# 7.3: Survey Sampling

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Sample: is a small group selected to represent the larger group that the survey researcher wants to learn about. Unless the researchers are going to conduct a census (talk to every member of the group in question), they must draw a sample. The main goal of the sample is to ensure the accurate representation of the entire population. there are various type of sampling technique including systematic sampling, stratified sampling, and random sampling.

Systematic Sampling: is a sampling method where set of individuals or object are selected from a larger population at a random starting point, but with a fixed or periodic interval. In this approach, progression through the list is treated circularly, with a return to the top once the end of the list is passed. The sampling starts by selecting an element from the list at random and then every kth element in the frame is selected, where k, is the sampling interval (sometimes known as the skip): this is calculated as:[1]

where n is the sample size, and N is the population size.

Example: Suppose a supermarket wants to study buying habits of their customers, then using systematic sampling they can choose every 10th or 15th customer entering the supermarket and conduct the study on this sample.

This is random sampling with a system. From the sampling frame, a starting point is chosen at random, and choices thereafter are at regular intervals. For example, suppose you want to sample 8 houses from a street of 120 houses. 120/8=15, so every 15th house is chosen after a random starting point between 1 and 15. If the random starting point is 11, then the houses selected are 11, 26, 41, 56, 71, 86, 101, and 116. As an aside, if every 15th house was a "corner house" then this corner pattern could destroy the randomness of the sample.

If, as more frequently, the population is not evenly divisible (suppose you want to sample 8 houses out of 125, where 125/8=15.625), should you take every 15th house or every 16th house? If you take every 16th house, 8*16=128, so there is a risk that the last house chosen does not exist. On the other hand, if you take every 15th house, 8*15=120, so the last five houses will never be selected. The random starting point should instead be selected as a non integer between 0 and 15.625 (inclusive on one endpoint only) to ensure that every house has equal chance of being selected; the interval should now be non integral (15.625); and each non integer selected should be rounded up to the next integer. If the random starting point is 3.6, then the houses selected are 4, 20, 35, 50, 66, 82, 98, and 113, where there are 3 cyclic intervals of 15 and 4 intervals of 16.

Stratified sampling: is a sampling method where the population is randomly divided into small group called strata before being sample.

A simple example of a stratified sampling could be a researcher select five customers from each of the the four postal code or he could select six transactions from each of the four postal code. In this example we use stratified sampling to ensure that the sample is balance geographically.

Random Sampling: is a sampling method where each member of the population being sample has equal chance of been selected. Example: 20 students out of 200 being selected out the hat, without any criteria taking into consideration.

The term population has been mention repetitively during our discussion of sample, so what is a population? How is a population deferent from a sample?

Population: the broad group of people with something in common that the researchers want to learn something about. For example, a reporter investigating the popularity of Facebook may define the population for her survey as 18-24 year-olds who have an account on Facebook. For a study to inform the creators of an advertising campaign for Toyota, researchers may want to look at the population of everyone who purchased a new Toyota in the last year in the state of Colorado. In other words, the population can be defined in any way that makes sense given who the researchers want to study for a particular purpose.

The difference between the population and sample is that population is an entire group that a researcher study whereas a sample is a small group selected from a larger group where you will collect information from.

7.3: Survey Sampling is shared under a not declared license and was authored, remixed, and/or curated by LibreTexts.