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Extra Anthropometric data quality checks

This session focuses on performing quality checks on anthropometric data, including tests for dispersion, normality, skewness, and kurtosis. The results can help identify potential issues and guide future research. Use in surveys with clusters to assess heterogeneity of malnourished children and the distribution of cases.

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Extra Anthropometric data quality checks

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  1. Extra Anthropometric data quality checks

  2. DispersionIndex Normality Skewness Kurtosis SAM/MAM ratio Mean Z-score Anthropometric data quality checks Objectives

  3. The tests on this session are often controversial and should be use with caution but they may give you ideas for future research Anthropometric data quality checks Data quality

  4. Use in surveys with clusters Examines the heterogeneity of the population in terms The number of malnourished children by cluster should follow a distribution statistically known as Poisson Anthropometric data quality checks Dispersion If data do not follow Poisson Distribution  Heterogeneous sample with ‘malnutrition pockets’.

  5. Poisson Distribution

  6. Test for random distribution or aggregation of cases over the clusters: pockets of malnutrition. 3 Options: • Uniform distribution: ID < 1 • Random distribution: ID = 1 • Aggregated distribution: ID > 1 Anthropometric data quality checks Index of Dispersion

  7. Index of Dispersion Random distribution Uniform distribution Aggregated distribution

  8. ID for WHZ<-2 is 1.33 and the p>0.05. Whatcanwe assume? Quick Excercise

  9. ID for WHZ<-2 is 1.33 and the p>0.05. Therefore, wecan assume that the distribution of the cases of wasting for thissurveywasrandom. Quick Excercise

  10. Basic Anthropometric data quality checks Normality

  11. Basic Anthropometric data quality checks Graphical and numerical summaries WAZ WHZ

  12. Basic Anthropometric data quality checks Graphical summaries WAZ WHZ

  13. Basic Anthropometric data quality checks Shapiro-Wilk Test for WHZ, WAZ, HAZ. Assessessignificantdifferencebetween data distribution and normal distribution Numerical summaries

  14. The results for Shapiro-Wilk test for normally (Gaussian) distributed data for W/H, when excluding SMART flags was p= 0.075. Quick Excercise

  15. Since it is higher than 0.05, we can therefore assume that the data for weight for height was normally distributed. Quick Excercise

  16. Measures asymetry If distribution issymmetrical value of skewness = 0. The value of skewness should lie between -1 and +1. Basic Anthropometric data quality checks January 2019 AddisAbaba Skewness

  17. Basic Anthropometric data quality checks January 2019 AddisAbaba Skewness

  18. Measures the “peakedness” of the distribution. Normal distribution: kurtosis = 3. The value of kurtosis should lie between 2 and 4. Basic Anthropometric data quality checks January 2019 AddisAbaba Kutosis

  19. Anthropometric data quality of our surveys January 2019 AddisAbaba Kurtosis

  20. Always provide histograms Research the reason for non-normality Check the tails of the HAZ, WHZ, and WAZ distribution. Did they end smoothly or abruptly • Skewifskew is <-0.5 or >+0.5 • Kurtosis if <+2 or >+4 Checking data quality How to present

  21. Conclusions cannot be drawn about the quality of the data based on values of skewness or kurtosis alone. Conversely, deviations from normality in the context of other problematic data quality checks should flag concern. Further research is required to understand distribution patterns for populations with different patterns of malnutrition and also to understand the extent to which values of skewness and kurtosis which deviate from normality represent data quality issues Basic Anthropometric data quality checks January 2019 AddisAbaba

  22. Analysis by Teams • Number of children. • Proportion of flags. • Age ratio. • Sex ratio. • Digit preference (weight, height and MUAC). • Standard deviation.

  23. SAM/MAM ratio • Fixed relation between MAM and SAM • Depends on: • Z score mean • Z score SD • another way of assessing the quality of the survey data • Digit preference (weight, height and MUAC). • Standard deviation.

  24. Divide in 4 groups The file ex05.csv is a comma-separated-value (CSV) file containing anthropometric data from a SMART survey in Kabul, Afghanistan.. Provide histograms for WHZ, HAZ and WAZ Calculate Saphiro-Wilks test Calculate Skewness and Kutosis Use this calculator online: http://www.statskingdom.com/320ShapiroWilk.html Excercise 5

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