Echantillonnage LQAS

You need to choose:

(1) The levels of the indicator(s) that are considered to be bad and good. You might (e.g.) have < 50% as bad and > 80% as good.

(2) Acceptable levels of error (type I / type II). I usually set these both at 5%.

(3) The population size. This is important for small populations such as when you assess coverage of SAM treatment. IF this is large then you can specify a population size of 5000 or higher.

I have an on-line calculator ... click this link. Here is an example sample size calculation:

I assume a large population so I set 'Population size' to 5000

I define good as > 80% so I set 'Upper threshold' to 0.8

I define bad as < 50% so I set 'Upper threshold' to 0.5

I want both errors to be <= 5% so I set both 'Maximum tolerable a error' and 'Maximum tolerable ß error' to 0.05

I click 'Find sample size and decision rule'. The calculator returns the LQAS sampling plan n = 28, d = 18. I need a sample size of 28. If I find >= 18 'good' cases then I classify the situation as good. If I find < 18 'good cases' then I classify the situation as bad.

I hope this helps.

BTW .. A three class LQAS (as in SLEAC) is possible. A sample size of n = 40 is usually large enough for most applications (see the SQUEAC / SLEAC technical reference for more information).

As with any survey ... you can have many indicators.

It is typical to calculate the sample size for all indicators and then pick the largest required sample size and collect that for all indicators. The one complication is that you need to recalculate the decision rules for the larger sample sizes for all of the indicators.

You can find a "reverse" calculator here. This needs you to enter the same information and the sample size.

A very easy method is to pick a reasonably large sample size (e.g. n = 40) and then use the simplified methods found in the SQUAC / SLEAC technical reference.

To be clear ... you can do many indicators with one sample.

Then you would need a separate sample from each target group.