Sampling error causes variability among estimates derived from different samples when keeping the same sample size and design, and the same estimation method used. In simple sample designs, such as Simple Random Sampling, the sampling variance can be calculated directly using a formula. In this case, an estimate of the sampling variance can be calculated using methods such as Taylor linearization or resampling methods such as the jackknife and the bootstrap. Regardless of which method is used for variance estimation, it has to incorporate sample design properties such as stratification, clustering and multistage or multi-phase selection, if applicable.
Except using sampling variance to measure sampling error, other frequently used methods also exist, including standard error, coefficient of variation, margin of error and confidence interval. The standard error is the square root of the sampling variance. This measure is easier to interpret since it provides an indication of sampling error using the same scale as the estimate whereas the variance is based on squared differences. The coefficient of variation CV assesses the size of the standard error relative to the estimate of the characteristic being measured.
It is the ratio of the standard error of the estimate to the average value of the estimate itself. CV is very useful in comparing the precision of sample estimates, where their sizes or scale differ from one another. In this case, confidence interval is more appropriate to use. The confidence interval CI gives a range of values around the estimate that is likely to include the unknown population value with a given probability. This probability is the confidence level of the IC.
For a given estimate in a given sample, using a higher confidence level generates a wider CI, meaning a less precise CI. The margin of error is half the width of the CI. When a subset or sample of a population is collected o represent the entire population, there is an error in the data collection process and this error extends to the results realized.
Here are some important things you should know about sampling error;. It is important that selecting a sample for analysis does not automatically cause sampling error, rather, it is when the sample selected fails to represent the entire data population that sampling error occurs.
For instance, when samples are randomly selected or the selection is influenced by biases, a sampling error is bound to occur. Analysts can prevent sampling errors by selecting samples or subsets of an entire data population that efficiently represents the entire data.
For instance, if the samples selected are small and inadequate to represent the entire population, an analysts can increase the samples selected for adequate representation. This illustration below is helpful for a better understanding of how sampling errors occur; Company ABC airs a programme on a local channel for teenagers, this company wants to analyze the percentage of teenagers that watch the programme and picks even randomized samples or viewers between the hours of 10am and 5pm, sampling errors can occur due to this.
It might be the entire family, the mother, or the children. The mother might make the purchase decision, but the children influence her choice.
Sample Frame Error—A frame error occurs when the wrong sub-population is used to select a sample. A classic frame error occurred in the presidential election between Roosevelt and Landon. The sample frame was from car registrations and telephone directories. In , many Americans did not own cars or telephones, and those who did were largely Republicans.
The results wrongly predicted a Republican victory. Selection Error—This occurs when respondents self-select their participation in the study — only those that are interested respond. Selection error can be controlled by going extra lengths to get participation. A typical survey process includes initiating pre-survey contact requesting cooperation, actual surveying, and post-survey follow-up.
If a response is not received, a second survey request follows, and perhaps interviews using alternate modes such as telephone or person-to-person. Non-Response—Non-response errors occur when respondents are different than those who do not respond. This may occur because either the potential respondent was not contacted or they refused to respond.
The extent of this non-response error can be checked through follow-up surveys using alternate modes. Sampling Errors—These errors occur because of variation in the number or representativeness of the sample that responds. Sampling errors can be controlled by 1 careful sample designs, 2 large samples, and 3 multiple contacts to assure representative response.
Be sure to keep an eye out for these sampling errors so you can avoid them in your research. Request a live demo of the Qualtrics platform. Request Demo. Related resources. Checking is simple:. Enter your school-issued email address:. A university-issued account license will allow you to: Complete assignments more easily Export your data in multiple formats Activate multiple surveys and emails Access additional question types and tools.
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