually to secure validation of the data.

The institutions can validate information, because information on individual institutions are portrayed on http://www.dagtilbudsportalen.dk.

Data revision - policy

Statistics Denmark revises published figures in accordance with the Revision Policy for Statistics Denmark. The common procedures and principles of the Revision Policy are for some statistics supplemented by a specific revision practice.

Data revision practice

Statistics Denmark revises published figures in accordance with the Revision Policy for Statistics Denmark. The common procedures and principles of the Revision Policy are for some statistics supplemented by a specific revision practice.

From 2022, the statistics are revised after publication of BOERN5 & BOERN61 and again with the publication of BOERN1-3, since the publications lead to an increase in data validation, which can improve the data quality.