in this video I want to show you how to run a power analysis using G*power for
the correlation coefficient and what we're going to do in this video is focus
on determining the necessary sample size to obtain a certain amount of power
given an effect size. Okay and that's the most common way to do a power analysis
where I want to know how many people do I need in my study to have a good chance
or good power at getting a significant result okay so how many people do I need
to have a good chance at getting significance if there really is a true
correlation in the population. Let me show you what I mean here
so this is G*power and if I go under test family and I select exact then
notice what comes up correlation bivariate normal model that's what I
want for just the ordinary Pearson r correlation coefficient and for type of
power analysis I do want a priori and this is what I was saying compute
required sample size so how many people do we need if I provide the Alpha like
.05 the power level that I'm interested in and I'm going to go ahead
and put in eighty percent or 0.80 and then the effect size and for the
effect size for correlation the typical or standard effect sizes as proposed by
Jacob Cohen where .10 for small .3 for medium and .5 for
large. OK so let's move this I'm going to run a two-tailed test which allows
for either a significant positive or a significant negative correlation and
then here is what is my correlation in the population and this I have to just
estimate it would be best to estimate it based on previous research I'm going to
go ahead and show you the sample size required for small, medium, and large
effect sizes. Okay here this is our alpha level so we'll use the standard at
.05 and then here is power now this to me is way too high in reality we
don't try and run power with a 95% chance of getting
significant our power of 0.95 the highest I usually see is .80
so I'm going to go ahead and put that in and we are testing against a
null hypothesis of zero correlation which is our most commonly used
hypothesis value for correlation by far so our first analysis we're going to run
is for a medium effect size on effect size of 0.3. Go ahead and click calculate
and here I can see the number of people required to obtain significance so here
it's 84 people are required if I really have a medium effect in the population
if I go ahead and run the study if I sample 84 people I will have an 80%
chance of getting significance which also implies on the other side of the
coin a 20% chance of not getting significance. Now this assumes that the
true correlation in the population is 0.3 between the two variables. Okay let's
try this for a large effect size and you can see a pattern here.
If I go to 0.5 with all these same conditions previously for a medium
effect size my sample size required was 84 now it drops all the way to 29 which
is quite doable in most research that I see 29 people is not really asking
that much in some situations it is like perhaps brain research or what have you
but 29 in the work I do a lot of Survey Research and other studies that's not
usually a problem at all so if I have a large effect size I will only need 29
people to have an 80% power or an 80% chance of getting significance if the
true population value really is 0.5. Now let's finish up here with a small effect
size and here it gets a little bit less encouraging and I'll show you what I
mean so the same conditions everywhere else to calculate notice how many people
that we need if we have a small effect size if our true correlation in the
population is 0.1 between our two variables and I run a
study and I want 80% power then I'm going to need 782 people in order to
have an 80% chance of getting significance that's pretty tough; I don't
see a lot of studies with that many people so a lesson that you can pull
from this is that with small effect sizes in research it can be quite hard
to get significance with medium and large effect sizes there's a lot greater
chance of getting significant results in your studies but in conclusion this
power analysis can be done for any type of test: t-test, correlation, ANOVAs, and
many others, regression, and so on and you can obtain using these options here the
sample size required for a given effect size input okay that's it thanks for watching.