Sampling Techniques

What are Sampling Techniques?

Sampling techniques are methods used to select a subset of data or observations from a larger population or dataset for analysis. The goal of sampling is to obtain a representative sample that accurately reflects the characteristics of the population, allowing for generalization of findings and inferences. Sampling techniques can be broadly classified into two categories: probability sampling and non-probability sampling.

Probability Sampling Methods

Probability sampling methods include:

  1. Simple Random Sampling: Each member of the population has an equal chance of being selected.

  2. Systematic Sampling: Selecting every kth member of the population.

  3. Stratified Sampling: Dividing the population into homogenous subgroups and selecting a proportionate number of members from each subgroup.

  4. Cluster Sampling: Dividing the population into clusters and randomly selecting a certain number of clusters for analysis.

Non-Probability Sampling Methods

Non-probability sampling methods include:

  1. Convenience Sampling: Selecting members based on their availability or ease of access.

  2. Judgment Sampling: Selecting members based on the researcher’s judgment or knowledge of the population.

  3. Quota Sampling: Selecting members to meet predefined quotas based on specific characteristics.

  4. Snowball Sampling: Selecting initial members and then asking them to refer additional members.

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