The findings of a research study
should be applicable to the population of
interest. The population of interest is the larger group to which study
results are generalized. For example, a group of researchers that intend
to study the effect of a treatment in the population of patients with type 2
diabetes should include a representative sample of research participants.
A representative sample is a sub-group of individuals who has similar
characteristics of the population of interest. The term
"sampling" refers to the process of selecting a representative
sample. Below is a Venn diagram that illustrates that a representative
sample is a subset of the population of interest (target population).
The
key to appropriate sampling is selecting a sample that would respond in the
study in the same manner as if the entire population was included in the
study. The process of sampling can be challenging for multiple
reasons. In human studies, populations are heterogeneous, meaning
variations in different variables exist between individuals. As an example, previous
research studies have investigated the effect of exercise training on
hemoglobin A1c (glycated hemoglobin) in patients with type 2 diabetes. (https://www.ncbi.nlm.nih.gov/pubmed/21098771)
Participants in such a study should have a diagnosis of type 2 diabetes and not
type 1 diabetes, since the target population of study is people with type 2
diabetes.
Sampling bias is defined as bias that
occurs when individuals are selected to serve as a representative sample of the
study population, however the selected individuals have different
characteristics from the study population. One type of sampling bias
occurs when a researcher unknowing
and unintentionally selects participants with different
characteristics from the study population. Another type of sampling bias
occurs when a researcher knowing
and intentionally selects participants with different
characteristics from the study population. Why might a researcher
knowing and intentionally introduce sampling bias? An investigator may
select patients with knee osteoarthritis and consciously choose study
participants who are most likely going to have a positive response to the
treatment. Certainly, intentional sampling bias is a concern.
A
method for selecting a representative sample is using inclusion and exclusion
criteria. Inclusion
criteria are a set of predefined characteristics used to
identify participants who will be included in a research study. Exclusion criteria are
a set of predefined standards that is used to identify individuals who
will not be included or will have to withdraw from a research study after being
included. Proper selection of inclusion and exclusion criteria will
minimize sampling bias, improve external and internal validity of the study,
minimize ethical concerns, help to ensure the homogeneity of the sample, and
reduce confounding. Click on the following link for an example of a study
that describes inclusion and exclusion criteria.
Sampling
procedures can be classified as either probability or non-probability methods.
Probability sampling methods are based on random selection. Random selection
means that every person who meets the criteria for being included in a study
has an equal chance or probability of being chosen. In statistical
theory, random selection also means that every person who meets the criteria
for study inclusion will have an equal probability of having the
characteristics of the target population. Thus, random selection should
eliminate bias in sampling and the sample should be representative of the
target population. One should consider that random selection is based on
probability and therefore does not guarantee that the sample will be a true
representation of the target population due to the possibility that the sample
and target population will have different characteristics.
For
a description of sampling techniques, click on the following link. https://towardsdatascience.com/sampling-techniques-a4e34111d808
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