Other strata may be religion, academic ability or marital status. Some examples of strata commonly used by the ABS are States, Age and Sex. A sample is then drawn from within these strata. In stratified sampling, the population is divided into groups called strata. However, if you form the population into groups, and sample from each group, you can make sure the sample is representative. A disadvantage is that you may need a list to start with, if you wish to know your sample size and calculate your sampling interval.Ī general problem with random sampling is that you could, by chance, miss out a particular group in the sample. It also gives a good spread right across the population. 20th) member on the list, than to select as many random numbers as sample size. The advantage of systematic sampling is that it is simpler to select one random number and then every ‘Ith’ (e.g. The sample of students would be those corresponding to student numbers 9, 29, 49, 69. If this number was 9, then the 9th student on the list of students would be selected along with every following 20th student. The starting point would be chosen by selecting a random number between 1 and 20. Note: if I is not a whole number, then it is rounded to the nearest whole number.Īll students would be assigned sequential numbers. If a systematic sample of 500 students were to be carried out in a university with an enrolled population of 10,000, the sampling interval would be: The appropriate sampling interval, I, is then calculated by dividing population size, N, by required sample size, n, as follows: In this case, it is first necessary to know the whole population size from which the sample is being selected. It may be that a researcher wants to select a fixed size sample. A market researcher might select every 10th person who enters a particular store, after selecting a person at random as a starting point or interview occupants of every 5th house in a street, after selecting a house at random as a starting point. This technique could also be used when questioning people in a sample survey. This technique requires the first item to be selected at random as a starting point for testing and, thereafter, every 20th item is chosen. This method is often used in industry, where an item is selected for testing from a production line (say, every fifteen minutes) to ensure that machines and equipment are working to specification.Īlternatively, the manufacturer might decide to select every 20th item on a production line to test for defects and quality. Systematic sampling, sometimes called interval sampling, means that there is a gap, or interval, between each selection. However, because every person or item in a population has to be listed before the corresponding random numbers can be read, this method is very cumbersome to use for large populations. The advantage of simple random sampling is that it is simple and easy to apply when small populations are involved. A sample of 6 numbers is randomly generated from a population of 45, with each number having an equal chance of being selected. A Tattslotto draw is a good example of simple random sampling.These numbers could then be matched to names in the telephone book, thereby providing a list of 2,000 people. If the sample size was to include 2,000 people, then 2,000 numbers could be randomly generated by computer or numbers could be picked out of a hat. For example, each name in a telephone book could be numbered sequentially. With simple random sampling, each item in a population has an equal chance of inclusion in the sample. Five common random sampling techniques are: Random sampling technique ensures that bias is not introduced regarding who is included in the survey. In random sampling, all items have some chance of selection that can be calculated.
In this section the two major types of sampling, random and non-random, will be examined.
A sample must be large enough to give a good representation of the population, but small enough to be manageable. To overcome these problems, samples are taken from populations, and estimates made about the total population based on information derived from the sample. Obviously, each car could not be crash tested to determine its strength! For example, a car manufacturer might want to test the strength of cars being produced. Sometimes taking a census can be impossible. This may be too expensive if every person in the population is to be included. For example, a survey that asks complicated questions may need to use trained interviewers to ensure questions are understood. However, this method is often impracticable as it’s often very costly in terms of time and money.
If you survey every person or a whole set of units in a population you are taking a census.
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