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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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