Stratified Sampling Essentials

Stratified sampling is a probability sampling technique where the researcher divides the population into distinct subgroups, known as strata. Each stratum is homogeneous within itself. Then, a random sample is drawn from each stratum. This ensures representation from all subgroups.

Analyzing Sampling Type Options

The question asks to identify the option that is NOT a type of stratified sampling. Let's examine the choices:

• Proportionate Sampling: This is a method within stratified sampling where the sample size from each stratum is proportional to the stratum's size in the total population.
• Disproportionate Sampling: This is also a technique used in stratified sampling, where the sample sizes are deliberately chosen to be unequal across strata, often to oversample smaller strata or strata with high variance.
• Optimum Allocation Sampling: This is a specific type of disproportionate stratified sampling. It aims to allocate sample sizes to strata in a way that minimizes the overall sampling error or variance for a fixed cost or sample size.
• Non Probability Sampling: This is a broad category of sampling methods where the selection is not random. Examples include convenience, quota, and judgment sampling. Stratified sampling fundamentally relies on random selection within strata, making non-probability methods incompatible with it.

Conclusion on Stratified Sampling

Since stratified sampling requires random selection within defined strata, any method that does not involve random selection, such as Non Probability Sampling, cannot be considered a type of stratified sampling.

Therefore, the correct option is Non Probability Sampling.