Systematic sampling refers to a probability sampling method where samples or limited members are selected from a large population at a regular interval (or k) planned from before.

Systematic sampling helps in concluding a large section of a random or random-like population by giving a representative sample.

Appropriate Time To Use Systematic Sampling
Systematic sampling is mostly similar to simple random sampling; however, it is comparatively easier to implement. Like simple random sampling, systematic sampling can be used for a list of entire populations. However, one can also use systematic sampling when the list of the population is not accessible in advance.

Order Of The Population
In systematic sampling, it is essential to consider the order of the population list to ensure that the chosen sample is valid.

If the population is arranged in ascending or descending order, then the systematic sampling helps give a fairly illustrative sample by including members from both ends of the population.

For example, if the sample from the population list is ordered by age, systematic sampling will take place from the large section of the population drawn based on age. If one uses simple random sampling, it will end up with either younger or older persons. However, one should avoid using systematic sampling if the population is organized cyclically or periodically, as the resulting sample fails to represent the actual population.

Example: Alternative List
For example, the population list contains alternatives such as men (on even numbers) and women (on odd numbers). Every tenth individual included in the sample will be a man in such a case.

Example: Cyclically Ordered List
For example, the sample is being chosen from a population list of 1000 nurses. The list is further divided into 50 departments, including 20 nurses each. Each department is further divided into age. In such a case, the list shows 20 repeated age cycles.

Thus, when every sample has 20 members, there will be the oldest nurse on each list. It will fail to represent the correct sample of the entire hospital.

Systematic Sampling In Absence Of Population List
Systematic sampling can also imitate the randomization of simple random sampling even if the complete list of the population is not accessible from advance.

Step 1: Define Your Population
Similar to other sampling methods, it is essential to determine the population under study. There are two data collection methods in systematic sampling:

• The sample can be chosen in advance from the population list and then approach the selected sample for collecting data, or
• The researcher can also approach every kth member of the target population and request them to participate in the study.

Listing The Population In Advance
In case of listing the population in advance, it is essential to confirm that the list includes the entire target population and not in a periodic or cyclic order.

Example: Listing the Population
For example, in the study of a department store, the customers are the target population. To choose a sample ahead of time, one needs to create a list of every customer visiting the store in a week. However, creating such a list would be challenging and thus not possible. In this case, one can use receipts to create the list; however, it would exclude customers that are not purchasing anything and thus create a biased result.

Selecting Sample On Spot
If the population is physically observable but cannot be accessed from before, one can choose the sample using systematic sampling during data collection. However, make sure to cover the entire target population under study while choosing the sample to avoid biased results.

Example: Sampling On The Spot
Continuing from the above example, as it is not possible to list a store’s customers, one can instead choose every kth customer visiting the store for the sample. This method helps in choosing both customers who are purchasing items and those who are not purchasing anything.

Step 2: Plan The Sample Size And Intervals
The sample size should be pre-planned before choosing the sampling interval. There are various ways to select a sample size; however, the most common method includes a sample size calculator.

Once the desired error margin and confidence level are chosen, it is time to plan the total target population for the study and the standard deviation of the variables. This information will help the calculator suggest the sample size that should be considered in the study.

Finally, the interval k can be calculated once the sample size is known by dividing the targeted population size by sample size. The calculation can give a rough estimate of the interval or exact result.

Step 3: Sample Selection And Data Collection Process
In the final step, one can consider the entire list of populations and randomly choose every kth member to participate in the sample. However, when the population list is unknown, every kth member can be chosen to be included in the sample at the time of data collection.

Like simple random sampling, one should ensure that every member included in the sample participates in the study and shares unbiased information. If the chosen sample decides to participate in the study for the variables, it will give a biased research outcome.

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