Written by Dr. Hannes Nel
I will discuss event sampling, extreme-case sampling, matched sampling, multi-phase sampling, non-probability sampling and opportunistic sampling in this article.
Of all the types of sampling, event sampling is the most a research method rather than a data collection method.
You would typically select a small group of individuals to investigate.
It requires counting the number of times that an occurrence is observed or a statement, word, etc. is found in a data source, for example a document.
Counting words, statements, etc. in a document is easy if it is available in electronic format.
An example of this is to count the number of times that the words “he” and “she” respectively are used in a book to determine if there is gender discrimination against one or the other.
You can use your laptop computer to count the words for you.
Any word processor on computer has the facility to count words.
When your research requires you to observe certain repetitive actions, you will need to develop statements or criteria for the action.
This can be compared to the cricket scorebooks that some clubs still use, although computer programmes for even that have been developed.
The task becomes more difficult if it is not only necessary to count the number of times actions are repeated, but also the chronological order in which the actions took place.
An example of this is where you need to determine what kind of response a certain action invites, such as how people respond to a smile, compliment or insult.
Extreme-case sampling is used when information about unusual cases that may be particularly troublesome, or enlightening are sought.
Such a sample might represent the purest instance of a phenomenon that you wish to investigate.
Obviously, such cases are likely to be exceptions to the rule.
You might identify and investigate extreme cases to develop a richer, more in-depth understanding of a phenomenon.
You can also use extreme-case sampling to lend credibility to your research project.
Extreme-case sampling is often used in conjunction with other sampling methods.
In such events the extreme-case will be investigated in the context of the other sampling method.
The extreme-case can be used as an outlier, that is an observation that takes on an extremely high or extremely low value, thereby strengthening the norm.
An extreme-case can also be investigated on its own.
For example, where you do research on a specific individual who is extremely gifted, rich, strong, etc.
In matched sampling one member of a control group is matched to a member of an experimental group.
Matching is done randomly, but the independent variables that are considered important for the research are taken into consideration.
Pairs of participants are matched in terms of the independent variables.
For example, age, gender, academic qualifications, etc. and only then assigned to the control or experimental group.
Matching pairs are used mostly when experimental research methods are used.
This implies that it is more valuable to quantitative research than to qualitative research.
However, experimental research methods can be used with qualitative research methodology.
Therefore, matching pairs can also be used to compose samples for such research.
Multi-phase sampling is a form of stage sampling.
The difference between multi-phase sampling and stage sampling is this:
In the case of stage sampling the criteria on each level are pretty much the same for each level of sampling in a particular region or field.
You will take samples of samples.
For example, your initial sample can be 1000 students randomly selected at a university.
Your next level sample can be 500 students who enrolled for psychology.
Your third level can be the 100 students who achieve the highest marks in the final exam.
In multi-phase sampling the criteria change on each ensuing level.
For example, the first stage criterion might be region.
The second stage criterion can be students who excel in numeracy skills.
The third stage criterion can be students with blue eyes.
Convenience sampling, quota sampling, dimensional sampling, purposive sampling and snowball sampling can all be conducted in a non-probability sampling manner.
In non-probability sampling you, as the researcher, select the sample based on your own subjective judgement.
Non-probability infers that randomness ins not required, and the accuracy of the research findings need not be accurate to a good measure of probability, for example 90%.
In non-probability sampling, you will do research on a specific group to obtain knowledge about the group.
You will not claim general applicability of your findings.
The group, therefore, is important rather than any wider population.
This is often the case with small-scale research.
For example, just two or three faculties in a university,
Or just the lecturers in a small number of faculties.
Small-scale research often uses non-probability samples.
Meaning that not all available individuals have an equal chance of being selected for the sample.
The more expertise you have of the field of study, the better will you be able to select your sample.
Such samples are far less complicated to set up than a randomly selected sample.
However, keep in mind that they probably do not represent a substantive population.
Then again, they are less expensive than random samples and can achieve the purpose of the research if the general applicability of the findings is not important.
Small-scale research can also be used to pilot data collection tools, such as questionnaires, before they are used in actual research.
Opportunistic sampling means taking advantage of unanticipated events, leads, ideas, hints, and issues.
Accidental sampling is similar, but slightly different, as we saw in my previous post on convenience sampling.
The sample is composed of items upon which you stumbled by chance.
People who are available at the time of your study can be included in the sample.
They will need to provide valuable information and they must fit the criteria that you need.
A friend, who unknowingly shares valuable information with you at a party can be included in your sample.
Opportunistic sampling can be risky because the data that you collect can often not be generalized.
It might, furthermore, be difficult to corroborate the information.
Your source might not be reputable and might not have any credentials or qualifications making him or her an authoritative or expert source.
Event sampling is where you would do research on one or a small number of people, occurrences, or phenomena.
Extreme-case sampling also focus on just one or a small number of people, occurrences, or phenomena. Only here you will look for an unusual case or cases.
In matched sampling members of a control group are compared in terms of independent variables to members of an experimental group.
In multi-phase sampling the sample is broken down in terms of different criteria for each phase or level.
Non-probability sampling is selecting a sample that does not necessarily represent the population.
The result of this would be that your research findings cannot be generalized.
Opportunistic sampling means using any data sources that you can find without deliberately selecting them.