Types of sampling methods in statistics

T. Dhasaratharaman*

Statistician, Kauvery Hospitals, India

*Correspondence: Tel.: +91 90037 84310; email: dhasa.cst@kauveryhospital.com

Probability sampling strategies typically use a random or chance process, although there are important exceptions to this rule. Random sampling is a strategy for selecting study participants in which each and every person has an equal and independent chance of being selected. What does it mean to be independent? The researchers select each person for the study separately.

The types of sampling roughly fall into two main categories – probability sampling and judgment sampling. Most sampling theory has been developed for probability sampling and we will later consider the detailed theory of some of these methods.

Simple Random Sampling

Simple random sampling is the most straightforward of the random sampling strategies. We use this strategy when we believe that the population is relatively homogeneous for the characteristic of interest.

For example, let’s say you were surveying first-time parents about their attitudes toward mandatory seat belt laws. You might expect that their status as new parents might lead to similar concerns about safety. On campus, those who share a major might also have similar interests and values; we might expect psychology majors to share concerns about access to mental health services on campus.

Stratified Random Sampling

Stratified random sampling is used when we have subgroups in our population that are likely to differ substantially in their responses or behaviour. This sampling technique treats the population as though it were two or more separate populations and then randomly samples within each.

For example, you are interested in visual-spatial reasoning and previous research suggests that men and women will perform differently on these types of task. Therefore, you divide your sample into male and female members and randomly select equal numbers within each subgroup (or “stratum”). With this technique, you are guaranteed to have enough of each subgroup for meaningful analysis.

Cluster Sampling

Cluster sampling is useful when it would be impossible or impractical to identify every person in the sample. Suppose a hospital does not print a staff directory. It would be most practical in this instance to sample staff from hospital. Rather than randomly sample 10% of staff from each hospital, which would be a difficult task, randomly sampling every staff in 10% of the hospital would be easier.

For example, suppose an organization wishes to find out Hospital Anniversary Year 11 staff are participating in across Trichy. It would be too costly and take too long to survey every staff, or even some staff from every hospital. Instead, 100 staff are randomly selected from all over Trichy.

These Hospitals are considered clusters. Then, every Year 11 staff in these 100 hospitals is surveyed. In effect, staff in the sample of 100 hospital represent all Year 11 staff in Trichy.

Multistage Sampling

Our final strategy within the broader category of probability sampling is multistage sampling. This is our most sophisticated sampling strategy and it is often used in large epidemiological studies. To obtain a representative national sample, researchers may select zip codes at random from each state. Within these zip codes, streets are randomly selected. Within each street, addresses are randomly selected. While each zip code constitutes a cluster, which may not be as accurate as other probability sampling strategies, it still can be very accurate.

Systematic Sampling

Systematic sampling yields a probability sample but it is not a random sampling strategy (it is one of our exceptions). Systematic sampling strategies take every nth person from the sampling frame. For example, you choose a random start page and take every 45th name in the directory until you have the desired sample size. Its major advantage is that it is much less cumbersome to use than the procedures outlined for simple random sampling. The appropriate sampling interval, I, is then calculated by dividing population size, N, by required sample size, n, as follows: I = N/n

For example, if a systematic sample of 500 staff were to be carried out in a hospital with an enrolled population of 10,000, the sampling interval would be:

I = N/n = 10000/500 = 20

Proportionate Sampling

Proportionate sampling is a variation of stratified random sampling. We use this technique when our subgroups vary dramatically in size in our population. For example, we are interested in risk taking among college students and suspect that risk taking might differ between smokers and non-smokers. Given increasing societal pressures against smoking, there are many fewer smokers on campus than non-smokers. Rather than take equal numbers of smokers and non-smokers, we want each group represented in their proportions in the population.


Mr. T. Dhasaratharaman