Determine the accuracy of your survey data
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In statistics, a sample refers to a subset of a larger population. The sample allows researchers to conduct their study on a part of their target population so that they can work with manageable data, in a timely and cost-effective manner. In order to acquire data that is generalizable to your target population, is integral to select a sample group that is representative of your target population.
A representative sample will have the same composition as that of the larger population. However, when researchers fail to select a target population that is representative, it results in sampling error.
Sampling error can be defined as a statistical error that occurs when a researcher fails to select a sample that is representative of the entire population. When sampling error occurs, the results obtained from the sample are not reflective of the results that would be obtained from the target population itself. Therefore, the findings of the study are less generalizable to the target population.
The only way to completely eliminate sampling error from a study is by observing every element in a population, which is not feasible and is even impossible in some cases. Therefore, sampling error cannot be completely avoided as no sample will ever be fully representative of the target population. However, by having an understanding of sampling error, we can estimate the size of it and take measures to minimize it, so as to make the findings of our study as generalizable to the larger population as possible.
Sampling errors can be caused by a range of different causes. By having an understanding of what causes sampling error, we can take measures to minimize it.
The following is a list of the five most common types of sampling errors:
Sample frame error occurs when the sample is selected from the wrong population data. Therefore, in such cases, the sample frame does not represent the population of interest from which the researcher thinks they are sampling. This error generally includes targeting the wrong population segments or completely missing out on certain demographics within the correct segments.
This error occurs when participants themselves opt to be a part of the study, and therefore only those who are interested participate in the survey. If researchers overlook respondents who didn’t initially respond, the outcome of the study will not be reflective of the target market. If instead, the researcher decides to follow up with the respondents that didn’t initially participate in the survey, the outcome is very likely to change.
This is a type of sample design issue that is caused when a researcher fails to clearly outline who they want to survey and therefore does not have a clear idea of their target population. When you don’t have a clearly defined target population, you may end up selecting inappropriate elements to be a part of your sample group. This error is generally the result of a lack of knowledge on which group(s) would be of most use and relevance to the study.
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Non-response errors occur from the failure to obtain responses from all units in the selected sample group. The decrease in the sample size and amount of information collected will result in a larger standard error. Additionally, a bias is introduced at the risk of non-respondents differing from the respondents within the selected sample. Many reasons could cause this, for example, a percentage of the sample group may not use the channel through which the survey was conducted. The extent of non-response error can be checked by using follow-up surveys through additional channels to obtain responses from those respondents who didn’t initially respond to the survey.
Sampling errors occur when there is a lack of representativeness of the target population in the sample group. This is generally the result of poor sample designing. Therefore, this error can be minimized or eliminated through careful sample designing and by ensuring the sample size is large enough to reflect the entire population.
To gain a deeper understanding of sampling error, let’s take a look at a real-life example where a study had a large sampling error. We will also take a look at what caused this sampling error.
In the 1936 presidential election, Alfred Landon, the Republican governor of Kansas was pitted against the incumbent President, Franklin D. Roosevelt. At the time, Literary Digest was one of the most respected magazines and had accurately predicted the winners of multiple presidential elections within the previous decades. For this election, Literary Digest conducted a poll about the election, and with the data collected, they predicted that Landon would win the election with 57% of the votes while Roosevelt will lose with 43%.
The actual outcome of the election was jarringly different, with 62% of the votes going to Roosevelt and 38% going to Landon.
In this case, the sampling error was a shocking 19% even though this was one of the largest and most expensive polls conducted by Literary Digest and had a sample size of around 2.4 million people.
This large sampling error was caused specifically due to sampling frame error, as the sample frame was from telephone directories and car registrations. However, at the time, many Americans did not own cars and phones and the ones who did were largely Republicans. For this reason, the results wrongly predicted a Republican Victory.
The margin of error that is seen in survey results is an estimate of sampling error. The following formula can be used to calculate your sampling error:
Sampling Error= Z x (σ/n)
Z = Z score value based on the confidence interval (approx=1.96)
σ = Population standard deviation
n = Size of the sample
It is important to note that as this value is simply an estimate, there is a small chance (5% or less) that the margin of error is more than what is stated in the report.
[Related Read: How to Ensure your Survey Delivers Better Result]
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There are many different measures that can be taken to reduce the 5 types of sampling error.
Let’s explore a few of the most effective ways to do so:
When you select a larger sample size, your sample size gets closer to the actual population size. This makes the sample more representative of your target population and reduces the margin of error.
You can reduce your sampling error by improving your sample design and accounting for the different sub-populations within your target population. For example, if a specific demographic makes up 40% of your target population, then you should ensure that 40% of your sample group’s population is also made up of this demographic.
This can be done by using a type of probability sampling known as stratified random sampling. In this method of sampling, a population is first divided into homogeneous sub-groups known as strata before simple random sampling is used to select elements from each stratum. This ensures that the sample group has a similar composition to that of the target population, and is, therefore, more representative of it.
Before you select a sample, it is integral that you have a thorough understanding of your target population and its demographic mix. Study your target population well so that you can clearly and accurately outline who makes up your target population so that this subpopulation can be targeted effectively.
To gain a more comprehensive understanding of sampling error, watch this video by Elon University’s Political Science Professor Kenneth Fernandez where he defines sampling error and how to reduce it:
Sampling error is the arch nemesis of a research. It ruins the credibility of your research outcomes and leads to wasted effort. Thankfully, there are many ways to control and prevent these sampling error as mentioned in the article.
Stay cautious of these types of sampling errors to avoid them from sneaking into your research.
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Sampling error occurs when the sample group employed in a study is not representative of the entire target population.
Let’s consider the following example of sampling error;
you want to conduct a study about kid’s shoes. Although children use these shoes and have an influence on the purchasing decision, their parents are ultimately the ones who make the final purchase. In such cases, it’s hard to discern whose opinions matter more and therefore who must be surveyed. This can cause a common kind of sampling error known as a population specification error.
Sampling error is generally caused by the following market research errors:
Some of the most common sampling errors are sample frame errors, selection errors, population specification errors, and non-response errors.