Systematic sampling is a type of probability sampling that takes members for a larger population from a random starting point. It uses fixed, periodic intervals to create a sampling group that generates data for researchers to evaluate. Each interval gets calculated by dividing the population size by the desired scope of the sample.
That means a population group of nine individuals with a 33% systematic rate would pull the #3, #6, and #9 individuals to collect data. The first person would be randomized, which creates a selection series that reduces bias because the starting point becomes unpredictable. It provides every member of the community an equal opportunity to get selected when using this technique.
Linear and circular systems are both available for researchers to use. The only difference is that the latter option restarts from the randomized starting point once the entire population receives consideration.
Several systematic sampling advantages and disadvantages occur when researchers use this process to collect information.
List of the Advantages of Systematic Sampling
1. It is simple and convenient to use.
Researchers can create, analyze, and conduct samples easily when using this method because of its structure. The algorithm to make selections is predetermined, which means the only randomized component of the work involves the selection of the first individual. Then the selection process moves across the linear or circular pattern initiated until the desired population group is ready for review.
That’s why systematic sampling is useful in situations when budget restrictions are in place. It’s well-suited for situations where money is a contributing factor to the research because it is an uncomplicated process to follow.
2. There isn’t a need to number each member of a sample.
Researchers can represent an entire population quickly and easily when using systematic sampling. There isn’t a need to number each member of the sample because the goal is to create representative data of the entire group without specific individualized identifiers. This advantage makes it possible to create data for analyzing quickly because the only step necessary to get started is to identify the targeted demographic.
Investigators will still need to assign a starting number to the first participant in the systematic sampling work. Then the research chooses an integer that’s less than the total number of people in the selected demographic to create results. The final integer is the constant difference between any two consecutive numbers.
3. The created samples are based on precision.
The samples that get created from systematic sampling have a higher level of precision than other randomized methods. Researchers know specifically who will become part of the research group once the first selection occurs. That means there is a much lower risk of favoritism occurring in the data because the individuals in charge of the research have no control over who gets to have their data included in the work. Everything is predetermined for them once the population group gets chosen.
This advantage also applies to unconscious bias that can occur when researchers have specific social preferences that get followed when selecting participants.
4. It reduces the potential for bias in the information.
Other methods of probability sampling can have a high risk of creating highly-biased clusters even when researchers take steps to avoid this issue. The processes of systematic sampling create an advantage here because the selection method is at a fixed distance between each participant. That’s why cluster, convenience, and stratified sampling methods quickly fall out of favor when compared to this process.
5. This method creates an even distribution of members to form samples.
The factor of risk that’s involved with this sampling method is quite minimal. Even when the population under review is exceptionally diverse, this process is beneficial because of the structured distribution of members to form the sample. That means the data collected during a research project has a better chance of being an authentic representation of the entire demographic.
That means the samples are relatively simple to compare, construct, and execute to understand the data that comes in from the work. It systematically eliminates the issue of clustered subject selection that other forms of randomization can subconsciously add to the research process.
6. It reduces the risk of favoritism.
Researchers have no control over who gets selected for systematic sampling, which means it creates the benefits of randomized selection while providing a buffer against favoritism in the data collection efforts. It provides a low risk of data manipulation during the work collection process while keeping the sampling work highly productive on broad subjects while there’s a negligible risk of error.
This advantage comes about because the researchers maintain a sense of control with the process. When studies have strict parameters or a narrow hypothesis to pursue, then it works well when the sampling can get reasonably constructed to fit those parameters.
List of the Disadvantages of Systematic Sampling
1. This process requires a close approximation of a population.
The systematic sampling method must assume that the size of the population in specific demographics is available and measurable. If that isn’t possible, then this method requires a reasonable approximation of the demographic in question. The selection process cannot occur correctly if that figure isn’t available, because the size of the pool pulled for participation comes from the division of that overall figure.
This issue becomes problematic when systematic sampling assumes that the population is larger or smaller than it actually is because that will impact the integrity of the samples in question.
2. Some populations can detect the pattern of sampling.
If a smaller population group is under review, then the systematic sampling method can get detected by some participants. When this disadvantage occurs, then it can bias the population as non-participants will be different than those who get to be part of the process. It can encourage some individuals to provide false answers as a way to influence the results for personal purposes, working against the perceived hypothesis under study.
This issue can be severe enough that it compromises the work of the entire study.
3. It creates a fractional chance of selection.
The systematic sampling method creates fractional chances for selection, which is not the same as an equal chance. Even the circular method encounters this disadvantage, especially with a small demographic. If people fall between the numbering system in their count, then there is no way for their perspectives to be included in the collected data. Although generalizations are possible with this method that apply to the whole demographic, the representation is not typically 100% accurate to each member.
That means the researchers who use systematic sampling are always going to miss something that could have led them to a new finding. Some participants may not want to take part in this effort if they detect a pattern that also excludes them.
4. A high risk of data manipulation exists.
Researchers can construct their systems of systematic sampling to increase the likelihood that a targeted outcome can occur. Instead of letting random data produce the repetitive answer organically, the information comes out with an inherent bias that no one else would recognize upon analysis. That means it is still possible to produce answers that are constructed instead of representative, negating the outcomes that occur with the work. Any statistics produced from a process influenced by this disadvantage could not be trusted.
5. Systematic sampling is less random than a simple random sampling effort.
If randomness is the top priority for research, then systematic sampling is not the best option to choose. Although it takes less time and isn’t as tedious as other methods of data collection, there is a predictable nature to its efforts that can influence the final results. The goal is still to reduce the sampling error, but the impact of the work may never get detected. It may not even be an authentic sampling option if mailing questionnaires or surveys because of lost mail or uncooperative subjects.
6. This method can potentially interact with hidden periodic traits.
The process of selection in systematic sampling can unintentionally interact with hidden periodic traits found in some demographics and communities. If this issue were to occur at random through the integer selection process, then the sampling technique would coincide with the periodicity of the trait. That means the final data set would not be a random representative of the entire group because it would over-emphasize the nature of the periodic trait.
7. The population group in question must have some randomness to it.
The processes of systematic sampling can only work when a population group has some degree of randomness to it. If the demographic has a standardized pattern to it, then there is a significantly high risk of accidentally choosing common cases when conducting research. That means the survey might skip key components of the population group without the researchers even realizing what is happening.
When this disadvantage occurs, it can skew the results in adverse ways that can lead researchers down the wrong direction toward a hypothesis. It can even lead to demographic changes that wouldn’t occur otherwise if the sampling process was more authentic to the results.
Systematic sampling is a probability-based method that provides some specific strengths and weaknesses to consider. It requires the first sample to be chosen randomly to ensure the probability aspect of this approach. If researchers do not take that approach, then those who fall between the regular samples have a chance of not being chosen for this process.
It can be a cost-effective way to conduct research, but this method can also produce an easier way to hide purposeful bias. That’s why independent verification of the randomness involved with this process is a useful component of its authenticity. It is a method of data collection that allows for geographically disperse cases to still receive inclusion in the work.
The advantages and disadvantages of systematic sampling also note that it is only possible to complete if an entire population list is available. If that is not possible, then this method is no longer useful.
Blog Post Author Credentials
Louise Gaille is the author of this post. She received her B.A. in Economics from the University of Washington. In addition to being a seasoned writer, Louise has almost a decade of experience in Banking and Finance. If you have any suggestions on how to make this post better, then go here to contact our team.