Sampling | Random, Systematic & Stratified

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1 Summary

1.1 What is sampling?

1.2 Some important terms that we must be familiar with are:

1.3 There are quite a few methods of sampling that we will go through in this article.

1.3.1 Reference

Summary

Statisticians collect the data to investigate the characteristics of a population. After taking a sample it is assumed that the result for the sample reflects the whole population.
Some important terms that we must be familiar with are: Census, Sampling unit and sampling frame.
Random Sampling: his method gives every item of the population an equal chance of selection.
Systematic sampling involves choosing items at regular intervals.
Stratified random sampling is a method of sampling that involves the division of a population into smaller groups known as strata.
Quota sampling is a non-probability sampling technique in which researchers look for specific qualities or traits in their respondents, and then take a sample that is in proportion to a population of interest.

What is sampling?

Statisticians collect the data to investigate a characteristic of a population. A population is the set of all the possible items to be observed. We hope that the sample taken by statisticians is representative of the population. When this doesn’t happen, we say that our sample is biased. In other words we can say that a sample must represent the whole population.

After taking a sample it is assumed that the result for the sample reflects the whole population.

Some important terms that we must be familiar with are:

Census: A census is a survey conducted on the full set of observation objects belonging to every member of a given population.
Sampling Unit: Before selecting the sample, the population must be divided into parts called sampling units. Hence, the individuals whose characteristics are to be measured in the analysis are called sampling units.
Sampling Frame: A sampling frame is a list or database from which a sample can be used.

There are quite a few methods of sampling that we will go through in this article.

Random Sampling: his method gives every item of the population an equal chance of selection. In other words in data collection, every individual observation has equal probability to be selected into a sample. In random sampling, there should be no pattern when drawing a sample.
Systematic sampling: Systematic sampling involves choosing items at regular intervals e.g. completing a beach transect every 20 metres or interviewing every tenth person.It is different from random sampling in that it does not give an equal chance of selection to each individual in the target group.
Stratified sampling: Stratified random sampling is a method of sampling that involves the division of a population into smaller groups known as strata. Samples are then pulled from these strata, and analysis is performed to make inferences about the greater population of interest.

An example will help understand stratified sampling better.

Example #1

If doing a survey to calculate the average wage earned in the manufacturing industry, you would split your population into shopfloor workers, managers and directors.

In your sample you would include a greater number of shop floor workers, as there are significantly more of these.

4. Quota sampling: Quota sampling is a non-probability sampling technique in which researchers look for specific qualities or traits in their respondents, and then take a sample that is in proportion to a population of interest. Non-probability sampling focuses on sampling techniques that are based on the judgement of the researcher. Unlike probability sampling techniques, especially stratified random sampling, quota sampling is much quicker and easier to carry out because it does not require a sampling frame and the strict use of random sampling techniques.

Example #2

Let’s assume that we need to examine the differences in the career goals among fresher, juniors and seniors. Suppose the university consists of 10,000 students. This is our population.

Now, we have to divide our population of 10,000 students into three categories; freshers, juniors and seniors. Suppose, we found that there are 3000 freshers (30%), 2500 junior students (25%) and 2000 senior students (20%).

Our sample must have these proportions. It means that if we sample 1000 students, then we must consider 300 freshmen, 250 juniors and 200 seniors. Lastly, we may start collecting samples from these student on the basis of our proportion.