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Scope of this data quality guidance note

To produce data that are of acceptable quality, it is necessary to ensure that key steps in the process of data collection are respecting a well designed protocol. Those key steps are:

  • the elaboration of the data collection instruments;
  • the training of surveyors;
  • the sampling;
  • the data collection;
  • the data entry;
  • the data cleaning;
  • the data quality tests.

In the context of NIPN, the data have already been collected, entered and cleaned.

When referring to “assess the data quality” in these guidance notes, we specifically refer to the data quality tests that can be performed on the cleaned datasets.

When conducting secondary data analysis, it is important to know the protocol and the process that have been effectively implemented to ensure the quality of the data collected. In particular, the data quality tests that have been performed are normally compiled in a separate report. In these guidance notes, we describe the main data quality tests.

There is no threshold for “data quality”. Some methods propose a “global score” for data quality which provides an overall indication but this score should not be used as a standard threshold. It is ultimately the responsibility of the NIPN data team to decide if the data quality is good enough for the planned analysis. The tests described in these guidance notes will be key to inform this decision.

Given the role of NIPN to influence policy decisions, it is highly recommended to take a conservative approach towards data quality in order to avoid criticism that could damage the reputation of the NIPN.

The main sources of information for NIPN are:

  • Population-based Survey” refer to cross-sectional surveys that are designed to be representative of the studied population (ex: national population; sub-national population).
  • Routine Data” refers to systematic information collected on a regular basis, typically from health centres (ex: disease registries ; births and deaths).

Population based survey data and routine data have a very different purpose, protocol and structure. Therefore, assessing the data quality will be very different depending on the source of data.

This guidance note provides details for both population-based surveys and routinely collected data.