Sampling methods are used to select a test panel for research. Probability and non-probability sampling methods are used to select participants. Stratified sampling is used to divide the population into groups and select participants in appropriate proportions. Convenience and quota sampling are non-probability methods that may not accurately represent the population.
There are several sampling methods used when selecting a test panel for research. This research may involve testing a specific theory or product, conducting an opinion poll, or any other research that aims to cover a particular group in its entirety. This group is known as a population, although it can involve any type of group, not just the citizens of a country.
With a small population, such as staff working in a particular office, it is usually possible to question or test everyone involved. This is known as a census study. With most populations, such as “everyone in China aged 65 and over,” it is impossible to question or test everyone, so a sample group must be selected. The different ways of choosing these participants are known as sampling methods.
Sampling methods fall into one of two main categories: probability and non-probability. In a probability sampling method, everyone has a known probability of being selected, although this probability can vary from person to person. In a non-probability sampling method, some people have no chance of being selected because participants are chosen from specific sections of the population. This may be cheaper, but it comes at a price: unlike probability sampling, non-probability sampling makes it impossible to estimate how accurately the sample group represents the entire population.
The simplest form of probability sampling is to randomly select people from a list of the entire population. A variation of this method, systematic sampling, is to select people at fixed intervals through the list, such as every cent. Both of these sampling methods are flawed in that the resulting sample group may not represent the composition of the population. For example, the sample group may have three children and seven adults, which is clearly not representative if the entire population is 20% children and 80% adults.
This can be addressed using stratified sampling, where the population is divided into particular groups that share common factors and participants are randomly selected from these groups in the appropriate proportions. In the example above, the researchers would randomly select two people from a list of all children and eight people from a list of all adults. Of course this can be extended to other group types, such as by gender, to create a sample group that more accurately reflects the entire population.
The simplest forms of non-probability sampling are known as convenience sampling. Researchers simply choose the participants who are easiest to connect with. Clearly there is a strong risk that this is very unrepresentative of the population. For example, if researchers knock on doors during the day, they’re less likely to get participants in full-time jobs.
Quota sampling combines stratified sampling and convenience sampling and usually involves researchers trying to find participants to fill quotas. In the example above, researchers could knock on doors until they’ve spoken to a total of two children and eight adults. While this method means that the sample group is in the right proportions, the selection process makes it impossible to know how representative it is. In our example, the eight adults could all be unemployed, making them unrepresentative of the views of the whole population in a question about social security benefits. For this reason, quota sampling is classified as a non-probability sampling type.
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