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Survey Sampling MethodsOn this page: Simple random sampling One-stage cluster-sampling Two-stage cluster-sampling Stratified random sampling Adaptive cluster sampling
Random and systematic sampling
Simple random sampling
You must first be able to list your individual sampling units in some way. This applies whether your sampling unit is a person, a rodent, a tree or an insect. In most cases they will need to be tagged in some way so they can be identified. If you cannot list the individual sampling units, you cannot take a simple random sample (although there are other options as we see below).
The best way to select your sample is to select n numbers from a table of random numbers. Another way is to generate n numbers on the computer using a random number generator. If all else fails, write all the numbers of the sampling units on pieces of paper. Fold the pieces of paper so the numbers are not visible, put them in a box and shake them up. Then select the required number of units, preferably with your eyes shut. Unfortunately it is not random, but hopefully will not be unduly biased.
For systematic sampling the starting point should be chosen randomly in order to avoid bias. In the diagram right, we wanted to select (n=) 12 units from a population of (N=100), so k = N/n = 100/12 = 81/3. We used random number tables to select a number between 1 and (k=) 8 as our starting point. The number selected was 6, so starting there, we then selected every 8th unit - giving a total sample size of 12.
Since N is fairly small, it would have been better had we employed a selection interval of k=81/3 unit - rounding the result to the nearest whole number. Thus, instead of (6+0)=6, (6+8)=12, (6+16)=22, (6+24)=30... we should have used (6+0)=6, (6+81/3)=12, (6+162/3)=23, (6+25)=31...
The need for an initial random selection means that, even for a systematic sample, you must be able to list all units in the population - or at least locate them unambiguously. You also have to know the total number of units in order to select the sampling interval to get your desired sample size. Sometimes the first unit is haphazardly selected, although this can lead to bias - especially if you interpret haphazard to mean convenience and also select a convenient value of k.
If systematic sampling is being used to select quadrats in a field, the distance between plots can measured by the number of paces. The distance between sampling units does not have to be measured too precisely, providing there is no risk of bias in the precise positioning of the sample. If there is, it is better not to look at the ground for the last few paces.
One stage cluster sampling
This is done in the following way.
We first take an example where there are the same number of secondary units in each cluster.
In this example there are an unequal number of secondary units in each cluster.
Let's take an example of doing a survey to determine the level of immunization coverage for children against measles in a district. You don't have a list of all the children in the district so you cannot take a simple random sample. But you do have a list of schools in the district. Hence you take a random sample of schools. You then sample all pupils from each
Two stage cluster sampling
This is done in the following way.
Stratified random sampling
Adaptive cluster sampling