A **systematic sampling***k *value. Using the given answer, researchers then select every k value from the total population. Systematic sampling works incredibly well in instances where the population is grouped randomly or represented orderly, e.g., in an alphabetical way.

In addition, this method is an implementation of probability sampling and, hence, it’s one of the primary methods used by researchers. It allows for a great spread across any population and helps to remove bias. Keep on reading and learn all ins and outs of this universal method.

## What Is Systematic Sampling

First, let’s make sure you understand what systematic sampling technique is. systematic sampling is a statistical probability method used to select population members by setting a fixed interval (k value). Researchers use this method to zero down on any population in a study. In simpler terms, zeroing down on a population means finding the nearest divisible number that can be determined in a population, which is divisible by sample.

Furthermore, conducting systematic sampling allows researchers to select a random starting point and then proceed to the nearest k value to figure out the interval. In addition, to ensure accurate results, one must determine a population order before using this method.

### When to Use Systematic Sampling

It is important that you learn when it is appropriate to use systematic sampling as the properties of any population greatly determine your research outcome. As was mentioned above, researchers should use a fixed k interval when the population is ordered. When it comes to obtaining the desired sample, a systematic method provides benefits similar to those found in randomization. On the flip side, this method is much easier to conduct as you can get an entire population list without having to manipulate data. However, avoid this method if patterns are already present in your dataset.

## Types of Systematic Sampling

There are 3 main types of systematic sampling:

- circular
- linear
- systematic random.

Each type has its own special advantages that can enhance your research if used right. Read on and find out which method is more applicable in your study and what benefits these types have.

### Circular Systematic Sampling

Circular systematic sampling with equal probability, as its name suggests, presents the population in a circular manner. In this type, your sample will begin from the same starting point. To choose your target data, you should calculate an interval first.

A formula for k interval can be represented as *N/n* where *N* is a total population and *n* is a sample size. For instance, if N=10 and n=2, your *k* will be 5. An *r* value can now be taken from any point of the population beginning up until *k* value. Sampling is considered done when a circular motion through a population is completed and your *n* value is achieved.

### Linear Systematic Sampling

Linear systematic sampling involves the use of a linear cluster in a tabulated row & columns format where your *k* interval is determined by *N/n*. This formula is identical to the circular one with the only difference being in how populations are represented. This method follows a linear direction and stops once a population ends.

To decide the probability of a selected element using this method, several columns are constructed based on your *k* value. This means that if *N/n* gives 3, you will use 3 columns to present your population. Then, your entire population is tabulated within these 3 columns, and results generated.

### Systematic Random Sampling

Systematic random sampling is a method that allows researchers to choose samples at a fixed interval.

Generally speaking, this method is similar to simple random one where the calculation of p and confidence intervals are done identically and as expressed below:

* r* = 1~*i where i* = *N/n*

In a nutshell, you should choose any starting point that goes between 1 and an interval. Then, you should repeat a fixed interval as many times as needed to choose other elements. As systematic random sampling does not comprise completely random selections, it delivers multiple advantages when you are selecting large sizes from long listed populations.

## How to Do Systematic Sampling

Systematic sampling technique requires researchers to select starting data which basically represents the first sample taken from population. Other samples are generated by taking an element which is ‘*n’* times the *‘k’ *value from your starting point *r*. Your *r* value is the first sample taken which lies between 1 and *k* interval.

Here are 5 steps you should follow:

**Step 1:** Find *k* value by dividing *N/n* where *N* is total population and *n* is sample size.

**Step 2:** Randomly select *r* between *N*1 and *k*.

**Step 3:** For each element do *r*, *r+k*, *r*+*2k*……*r*+*nk*

**Step 4:** Take multiple samples with different *r* values to normalize data

**Step 5:** End sample by taking mean.

### Systematic Sampling: Example

An accurate example of systematic sampling is the use of households in any neighborhood that corresponds to a population of any city or town. Using this population, we can determine an ordered list by generating sample using *k *interval.

For example, we can determine the total population in terms of households that are conflict affected. To generate your required sample, we first divide total households by number of specific elements required. Then, a value between the first element of this population and *k *value is taken to determine subsequent elements.

## Pros and Cons of Systematic Sampling

To find systematic sampling advantages and disadvantages, researchers should list correct properties of a population in order to choose the best methodology. In order to create a list, a researcher should determine if the population is ordered. This way, you will be able to benefit from this method rather than face issues with selection bias later on.

### Advantages of Systematic Sampling

The advantages of systematic sampling include the ease of understanding and execution along with a reliable control process. In addition, bias is removed from population and its sample since any values that researchers select are based on a randomized principle.

Keep in mind that systematic sampling has an extremely low risk factor and allows to generate sampled data from an entire population. All these factors make it a perfect method for researchers.

### Disadvantages of Systematic Sampling

The disadvantage of systematic sampling can range from the simple fact that data can be easily manipulated as ordered lists of populations might follow some pattern. Also, it requires that you find data sets that have random selection embedded in its population. People or researchers who use this method only apply this method when working with large datasets.

## Final Thoughts on Systematic Sampling

The use of systematic sampling methods has its pros and cons. It’s an excellent method if you are dealing with a large data set. But you should remember that the outcomes can be biased if you use an alphabetical population order.

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## Comments

Both the cluster and systematic sampling solve different problems. They both can reduce your listing work and they both do regrettable things to your second-order selection probabilities, but cluster picks units close to each other and systematic is a simple way to guarantee a sample spread out over a list.

Hope this helps!

Simple random sampling demands that each member of the population is identified and selected. At the same time, systematic sampling is based on the rule of sampling interval when choosing all the individuals.

We hope this is helpful!