Copyright © All rights are reserved by Joel Snell. 606
Journal of Clinical &
Community Medicine
Research Article
Research Sampling in Medicine
Joel Snell*
Kirkwood College, Cedar Rapids, Lowa, USA
Corresponding author: Joel Snell, Kirkwood College, Cedar Rapids, Lowa, USA Received: October 21, 2024 Published: October 25, 2024 DOI: 10.32474/JCCM.2024.05.000219 ISSN: 2690-5760 Abstract Convenience sampling is often overused in medical research due to its ease of application, despite its inherent weaknesses. Researchers frequently employ alternative strategies to mitigate these limitations. However, reliance on flawed sampling techniques can lead to misleading results, affecting how students and professionals understand research methodologies. This article explores various sampling methods and discusses how improvements can be made to enhance the validity and reliability of medical research. Introduction Medical research faces numerous challenges, particularly in adopting the most effective strategies to diagnose, treat, and improve patient outcomes. All areas of medicine, from clinical trials to pharmaceutical research, must rely on sound research methodologies to produce accurate, reliable results. One of the central issues is the type of sampling used to gather data. Choosing the correct sampling method is critical to ensuring that findings are valid and can be generalized to a broader population. Description of Sampling Methods Convenience Sampling Convenience sampling is commonly used in medical research due to its ease of application, but it presents significant limitations. Researchers often select participants who are easily accessible, such as students, clinic patients, or individuals at a specific location. While this saves time, labor, and funding, it often results in biased outcomes that do not accurately reflect the target population. The over-reliance on convenience sampling can undermine the validity of the research and weaken its findings [1]. Random Sampling In contrast to convenience sampling, random sampling involves selecting participants in a way that every individual in the target population has an equal chance of being chosen. This method is considered the gold standard in research because it minimizes bias and allows for generalization to the larger population. Random samples are often smaller but, when done correctly, they can yield more reliable results that reflect the characteristics of the larger group [2]. Types of Biased Samples Purposive Sampling Purposive sampling involves selecting participants based on specific criteria, such as a particular health condition. While this method is useful for targeting a specific population, it is not random and may introduce biases if not carefully controlled. For example, selecting participants who all share a common treatment experience may lead to results that don’t apply to a wider population [3]. Cross-sectional Sampling Cross-sectional sampling compares different groups at a single point in time. For instance, patients with and without nausea might be compared based on their responses to a questionnaire. While this method offers more structure than convenience sampling, it J Clin & Commun Med Volume 5 – Issue 4 Copyrights @ Joel Snell Citation: Joel Snell. Research Sampling in Medicine. J Clin & Commun Med 5(4)- 2024. JCCM.MS.ID.000219.
DOI: 10.32474/JCCM.2024.05.000219 607
still lacks randomness since participants are often selected based
on pre-existing conditions [4].
Longitudinal Sampling
Longitudinal sampling involves collecting data from
independent samples at multiple time points. While this method
allows researchers to observe changes over time, it can also
introduce bias if the sample is not random or if participants drop
out of the study. Unlike cross-sectional sampling, which provides a
snapshot, longitudinal studies track trends but may be limited by
their non-random design [5].
Panel Sampling
Panel sampling involves repeatedly collecting data from the
same group of participants. While this method offers valuable
insights into changes within a population, it risks bias due to
participant dropouts or external influences. If new participants
are added, the sample is no longer completely consistent, and
randomness may be compromised [6].
Adjusted and Specialized Sampling Methods
Adjusted Sampling
In adjusted sampling, researchers compare two groups that
differ in size or demographic characteristics. For example, a large
city and a small town may have different rates of a condition like
nausea. To account for these differences, researchers adjust the
sample size by applying a constant multiplier to ensure fairer
comparisons. While this improves the comparability of the results,
the samples remain non-random [7].
Meta-analysis Sampling
Meta-analysis involves combining data from multiple studies to
generate a larger sample size for analysis. While this method can
provide stronger statistical power, it relies on convenience because
the data comes from pre-existing studies. As such, the results may
not be as precise as data collected from a truly random sample [8].
Systematic Sampling
Systematic sampling selects participants at regular intervals,
such as every tenth patient in a clinic. While this approach ensures
a consistent selection process, it is still not truly random, as it may
miss important segments of the population [9].
Cluster Sampling
Cluster sampling is used when the population is geographically
dispersed. Researchers divide the population into clusters, such as
hospitals or regions, and randomly select some clusters for study. If
not carefully managed, this method can introduce bias and may not
represent the entire population [10].
Stratified Sampling
Stratified sampling divides the population into subgroups
based on characteristics like age, gender, or health status.
Researchers then randomly sample from each subgroup to ensure
representation. Although the samples are not always random,
this method allows for better control over population variability.
However, it is not random [11].
Importance of Randomness in Sampling
Random sampling is essential for ensuring that research
findings can be generalized to a larger population. A small
random sample can have a margin of error of approximately 6%,
but when comparing a medication to a placebo, this margin may
be acceptable if the difference is substantial. However, when the
difference is minimal, larger samples (e.g., 5,000 participants) are
necessary to achieve statistical significance. Even with precautions,
if the sample is not random, the results cannot be considered truly
representative [12]
Randomized Sampling
Randomized sampling remains the ideal in medical research,
allowing for the most accurate, unbiased results. Researchers must
carefully design their studies to ensure randomness, often using
techniques to simulate random assignment. True randomization
is difficult to achieve, but it is critical for producing reliable,
generalizable findings. Further, randomization is a statistical
process where a random mechanism may be employed [13].
Discussion
The reliance on convenience and non-random sampling in
medical research can be likened to a boat with leaks covered by
patches. While some methods come closer to randomization,
they often fall short. The inability to replicate many studies, as
highlighted by Ioannidis and others, raises concerns about the
reliability of published research. Statistical tests like chi-squared or
Fischer Corrected can help assess data, but randomness remains
the key to producing trustworthy results [14].
There is hope, however, for improving research methodologies.
As noted by Babalola, Nawanzu, and Serpa (2021), the current
phase of social science research is evolving, with new approaches
emerging to address these concerns [15].
Conclusion
A perfect random sample means that the total is randomly
chosen from a target population. Other strategies are used with
differing outcomes. Some are superior to others. However, they do
not mean that these alternatives can match a true random sample.
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  DOI: 10.32474/JCCM.2024.05.000219