Sampling and Sample Size

INTRODUCTION: When population appears to be too large, there is need to employ sampling technique to arrive at a small sample size. Thus, sampling and sample size are essential to research.

“Can you study over 170 million Nigeria population in a research?” NO, “Can you content analysis all Nigeria newspapers, all editions in just a research?” NO and “Can you study all Nigerian Students in just a study?” NO

                                                                                                           Why?

Because the population is too much coupled with time to complete the study apart from fund and personnel to achieve the task. Therefore, you need to employ sampling technique to get part of the population to study and this part represent the population

 Is that clear? Let go…

Sampling is one of the frequently mentioned terms in research especially social science research. We often talk about this concept when we have large population or we want to conduct a survey research or even content analysis.

Sampling comes to mind when we felt or we notice that we cannot study a whole population e.g a study of Nigerian voters in the 2015 General Election or A study of Unilag students etc, therefore, there is need to narrow the study a manageable size. To narrow a study to a manageable size often requires the application of certain sampling technique to arrive at a small sample size.   

Sampling is the statistical process of selecting  a  subset  (called  a  “sample”)  of  a population  of  interest for  purposes  of  making  observations  and statistical  inferences  about that population (Bhattacherjee, 2012).

Ajayi (2009) defines sampling as a process of selecting a portion of the population for the purpose of generalizing the findings about the sample itself. It is the sample size that would be studied and not the population while the outcome of the result from the data collected from the sample size can be generalized on the population. However, the way we select our sample has major implications on the quality and quantity of the data that we collect. It also determines whether the sample has the characteristics that can make it represent the population. There are various types of sampling techniques:

1. Probability sampling
2. Non-probability sampling

These types shall be discussed in the subsequence post including the advantages of one over another and some of their forms too.

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