Vol. 10, No. 10, October 2014
“Happy Trials to You”
Making Sense of Biostatistics: Types of Nonprobability Sampling
By Kathleen Mathieson
Probability sampling methods, such as simple random sampling, as discussed in the last
column, are the best way to reduce sampling bias and achieve a representative sample of
patients for a clinical study.1,2,3 These methods, however, require a comprehensive list of
eligible patients, which is rarely available in practice. Therefore, clinical studies must often
use methods of nonprobability sampling, in which subjects are chosen on a basis other than
random selection.4,5 This column describes several common types of nonprobability
sampling methods and factors that can reduce bias and increase generalizability. Regardless
of the sampling method used, assignment to treatment arms should still be random.
Convenience sampling entails selecting patients on the basis of their availability. For
example, patients may be selected from an easily accessible geographic region or specific
clinical site. Patients with certain characteristics are more likely to be chosen via
convenience sampling. For example, in community-based research, healthier patients are
often more accessible than those with more serious illness. This factor and others can lead
to over- or under-representation of certain population attributes that are being studied, and
therefore decrease the generalizability of study results.
The type of convenience sampling used most commonly in clinical studies is consecutive
sampling. In consecutive sampling, each consecutive eligible patient who presents for care
is approached for enrollment. Consecutive sampling provides some structure and thus
additional rigor in that it includes all patients who are accessible within the defined study
time period.4 The resulting sample is thus more likely to represent the target population
than one resulting from simple convenience sampling. When consecutive sampling is used,
researchers must ensure that the study period spans a long enough time period to avoid
potential bias. For example, in studies where seasonal variations may be important (e.g.,
allergy, depression, or arthritis trials), it is important for the study period to be long enough
to achieve a broad, generalizable sample of patients.6
Another way to enhance the rigor of convenience sampling is to use quota sampling to
obtain a certain number of participants with specific characteristics. For example, many
more women than men are diagnosed with lupus. If researchers wish to test the efficacy of
a treatment in both men and women, they may need to set quotas by sex to ensure
appropriate statistical power. Using consecutive sampling, researchers may enroll patients
of both sexes until the quota for women is filled, and then continue recruiting men until
their quota is filled.
Purposive sampling is a precise form of convenience sampling in which patients are handpicked based on certain criteria when a researcher is interested in a very specific group of
patients for reasons of feasibility or efficiency.6 Thus, studies with very specific eligibility
criteria are engaging in purposive sampling; it’s a matter of degree.
For studies of rare conditions or for difficult-to-reach populations, snowball sampling may be
an efficient and cost-effective method. This method involves identifying an initial individual
or set of individuals who meet study criteria, then using those individuals to find and contact
others for the study. In the case of a rare illness, a researcher might identify the initial
“seed” of patients via a support group and then use those patients to reach other eligible
patients.
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Convenience methods that can be expected to bias the sample should be avoided. For
example, recruiting patients that live near the research site might not introduce substantial
bias, but selecting patients based on their Medicare/Medicaid status might.
Like all study design decisions, choosing a sampling technique for clinical research involves
trade-offs. When choosing a technique, researchers must balance the objective of
generalizability with real-world factors, such as cost, efficiency, feasibility and practicality.
Although probability sampling is the gold standard for ensuring that the study sample
accurately represents the target population, in practice it is usually more of a yardstick
against which more feasible methods can be measured. Nonprobability sampling methods,
such as those described in this column, are frequently used in clinical research, and while
not as rigorous as probability methods, can provide efficient and cost-effective ways to
recruit patients.
References
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Mathieson, K. Making Sense of Biostatistics: Probability Versus Nonprobability Sampling.
Journal of Clinical Research Best Practices. 2014; 10(8). Available from
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Mathieson, K. Making Sense of Biostatistics: Types of Probability Sampling. Journal of
Clinical Research Best Practices. 2014; 10(9). Available from
https://firstclinical.com/journal
Kandola D, Banner D, O’Keefe-McCarthy S, Jassal D. Sampling Methods in
Cardiovascular Nursing Research: An Overview. Canadian Journal of Cardiovascular
Nursing. 2014; 24(3): 15-18.
Straus, SE, Richardson, WS, Glasziou P, Haynes, RB. Evidence-Based Medicine: How to
Practice and Teach EBM (Third Edition). New York, NY: Elsevier; 2005.
Acharya AS, Prakash A, Saxena P, Nigam A. Sampling: Why and How of It? Indian
Journal of Medical Specialties. 2013; 4(2): 330-333.
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Portney LG, Watkins MP. Foundations of Clinical Research: Applications to Practice
(Third Edition). Upper Saddle River, NJ: Pearson Prentice Hall; 2009.
Author
Kathleen Mathieson, PhD, CIP, is an Associate Professor in the Doctor of Health Sciences
Program at A.T. Still University (ATSU) in Mesa, Arizona. She is also a Vice-Chair of the
ATSU-Arizona Institutional Review Board and a Still Research Institute Scientist. Contact her
at 1.602.573.6547 or [email protected].
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