Written by Dr J.P. Nel
On face value sampling seems like a simple job.
And so it mostly is.
However, if you choose the wrong sample or you choose
the sample incorrectly, you can destroy the validity and accuracy of your
And you might have wasted a good number of years on
Think carefully about the examples that I offer in
this post, especially the ones on systematic sampling.
I will discuss:
- Stratified sampling.
- Systematic sampling.
- Theoretical sampling.
- Time sampling.
In this post.
Stratified sampling is used to ensure that the sample
is representative of a population with multiple characteristics that are evenly
distributed amongst the population.
The research population is divided into homogeneous
groups, with each group containing subjects with similar characteristics.
Such homogeneous groups are often called ‘sub-units’
Samples are selected by making use of random selection
procedures such as systematic sampling or simple random sampling.
For example, students at a specific university, which
are the population on which the research will be conducted, can be grouped into
a male and a female group.
Or they can be grouped per faculty.
Or per leisure time interests, etc.
If, for example, you wish to do research on the impact
of leisure time interest on academic performance, you will need to investigate
a representative sample from each leisure time interest group.
Representivity is achieved by identifying the
parameters of the wider population and taking them into consideration when composing
So, in this example leisure time utility will be the
Factors such as gender, age, occupation, academic
year, etc. can be the dependent variables.
For the sake of coming to valid conclusions and
findings, the process of organizing a stratified sample requires ensuring that
the characteristics typical of the wider community are present in the sample.
If you draw more than one sample, you must also ensure
that the samples are homogeneous in terms of at least one characteristic, which
would be your independent variable.
In qualitative research, you will probably not
calculate the size of the sample in terms of a statistical level of accuracy.
Therefore, representivity will depend on your own
You do need to take certain factors into
For example, the size of the population, time and cost
constraints, access to data, the need for simplicity and the purpose of the
Although some simple calculations need to be done with
systematic sampling, it is not enough to be called quantitative research.
Systematic sampling is a modified form of random
For example, in a population of 10,000 individuals, you
might feel that you should have a sample of, say, 200 people.
This will mean that you should select every first person
from each sub-unit of 50 for your sample.
Fifty is, thus, your sample interval, i.e. the
distance between each element selected for the sample.
You can choose numbers 1, 51, 101, 151, etc. for your
If, for example, the first 57 people on the list that
you use are the only females, you will have only two females in your sample.
This will probably distort your findings.
Another example of a skewed list would be if you work
on a number of name lists for different faculties and the faculty lists are
ordered normatively with the best performing student as the first name on the
list and the poorest performing student as the last name on the list.
To use an extreme example, if there are fifty names on
each list, your sample will include only the poorest performing students.
Periodicity can be a problem with systematic sampling
if the list is not a fully random one.
For example, if you start by selecting the first name
on the list, then names numbered 1 to 49, 50 to 99, 100 to 149, etc. do not
have any chance of being selected for the sample.
You can eliminate this problem by ‘randomizing’ the
list first by ‘shuffling’ the names like you would a deck of playing cards, and
then selecting the sample.
You can also deviate somewhat from a systematic
selection. A sampling procedure is not cast in concrete and you can adapt it to
your research needs.
Just as long as it improves the randomness of your
You can, for example, use systematic sampling coupled
with the condition that both genders are fairly represented in the sample.
This can be achieved by splitting the name lists for
males and females and then selecting a number of males and females from each
Theoretical sampling is the process of investigating
incidents, events, occurrences or people over a period based on their potential
to represent or demonstrate important theoretical constructs.
Researchers who follow an approach where deductions
can be made from data with which theory can be developed or extended would be
interested in finding individuals with the right makeup, or cases that embody
Theoretical sampling, therefore, is a special type of
As the name implies, theoretical sampling is best used
when the research focuses on theory and concept development.
Your goal would be to develop theory and concepts that
are connected to, are grounded in, or emergent from real life events and
To generate new theory will often require substantial
research and is, therefore, a time-consuming process.
You will need to analyze and probably reconstruct and
deconstruct existing theory until you manage to develop new theoretical ideas.
As opposed to purposeful sampling, you cannot know in
advance precisely what to sample for and where it will lead you when you use
That is why theoretical sampling fits in well with
grounded theory and why you will probably work towards solving a problem
statement or question rather than hypothesis testing.
Time sampling is where observations of occurrences or
events need to be taken and recorded at certain times.
You can use it if it is important to know the chronology
Time sampling can be instantaneous, at specific
intervals or measured in terms of how long it takes, i.e. duration.
Instantaneous sampling is achieved by observing an
event or occurrence at standard intervals, for example every minute, hour, etc.
on the dot.
You will need to take observations at a specific time
and intervals that you planned on, or that are prescribed or necessary for
whatever reason and you will take notes of your observations.
In the case of interval recording, you will observe
and record events or occurrences during each interval.
This means that you will record observations during
each interval for the entire time of the interval and not just at the moment
each interval starts.
For example, you might wish to conduct research on the
emotional state of a patient over a period and at set intervals, say every
fifteen minutes for a day.
You can then count the number of times that the
patient is happy, sad, angry, etc. and how the patient expressed the emotions.
The patient might have smiled, cried, laughed, etc.
You might also need to take note of what possibly
might have triggered the emotion.
Duration sampling is where you will measure how long
it takes for an individual or group to complete a certain task.
With duration sampling, you might, for example, need
to record how long it takes the patient to overcome an anger attack or any
other emotional state.
Or you might need to record how often the individual
switches between different emotions, and many more.
The format of your notes will depend on the type of
data that you wish to collect.
Time sampling will often require a quantitative
research approach or at least some calculations.
This, however, does not mean that it cannot be used in
It might also be necessary to use a mixed approach,
i.e. quantitative and qualitative research.
Stratified sampling is where you will select a
representative sample from a diverse community. You will use random sampling to
select the sample or samples.
In systematic sampling you will select individuals or
items from a population at structured intervals.
In theoretical sampling you will select the sample
based on certain criteria that will enable you to develop new theories and
Time sampling is where you will take samples at
certain times. It can be instantaneous, at specific intervals or measured in
terms of duration.