Written by Dr. Hannes Nel
I discussed an introduction to sampling in my previous post.
I also discussed the first two types of sampling, namely boosted sampling and case study sampling.
To refresh your memory, boosted sampling is a type of sampling where you take specific steps to ensure that individuals who might otherwise have been omitted from the sample, are included.
Case study sampling is an investigation into a specific instance or phenomenon in its real context.
I will discuss cluster sampling, convenience sampling, critical-case sampling and dimensional sampling in this post.
A large and widely dispersed population brings with it, administrative problems.
Let’s say you want to investigate the impact of video games on teenagers.
It would be difficult to select and investigate a sample of, say, 1000 teenagers across the entire country.
You can reduce the time and work by selecting a smaller geographical area within the country.
This might have a negative effect on the generalizability of your findings.
It depends on your purpose with your research if this would be a problem or not.
But at least your research topic will now be viable.
This is an example of cluster sampling.
You can go one or more steps further by subdividing the geographical region into towns and towns into suburbs.
This is called ‘multi-stage cluster sampling’.
Limitations can be:
- Younger teenagers will probably not be affected the same as older ones.
- Teenagers in rural areas will not be affected the same as children living in urban areas.
- There are many other possible variables that can have an impact on the results of the research.
- For example, single-parent versus both parents in the family, personality traits, home conditions, etc.
The obvious risk with cluster sampling is that you can build in bias by excluding some possibilities.
The type and purpose of the research will determine if cluster sampling should be used or not.
Convenience sampling means selecting inputs and data from whoever happens to be available.
It is, thus, a rather opportunistic type of sampling.
It can also be accidental in nature, although there is a subtle difference between convenience sampling and opportunistic or accidental sampling.
Convenience sampling means making use of sources of information that you know of and that are easy to obtain.
Opportunistic or accidental samples are sources of information that you are not aware of in advance and that ‘falls into your lap’.
Convenience sampling is not a scientific way in which to collect data or to compose a sample.
It involves choosing the nearest individuals to serve as respondents.
It also means continuing the process until the required sample size has been reached.
Captive audiences such as students in a class are sometimes asked to discuss issues or complete questionnaires.
Convenience sampling is unlikely to deliver generally applicable findings.
Therefore, you will only use convenience sampling if achieving generalizable findings is not important to you and your research.
You must acknowledge that your findings are not applicable to a larger population than the small sample that you selected for your research.
It is even possible that your findings apply to your sample only.
Critical-case sampling is sampling where it is important to obtain maximum applicability.
If the information and findings hold true for a critical case, it is likely to hold true for other cases and communities as well.
Critical cases are those that are likely to yield the most or most important information that will prove a hypothesis or solve a problem and that will have the greatest impact on the development of knowledge.
When selecting a critical-case sample, you would be looking for a ‘decisive’ case that would help you decide about which of several different explanations for a phenomenon, event or behavior is most plausible or most widely regarded as representing the general profile of a community.
For example, if one or a small number of people living in a jungle somewhere proves to have an immunity to the venom of a snake that can be found in that part of the world, one can probably deduce that there are people who have developed an immunity to the venom of that snake.
It does not necessarily mean that the tribe from which the sample was taken is immune to the venom of the snake.
This is a rather dramatic example and one will probably need to do more research and tests before you can claim with certainty that all people belonging to that tribe are immune to the venom.
It would also be interesting to do additional research on why the people are immune to the venom.
But please keep in mind that this example is a figment of my imagination – I don’t think there is such a tribe or snake.
Critical-case sampling is an efficient way in which to conduct research.
It need not be expensive because only one or a few items should be enough to prove your hypothesis or solve your research problem statement or question.
Dimensional sampling is a refinement of quota sampling.
It can be used to reduce the sample size.
Dimensional sampling means selecting participants in the sample group in terms of a combination of criteria that you feel they should meet.
For example, when doing research on the correlation between perseverance and cognitive thinking ability, you can draw up a table with criteria for perseverance at the top (as column headings) and cognitive thinking ability down the side (as row headings).
A table like the one in the example can easily be converted into graphs of different types.
Cluster sampling is mostly used where the population is widely dispersed.
You will need to choose a cluster in the population that is representative of the population and that can realistically be investigated.
Convenience sampling means accepting any relevant data sources that you can easily find for your sample.
Convenience samples can be found by accident or coincidently.
Critical-case sampling focuses strongly on the purpose of your research.
The sample will often be too small to deliver generalizable findings.
Dimensional sampling is sampling where different characteristics of the sample are compared in the hope that the intersections of comparative data will deliver significant findings.