Theory of Sampling: Meaning, Objectives, and Methods (Detailed Explanation)
29/June/2025 01:30
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Theory of Sampling: Meaning, Objectives, and Methods (Detailed Explanation)
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? What is Sampling? (Theory of Sampling)
Sampling is the statistical process of selecting a subset (sample) from a larger population to estimate characteristics of the whole population. Since it’s often impractical or expensive to study every member of a population, sampling offers a way to gain insights economically and efficiently.
> Definition:
Sampling is the process of selecting a few units from a whole population such that the knowledge gained from the sample helps in generalizing about the population.
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? Objectives of Sampling
1. To reduce cost of data collection and analysis.
2. To save time while maintaining reasonable accuracy.
3. To improve quality of data analysis through focused efforts.
4. To make decisions quickly with adequate information.
5. To conduct feasibility studies or pilot tests before full-scale operations.
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? Methods of Sampling
Sampling methods are mainly divided into two categories:
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1. Probability Sampling (Random Sampling)
Every unit in the population has a known and equal chance of being selected.
✅ Types:
Simple Random Sampling: Each unit is selected entirely by chance (e.g., lottery method).
Systematic Sampling: Every kᵗʰ item is selected after a random start.
Stratified Sampling: Population is divided into strata, and random samples are taken from each.
Cluster Sampling: Divides the population into clusters, and a few clusters are selected randomly.
> Advantages: Minimizes bias, enables use of probability theory, provides representative results.
> Disadvantages: More complex to organize, needs a full population list.
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2. Non-Probability Sampling
Units are selected based on judgment or convenience, not by chance.
✅ Types:
Purposive or Judgmental Sampling:
Also called Deliberate Sampling.
The researcher uses expertise to select the most useful units.
Example: Selecting only experienced managers to study management practices.
Convenience Sampling:
Sampling those who are easily accessible.
Example: Surveying people in a nearby store.
Quota Sampling:
Ensures representation of certain characteristics but selection is non-random within groups.
> Advantages: Cost-effective, time-saving, simple to conduct.
> Disadvantages: Higher risk of bias, lacks generalizability.
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? Which Sampling Method Should Be Adopted?
The choice depends on:
1. Purpose of the study: If representativeness is key, use probability sampling.
2. Population size and accessibility: Large, accessible populations favor probability methods.
3. Resources available: Budget and time constraints may favor non-probability methods.
4. Desired accuracy: Probability methods offer statistically valid conclusions.
5. Expert knowledge: If domain expertise is crucial, purposive sampling may be used.
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✅ Conclusion
Sampling plays a critical role in modern statistics and research. While probability sampling is ideal for scientific and generalizable studies due to its objectivity and statistical soundness, non-probability methods like purposive sampling are valuable in exploratory research and specialized studies. The right sampling method should balance accuracy, cost, time, and research objectives.
