Written by Dr. Hannes Nel
Sampling types mostly differ only in the way in which the sample is composed.
Ultimately, samples are all small extracts from a larger population.
They are studied because the entire population is too large for viable research.
Obviously, the sample should display behaviour, phenomena, events, etc. that would be typical for the entire population.
That means that the research findings based on the sample should be generalizable.
This, however, is not always the case.
Researchers sometimes limit their research to the sample without even trying to achieve generalizability.
In effect, what is supposed to be a sample becomes the population.
I will discuss the following sampling methods in this article:
- Probability sampling.
- Purposive sampling.
- Quota sampling.
- Random sampling.
- Reputational-case sampling.
- Snowball sampling.
- Stage sampling.
A probability sample is drawn randomly from the wider population.
Therefore, it will be useful if you need to generalize.
Or if you seek representativeness of the wider population.
The name already reveals that it should be used with quantitative research.
Obviously, statistical calculations will form part of the research process.
Probability or representativeness is calculated with a wonted measure of accuracy probability.
For example, 97% (giving an error factor of 3%).
The risk of bias is less than if you were to rely on your gut feeling or subjective judgement.
However, all samples, regardless of whether they are based on statistical calculation or your judgement, are hampered by a measure of sample error.
Purposive sampling is also sometimes called purposeful sampling or judgmental sampling.
In purposive sampling the researcher collects data according to the aims of the research.
To develop a purposive sample that will sufficiently represent the population:
- You will need to know the profile of your target group.
- And you will select items for the sample according to a list of important characteristics.
You can select purposive samples after you have studied the bigger population from which to select the sample.
This might require fieldwork or studying written profiles, such as curriculum vitae.
It can also require fieldwork and literature study.
You will need to find certain types of individuals displaying attributes that are needed for your research.
In horticultural research you will probably use categories such as resistance to plant diseases, flower colour, size and shape, etc.
In social research categories such as age, gender, status, role or function in organizations, stated philosophy or ideology, may serve as starting points.
For study in-depth, you will need to select information-rich cases.
As your research progresses, you may discover new categories which would lead you to more sampling in that particular dimension.
For quota sampling you will need to choose representative individuals from a specific subgroup.
For example, numbers in accordance with the demographic composition of the total population.
You can simplify the process by first preparing a matrix table that creates cells or strata.
You may wish to use gender, age, education, or any other attributes to create and label each stratum or cell in the table.
The research question and the focus of your study will determine which attributes you should select for your sample.
You should use a nonprobability method to fill the cells.
Each category in the overall sample must be filled using the same recruitment procedure for the resulting groups to be comparable.
Next, you need to determine the proportion of each attribute in the full study population.
Let’s say you want to study perceptions of violence among people over age 50.
You can, for example, group the people in your sample as younger than 30; 30 to 39; 40 to 49; 50 to 59 and 60 and older.
You will need to determine the proportion of people in each age group, although it will often be best to have the same number of individuals in each group.
The groups younger than 50 are included for comparison purposes.
In random sampling, each possible sample has an equal probability of being chosen as the sample.
The sample needs to be large enough to deliver findings that will apply to the entire target group with a reasonable accuracy.
This depends on the size of the population and the measure of heterogeneity.
The larger the population, the larger the sample drawn should be.
A random sample is mostly used when each element of the population has the same chance of being selected to be part of the sample.
This is sometimes called ‘simple random sampling’.
The procedure used to draw the sample needs to ensure that as little bias as possible is present.
Drawing names from a hat, putting the names on a dart board and throwing darts at them to choose sample items, the roll of a dice are examples of how a random sample can be composed.
Some dedicated computer software can also select a random sample from a list of names.
Reputational-case sampling is a variant of extreme-case and unique-case sampling.
Like in the case of the extreme-case and unique-case sampling, you will choose a sample recommended by an expert in the field.
For example, you might be looking for individuals who have particular expertise that is most likely to advance your research interests.
Snowball sampling is also called chain referral sampling or respondent-driven sampling.
Snowball sampling is an effective way to locate subjects with certain attributes or characteristics necessary in the study.
Snowball samples are particularly popular among researchers who are interested in studying various classes of deviance.
Sensitive topics are often studied, and the sample often come from difficult-to-reach populations.
In snowball sampling, you would identify a small number of individuals who have the characteristics in which you are interested.
These people are then asked to identify and put you in touch with others who also have the wonted characteristics.
For example, if you are doing research on people who suffer from arachnophobia, you need to find only one person who is intensely scared of spiders and would be willing to put you in touch with others who suffer from arachnophobia to get the snowball rolling.
It so happens that this sampling method is especially useful for sampling a population where access and obtaining co-operation are difficult.
The basic procedure of snowball sampling begins by identifying one or more people with relevant characteristics and then interviewing them or asking them to complete a questionnaire.
After interviewing the subjects or after they have completed a questionnaire, you would ask them for the names of other people who possess the same attributes as them.
In this manner, a chain of subjects driven by the referral of one respondent to another is formed.
Stage sampling is an extension of cluster sampling.
It involves selecting the sample in stages, i.e. taking samples of samples.
Doing research on the value systems of universities, you can first select a cluster of universities as your sample, then certain faculties in each university as a sub-sample, then a number of students from each faculty as your sub-sub sample.
A probability sample is a random sample for which the measure of probable accuracy is calculated.
In purposive sampling you would collect data based on the aim of your research. The characteristics of the population from which you will draw a sample will be taken into consideration.
A quota sample is drawn from a population that represents the profile of your research target. The profile is based on your research question and the focus of your study.
In random sampling each possible sample has an equal chance of being chosen for your research.
In reputational-case sampling you will choose a sample recommended by an expert in the field.
In snowball sampling you would identify a small sample or perhaps just one individual who meets the requirements for your research. This individual or small group can recommend other individuals or groups that meet your requirements.
Stage sampling is the selection of a sample in stages, that is taking samples from samples.