Summary - Sampling

Based on the reading, there are seven types of sampling that be used for research purpose depending on our research questions and outcomes. I will briefly explain all the seven types.

Random sampling

It is the simplest form of probability sampling. Each member of the population has an equal and known chance of being selected. When there are very large populations, it is often difficult or impossible to identify every member of the population, so the pool of available subjects becomes biased.

Systematic sampling

It is also called an Nth name selection technique. After the required sample size has been calculated, every Nth record is selected from a list of population members. As long as the list does not contain any hidden order, this sampling method is as good as the random sampling method. Its only advantage over the random sampling technique is simplicity. Systematic sampling is frequently used to select a specified number of records from a computer file.

Stratified sampling

It is commonly used probability method that is better to random sampling because it reduces sampling error. Examples of stratums might be males and females, or managers and non-managers. Random sampling is then used to select a sufficient number of subjects from each division. “Sufficient” refers to a sample size large enough for us to be reasonably confident that the division represents the population. Stratified sampling is often used when one or more of the division in the population has a low incidence relative to the other division.

Convenience sampling

It is used in exploratory research where the researcher is interested in getting an inexpensive approximation of the truth. As the name implies, the sample is selected because they are convenient. This non probability method is often used during preliminary research efforts to get a gross estimate of the results, without incurring the cost or time required to select a random sample.

Judgment sampling

It is a common non probability method. The researcher selects the sample based on judgment. This is usually an extension of convenience sampling. For example, a researcher may decide to draw the entire sample from one “representative” city, even though the population includes all cities. When using this method, the researcher must be confident that the chosen sample is truly representative of the entire population.

Quota sampling

It is the non probability equivalent of stratified sampling. Like stratified sampling, the researcher first identifies the division and their proportions as they are represented in the population. Then convenience or judgment sampling is used to select the required number of subjects from each division. This differs from stratified sampling, where the divisions are filled by random sampling.

Snowball sampling

It is a special non probability method used when the desired sample characteristics are rare. It may be extremely difficult or cost prohibitive to locate respondents in these situations. Snowball sampling relies on referrals from initial subjects to generate additional subjects. While this technique can dramatically lower search costs, it comes at the expense of introducing bias because the technique itself reduces the likelihood that the sample will represent a good cross section from the population.

I did further reading on this topic at this webpage. It elaborates and exemplifies with better examples and description.

Have a good weekend everyone!