If you are performing research on a large community, organisation, or country, then it may not be possible to collect data individually from each participant. To deal with this issue, you can use a group of a specific number of participants, and this group is referred to as a sample.
The method you apply for selecting your participants is known as the sampling method. It helps in concluding the entire population based on the outcomes of the research.
Example:
If you want to research China’s entire population, it isn’t easy to gather information from 1.38 billion people. You can use a sampling method by conducting your research on a specific number of participants and drawing a conclusion about the entire population based on your study’s outcomes.
Uses of Sampling Method
- Gather data from a large group of people.
- Counter check data collection.
- Speed up tabulation and publication of results.
- Increase the efficiency of research.
- Conduct experimental research.
- Obtain data for population census studies.
Population vs. Sample
Before exploring sampling methods, it is important to understand the difference between the population and the sample.
| Aspect | Population | Sample |
|---|---|---|
| Definition | The entire group of individuals, objects, or items that the researcher is interested in drawing conclusions about. | A subset of the population that is selected for the study. It is the group from which data is actually collected. |
| Size | Larger, includes all members that fit the study criteria. Often impractical or impossible to measure entirely. | Smaller, a manageable group that is intended to represent the larger population. |
| Example | All undergraduate students enrolled in U.S. universities during the 2024 academic year. | A randomly selected group of 500 undergraduate students enrolled in U.S. universities during the 2024 academic year. |
| Purpose | To establish the group to which broad conclusions and generalizations will apply. | To allow for practical data collection and to use the data to make inferences about the population. |
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Sampling Frame vs. Sampling Size
| Aspect | Sampling Frame | Sample Size ($\text{n}$) |
|---|---|---|
| Definition | The actual list or source from which the sample is drawn. It is a working definition of the target population. | The number of units (individuals, organizations, items) that are selected to be included in the study. |
| Example | To study university students, the sampling frame might be the official university enrollment database or a current student email list. | A study surveying $\text{1,000}$ registered voters is to be conducted. The $\text{1,000}$ is the sample size ($\text{n}$). |
| Accuracy | Depends on the completeness and accuracy of the list; errors in the frame (under- or over-coverage) can lead to selection bias. | The larger the appropriate size, the more precise and representative the study’s estimates are likely to be (i.e., smaller margin of error). |
Methods of Sampling
There are usually two methods of sampling that are used widely
- Probability Sampling
- Non-Probability Sampling
Probability Sampling
This method of sampling is conducted by using the method of randomisation. In this method, each individual has an equal and independent opportunity to be selected. It has further sub-categories.
- Simple Random Methods
- Stratified Methods
- Systematic Method
- Cluster Method
- Multi-Stage
- Matching sampling
1. Simple Random Sampling
Participants are selected randomly and assigned to groups. If the selection is not random, it’s considered non-probability sampling.
Example:
- You want to identify how much time people spend on social media. You need to randomly select the participants and assign a specific number of hours to spend on social media.
2. Systematic Sampling
In this type of sampling, participants are selected according to a fixed period interval and starting point. The fixed period interval can be calculated by dividing the sample size by the respective population size.
Example:
- The Framingham study, which includes selecting every second person from a list of two residents.
3. Stratified Sampling
Stratified sampling is a random selection of the participants by dividing them into strata and randomly selecting participants from each level.
Example:
- You want to find out the benefits of a balanced diet. You need to divide participants into various groups based on their age, gender, and health conditions, and assign them to each group’s treatment group.
4. Matching Method
Even though participants are selected randomly, they can be assigned to the various groups of comparison.
Another procedure for selecting the participants is ‘matching.’ The participants are selected from the control group to match the experimental group’s participants in all aspects based on the dependent variables.
Example
- A teacher pairs each student with another who has similar math scores to compare study habits, ensuring both groups are balanced for fair analysis of learning improvement through different teaching strategies.
5. Cluster Sampling
The population is divided into clusters, and entire clusters are randomly selected.
Example:
- You want to check high school students’ communication skills, and there are more than 50 schools in the city. You can’t visit each school to gather information. In such situations, you can select any five schools, and these schools will be your clusters.
Non-Probability Sampling
This method does not use randomisation. Instead, participants are selected based on the non-probability sampling technique, not based on randomisation selection. Instead, participants are chosen on the basis of the researcher’s judgment, convenience, or availability.
It is often used in qualitative or exploratory research and qualitative research. This type of sample is not to test a hypothesis about a broad population but to develop an initial understanding of a small or under-researched population.
Types of Non-Probability Sampling:
- Purposive Sampling
- Referral (Snowball) Sampling
- Convenience Sampling
- Quota Sampling
1. Purposive Sampling
This type of sampling is based on the aims of the research. Therefore, only such elements of the population will be selected, which are according to the research’s purpose.
Example:
- You want to find out the opinion of people about jobs and businesses. You can select a few participants interested in doing 9-5 jobs and a few interested in doing business.
2. Referral (Snowball) Sampling
In this technique, one participant is selected according to defined criteria. After that, the same selected participant is asked to refer to other samples fulfilling the study’s criteria. In this way, it enlarges its size with the help of the referral.
Example:
- You can use it while conducting a study on the victims of physical harassment at workplaces. No matter how smoothly you approach them, not all women respond openly to your questions as they feel uncomfortable, or they get afraid of being humiliated. You can select the people from these victims’ circles (ex, their colleagues, friends, relatives) to get in touch with them and gather the required information for your research.
3. Convenience Sampling
Inconvenience sampling participants are selected on the basis of their ease of access and availability.
Example:
- Collecting data in election campaigns by surveys from candidates you encounter or meet.
4. Quota Sampling
Participants are selected on the basis of a predetermined quota that represents subgroups.
Example:
- Selection of 25 students from each 9th, 10th, 11th, and 12th grade to analyze their academic performance.
Advantages of Sampling Method
Sampling has many advantages, such as:
- It saves a lot of time, as contacting the entire population would be difficult and time-consuming.
- It’s cost-effective.
- It has a greater scope and adaptability.
- It provides accurate results.
- It can be managed easily.
Disadvantages of Sampling Method
- It may cause a feeling of discrimination among the participants who are not selected for the study.
- The researcher needs to be skilled, experienced, and qualified to ensure efficient sampling.
- It requires a lot of time, and results may not be reliable.
Frequently Asked Questions
Randomise sampling technique is considered the best for experimental research as it reduces the brightness and ensures that the individual has an equal chance of selection.
The larger the sample size greater the accuracy and reliability of results, while a smaller sample size can lead to bias in precise findings.
Sampling methods can’t replace a census as sampling method provides representative data, but a census covers the entire population. Sampling is mainly preferred when a census is too expensive or time-consuming.
