Transcript final_Draft_RCSI_Sample Size poster_2015 IC+HN+HM edits.pptx

How Good is your Estimated Sample Size?
Getting the Essential Parameters into Focus
Dr. Helen Malone – Visiting Research Fellow (Nursing) Trinity College, Dublin.
Dr. Honor Nicholl – Assistant Professor (Nursing) Trinity College, Dublin.
Professor Imelda Coyne – Professor (Nursing) Trinity College, Dublin.
Background:
This poster presentation is aimed at the novice
nursing and midwifery researcher. Findings from
healthcare research have the potential to be
translated into clinical practice. As such, sample
size determination warrants adequate time and
attention in the planning stage of a research
study. Too small a sample size may have
insufficient power to statistically detect a specified
meaningful effect. Too large a sample size may
be statistically unnecessary, and unethical.
Aims and Objectives
The aim of this literature review is to highlight the
importance of inputting appropriate parameter
values into sample size calculations or software
programs.
How can service users contribute
to healthcare research?
Data Sources
A review of the literature included data sources
CINAHL, Google Scholar and Statistical texts
1974- 2014.
The first three parameters are chosen by the
researcher. Consequently sample size estimation
should be considered to be an estimate only.
Increasing Significance level, (i.e. 10% to 5%),
power (i.e. 80% to 90%) and Standard deviation
increases sample size. Decreasing Significance
level, Power and Standard deviation decreases
the estimated sample size.
In contrast, increasing Effect size decreases the
estimated sample size and increasing the Effect
size decreases the estimated sample size.
A small adjustment of Effect size can result in
large changes in estimated sample size.
G*Power where appropriate allows for the input of
either the standardised or the absolute effect size.
Absolute effect size for a mean is │µ1 - µ2│
Absolute effect size for a proportion is │p1 - p2│
The absolute effect size changes to the
standardised effect size when divided by the
standard deviation i.e. │µ 1 – µ2 / σ for means
and │p1 - p2│/ σ for proportions.
Key Findings
Conclusions
Four parameter values required for basic sample
size estimation are: Significance level, Power,
Effect size and Standard deviation.
Carefully estimated sample size estimates are
important because:
(1)Conventional Significance level is typically 5%.
(2)Conventional Power chosen is typically 80%.
(3)Effect size is the clinical meaningful difference
which can justify a change in clinical practice
i.e. a clinically important difference.
(4)Standard deviation is usually unknown and
can be estimated by utilising the standard
deviation from previously analogous studies
or a representative and appropriately sized
pilot study.
The research informing Nursing and Midwifery
practice needs to be evidenced-based.
Underpinning the concept of evidence-based
practice is the application of research findings that
are statistically sound.
Further information from:
[email protected]
[email protected]