What are the implications of using a convenience sample for the way you interpret and use findings?
Convenience sampling belongs to the non-probability sampling family where the researcher selects a sample that is accessible for purposes of data collection. For instance, a researcher can choose to interview or poll people at a subway station because it provides an opportunity to access people in a central location. However, the researcher would miss out on crucial data from people driving private cars, cyclists, and those using taxis among other segments of the population using different means of transport.
It is clear that the major challenge of convenience sampling would be its inadequacy in generalizing the findings of the study to the entire population. Consequently, the findings can only be used to confidently make conclusions about the sample that was selected. However, it is important to observe that in some situations convenience sampling provides the only means of sampling even though it presents challenges in the applicability of the results and findings.
The convenience samples are the ones that are easy to find. In the case of survey studies or legal parlance, convenience samples would also mean willing participants.
The problem with convenience sampling lies in the fact that such samples are not representative of the population and any results available from these samples can, at best, be utilized for pilot-scale testing. The findings or implications are not valid for the entire population.
For example, to determine the time people spend on mobile phones, one can ask his immediate friends and family and get some initial ideas for theory validation (that, too, would be preliminary). However, to be able to imply results on people's mobile phone usage, one has to gather data from a much larger and diverse sample (representative, in other words).
Hence, convenience sampling, though simple and easy, provides results that are only applicable to pilot-testing and are of limited applicability.