Probability Sampling Techniques Quiz

Study Notes

Simple Random Sampling: Every individual in the population has an equal chance of selection, ensuring unbiased representation.
Systematic Sampling: Members are chosen at fixed intervals from an ordered list, enhancing efficiency. For example, with a sample size of 100 from 1,000 employees, every 10th employee could be selected.
Stratified Sampling: The population is divided into strata based on specific characteristics (e.g., age groups). Random samples are drawn from each stratum ensuring that all segments are represented proportionately.
Cluster Sampling: The population is split into clusters; entire clusters are randomly selected. This method can simplify logistics but may introduce higher variability within clusters.

Simple Random Sampling Example: Surveying 100 out of 1,000 employees from Raymond Mhlaba Municipality using a random number generator ensures equal selection opportunity.
Systematic Sampling Example: In the Eastern Cape Department of Education, every 10th employee is selected after calculating the interval (1,000/100) and starting from a randomly chosen point.
Stratified Sampling Example: At UFH, second-year students (2,000 total) are divided by age groups, with sampled individuals reflecting the overall distribution (e.g., 30 students aged 16-20).
Cluster Sampling Example: Evaluating community policing initiatives can involve randomly selecting entire neighborhoods as clusters for research.

Non-probability sampling allows for selection based on researcher’s judgment rather than random methods.
This technique can be used when accessing specific populations is challenging, but it introduces bias making findings less generalizable.

Defining the population helps determine the sampling frame and allows for the selection of a suitable sample.
A well-defined population ensures that the sample is representative, facilitating accurate generalizations of research findings.

Sampling involves selecting a portion from a larger population to conduct research efficiently.
Accurate sampling is crucial for generalizing findings to the entire population without having to study every individual.

Probability sampling reduces bias and supports valid statistical inferences, allowing researchers to make broader conclusions about the population.
Despite its advantages, it may require more resources and effort compared to non-probability methods.