Systematic sampling, a crucial part of research methodology, is a method of probability sampling. In this type of sampling, a random sample is acquired from a large population using sampling intervals. This sampling is preferred in research because it is consistent and time-efficient.
Definition: Systematic Sampling
To understand systematic sampling, it is essential to comprehend probability sampling. Researchers often analyze an extensive data set to answer research problems. They use samples from these populations for more scrutiny. Systematic sampling involves assigning numbers to sample size and choosing every nth member. For instance, an interval of 2, 4, 6, and so on.
When is it relevant to use systematic sampling?
Researchers opt to use systematic sampling in data sets that lack apparent patterns. Additionally, this type of sampling is better suited for projects with time and budget constraints. For example, by defining the nth term in a sample, a study can interview every 10th visitor to a hotel.
Ascending vs. descending order of population
Ascending order refers to the arrangement of numbers from the smallest to the largest. It is a way of ordering numbers to visually represent the increasing order of a data set. For instance, 25, 37, 41, 54, 69…
Descending order is where numbers are arranged in decreasing order. Alphabetically you would arrange from Z to A. For instance, 98, 67, 54, 41, and so on. Researchers use both descending and ascending orders in for different types of data.
While this sampling has many practical advantages, it may not be appropriate for cyclically ordered data. Cyclical data is a circular arrangement of data in a wheel-like format. For instance, in an analysis of incomes from a population of 200 workers in a factory, the data may be collected from different departments.
A cyclical approach would collect information in an ordered system, i.e., from the lowest to the highest. If there are 10 departments, the sample size will pick the 20th individual from each department. This would lead to an inaccurate sample because only the highest-earning individual from each department would be considered.
Systematic sampling without a population list
In some research scenarios, a researcher may not have a population to study. In this case, systematic sampling can be used to analyze every nth occurrence of an event without a defined population size.
For example, a study could be done to measure how satisfied students are with a common course. The researcher can interview every 10th person who attends the class. This method can still be classified as a random test since the order of attendance is not controlled.
Choosing a population for systematic sampling
The main types of data collection in systematic sampling are:
- Selecting samples in advance
- Selecting samples on the spot
Selecting samples in advance
The research process begins by targeting a population and selecting the most representative sample. Researchers may choose samples from a pre-determined list of participants or variables. For example, a study on patients in a hospital could examine a list of patients who have visited the hospital in the last month.
Selecting samples on the spot
Where no defined list is available, researchers may use observation to study a population. Here, samples are collected on the spot, and systematic sampling is used to create a sample criterion on the day of the study.
For example, in our previous model, you may study every nth patient who comes to the hospital.
Determining the sample size and sampling interval in systematic sampling
Researchers choose the sample size that reflects the variables under study. You can use a sample calculator and other methods to select your sample size. Each research undertaking needs to define the margin of error and any deviations from trends and patterns in the data set. A sample size calculator uses these inputs to output the optimal sample size.
After creating a sample size, the sample interval is calculated. The sample interval is mainly calculated by dividing the population by the sample size. Other adjustments may be made to accommodate other variables not represented in data collection.
For example, in the study of patients, you can estimate that 500 people visited in the last month. From this, you can use a sample size of 50. Using these variables, you can use a sample interval of 10, i.e., 500 divided by 10.
Limitations of systematic sampling
Systematic sampling is used in different fields of study because it can be applied to study other variables. However, several limitations should be considered:
- There is a significant risk of bias in cyclical data
- When the population size is unknown, it may be challenging to get an accurate sample
- In cases where there are noticeable patterns, the results of the study may be irrelevant
- Sampling may fail to consider a large set of variables in favor of creating a straightforward sample.
Pros and Cons of systematic sampling
|It is relatively easy to conduct
|There is a high chance of manipulation if the findings affect external parties
|It is possible to control critical variables such as population and sample size
|It requires a population that exhibits randomness
|It creates a diverse range of samples due to its randomness
|This method relies on an approximation of some variables, such as the margin of error
|It is generally low risk because a small sample is used
|It may fail to identify undefined parameters affecting a research proposal's findings
It is a common research approach that helps researchers study and understand distant and unfamiliar communities and cultures.
Depending on the topic, this type of research could last from several months to years. Therefore, it takes no specific amount of time to complete ethnography research and write a report.
It is a common approach in anthropology and other social sciences.
A basic study features five essential parts; the thesis statement, literature review, data collection, data analysis, and reflexivity.