Last Updated 14 Feb 2023

Expert Sampling Is A Sampling Method In The Purposive Sampling Family

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There comes a time in all areas of study when significant research in the area hasn’t taken place yet, thus formal findings are minimal. One could conduct a large probability-based sample and attempt to find the most glaring trends or root causes of an issue, although that would most likely be extremely time-consuming and financially irresponsible. In cases like this, where more exploratory knowledge is needed before probability-based testing, it can be extremely helpful to call upon the help of experts. Expert sampling is a sampling technique where instead of sampling a group of individuals, a group of experts in related fields is sampled to try and draw generalizations about a relatively unknown field of study. This type of sampling is very cost-effective, although it does come with its own set of biases that must be recognized.

Expert sampling is considered to be a type of sampling method within the purposive sampling family because it is a non-probability-based model. That means the subjects selected for the study were not derived from a situation involving probability, like the convenience sampling of studying every third individually at a clinic or a simple random sample of 100 diseased individuals from every state in the USA, rather the experts were chosen for a specific reason by those that created the study. The type of experts elicited in a particular study can have a great effect on the results obtained, thus it is very important to pool experts with a variety of expertise and occupational industry. While expert sampling is an extremely cost-effective way to perform analysis in an emerging field of study, it doesn’t come without its drawbacks.

Unfortunately, because experts are human, they can introduce bias into the experiment. These cognitive biases have been documented rigorously in the field of psychology, although some of the larger biases that affect expert sampling are anchoring and availability. When an expert is first presented with an estimation question, they first latch onto a value (an anchor) and then move their estimate up or down, but generally do not move it enough to overcome that initial anchoring bias (PNAS). Availability bias comes about when an expert links the probability of an event with the ease at which examples or instances come to mind (PNAS). Lastly, humans aren’t built to make perfect statistical computations all the time.

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When experts are asked to give statistical-based answers, like estimating prevalence/rates of interest or confidence intervals, the answers may not be perfect. A common way to combat these biases is to find experts with similar, but different expertise and to find experts employed in different sectors. Though expert sampling does come with biases, those can be minimized and therefore utilizing expert opinions can lead to cheap and quick findings. An example of note for expert sampling was published in the Journal of Food Protection in 2007 titled Using Expert Elicitation To Link Foodborne Illnesses in the United States to Foods. Instead of sampling a large number of people that had contracted a foodborne illness all throughout the country, the researchers chose to question 42 experts. These experts were asked to estimate the distribution of foodborne illnesses associated with specific food categories and provide 90% confidence bounds for each of their estimates.

In order to combat the previously discussed biases, the researchers chose experts with educational backgrounds in medicine, food science, public health, microbiology, and veterinary medicine, as well as employment areas that were governmental, industry, and academia. Once data was collected from the experts the researchers applied simple descriptive statistical measures and tests to find that 15 food-illness combinations pair for 90% of foodborne illnesses, amongst other findings. In using expert sampling, the researchers were able to identify the foods that are most likely to cause food related illness and perhaps help enact public health policy change in order to lower foodborne illness.

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