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:

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

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

When referring to “the assessment of 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 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 the information platforms 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 platform.

The main sources of information for NIPN are:

  • Population-based survey data” are collected in cross-sectional surveys designed to be representative of the studied population (e.g. national or district population).
  • Routine Data” refer to systematically and regularly collected information, typically from health centres (ex: disease, birth and death registers).

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

This guidance note provides details for both population-based surveys (pages 3 to 5) and routinely collected data (pages 6 onwards).

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