Sampling in Research

Introduction

• Sampling: A method allowing researchers to infer information about a population based on the results from a sample.
• Population vs. Sample:
  • Population: The entire group from which a sample is drawn.
  • Sample: A smaller group selected from the population to represent it.

Importance of Sampling

• Reduces the cost and workload of research.
• Helps in obtaining high-quality information.
• Necessary to carefully select a representative sample.

Types of Sampling

Probability Sampling

• Every member of the population has a known and equal chance of being selected.
• Types of Probability Sampling:
  • Simple Random Sampling: Each member has an equal chance of selection, based purely on chance.
  • Systematic Sampling: Select the first element randomly, then every nth element.
  • Cluster Sampling: Randomly select clusters and study all elements within those clusters.
  • Stratified Sampling: Divide the population into strata and randomly sample from each stratum.

Non-Probability Sampling

• Selection is based on non-random criteria, not all individuals have a chance of being selected.
• Easier and cheaper but can't make valid statistical inferences about the whole population.
• Types of Non-Probability Sampling:
  • Convenience Sampling: Select individuals who are most accessible.
  • Snowball Sampling: Existing subjects help recruit future subjects.
  • Quota Sampling: Tailored sample proportionate to some population traits.
  • Purposive Sampling: Selection based on the researcher's judgment.

Differences between Sampling Methods

• Stratified vs. Cluster Sampling:
  • Stratified Sampling: Samples include elements from each stratum.
  • Cluster Sampling: Samples include elements from selected clusters only.

Conclusion

• Sampling is crucial for efficient research.
• The choice of sampling method depends on research needs and resource availability.

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