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The sample size is a term used to define the number of individuals in a research study to represent the whole population. It is often broken down into smaller groups or sub-groups by demographics such as location, gender, and age so that the total sample represents the entire population.
Typically, you are interested in the entire group when surveying a large population of respondents. However, getting answers or results from everyone is not realistically possible. Therefore, you need to take a random sample of individuals representing the whole population.
The sample size is crucial for getting accurate, statistically significant results and successfully running your study.
There will always be the presence of uncertainty and imprecision since a sampling pool (your survey respondents) represents a larger population, while survey samples represent a chunk of your target population.
The existence of MOE, hence, is to calculate this inherent imprecision, which allows researchers to see how conclusive their survey results represent a particular population.
In addition to that, the MOE is also beneficial when you can not record responses from every person you want to survey. For example, polling the entire population of Hong Kong is not feasible. However, you can pick out a set of people representative of that group and then base your survey results on them.
MOE helps you determine the accuracy of a value by expressing it in a range, which represents the answer in relation to the entire target population.
Generally speaking, the calculation of MOE will help specify the likelihood that your survey result is close to the result had the whole population been surveyed. Its value goes a long way in determining the accuracy level of your survey.
To define your sample size, you must first consider the five following variables.
To determine the total number of people you are surveying or the population size, you must first be clear about who does and doesn’t fit into your group.
For instance, if you want to know about cat owners, you need to include those who have owned at least one cat. (Depending on your research goals, you may include or exclude those who previously owned a cat.)
Don’t worry if you’re unable to calculate the exact number. It’s common to have an unknown number or an estimated range.
The margin of error (MOE) or confidence interval is the error allowed in a research study. It shows you how close the sample results will be concerning the actual value of the overall population included in the study.
The confidence level is closely related to the confidence interval or the margin of error. This value measures the degree of certainty that a sample represents the whole population within the margin of error.
The standard deviation indicates how much variation can be foreseen from the responses of the study.
Since standard deviation is difficult to calculate in an actual survey, most people use 0.5 (50%) as the value, which is the worst-case scenario percentage.
Using this value will ensure that the figured sample size represents the overall population within the confidence interval and the confidence level in an accurate manner.
The Z-Score is a constant value that depends on the confidence level. Z-Score shows the typical standard score between the average/mean of the population and any selected value.
Since the confidence levels are all standardized, most researchers memorize the required Z-Score for most of the commonly used confidence levels, as below:
The larger the sample size, the more statistically significant it is. It means there’s less chance that your results happened by coincidence.
The sample size is essential for collecting accurate results. The formula calculates the sample size for an infinite population: S= Z2 × P × (1−P)M2(1−P)M2
Note that:
Slovin’s formula allows sampling the population with a preferred degree of accuracy. It gives an idea of how large the sample size should be to guarantee reasonable accuracy of results.
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