Question

Suppose you are conducting a quantitative research study for a major car dealership in the United States with the objective to rate the importance of typical obstacles in consumers' car purchasing process (such as long wait times, complicated paperwork, aggressive salespeople, or insufficiently trained sales executives). Suppose you came to the conclusion that a random sample is not feasible or cost effective for this study. Which of the following nonprobability sampling designs (convenience sampling, judgment sampling, quota sampling, or snowball sampling) would you prefer for your study? And which of these four sampling design would be the least desirable? Please justify your assessment.

Answer 1

Among the non probability sampling methods(convenience sampling, judgment sampling, quota sampling, or snowball sampling),snowball sampling would be the most preferable for the study under consideration.

snowball sampling is a non probability sampling technique where existing study subjects recruit future subjects from among their acquaintances. Thus the sample group is said to grow like a rolling snowball.

This study is specifically for a US based car dealership.So given that a sample is taken for the study, collecting data from another individual who is related to the car dealer may be more efficient. This can be achieved more effectively by snowball sampling. Under the context, sampling would be easier given the subjects are interrelated. There is more chances for them to be related too. So, snowball sampling can be the best in this case.

The least recommendable among the four samplings would be Convenience sampling.

Convenience sampling (also known as grab sampling, accidental sampling, or opportunity sampling) is a type of non-probability sampling that involves the sample being drawn from that part of the population that is close to hand.

Clearly, convenience sampling would be the least recommendable as there is higher chances of bias to be present from the data collected from the samples as sample may not be able to represent the population effectively. This in turn will seriously affect the future analysis.