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The Different Types Of Systematic Sampling & How To Use Them?

Does statistics utterly enthral you? If yes, we bet you would have heard about systematic sampling. And, if not, don’t worry; we have got your back. Here’s an in-depth, comprehensive guide to what it is, the different types and how to use it in various instances.

Table of Contents

What Is Systematic Sampling?

Statistically speaking, systematic sampling involves choosing items or individuals from a particular group as samples based on calculations. The calculations are involved in a way that every “nth” item or individual is selected from a population. This technique is also called skip intervals.

These intervals can be calculated by dividing the total population by the desired sample size.

Here’s a systematic sampling example for a better understanding:

If the population size is 1000 and the desired sample size is 200, you’ll pick every 5th item(1000/200) or individual to gather insights.

`RELATED: Systematic Random Sampling: Overview, Advantages, and Disadvantages`

How Is Systematic Sampling Different Than Simple Random Sampling?

Systematic sampling and simple random sampling differ in several ways. Here are some of the differences:

• Execution: Unlike random sampling, where every item is individually identified and selected, the systematic technique uses the interval rule to choose the samples.
• Probability: Systematic rule chooses every item at a predetermined interval. This differs from random sampling, where each item is likely to be selected or identified.
• Sample Size: Simple random sampling can be a great technique, but only if your audience is smaller. In contrast, systematic sampling, which is based on the interval equation, helps identify items systematically from a large audience.
• Accuracy: Again, random sampling can be accurate if the audience count is low. However, if you want to deal with a massive amount of target groups, you would need to switch to the systematic technique to gain accuracy.

Different Types Of Systematic Sampling

Now that you are all clear with systematic sample definition let’s examine the different types with the appropriate systematic sample examples.

1. Systematic Random Sampling

This one is the most basic type. All you need to do is determine a random starting point for identifying your items.

For instance: If a supermarket wants to conduct a survey on customers’ shopping habits, they can randomly choose every 10th or 15th customer that walks in and ask them survey questions.

2. Stratified Systematic Sampling

This one might seem a little complicated, but it offers the most accurate results. The stratifying technique involves the researcher dividing the entire population into different strata. These strata should have additional attributes, like another gender, ethnicity or belonging to a diverse age group. Once you have divided the population into different strata, you can choose a sample member from each stratum and ask them survey questions.

For instance: A researcher might divide a population age-wise to ask about their shopping habits if they were studying shopping preferences among different age groups.

`RELATED: Stratified Random Sampling: A Microscopic Introduction`
3. Linear Systematic Sampling

Linear sampling involves listing your audience as a fixed line, differentiated by periodic sampling. Samples are considered complete once they reach the end of the line.

You can use this technique if you want to collect a one-time sample and know how many exact units of audience you need to focus on.

For instance: If you are conducting sample research for a work stress study from April to January, you can easily find your target audience by applying this method.

4. Circular Systematic Sampling

This approach views the audience as an endless list. You can pick again from the beginning of the list after you’ve reached the finish. This may be thought of as a clock, with the hour lines denoting the passage of time.

For instance: If you have to survey a large population, this method allows you to get N samples by assuming N is the total audience.

5. Proportionate Systematic Sampling

By definition, a proportional sample takes a percentage of the population from each stratum that corresponds to the stratum’s size.

For instance: Let’s say you are conducting a customer satisfaction survey among four different audience bases, each with 50 customers. You can select proportional samples of 10 customers from each audience group as a time-saving measure.

6. Disproportionate Systematic Sampling

In this technique, the sample size is not proportional to the relative size of the data.

For instance: If you want to survey the ice cream sales in a city, you could divide the strata based on the ice cream chains. Though they might only include just 20% of the shops around the city, you will get about 70% of customer footfall.

How To Do Systematic Sampling?

Here’s how you can do systematic sampling with six easy steps:

• Determine the target population and its size.

• Divide the target audience into different strata. Each stratum should contain an equal number of people.
• After settling on sufficient subjects to include in the sample, you may start giving them all unique identifiers.
• Limit your sample to a specific range. Your chosen separation will serve as the baseline for all subsequent comparisons. As an example, your sample interval would be 40 if your population size (N) was 10,000 and your sample size (n) was 250.
• In this step, you’ll pick the list’s one-in-twenty-fifth resident who satisfies your criterion. Analyze your data through real-time tracking. Track and record the data using an online survey tool like SurveyPoint.
• Conduct a conclusion and make an insightful survey report. In order to generate the initial subject (r), pick a random number from the sample and multiply it by the interval. Maintain a growing sample size until it accurately represents the whole population. Thus, we will begin with r, r+i, r+2i, etc.

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Heena Shah – Content Writer at Sambodhi

Heena Shah
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