If you have an interest in statistics and data analysis, you may have heard the term systematic sampling.
If not, no worries – you’ll find all the details you need here. What systematic sampling types are, what they look like, how to use them and more.
What is systematic sampling?
In statistics, a sampling method is systematic if it involves selecting individuals or items for a sample in such a way that every nth item is selected.
These intervals are known as skip or sampling intervals. This interval is calculated by dividing the population size by the desired sample size.
For eg., Population size (N) = 1000
Desired sample size (n) = 200
Sampling interval (I) = N / n
1000 / 200 = 5.
So every 5th item will be selected for the sample.
What is the difference between simple random and systematic sampling?
- Execution: In simple random sampling, we individually identify and select each item. In systematic sampling, we use a sampling interval rule to select items.
- Probability: In simple random sampling, each item has an equal likelihood of being chosen. However, systematic sampling chooses an item after a predetermined interval.
- Audience/sample size: Simple random sampling is ideal if the audience size is small, and the sample numbers or sizes are relatively small. However, as the audience size and sample size increases, random sampling becomes more time consuming. This is where systematic samples are the better option.
- Accuracy: The level of accuracy in random sampling depends to an extent on the sample size. But in systematic sampling, accuracy depends on how well the sample reflects relevant characteristics of the audience.
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Systematic sampling types (with examples)
Each type of systematic sampling can be used for single or multi-phase surveys.
Systematic Random Sampling
Simple systematic sampling is the most basic type. You just need to select from a random starting point but with a fixed, periodic sampling interval.
Example: Suppose a supermarket wants to study their customers’ buying habits. With systematic random sampling, they can choose every 10th or 15th customer entering the supermarket. Then, they can conduct the study on this sample.
Stratified Systematic Sampling
Stratified sampling divides your audience into sub-groups called strata. Any characteristic can be the basis for this strata, like age, ethnicity, religion, etc. Then, using sampling intervals, you can choose sample members from each strata.
Example: Let’s say you’re researching the factors that influence consumer preferences towards bread. Age does play a role here, so you would want to divide your audience into age groups like 18-25, 25-40 etc. From each strata, you can select individuals to study using sampling intervals.
Linear Systematic Sampling
This type treats the audience list as a fixed line divided at periodic sampling intervals. So once you reach the end of the line, you have exhausted your list and the sampling ends there.
Example: This is a helpful sampling type if you require only a one-time sample and know exactly how many units are there in your audience. For example, if you are sampling for a work stress study within your organization between March-December, you can easily find out the current number of employees and apply the linear method.
Circular Systematic Sampling
This type treats the audience as a circular list. Once you reach the end of the list, you can continue the selection from the beginning. You can visualize this as a clock, with the hour lines symbolizing intervals.
Example: What if you have a huge population to draw from? Or you need multiple sets of samples? Circular systematic sampling is the best option. Because assuming the total audience is N, you can potentially get an N number of samples to work with.
Proportionate Systematic Sampling
In proportionate sampling, the sample size from each strata is proportional to the strata size.
Example: When you’re doing a Teacher Feedback survey among three classes of 30 students each. To save time, you choose proportional samples of 10 students from each class.
Disproportionate Systematic Sampling
This is a sampling method in which the size of the sample from a strata is not proportional to the relative size of that strata.
Example: Let’s say you’re doing a study of pizza sales in your city. One strata could be fast food chains. Though they only account for 20% of all the pizza shops in your city, they get 70% of customer footfalls. So they will be disproportionately represented in your final sample.
How to use systematic samples in 7 steps
- Select a population and determine its size. For example: If you’re doing a retail store study, the customers make up your target population. To determine the population size, you will need a list of every customer who has ever shopped at the store during the time period relevant to your study.**
- Divide that population into subgroups or strata of approximately equal size.
- Decide your sample size and sampling interval with the formula in the first section.
- Record the data with survey software.
- Analyze your data using real-time analysis.
- Form conclusions based on that analysis.
- Repeat steps 2 through 6 with another sample subgroup until you use all available samples.
**Creating such a list can be time-consuming, if not impossible. So if you are able to physically observe your audience, you can instead choose to sample them at the time of the study.
For example, you can survey every nth customer as they exit the store.
But in order to get a representative sample, you have to allot enough time to collect the participants you need, because different types of customers shop at different times. Need-based shoppers might shop during lunch hour or later in the evening, while casual browsers usually come in on the weekends.
That’s all folks! Hopefully, after reading this article, you’ll be able to determine the most appropriate method for your project, whether it’s about analyzing survey data or collecting customer experience feedback.
So, let’s get started!