Sampling Techniques
Sampling is the process of selecting a subset of individuals, items, or observations from a larger population to estimate characteristics of the whole population. It is a fundamental aspect of statistical analysis, enabling efficient data collection and analysis.
Why Use Sampling?
- Efficiency: Collecting data from an entire population can be time-consuming and expensive.
- Feasibility: In some cases, it is impossible to study the whole population (e.g., testing every product).
- Accuracy: Well-designed samples provide accurate estimates of population characteristics.
Types of Sampling Techniques
Sampling methods are broadly classified into probability sampling and non-probability sampling.
1. Probability Sampling
In probability sampling, every member of the population has a known, non-zero chance of being selected. This method reduces bias and allows for generalization to the entire population.
1.1. Simple Random Sampling
- Description: Each individual has an equal chance of being selected.
- Method: Use random numbers or draw names from a hat.
- Example: Selecting 100 students randomly from a school.
1.2. Stratified Sampling
- Description: The population is divided into strata (subgroups) based on shared characteristics, and a random sample is taken from each stratum.
- Example: Sampling students by grade level (e.g., 10th, 11th, 12th grades).
1.3. Systematic Sampling
- Description: Every $k$-th individual is selected from a list, starting at a random point.
- Example: Selecting every 10th person from a customer database.
1.4. Cluster Sampling
- Description: The population is divided into clusters, and entire clusters are randomly selected for sampling.
- Example: Sampling all households in randomly selected neighborhoods.
1.5. Multistage Sampling
- Description: Combines multiple probability sampling methods.
- Example: First selecting clusters, then randomly sampling individuals within those clusters.
2. Non-Probability Sampling
In non-probability sampling, individuals are selected based on non-random criteria, making it less representative of the entire population.
2.1. Convenience Sampling
- Description: Selecting individuals who are easiest to reach.
- Example: Surveying people in a shopping mall.
2.2. Judgmental (or Purposive) Sampling
- Description: The researcher selects participants based on their expertise or knowledge.
- Example: Selecting experienced managers to evaluate a training program.
2.3. Snowball Sampling
- Description: Participants recruit other participants, forming a chain.
- Example: Surveying members of a niche community.
2.4. Quota Sampling
- Description: Selecting individuals to meet predefined quotas for subgroups.
- Example: Ensuring equal numbers of men and women in a sample.
Comparison of Sampling Techniques
| Feature | Probability Sampling | Non-Probability Sampling |
|---|---|---|
| Selection Process | Random | Non-random |
| Representativeness | High | Lower |
| Examples | Simple Random, Stratified, Cluster | Convenience, Snowball, Judgmental |
| Use Case | Generalizable studies (e.g., population surveys) | Exploratory research (e.g., pilot studies) |
How to Choose a Sampling Technique?
- Purpose of Study: Use probability sampling for generalizable studies and non-probability for exploratory or qualitative research.
- Population Characteristics: Consider factors like diversity and size of the population.
- Resources: Time, budget, and manpower may limit the sampling method.
Applications in Real Life
- Market Research:
- Stratified sampling to understand customer preferences by age groups.
- Healthcare Studies:
- Random sampling to test a new drug’s effectiveness.
- Social Research:
- Snowball sampling to study hard-to-reach populations like refugees.
Conclusion
Choosing the right sampling technique is crucial for obtaining reliable and valid results. Probability sampling ensures generalizability, while non-probability sampling is often more practical for specific scenarios.
Next Steps: Confidence Intervals