2. • When you conduct research about a group of people, it’s rarely
possible to collect data from every person in that group.
Instead, you select a sample. The sample is the group of
individuals who will actually participate in the research.
3. • To draw valid conclusions from your results, you have to
carefully decide how you will select a sample that is
representative of the group as a whole.
4. There are two types of sampling
methods:
• Probability sampling involves random selection, allowing you
to make strong statistical inferences about the whole group.
• Non-probability sampling involves non-random selection
based on convenience or other criteria, allowing you to easily
collect data.
5. What Is Non-Probability Sampling? |
Types & Examples
• Non-probability sampling is a sampling method that uses
non-random criteria like the availability, geographical proximity,
or expert knowledge of the individuals you want to research in
order to answer a research question.
• Non-probability sampling is used when the population
parameters are either unknown or not possible to individually
identify. For example, visitors to a website that doesn’t require
users to create an account could form part of a non-probability
sample.
6. Be careful not to confuse probability and
non-probability sampling.
• In non-probability sampling, each unit in your target
population does not have an equal chance of being included.
Here, you can form your sample using other considerations,
such as convenience or a particular characteristic.
• In probability sampling, each unit in your target
population must have an equal chance of selection.
7. Non-probability sampling
Simple random sampling
• Simple random sampling gathers a random selection from the
entire population, where each unit has an equal chance of
selection. This is the most common way to select a random
sample.
• To compile a list of the units in your research population,
consider using a random number generator. There are several
free ones available online, such as random.org, calculator.net,
and randomnumbergenerator.org.
8. Stratified sampling
• Stratified sampling collects a random selection of a sample from
within certain strata, or subgroups within the population. Each
subgroup is separated from the others on the basis of a
common characteristic, such as gender, race, or religion. This
way, you can ensure that all subgroups of a given population
are adequately represented within your sample population.
• For example, if you are dividing a student population by college
majors, Engineering, Linguistics, and Physical Education
students are three different strata within that population.
9. • To split your population into different subgroups, first choose
which characteristic you would like to divide them by. Then you
can select your sample from each subgroup. You can do this in
one of two ways:
• By selecting an equal number of units from each subgroup
• By selecting units from each subgroup equal to their proportion
in the total population
10. Systematic sampling
• Systematic sampling draws a random sample from the target
population by selecting units at regular intervals starting from a
random point. This method is useful in situations where records
of your target population already exist, such as records of an
agency’s clients, enrollment lists of university students, or a
company’s employment records. Any of these can be used as a
sampling frame.
11. • To start your systematic sample, you first need to divide your
sampling frame into a number of segments, called intervals. You
calculate these by dividing your population size by the desired
sample size.
• Then, from the first interval, you select one unit using simple
random sampling. The selection of the next units from other
intervals depends upon the position of the unit selected in the
first interval.
12. Cluster sampling
• Cluster sampling is the process of dividing the target population
into groups, called clusters. A randomly selected subsection of
these groups then forms your sample. Cluster sampling is an
efficient approach when you want to study large, geographically
dispersed populations. It usually involves existing groups that
are similar to each other in some way (e.g., classes in a
school).
13. • There are two types of cluster sampling:
• Single (or one-stage) cluster sampling, when you divide the
entire population into clusters
• Multistage cluster sampling, when you divide the cluster further
into more clusters, in order to narrow down the sample size
14. Non-probability sampling
Convenience sampling
• Convenience sampling is primarily determined by
convenience to the researcher.
• This can include factors like:
• Ease of access
• Geographical proximity
• Existing contact within the population of interest
• Convenience samples are sometimes called “accidental
samples,” because participants can be selected for the sample
simply because they happen to be nearby when the researcher
is conducting the data collection.
15. Quota sampling
• In quota sampling, you select a predetermined number or
proportion of units, called a quota. Your quota should comprise
subgroups with specific characteristics (e.g., individuals, cases,
or organizations) and should be selected in a non-random
manner.
• Your subgroups, called strata, should be mutually exclusive.
Your estimation can be based on previous studies or on other
existing data, if there are any. This helps you determine how
many units should be chosen from each subgroup. In the data
collection phase, you continue to recruit units until you reach
your quota.
16. Self-selection (volunteer) sampling
• Self-selection sampling (also called volunteer sampling) relies on
participants who voluntarily agree to be part of your research. This is
common for samples that need people who meet specific criteria, as
is often the case for medical or psychological research.
• In self-selection sampling, volunteers are usually invited to
participate through advertisements asking those who meet the
requirements to sign up. Volunteers are recruited until a
predetermined sample size is reached.
• Self-selection or volunteer sampling involves two steps:
1.Publicizing your need for subjects
2.Checking the suitability of each subject and either inviting or
rejecting them
17. Snowball sampling
• Snowball sampling is used when the population you want to
research is hard to reach, or there is no existing database or other
sampling frame to help you find them. Research about socially
marginalized groups such as drug addicts, homeless people, or sex
workers often uses snowball sampling.
• To conduct a snowball sample, you start by finding one person who
is willing to participate in your research. You then ask them to
introduce you to others.
• Alternatively, your research may involve finding people who use a
certain product or have experience in the area you are interested in.
In these cases, you can also use networks of people to gain access
to your population of interest.
18. Purposive (judgmental) sampling
• Purposive sampling is a blanket term for several sampling
techniques that choose participants deliberately due to qualities
they possess. It is also called judgmental sampling, because it
relies on the judgment of the researcher to select the units (e.g.,
people, cases, or organizations studied).
• Purposive sampling is common in qualitative and mixed
methods research designs, especially when considering specific
issues with unique cases.
19. Non-probability sampling examples
• There are a few methods you can use to draw a non-probability
sample, such as:
• Social media
• River sampling
• Street research
20. Social media
• Suppose you are researching the motivations of digital nomads
(young professionals working solely in an online environment).
You are curious what led them to adopt this lifestyle.
• Since your population of interest is located all over the globe, it
clearly isn’t feasible to conduct your study in person. Instead,
you decide to use social media, finding your participants
through snowball sampling.
21. • You start by identifying social media sites that cater to digital
nomads, such as Facebook groups, blogs, or freelance job
sites. You ask the administrators for permission to post a call for
participants with information about your research, encouraging
readers to share the call with peers.
22. River sampling
• You are part of a research group investigating online behavior
and cyberbullying, in particular among users aged 15 to 30 in
your state. You are collecting data in two ways, using an online
survey.
• You first place a link to your survey in an online news article
about cyber-hate published by local media. Second, you create
an advertising campaign through social media, targeted at
users aged 15 to 30 and linking back to your survey. To entice
users to participate, a prize draw (movie tickets) is mentioned in
all ads. The survey and the campaign are active for the same
length of time.
23. • These two data collection methods are river samples. The
name refers to the idea of researchers dipping into the traffic
flow of a website, catching some of the users floating by.
24. Street research
• You are interested in the level of knowledge about myocardial
infarction symptoms among the general population.
• For a week, you stand in a shopping mall and stop passersby,
asking them whether they would be willing to take part in your
research. To try to allow as broad a range of respondents as
possible to be included, you interview equal numbers of people
from Monday to Friday during working hours.
