In: Statistics and Probability
Describe four types of sampling possible for researchers.
Write a multi-paragraph response.
Probability sampling methods
Probability sampling means that every member of the population has
a chance of being selected. If you want to produce results that are
representative of the whole population, you need to use a
probability sampling technique.
There are four main types of probability sample.
Probability sampling
1. Simple random sampling
In a simple random sample, every member of the population has an
equal chance of being selected. Your sampling frame should include
the whole population.
To conduct this type of sampling, you can use tools like random
number generators or other techniques that are based entirely on
chance.
Example
You want to select a simple random sample of 100 employees of
Company X. You assign a number to every employee in the company
database from 1 to 1000, and use a random number generator to
select 100 numbers.
2. Systematic sampling
Systematic sampling is similar to simple random sampling, but it is
usually slightly easier to conduct. Every member of the population
is listed with a number, but instead of randomly generating
numbers, individuals are chosen at regular intervals.
Example
All employees of the company are listed in alphabetical order. From
the first 10 numbers, you randomly select a starting point: number
6. From number 6 onwards, every 10th person on the list is selected
(6, 16, 26, 36, and so on), and you end up with a sample of 100
people.
If you use this technique, it is important to make sure that there
is no hidden pattern in the list that might skew the sample. For
example, if the HR database groups employees by team, and team
members are listed in order of seniority, there is a risk that your
interval might skip over people in junior roles, resulting in a
sample that is skewed towards senior employees.
3. Stratified sampling
This sampling method is appropriate when the population has mixed
characteristics, and you want to ensure that every characteristic
is proportionally represented in the sample.
You divide the population into subgroups (called strata) based on
the relevant characteristic (e.g. gender, age range, income
bracket, job role).
From the overall proportions of the population, you calculate how
many people should be sampled from each subgroup. Then you use
random or systematic sampling to select a sample from each
subgroup.
Example
The company has 800 female employees and 200 male employees. You
want to ensure that the sample reflects the gender balance of the
company, so you sort the population into two strata based on
gender. Then you use random sampling on each group, selecting 80
women and 20 men, which gives you a representative sample of 100
people.
4. Cluster sampling
Cluster sampling also involves dividing the population into
subgroups, but each subgroup should have similar characteristics to
the whole sample. Instead of sampling individuals from each
subgroup, you randomly select entire subgroups.
If it is practically possible, you might include every individual
from each sampled cluster. If the clusters themselves are large,
you can also sample individuals from within each cluster using one
of the techniques above.
This method is good for dealing with large and dispersed
populations, but there is more risk of error in the sample, as
there could be substantial differences between clusters. It’s
difficult to guarantee that the sampled clusters are really
representative of the whole population.
Example
The company has offices in 10 cities across the country (all with
roughly the same number of employees in similar roles). You don’t
have the capacity to travel to every office to collect your data,
so you use random sampling to select 3 offices – these are your
clusters.
Non-probability sampling methods
In a non-probability sample, individuals are selected based on
non-random criteria, and not every individual has a chance of being
included. This type of sample is easier and cheaper to access, but
you can’t use it to make valid statistical inferences about the
whole population.
Non-probability sampling techniques are often appropriate for exploratory and qualitative research. In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population.
1. Convenience sampling
A convenience sample simply includes the individuals who happen to
be most accessible to the researcher.
This is an easy and inexpensive way to gather initial data, but
there is no way to tell if the sample is representative of the
population, so it can’t produce generalizable results.
Example
You are researching opinions about student support services in your
university, so after each of your classes, you ask your fellow
students to complete a survey on the topic. This is a convenient
way to gather data, but as you only surveyed students taking the
same classes as you at the same level, the sample is not
representative of all the students at your university.
2. Voluntary response sampling
Similar to a convenience sample, a voluntary response sample is
mainly based on ease of access. Instead of the researcher choosing
participants and directly contacting them, people volunteer
themselves (e.g. by responding to a public online survey).
Voluntary response samples are always at least somewhat biased, as
some people will inherently be more likely to volunteer than
others.
Example
You send out the survey to all students at your university and a
lot of students decide to complete it. This can certainly give you
some insight into the topic, but the people who responded are more
likely to be those who have strong opinions about the student
support services, so you can’t be sure that their opinions are
representative of all students.
3. Purposive sampling
This type of sampling involves the researcher using their judgement
to select a sample that is most useful to the purposes of the
research.
It is often used in qualitative research, where the researcher
wants to gain detailed knowledge about a specific phenomenon rather
than make statistical inferences. An effective purposive sample
must have clear criteria and rationale for inclusion.
Example
You want to know more about the opinions and experiences of
disabled students at your university, so you purposefully select a
number of students with different support needs in order to gather
a varied range of data on their experiences with student
services.
4. Snowball sampling
If the population is hard to access, snowball sampling can be used
to recruit participants via other participants. The number of
people you have access to “snowballs” as you get in contact with
more people.
Example
You are researching experiences of homelessness in your city. Since
there is no list of all homeless people in the city, probability
sampling isn’t possible. You meet one person who agrees to
participate in the research, and she puts you in contact with other
homeless people that she knows in the area.