Analysis of surveys. Effect of measurement error Shape of distribution as population gets more malnourished. Possibility of development of new methods Age/ height profile during famine The problem of oedema Implications for relief programs. Effect of measurement errors on survey results.
Suppose an imprecise error moves a value from one segment to another (up or down)
If the errors are random then the same number of values will move from one half of the distribution to the other (orange to green and green to orange).
There will be no change in the mean of the distribution provided that there are as many positive as negative measurement errors
1 Take a normally distributed population with mean Z-score of minus 1 Z-score WFH and Standard deviation of 1 Z-score unit.
2 Introduce an error height with a mean of 0.0cm and an SD of 1.0cm.
3 Introduce an error in weight with a mean of 0g and an SD of 100g
4 Introduce a 5 cm height error and 500g weight error to 0.25% of population
5 Introduce a 10cm height error and 1kg weight error to 0.15 of population
5 Remove all flags (plus or minus more than 4SD from population mean)
The actual Observed and theoretical prevalence of malnutrition are related within the confidence intervals of the survey – maybe calculation would be more precise?
Wasting by height group as the population nutritional state deteriorates:All groups are affected – as the situation becomes desperate older children have a high prevalence