Basic Anthropometric data quality checks Basic Anthropometric data
Basic Anthropometric data quality checks
Basic Anthropometric data quality checks Objectives • Outliers and flags • WHO and SMART flags. • Standard Deviation 2
Basic Anthropometric data quality checks Outliers and flags • values that fall outside of an acceptable range • values outside of the plausible range are frequently due to poor measurement, inaccurate date of birth, or data recording errors. • an important indication of data quality • Flagged records can be checked and corrected, or censored 3
Basic Anthropometric data quality checks Flagging • usually applied to – – HAZ WHZ BAZ (adults) • Flagged records can be checked and corrected, or censored 4
Basic Anthropometric data quality checks Flagging criteria • Two flagging methods are in common use – WHO flags – SMART flags 5
Basic Anthropometric data quality checks Flagging criteria 6
Basic Anthropometric data quality checks Flagging criteria 7
Basic Anthropometric data quality checks Flagging criteria 8
Basic Anthropometric data quality checks Flags 9
Basic Anthropometric data quality checks Flags 10
11 Standard deviation SD = 1 SD < 1 SD > 1
Quick Excercise We must separate two exam results of a class of 30 students; the marks of the first exam vary between 31 % and 98 % and those of the second between 82 % and 93 %. Given this range, which standard deviation will be higher? 12
Quick Excercise the standard deviation will be higher for the results of the first exam. 13
Anthropometric data quality checks Standard deviation (SD) • The higher the SD, the more likely poor data quality is • Very difficult to put acceptable ranges • SDs are typically wider for HAZ • SDs for HAZ are largest for younger children • No difference between girls and boys 14
Standard deviation SD = 1 SD > 1 15
Standard Deviation • Calculated by most softwares • Apply only to cleaned data from which erroneous data and flagged records have been censored. 16
Anthropometric data quality of our surveys Conclussions • For SD further investigations are needed to – (i) develop guidance on how to tease out the relative contribution of measurement error from expected population-associated spread for any given survey; – (ii) to ascertain a cut off at which the SD might be more conclusively related to data quality for each anthropometric index. • Other approaches still need more testing January 2019 Addis Ababa 17
Excercise 4 • Divide in 4 groups • The file ex 04. csv is a comma-separated-value (CSV) file containing anthropometric data from a recent SMART survey in Sudan. • Calculate WHO and SMART flags • Team B: present on WHO flags • Team C: present on SMART flags 18
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