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:
Simple Random Sampling: Each member of the population has an equal chance of being selected.
Systematic Sampling: Selecting every kth member of the population.
Stratified Sampling: Dividing the population into homogenous subgroups and selecting a proportionate number of members from each subgroup.
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:
Convenience Sampling: Selecting members based on their availability or ease of access.
Judgment Sampling: Selecting members based on the researcher’s judgment or knowledge of the population.
Quota Sampling: Selecting members to meet predefined quotas based on specific characteristics.
Snowball Sampling: Selecting initial members and then asking them to refer additional members.
Additional resources on Sampling Techniques:
Saturn Cloud for free cloud compute