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What is it?
What is it?
Sampling is where researchers select a suitable sample, which they intend to represent the ideas or characteristics of the whole population. This is done because it is often more cost and time efficient to gather primary data or evidence for a research hypothesis or question by selecting just a sample group of people rather than trying to gather data from the whole population.
Sampling Methods
Sampling Methods
Classifying Sampling Methods
🔹 Probability Sampling
Definition: Every individual in the target population has a known, non-zero chance of being selected.
Key Feature: Uses random selection → reduces researcher bias.
Examples:
Simple random sampling (names drawn from a hat).
Stratified sampling (dividing population into subgroups like age/gender, then randomly sampling within each).
Cluster sampling (randomly choosing whole groups, like schools or suburbs).
Strengths: Results are more representative, findings can be generalised to the wider population.
Limitation: Can be expensive and time-consuming.
🔹 Non-Probability Sampling
Definition: Not everyone in the population has a chance of being selected; samples are chosen based on convenience, judgement, or availability.
Key Feature: Does not use random selection → may introduce bias.
Examples:
Convenience sampling (using whoever is available, like classmates).
Purposive sampling (selecting people with specific characteristics, e.g., migrants for a cultural identity study).
Snowball sampling (asking participants to refer others, useful for hard-to-reach groups).
Strengths: Easier, quicker, and cheaper; useful for exploratory or qualitative research.
Limitation: Less representative, results can’t be confidently generalised.
✅ In short:
Probability sampling = random, representative, generalisable.
Non-probability sampling = non-random, easier, but less representative.
Convenience Sampling
Convenience Sampling
A convenience sample is obtained by randomly selecting people from the population who are easy to access.
For example, you might interview the first 10 people you meet in one day, or randomly select 10 of your close friends to survey.
This can save time, money and effort, but it is the poorest way of selecting a sample as it can result in a lack of relevant data being collected or unreliable research outcomes.
Simple Random Sampling
Simple Random Sampling
Simple random sampling means that each individual within the population has an equal chance of inclusion in the sample.
For example, if you want to select 10 people randomly from a population of 100, you could write or print their names on separate pieces of paper, fold them up, mix them thoroughly and then select 10 pieces of paper. In this case, every person has an equal chance of being picked.
Systematic Sampling
Systematic Sampling
A systematic sample is obtained by selecting one person on a random basis, and then choosing additional people at evenly spaced intervals until the desired number of units has been obtained.
For example, say you have a list of 100 students, in alphabetical order, and you want to select a sample of 20.
Using systematic sampling, you divide 100 by 20, to get 5.
You then randomly select any number between 1 and 5. Suppose that the number you pick is 4: that will be your starting number.
You then select student number 4, and then select every 5th name on the list until you reach the end of the list. You will end up with 20 students in your sample.
Cluster Sampling
Cluster Sampling
Cluster sampling involves taking random samples from clusters or groups that exist in the population.
Stratified Sampling
Stratified Sampling
To use this method, the researcher first needs to break the population into groups or divisions based on one particular characteristic or feature, such as education, income level, age or gender.
For example:
Group A = people who have completed less than five years of education
Group B = people who have completed five to 10 years of education
Group C = people who have completed 10 to 15 years of education.
A stratified sample is then obtained by selecting a simple random sample from each of these groups. The simple random sample can also be based on a proportion of the people in each group. For example, if Group A comprises 100 people, Group B only comprises 50 and Group C comprises 30, you might decide to select a 10 per cent sample from each group, meaning you would select 10 people from Group A, five from Group B and three from Group C.
Sample Group
Sample Group
The sample group is the representative sample that has been chosen to be the target population.
In order to determine the most appropriate sample size, you first need to consider the resources you have available, such as:
Money: how much will it cost to collect data from each person? This might include the costs of photocopying a questionnaire for each person, travelling to interview each person, and so on
Time: how much can you do within the available time? How many people do you have time to survey? How long will it take to collate the data you collect, based on the size of your sample?
Knowledge: do you know enough people who fit the population that you intend to study? For example, if you want to study the refugee population, how many refugees or refugee organisations do you know of?
Access to services: are you able to access the sample group? For example, if you want to research a topic specifically related to hospitals, how many hospitals do you have access to?
Sample Size
Sample Size
The sample size is how many representatives will make the sample group and may be dependant on the focus of the research, the purpose, sampling method, time, money and access to services.
ACTIVITY - Mix & Match
APPLICATION
1. For each sampling method write your own example of a study that may use the sampling method and justify your choice.
1. For each sampling method write your own example of a study that may use the sampling method and justify your choice.
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