Sampling Frames and Errors in Sampling

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  The importance of sampling frames and the types of errors associated with sampling. Here's a breakdown of the key points:

Sampling Frame:

  • Definition: The sampling frame is the list of individuals from which a sample is selected.
  • Types: It can be a physical list (e.g., students enrolled at a college) or a theoretical list (e.g., patients presenting to an emergency department).
  • Purpose: It represents the part of the population from which the sample is drawn, ensuring everyone in the frame has a chance of being selected.

Errors in Sampling:

  • Under Coverage: Occurs when population members are omitted from the sampling frame, leading to biases.
  • Sampling Error: Inevitable discrepancy between population parameters and sample statistics due to random variation.
  • Non-Sampling Error: Results from mistakes in the sampling process, such as using an incomplete or inaccurate sampling frame.

Types of Sampling:

Simple Random Sampling:

  • Definition: Each member of the population has an equal chance of being selected.
  • Methods: Can be achieved by drawing random samples from a list (e.g., hospitals) or assigning unique random numbers to the population.
  • Limitation: Requires a complete and accurate sampling frame.

Stratified Sampling:

  • Definition: Population divided into distinct subgroups (strata), and samples are randomly drawn from each stratum.
  • Purpose: Ensures representation from each subgroup, especially useful when there are distinct groups or minorities.
  • Limitation: May result in oversampling of smaller groups and requires careful stratification.

Overall, understanding sampling frames and errors is crucial for obtaining reliable and representative samples in research studies.

🌐 Sources

  1. classroom and lecture notes

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