Data analysis methods suited to the NIPN set-up (1/3)

Given the principles of data analysis specific to NIPN, and given the nature of the data typically available in population-based surveys or routine information systems, as described in the previous section, some data analysis methods are better suited to result into actionable recommendations to inform nutrition policy, programme and budget decisions than others. The following section describes why:

  1. meaningful descriptive or comparative data analysis methods should be the starting point of NIPN;
  2. population-based survey data or routine data do not permit a robust analysis of causal relationships or impact evaluation;
  3. analysis of the cost-effectiveness of interventions requires a research setting.
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1. Meaningful descriptive or comparative data analysis methods should be the starting point of NIPN

Descriptive multi-sectoral analysis, budget analysis, and trend analysis, using different sources of information, are typical data analysis methods that lend themselves to answering policy questions.
These methods:

  • can be applied to population-based surveys, routine data or modelled data;
  • deliver a simple message that is easy to communicate to policy makers;
  • provide results that cannot be disputed and are actionable;
  • provide timely results;
  • maximise the use of underutilised data on investments in nutrition and coverage of interventions (left part of the impact pathway, section 2.3, page 5).

These methods follow the NIPN principles of data analysis well (see previous pages) and fit with the type of data NIPN typically uses (this section, page 1).
Take a look at the six examples below of descriptive or comparative analysis methods which can be instrumental for policy makers.

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Examples
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