Systematic sampling is a type of probability sampling method where sample members from a larger population are selected based on a random starting point but with a predetermined, fixed interval. This interval, known as the sampling interval, is calculated by dividing the population size by the desired sample size. Although the sample population is predetermined, systematic sampling is still considered random if the interval is set beforehand and the starting point is chosen randomly.
When executed properly on a large population of a defined size, systematic sampling enables researchers, including marketing and sales professionals, to obtain representative findings from a wide audience without having to reach each individual member.
Key Takeaways
- Systematic sampling is a probability sampling method involving a fixed, periodic interval.
- The sampling interval is calculated by dividing the population size by the desired sample size.
- Advantages include eliminating cluster selection and reducing data contamination likelihood.
- Disadvantages include the potential for overrepresentation or underrepresentation and a higher risk of data manipulation.
- Three main types of systematic samples are Random systematic samples, Linear systematic samples, and Circular systematic samples.
Understanding Systematic Sampling
Unlike simple random sampling, which can be inefficient and time-consuming, systematic sampling is straightforward. Once a fixed starting point is identified, a constant interval is selected to facilitate participant selection.
Systematic sampling is more appropriate than simple random sampling when there’s a low risk of data manipulation. If the risk is high and interval lengths can be manipulated to obtain desired results, then a simple random sampling technique would be more suitable.
Researchers favor systematic sampling for its simplicity. Results are generally assumed to represent normal populations unless a disproportionate random characteristic exists within each nth data sample.
Researchers establish a target population before selecting participants, based on desired characteristics suiting the study. Potential characteristics include age, gender, location, etc. Systematic sampling organizes participants into a sample from the larger population based on these characteristics.
Steps to Create a Systematic Sample
Follow these steps to create a systematic sample:
- Define your population: Identify the group from which you are sampling.
- Settle on a sample size: Determine how many subjects you need to get a representative idea of the population.
- Assign every member of the population a number: If surveying 10,000 people, number them from 1 to 10,000.
- Decide the sampling interval: Divide the population size by the desired sample size.
- Choose a starting point: Select a random number as your starting point.
- Identify members of your sample: If starting at 15 with a sample interval of 100, the first sample member is 115, and the pattern continues.
Examples of Systematic Sampling
Imagine in a population of 10,000 people, every 100th person is selected for sampling. Intervals could also be timed, like sampling every 12 hours.
If selecting a random group of 1,000 from 50,000 people, list all potential participants and choose a starting point. For example, with intervals of 50 and starting at 20, the 60th, 120th, 180th persons, and so on, are sampled until reaching the list’s end, at which point count resumes from the top until completion.
Types of Systematic Sampling
Random Systematic Sampling
Classic form of systematic sampling, where subjects are selected at predefined intervals.
Linear Systematic Sampling
Follows a linear pattern instead of a random interval selection.
Circular Systematic Sampling
The sample recommences sampling from the same point after one cycle ends.
Systematic Sampling vs. Cluster Sampling
Systematic sampling takes regular intervals from a larger population, whereas cluster sampling divides the population into clusters, then takes a simple random sample from each cluster.
Cluster sampling is less precise but can reduce costs. It involves a two-step process: create a random subset of clusters and sample randomly within them. This method can simplify processes like customer studies for a chain of stores, avoiding extensive population lists.
Limitations of Systematic Sampling
A key risk is biased samples if population lists align cyclically with sampling intervals. For example, when surveying employees grouped in clusters, a biased sample may result from unintentionally selecting entire or none of the teams based on list order.
Advantages of Systematic Sampling
Systematic sampling’s simplicity and widespread assumption that results reflect normal populations make it favored by researchers. It provides better control versus other methodologies and a low data contamination risk.
Disadvantages of Systematic Sampling
It requires precise population sizes. Without them, systematic sampling is ineffectual. Additionally, populations need inherent randomness, or risk choosing similar cases repeatedly, undermining sample diversity.
The Bottom Line
Systematic sampling is a cost-effective method to study large groups, ideal for populations without cyclic patterns. While it’s not perfect, systematic sampling offers a reliable way to gather representative samples efficiently.
Related Terms: Cluster sampling, Simple random sampling, Sampling interval, Human resources sampling, Population sampling.
References
- University of Connecticut, Neag School of Education. “Educational Research Basics by Del Siegle: Systematic Sampling”.
- The University of Arizona, College of Agriculture, Life & Environmental Sciences. “Chapter 3: Simple Random Sampling and Systematic Sampling”. Page 6.
- London School of Hygiene & Tropical Medicine. “Systematic Random Sampling”.
- United Nations, Economic and Social Commission for Asia and the Pacific. “Pacific Training on Sampling Methods for Producing Core Data Items for Agricultural and Rural Statistics”.
- Professor, John Borkowski, Montana State University. “Sampling: Design and Analysis, (Second Edition); 7. Cluster Sampling and Systematic Sampling”. Pages 147-149.
