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P r a c t ic e M a t t e r s
R e s e a r c h 101
Sample size in quantitative
research
Sample size will affect the significance of your research.
By Susan B. Fowler, PhD, RN, CNRN, FAHA, and Valerie Lapp, PhD, RN, NEA-BC, CPN
Editor’s note: This is part o f the American Nurse Today
Research 101 series. To read other articles in the series,
visit americannursetoday.com/category/Researcbl01.
You’ve probably been asked (or have asked) the
question: How many subjects do I need for my re
search study? That’s your sample size—the number
of participants needed to achieve valid conclusions or
statistical significance in quantitative research. (Quali
tative research requires a somewhat different approach.
In this article, we’ll answer these questions about
sample size in quantitative research: Why does sample
size matter? How do I determine sample size? Which
sampling method should I use? What’s sampling bias?
Why does sample size matter?
When sample sizes are too small, you run the risk of
not gathering enough data to support your hypotheses
or expectations. The result may indicate that relation
ships between variables aren’t statistically significant
when, actually, they are. You also may be missing sub
jects who might give a different answer or perspective
to your survey or interview. Samples that are too large
may provide data that describe associations or relation
ships that are due merely to chance. Large samples al
so may waste time and money.
How do I determine sample size?
Larger sample sizes typically are more representative of
the population you’re studying, but only if you collect
data randomly and the population is heterogeneous.
Large samples also reduce the chance of outliers. How
ever, large samples are no guarantee of accuracy. If
your population of interest is homogenous, you may
need only a small sample.
If you’re studying subjects over longer periods of
time, as in longitudinal designs, you can expect subject
attrition. Know your population and how responsive
they may be to repeated questionnaires and interven
tions. Even if you’re not conducting a longitudinal study,
be realistic about how many people would agree to par
ticipate in research.
For a pilot study (a small-scale version of a bigger
study testing the efficacy of an intervention), you’d
usually need around 30 subjects, although that number
varies according to different experts.
No matter the type of study you’re conducting, take
into account time (yours and the subjects’), subject co
operation, and resources (such as statistical assistance,
access to subjects, managerial support for your study,
and co- or sub-investigators).
I
r
I
i
Power analysis
Power analysis is a robust way to determine sample size
and decrease the risk of type II errors (false-negative
conclusions that a finding was due to chance when ac
tually it was the result of the intervention). A power
analysis calculation includes a significance criterion, ef
fect size, and power to arrive at a sample size. The sig
nificance criterion is referred to as alpha and usually is
set at 0.05, which means that in 5 of 100 situations the
result would be due to chance and not the intervention.
Effect size (usually described as small, moderate, or large)
is the magnitude or strength of the relationship between
the variables you’re studying. In nursing, we often
propose that variables moderately affect one another
AmericanNurseToday.com May 2019 A merican Nurse Today 61
or are correlated. For
example, when on
cology nursing stud
ies about the effec
tiveness of symptom
management interven
tions were combined
and analyzed, a mod
erate to large effect
was found. Power (1-
beta) usually is set at
.80, which means that
there’s a 20% risk of
committing a type II
error. (See Feel the
power?)
Which sampling
method should
I use?
The sampling method
isn’t the same as the
sample. It’s the proce
dure you’ll use to select
study participants. We’ll
look at two sampling
methods: nonproba
bility and probability.
Nonprobability
sampling
Convenience sampling
and snowball sampling are common nonprobability
methods. Convenience samples consist of people who
are easily accessed and volunteer; however, the sample
may not be representative of the population of interest
in your study. Convenience sampling is considered the
weakest form of sampling.
With snowball sampling, participants are referred by
other participants. This method can be used when you
have difficulty locating participants. For example, when
interviewing undocumented immigrants, the researcher
gains the trust of a few participants and relies on them
to identify other undocumented immigrants who might
participate.
Probability sampling
With probability sampling, everyone in an identified
population has an equal chance of being in the sam
ple. You can use a variety of approaches, including
simple random, stratified random, multistage cluster,
and systematic random sampling. For example, system
atic random sampling of patients on a medical-surgical
floor for an intervention study may include selecting
every sixth room number. (Visit bit.ly/2FZLzYX to learn
more about types of
probability sampling.)
What’s sampling
bias?
Sampling bias can
occur w hen a partic
ular overrepresen
tation or underrep
resentation of the
population occurs.
For example, if a re
searcher wants to
study which method
of education is more
effective by gender
in reducing hospital
readmissions, the
num ber of men and
wom en should be
evenly distributed.
Bias occurs when the
researcher deliberate
ly omits or makes a
conscious decision to
exclude a participant
w ho’s had several re
admissions for exac
erbation of his heart
failure. Both omis
sions reflect bias and
may distort study re
sults and underm ine the validity of the study.
What are the practice implications?
As nurses becom e more involved in evidence-based
practice projects and research investigations, they’ll
need to understand key elements of research, such as
sample size, so they can critically appraise and gener
ate evidence. Remember that the “right” num ber of
subjects in your investigation impacts statistical and clin
ical significance support for your study findings. ★
Susan B. F o w le r is a n u rse s c ie n tis t a t O rla n d o H e a lth in O rla n d o , F lo rid a , m e n
t o r f a c u lt y a t T ho m as Edison S ta te U n iv e rs ity in T re n to n , N e w Jersey; a n d co n
t r ib u t in g fa c u lt y a t W a ld e n U n iv e rs ity in M in n e a p o lis , M in n e s o ta . V a le rie Lapp
is a p ro g ra m m a n a g e r fo r n u rs in g a n d sp e cia l p ro je c ts a n d M a g n e t® c o o rd in a to r
a t A rn o ld P a lm e r M e d ic a l C e n te r in O rla n d o , F lo rid a .
Selected references
Faber J, Fonseca LM. How sample size influences research outcomes.
Dental Press J Orthod. 2014; 19C4):27-P.
Polit DF, Beck CT. Nursing Research: Generating a n d Assessing Evi
dence fo r Nursing Practice. Philadelphia, PA: Wolters Kluwer; 2017.
Schmidt SAJ, Lo S, Hollestein LM. Research techniques made simple:
Sample size estimation and power calculation. / Invest Dermatol. 2018;
138(8):l678-82.
Feel the power
Betty, a pediatric nurse, w ants to study th e effect o f distraction on children’s
disco m fo rt d u rin g insertion o f an I.V. catheter before a procedure in th e ra
d io lo g y d epartm ent. She reaches o u t to experts at her fa cility to help her
determ ine how many subjects she needs fo r her study.
Researchers assist her using G*Power, a free o nline pow er analysis tool.
(V anderbilt U niversity also has a free pow er and sample size calculation
program th a t can be d o w n lo a d e d at b io sta t.m c.va n d e rb ilt.e d u /w iki/M a in /
PowerSampleSize.)
W ith significance set at 0.05, a m oderate effect size o f 0.3, and pow er at
.80, Betty w ill need 82 subjects (see below).
62 American Nurse Today Volume 14, Number 5 AmericanNurseToday.com
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