In calculating the minimum sample size using Slovin’s formula, researchers can choose a 5% or 10% margin of error. What’s the difference, and how do you choose the right one? In survey research, when observing a population, we are often faced with the challenge of a large population size that needs to be observed.
A larger population size is closely tied to increased costs, time, and effort. Selecting a sample that represents the population is a great solution while still adhering to the principles of scientific research.
It’s no secret that Slovin’s formula is often used by researchers to determine the minimum sample size in survey research. Determining the correct and representative sample size is crucial for obtaining accurate results.
One of the key components of Slovin’s formula is the margin of error, which plays a role in determining the level of accuracy in identifying the minimum sample size that represents the observed population. This article, brought to you by Kanda Data, discusses how to select the correct margin of error, whether 5% or 10%, when using Slovin’s formula.
Slovin’s Formula for Determining Minimum Sample Size
As mentioned in the previous paragraph, Slovin’s formula is a simple and widely used tool to determine the minimum sample size of a population. Researchers using simple random sampling techniques with a homogeneous population can use Slovin’s formula to determine the minimum sample size. The formula is as follows:
n = N / (1+N.e^2)
Dimana:
n = minimum sample size
N = population size
e = margin of error
Using this formula, we can calculate how many samples are needed to obtain representative survey results from the observed population. Therefore, researchers can use this formula as a basis for determining sample size in survey research with homogeneous data. Additionally, for data with strata where each stratum is homogeneous, Slovin’s formula can also be applied.
Definition of Margin of Error in Slovin’s Formula
Based on Slovin’s formula, one component to calculate is the margin of error (e). Before calculating the minimum sample size using Slovin’s formula, we need to first determine the margin of error based on the characteristics of the observed population.
The margin of error is the level of error allowed in sampling and indicates how much the sample results can differ from the actual population value. Many researchers typically use a 5% or 10% margin of error. The smaller the margin of error, the more accurate the survey results. However, this has the consequence that the required minimum sample size will be larger.
A 5% margin of error is a commonly used standard in Slovin’s formula. A 5% margin indicates that the survey results could deviate by up to 5% from the actual population value. With a maximum allowable error of 5%, the sample size is considered sufficiently accurate to represent the observed population.
On the other hand, a 10% margin of error has a higher level of error and is generally used in exploratory research or studies with limited resources. A 10% margin yields less accurate results compared to a 5% margin.
How to Choose the Correct Margin of Error
After thoroughly understanding the difference between a 5% and 10% margin of error in Slovin’s formula, we need to consider several factors when choosing the appropriate margin of error.
First, we need to review the research objectives. From the objectives, we must determine if we aim to obtain very accurate and detailed results. If so, a 5% margin of error is the better choice. However, if the research is only preliminary or exploratory, a 10% margin may be acceptable.
Second, for research with limited resources (time, effort, funds), a 10% margin of error is more suitable since the required sample size will be smaller.
Case Study Example in Research
To better understand the implementation of Slovin’s formula, let’s consider a case study in research. Suppose a researcher wants to survey 1,000 farmers to determine their preferences for new agricultural technology. If the researcher chooses a 5% margin of error, the required sample size is:
n = 1000 / (1 + 1000 * (0.05^2)) = 286 farmers
However, if the researcher has limited resources and decides to use a 10% margin of error, the required sample size will be smaller:
n = 1000 / (1 + 1000 * (0.10^2)) = 91 farmers
In this case study, choosing a 5% margin of error results in a larger minimum sample size, whereas a 10% margin reduces the minimum sample size, but a 5% margin produces more accurate results.
Conclusion
Choosing the correct margin of error is a crucial step in determining the minimum sample size that represents the observed population using Slovin’s formula. A 5% margin of error provides higher accuracy but requires a larger sample size, while a 10% margin of error reduces the sample size but may result in less accurate findings.
The choice of margin of error should be based on the research objectives, population size, and available resources. This is the article that Kanda Data has prepared for now. We hope it’s useful and provides new insights for all of us. Stay tuned for the next article update from Kanda Data. Thank you!
