A systematic sampling is one in which an element is chosen at random and, to choose the rest of the sample, regular intervals based on a numerical value are used.
With random sampling, therefore, what we do is count the elements of the population in order to choose those that we want to study. Unlike others, such as stratified, we do not make homogeneous groups; instead we use a default value to count.Stratified sampling
The elements chosen will have a high degree of heterogeneity.
Why do systematic sampling?
This type of sampling is very useful in certain circumstances.
So, let's see, for this, its advantages and disadvantages:
- First of all, the selection method is simple, it does not require any preparation. The same system, used in other random samples, allows us to choose the first case. From here you just have to count, as we will see in the example.
- On the other hand, it eliminates the possibility of autocorrelation, which can occur in other types of sampling. This is a problem for the researcher, since two correlated variables may be measuring the same thing.
- Among its drawbacks, we can highlight that, unlike the simple one, the probability of choosing an individual is not the same in all cases. In addition, it can increase the variability of the chosen sample.
Steps for Systematic Sampling
The steps to do this are similar in any random sampling. Above all, we must take into account what we want and what we are going to count on.
- Select the town: First, you have to choose the town. This is the essential step when researching a topic. We have to know to whom, or to what, our analysis is going to be directed.
- Sample size: Once we have carried out the first step, it is time to decide the size of the sample. There are different formulas to calculate it, all taking into account whether the population is finite or not.
- Intervals: Once we have the sample, we divide the population by it and round the number that comes out, if it has decimals. This number is called the sampling interval.
- So, all of the above done, we start counting. We choose the first case at random and, from this, we add the previous number. It is a simple process, as we will see in the example.
Example of a systematic sampling
Imagine a study in which we want to measure the level of mercury in salmon from a certain location. The values are fictitious for this example. We have decided to do a systematic sampling. The first step will be to divide the population by the minimum value of the sample that we want, which in this case we assume is five.
So this would be the sampling interval:
Systematic sampling presents a simple process. First, we choose one of the data, to do so, using the random numbers option from a spreadsheet.
Once we have them, we order them from highest to lowest, or vice versa. We must know that, in reality, they only reposition themselves, and we choose the first one.
Lastly, we are counting from five to five and, in this way, we obtain what the sample will be.
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