The Challenges of Sampling

It is seldom that a researcher is able to study the entire population. When the population is huge, a subset of the population has to be chosen. This creates room for many kinds of errors. Many a times it happens that there occurs a discrepancy between the chosen sample of the study and the population in the case of a specific parameter. This kind of error is called a sampling error. There is no fault of the researcher here.

A step ahead in complexity is the systematic error.  This type of error is related to the difference that occurs in the sample and the population because if some systematic differences between the two and not random chance only. In this context the response rate problem has relevance and it focuses on the fact that some times the sample can also become self-selecting. And the   characteristics of the people who chose to participate in the study leaves an impact on the different variables of interest. Systematic sampling error of another kind would be the coverage error. It is an error that takes place from the  end of the researcher when he by mistake  contains or restricts the sampling frame to only a subset of  the population. This kind of error means in a simple language that there is a systematic variation in the sample chosen for study and the population for which the outcomes have to be generalised.

With some careful steps and initiatives taken from the end of the researcher, it is possible to reduce the errors. The researcher needs to carefully think about the population that has to be included and careful planning and consideration can help in reducing the chances of the errors to a great extent.  When the sample size is large as in the number of participants in the study are more, the chances of a sampling error are relatively lesser. When the problem is pertaining to response rate, there can be an active involvement from the side of the researcher to increase the response rate. One crucial concern that the researcher should take into consideration is to remove out or eliminate all those variables that he cannot control. It is near to impossible in research to completely be able to isolate the variables of interest but the close the researcher is in doing so, the better is the quality of the research.