Introduction
Sampling is an important step in research. It refers to the process of selecting a small group of people or items (called a sample) from a larger group (called a population) to study. Instead of studying the whole population, which is often difficult, researchers collect data from a sample and draw conclusions. This blog will explain the major types of sampling in a simple way, useful for students of social work and others.
What is Sampling?
Sampling is the technique used to select a portion of the population to represent the whole. A good sample should be representative, unbiased, and reliable. It helps in saving time, money, and effort during the research process.
Main Types of Sampling
Sampling methods are generally divided into two broad categories:
- Probability Sampling
- Non-Probability Sampling
1. Probability Sampling
In probability sampling, every member of the population has a known and equal chance of being selected. It is mostly used in large-scale and scientific research where accuracy is very important.
a. Simple Random Sampling
Each member of the population is selected randomly, like picking names out of a hat.
Example: Choosing 50 students randomly from a list of 500 students.
b. Systematic Sampling
Every nth member of the population is chosen after selecting the first member randomly.
Example: Choosing every 10th person from a list of patients.
c. Stratified Sampling
The population is divided into smaller groups (strata) based on characteristics like age, gender, etc. Then random samples are taken from each group.
Example: Dividing a school into grades and picking a few students from each grade.
d. Cluster Sampling
The population is divided into clusters (like villages or schools), and then entire clusters are randomly selected for study.
Example: Choosing 3 villages randomly from a district and surveying everyone in them.
2. Non-Probability Sampling
In non-probability sampling, not every member has an equal chance of being selected. It is often used in social work research when quick or specific samples are needed.
a. Convenience Sampling
Samples are selected from people who are easy to reach or available.
Example: Interviewing people at a local market because it is close by.
b. Purposive (Judgmental) Sampling
The researcher chooses people who are believed to be most useful or relevant for the study.
Example: Interviewing only community leaders to understand village problems.
c. Snowball Sampling
Used when the population is hard to reach. One participant refers others, and the sample grows like a snowball.
Example: Finding drug users through their networks.
d. Quota Sampling
The population is divided into groups, and a fixed number (quota) is selected from each group. However, selection is not random.
Example: Choosing 10 men and 10 women from each community for a survey.
Comparison Between Probability and Non-Probability Sampling
| Aspect | Probability Sampling | Non-Probability Sampling |
|---|---|---|
| Selection Method | Random | Non-random |
| Chance of Bias | Low | High |
| Used In | Scientific and large studies | Exploratory or quick studies |
| Cost | Expensive | Low cost |
Conclusion
Sampling is a key part of any research. It helps researchers gather accurate and useful data without having to study the entire population. Understanding the different types of sampling helps in choosing the right method for a research project. Whether it is probability or non-probability sampling, the goal is always to get a sample that best represents the whole population.