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How to minimize the problem of getting less SAM children than minimum sample size due to big variation in village population figure?

This question was posted the Assessment and Surveillance forum area and has 1 replies.

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Anonymous 1310


Normal user

3 Jul 2013, 07:15

Some villages may have very high population while other villages have very low population. During wide area survey, we select number of villages on the basis of average village population to get minimum sample size. There may be the chance of getting selected very small villages and so we can not get adequate SAM children to meet our sample size as selected villages are very small. Will it affect the result? how to minimize this problem?

Mark Myatt

Frequent user

3 Jul 2013, 17:04

There can be a problem with using the arithmetic mean. This is due to community sizes frequently following a log-normal distribution. In this case the arithmetic mean will tend to overestimate the central tendency (average) because the arithmetic mean can be strongly influenced by a small number of very large communities. The problem of the relevant average to use can also apply to variables such as household size, livestock ownership, income, weight, growth, lengths of stay in a CMAM program. The arithmetic mean is often not an appropriate measure. The effect of this is that you may fail to meet the planned survey sample size. This means that your result will have worse precision than you wanted. You can minimise the problem by using the geometric mean (the antilogarithm of the means of the logarithms of community populations) or the median of community populations. Another (secondary) issue is that a "law of averages" does not really exist in the sense that a small sample will inevitably even out a random variable. In our context this means that we have no guarantee that the average community size in the sample will be very close to the average community size in the population. Sometimes we will not achieve the planned sample size and sometimes we will exceed the overall sample size. The way to minimise this issue is to increase the number of communities that you sample. This is not a big issue because the number of communities sampled is usually quite a large proportion of the number of communities that could be sampled (i.e. we have a high sampling fraction). I hope this helps.

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