Why should a researcher be wary of results of an analysis using a convenience sample?
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Sampling > Convenience Sampling (Accidental Sampling) Show
Watch the video for an overview of convenience sampling: Can’t see the video? Click here. Convenience sampling (also called accidental sampling or grab sampling) is where you include people who are easy to reach. For example, you could survey people from:
Convenience sampling is a type of non-probability sampling, which doesn’t include random selection of participants. The opposite is probability sampling, where participants are randomly selected, and each has an equal chance of being chosen. Why Use Convenience Sampling?Although convenience sampling is, like the name suggests—convenient—it runs a high risk that your sample will not represent the population. However, sometimes a convenience sample is the only way you can drum up participants. According to Barbara Sommer at UC Davis, it could be “…a matter of taking what you can get”. Convenience sampling does have its uses, especially when you need to conduct a study quickly or you are on a shoestring budget. It is also one of the only methods you can use when you can’t get a list of all the members of a population. For example, let’s say you were conducting a survey for a company who wanted to know what Walmart employees think of their wages. It’s unlikely you’ll be able to get a list of employees, so you may have to resort to standing outside of Walmart and grabbing whichever employees come out of the door (hence the name “grab sampling”). Advantages of Convenience Sampling
Disadvantages of Convenience SamplingThe method cuts out a large part of the population. As a result, this leads to several issues, including:
How to Analyze a Convenience SampleResults from these samples are easy to analyze but hard to replicate. While you can use any analysis method you like, you won’t be able to generalize your results to the larger population. Perhaps the biggest problem with convenience sampling is dependence. Dependent means that the sample items are all connected to each other in some way. This dependency interferes with statistical analysis. Most hypothesis tests (e.g. the t-test or chi-square test) and statistics (e.g. the standard error of measurement), have an underlying assumption of random selection, which you do not have. Perhaps most problematic is the fact that p-values produced for convenience samples can be very misleading. Recommendations for analysisThe biggest recommendation is simple: If possible, use probability sampling (Berk & Freedman, 2003). Other recommendations:
References:Berk R. A. (1991) “Toward a Methodology for Mere Mortals,” in P. V. Marsden (ed.),
Sociological Methodology, Volume 21, Washington, D. C.: The American Sociological Association. ---------------------------------------------------------------------------
 Need help with a homework or test question? With Chegg Study, you can get step-by-step solutions to your questions from an expert in the field. Your first 30 minutes with a Chegg tutor is free! Comments? Need to post a correction? Please Contact Us. What is the problem with using convenience samples for research?Because the generalizability of convenience samples is unclear, the estimates derived from convenience samples are often biased (i.e., sample estimates are not reflective of true effects among the target population because the sample poorly represents the target population).
What is a convenience sample and why is it bad?A convenience sample is one that is drawn from a source that is conveniently accessible to us. This sample, however, may not be representative of the population at large.
Why would a survey of a convenience sample not be trustworthy?There is always a chance that the randomly selected population may not accurately represent the population of interest, thus increasing the chances of bias.
What is the largest problem with samples of convenience?Disadvantages of Convenience Sampling
It is prone to several forms of bias, including selection bias, researcher bias, and sampling bias. All of these biases can distort your research outcomes significantly. Convenience sampling often leads to a high level of sampling error in a systematic investigation.
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