[SMARTKeymaster]

As aforementioned, SMART Flags are applied on the basis of statistical plausibility to ensure better data quality. If the surveyed population follows a normal distribution, SMART assumes a standard deviation of 1 (statistical properties of a normal distribution) to identify any SMART Flags that lie outside of ±3 of the observed mean. If you have 1000 children measured and for example there were 20 SMART flags identified, only 3 of those children are expected versus the remaining 17 are big mistakes.
On a day-to-day basis, you are generally only looking at WHO Flags (±5 of the reference population’s mean). It doesn’t make sense to use SMART Flags throughout the data collection as the observed mean on a day-to-day basis or for a given team will be different from the overall observed mean once the data collection has finished. You may make sense at start looking at the number of Flags after one week of data collection after retrieving all of the data from the teams; however, any flagged data should never be erased or removed at any stage. The reason for Flags is to highlight any big mistakes, whether it is during data collection with WHO or at the end of data collection with SMART. Consequently, the survey manager should double-check each flagged measurement with the paper or electronic copy of the questionnaire to ensure that no data entry errors were made. If the same values appear on the questionnaire, then these flags remain and you will obtain the associated amount of penalty points based on the SMART Plausibility Check.

[SMARTKeymaster]

Can you kindly indicate what exactly is the the problem you are having with downloading the ENA software?

[SMARTKeymaster]

WHO flags are based on a reference population. For weight for height, the range is -5 to +5 standard deviations. If a child is found to be outside of this range the data is not included in the analysis because a measurement or data entry error has likely occurred.
SMART flags are based on the observed population (the specific survey population as opposed to a reference population). The range is -3 to +3 standard deviations for weight for height, height for age, and weight for age.
Most national level data is reported using WHO flags whereas, regional or sub-national data can be reported using SMART flags. Data analysis with no exclusion is not performed often. On occasion no exclusion can be applied if all flagged results can be double checked in person. In most circumstances this is not feasible or necessary.

[SMARTKeymaster]

Dear Sameh,
Thank you very much for your email. SMART on-line training has been discussed and will likely be included on the website in the future. The first on-line training included will be the enumerator training. The SMART Capacity Building Toolbox currently contains the Enumerator package (training manual, presentations, handouts) that is free to download.

[SMARTKeymaster]

Currently in ENA you are able to select age ranges for mortality analysis, so if you change the age ranges to apply to women of childbearing age you will get the death rates and associated confidence intervals, overall and by sex.

As with U5 Death Rates (and other mortality sub-populations) you must interpret results with caution seeing as though your n (sample size) will be smaller and you will have wider confidence intervals.

[Author]

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