ARTICLE 35: Research Methods for Ph. D. Studies: Sampling Part 3 of 6

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.

Event sampling

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

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.

Matched sampling

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

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.

Non-probability sampling

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

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.

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ARTICLE 34: Research Methods for Ph. D. Studies: Sampling Part 2 of 6

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.

Cluster sampling

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

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

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

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.

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