Sampling is a fundamental research technique that allows investigators to draw meaningful conclusions about a larger population by studying a selected subset, ensuring that findings are reliable and applicable. One effective method for sampling is systematic sampling, which involves selecting participants based on a fixed interval from a randomly selected starting point. This approach can help researchers ensure that their sample is representative of the population while reducing the potential for bias.
Systematic sampling is beneficial when the population is ordered in some way, allowing for a more organized selection process. By using a predetermined interval, researchers can efficiently select participants while maintaining randomness. This method suits diverse populations and can facilitate generalizing research findings to the larger population.
Systematic sampling finds application in quality control inspections in manufacturing, public health surveys of households, customer satisfaction studies in retail, census data collection, environmental assessments of ecosystems, educational research on student performance, and employee satisfaction surveys in organizations.
What is Systematic Sampling?
Systematic sampling definition: Systematic sampling is as a statistical method used to select a sample from a larger population by choosing every kth individual or unit after a random starting point.1 This technique ensures that the sample is spread evenly across the population, reducing the risk of bias in purely random sampling methods. The characteristics of systematic sampling include:
- Interval Selection: The sample is chosen based on a fixed interval k, calculated by dividing the total population size by the desired sample size. For example, if there are 100 individuals and a sample size of 10 is needed, every 10th individual would be selected.
- Random Start: The selection begins with a randomly chosen individual within the first interval to maintain the randomness of the sample.
- Efficient and Simple: Systematic sampling is often easier to implement than simple random sampling, especially in large populations.
This article covers the fundamental aspects of systematic sampling, providing a comprehensive understanding of when and how to use systematic sampling with examples, types of systematic sampling, as well as its advantages and disadvantages. The difference between systematic and cluster sampling is also given to understand their practical significance.
When to Use Systematic Sampling?
Systematic sampling is best used in the following situations:[2], [3]
- When the Population is Homogeneous: Systematic sampling works well when the population has relatively uniform characteristics. For instance, when conducting a quality control check in a manufacturing process where all products are similar, systematic sampling ensures an evenly distributed sample across production.
- When a Sampling Frame is Available: If you have a complete, up-to-date list of the population, systematic sampling is easy to implement. For example, in a university setting, a list of enrolled students can be used to select every 10th or 20th student for a study.
- When Simplicity and Efficiency are Needed: Systematic sampling is quicker and simpler to administer than random sampling. If time and resources are limited, it allows for fast sample selection, like picking every 5th house in a neighborhood for a door-to-door survey.
- When Avoiding Clustered Data: If you want to avoid over-sampling specific clusters in the population, systematic sampling evenly spreads the selection across the entire list, such as selecting every 100th customer in a database to get a diverse set of respondents.
Steps for Systematic Sampling
The step-by-step process for conducting systematic sampling is explained in the table below with examples.[2], [3]
| Step | Explanation | Example |
| 1. Define the population | Clearly define the entire group you want to sample. | A university wants to survey 1,000 students about their campus experience. The population is all students. |
| 2. Create a sampling frame | Develop or obtain a complete list of the population members. | The university creates a list of all 1,000 students, ordered alphabetically. |
| 3. Decide on the sample size | Determine how many individuals you need to sample from the population. | The university decides to survey 100 students. |
| 4. Calculate the sampling interval | Divide the population size by the desired sample size to determine the interval (k). | Population size (1,000) ÷ Sample size (100) = Interval (k) of 10. |
| 5. Randomly select a starting point | Choose a random number between 1 and the sampling interval to determine where to start selecting from the list. | A random number generator selects a number, say 7, as the starting point. |
| 6. Select every kth member | Starting from the chosen point, select every kth member until you reach the desired sample size. | Starting at the 7th student, select every 10th student (7th, 17th, 27th, etc.) until 100 students are selected. |
This systematic approach ensures a well-distributed sample that is easy to implement and reduces bias when a random starting point is used.
Examples of Systematic Sampling
The below systematic sampling examples demonstrate how it can be used in different contexts:
- Scenario 1: The management wants to survey 100 employees from its list of 10,000 employees about job satisfaction.
- Method: The researcher selects every 100th (10,000 ÷ 100= 100) employee for sampling. Initially, a random starting point, say the 20th employee on the list, is chosen. Next, the 120th, 220th, and so on, are selected. If the list is exhausted and more employees are needed, the count resumes from the beginning of the list.
- Scenario 2: A researcher wants to select 500 individuals from a population of 25,000 for a study on consumer behavior.
- Method: All 25,000 individuals are listed, and every 50th person (25,000 ÷ 500 = 50) is selected. If the starting point is randomly chosen as the 15th person, the 65th, 115th, and so on, are chosen. If the list ends and more participants are required, the count wraps around to the beginning of the list.
- Scenario 3: A city wants to monitor air quality and take readings every 6 h from 1,000 locations over 1 month.
- Method: The systematic interval is set to every 12 h to account for changing environmental conditions. The first measurement is taken at 6 AM on day 1, and again at 6 PM on the same day. The process continues every 12 h, ensuring consistent data collection across the month.
Types of Systematic Sampling
The table below highlights the types of systematic sampling based on how the starting point and sampling interval are applied, especially when handling the end of the list.
| Type of Systematic Sampling | Explanation | Example |
| Systematic Random Sampling | A random starting point is selected, and then every kth element is chosen from the population list. | In a population of 5,000 people, the statistician randomly selects the 10th person and then every 50th person thereafter (e.g., 10th, 60th, 110th). |
| Linear Systematic Sampling | A fixed interval (k) is applied to the list, and elements are chosen without wrapping around after reaching the end. | In a list of 1,000 items, every 20th item is selected, starting from a random point. Sampling stops when the end of the list is reached. |
| Circular Systematic Sampling | After reaching the end of the list, the process continues by wrapping around to the beginning until the sample size is met. | In a population of 200 employees, every 10th employee is selected, and when the list ends, the counting resumes from the first employee. |
Systematic Sampling vs Cluster Sampling
The table below highlights the key differences between systematic and cluster samplings and when to use each method:
| Dimension | Systematic Sampling | Cluster Sampling |
| Definition | A sample is selected by choosing every kth element from a population list, starting from a random point. | A sample is selected by dividing the population into clusters (groups) and randomly selecting entire clusters. |
| Sampling Method | Involves selecting individuals at regular intervals from a list. | Involves dividing the population into clusters, then randomly choosing one or more clusters for study. |
| Population Structure | Best for populations that are homogeneous or have no inherent structure. | Best for populations that are naturally divided into distinct groups or clusters (e.g., regions, schools). |
| Sample Selection | Individuals are selected at regular intervals based on a sampling interval. | Clusters are randomly selected, and all individuals within chosen clusters are sampled. |
| Ease of Implementation | Easy to implement if a complete list of the population is available. | Easier when the population is too large to list every individual, but clusters are easily identifiable. |
| Risk of Bias | Can introduce bias if the population list has periodic patterns. | Can introduce bias if selected clusters are not representative of the whole population. |
| Example | Surveying every 20th customer in a list of 2,000 to gather customer feedback. | Surveying employees by randomly selecting 5 out of 20 departments and interviewing everyone in those departments. |
Advantages and Disadvantages of Systematic Sampling
Let’s take a look at the main advantages and disadvantages of systematic sampling simply explained in the table below.
Advantages of Systematic Sampling
| Advantage | Explanation |
| Simplicity | Easy to understand and implement, requiring only a list and a regular interval. |
| Time-Efficient | Quicker to execute than random sampling because only the first point needs to be randomly chosen, and the rest follow systematically. |
| Even Distribution of Sample | Ensures that the sample is spread evenly across the entire population, reducing clustering. |
| Reduces Selection Bias | Reduces intentional or unconscious bias possibilities in the selection by using a random starting point. |
| Cost-Effective | Less resource-intensive than other sampling methods as it requires fewer steps and random selections. |
| Useful for Large Populations | Works well for large populations where simple random sampling might be impractical. |
Disadvantages of Systematic Sampling
| Disadvantage | Explanation |
| Periodic Bias | Possibility of a bias increases if the population has a periodic pattern. |
| Sampling Frame Requirement | A complete and accurate sampling frame is required, which may be difficult to obtain. |
| Not Truly Random | Selection process is not completely random, leading to less generalizable results. |
| Overlooked Subgroups | Systematic sampling may overlook smaller or less frequent subgroups within the population. |
| Inflexibility | Once the sampling interval is determined, it cannot be adjusted without restarting the sampling process. |
Key Takeaways
Systematic sampling is a probability sampling technique in which researchers select every kth individual from a population list. This method provides a clear and efficient means of data collection. The procedure involves determining the total size of the population and the desired sample size, calculating the appropriate sampling interval, and randomly selecting a starting point.
The advantages of systematic sampling include its ease of use, speed of execution, and uniform coverage of the population. However, the potential for periodic bias if the population possesses an inherent structure and the requirement for a complete and accurate sampling frame cannot be overlooked. When used with caution, it can be a valuable data collection technique in various fields, including quality control and survey research, as it minimizes the risk of bias.
Frequently Asked Questions
- What are the mistakes to avoid in systematic sampling?
Take a look at the common errors, with an explanation and tips to avoid them in the simple table below.
| Mistake | Explanation | How to Avoid |
| Inappropriate sampling interval | A small interval may cause oversampling and error; a large interval may lead to undersampling and reduce sample representativeness. | Understand the full scope of the population before selecting an appropriate interval. |
| Bias in the sampling frame | If the sampling frame is not representative (e.g., only includes certain demographic groups), the sample will be biased. | Ensure the sampling frame is inclusive and representative of the entire population. |
| Ignoring systematic patterns in the population | Periodic patterns in the population may cause certain segments to be over- or under-represented (e.g., selecting the same positions on baseball teams). | Check for periodic trends in the population and adjust the sampling interval as needed to avoid cyclical bias. |
2. How is systematic sampling conducted in research?
Systematic sampling follows three key steps:
- Defining the Population and Sampling Frame: First, clearly define the entire population of interest and ensure that you have a comprehensive, up-to-date list or frame that includes every member of the population.
- Determining the Sampling Interval (k): Calculate the sampling interval (k) by dividing the population size (N) by the desired sample size (n). Thus, k = N/n. For example, N = 1,000 and n = 100, k = 10. This means you’ll select every 10th person from the list.
- Randomly Selecting a Starting Point and Applying the Interval: Randomly select a starting point from the population list (between 1 and k). From this point, select every kth element in the population. For example, if your starting point is 6 and k = 50, select the 56th, 106th, 156th person, and so on until the sample size is met.
3. What are the limitations of systematic sampling?
A key limitation of systematic sampling is the overrepresentation or underrepresentation of certain subgroups when the population list follows a cyclical pattern that aligns with the sampling interval. For example, if a university’s student list is organized by departments, and a survey samples every 100th student, it may include only students from one department, like medicine, while missing others, leading to biased results.
Additionally, systematic sampling assumes homogeneous population and may not capture all variations in diverse populations. For instance, in a customer satisfaction study, selecting customers based on visiting times may overlook peak periods, skewing the Moreover, systematic sampling requires a complete and accurate sampling frame. An incomplete or outdated list that fails to reflect the entire customer base can weaken the sample’s representativeness.
References:
- Levy, P. S., & Lemeshow, S. (2013). Sampling of Populations: Methods and Applications. Wiley.
- Pandey, P., & Pandey, M. M. (2021). Research methodology tools and techniques. Bridge Center.
- Bellhouse, D. R. (1988). 6 Systematic sampling. Handbook of statistics, 6, 125-145.
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