The research sample is a
critical aspect of any study, allowing researchers to study large populations
without surveying every individual. Sample source, size, and selection method
significantly influence result reliability and validity, impacting how
trustworthy and representative the findings are.
Sample: The
sample is the group of individuals who will actually participate in the
research.
A sample is a subset of individuals from a larger
population. Sampling means selecting the group that you will actually collect
data from in your research.
`When we conduct research about a group of people, it’s
rarely possible to collect data from every person in that group. Instead, we
select a sample. For example, if you are researching the opinions of students
in your university, you could survey a sample of 100 students.
A
sample is a smaller set of data that a researcher chooses or selects from a
larger population using a pre-defined selection bias method. These elements are
known as sample points, sampling units, or observations.
Population vs. sample: First, we need to understand the difference between a population and a sample, and identify the target population of our research.
· The population is the entire group that you want to draw conclusions about.
· The sample is the specific group of individuals that you will collect data from.
Types of Sampling
Method: The two different types
of sampling methods are:
1. Probability
Sampling: In probability sampling,
each sample has an equal probability of being chosen. We can say a probability
sample is one in which each element of the population has a known non-zero
probability of selection. This method of sampling gives the probability that
our sample is representative of a population.
i. 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:
Tossing a coin, Throwing a dice, lottery method, blindfolded method. We want to
select a simple random sample of 1000 employees of a social media marketing
company. We assign a number to every employee in the company database from 1 to
1000 and use a random number generator to select 100 numbers.
ii. Systematic
Sampling: Systematic sampling is an improvement
over the 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: Employees
listed alphabetically are chosen by selecting a random starting point, like
number 6, and then every 10th person is included (6, 16, 26, 36, and so on)
until you have a sample of 100 people.
iii. Stratified Sampling: Stratified sampling is where the population
is divided into strata (or subgroups) and a random sample is taken from each
subgroup. Subgroups might be based on company size, gender, or occupation (to
name but a few). Stratified sampling is often used where there is a great deal
of variation within a population. Its purpose is to ensure that every stratum
is adequately represented.
Examples:
To achieve a stratified sampling, from a company with 800 female and 200 male
employees, we divide them into two gender-based groups. Then, we randomly
select 80 women and 20 men, creating a balanced sample of 100 people.
iv. 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.
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). We 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.
2. Non-probability Sampling: Unlike probability sampling method,
non-probability sampling technique uses non-randomized methods to draw the
sample. Non-probability sampling method mostly involves judgment. Instead of
randomization, participants are selected because they are easy to access. For example,
your classmates and friends have a better chance to be part of your sample.
Even though in certain cases, non-probability sampling is a useful and
convenient method of selecting a sample, the method is appropriate and the only
method available in certain cases.
Non-probability sampling
techniques are often used in 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.
i. 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. Convenience samples are at
risk for both sampling
bias and selection
bias.
Example: We
gather opinions on student support services by surveying fellow students after
class. While convenient, our sample is limited to students in the same classes
and level, making it non-representative of the entire university population.
ii. Voluntary response sampling: oluntary response sampling is when people
decide to join a study themselves, often by participating in things like online
surveys. It's not a random selection, and it can be unfair because only those
who want to participate do so. This can create a bias known as
"self-selection bias," making the results less reliable for the whole
population.
Example: We
put up a sign asking for favorite colors, those who really love colors might
respond, and this voluntary response sample could be biased towards colorful
preferences. To get a fairer picture, you could randomly choose students from
different classes and ask about their favorite colors, which would be a more
representative sample.
iii. Purposive sampling:
This type of sampling, known as purposive or judgment sampling, relies on the
researcher's expertise to select a sample suited to the research goals. It's
common in qualitative studies, especially when detailed insights are needed, or
the population is small. As their knowledge is instrumental in creating the
samples, there are the chances of obtaining highly accurate answers with a
minimum marginal error.
Example: If a researcher wants to understand the opinions of tech experts on the
latest smartphone technology, they would purposively select individuals with
technical knowledge and experience in the smartphone industry rather than
randomly choosing people from the general population.
iv. Snowball sampling: If the population is hard to access, snowball sampling can be used to
recruit participants via other participants. A researcher
starts with one or a few participants who fit the research criteria, and then
asks them to help identify and recruit more participants. The downside here is also
representativeness, as you have no way of knowing how representative your
sample is due to the reliance on participants recruiting others. This can lead
to sampling
bias.
Example: We are researching experiences of thieves in our city. Since there is
no list of all thieves people in the city, probability sampling isn’t possible.
We can meet one person who agrees to participate in the research, and she puts us
in contact with other thieves people that she knows in the area.
v. Quota sampling: Quota
sampling is when a researcher chooses people based on specific categories, like
age or gender, rather than randomly. They make sure they get a certain number
of people from each category.
Example: To
see if people in Dhaka are interested in a new food delivery service, we split
them into three groups: meat eaters, vegetarians, and vegans. We want 1000
people in our study, with 200 from each dietary group, so it's fair. We keep
recruiting until we have 200 people from each category. This helps us compare
their interests.
Conclusion: The
quality of a research study hinges on the careful selection and handling of the
sample, which greatly influences the reliability and validity of the results.
Researchers should prioritize representative samples and transparency in their
methodologies to ensure trustworthy findings.
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