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Welcome to a brief tutorial
on how to calculate

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a correlation in JASP.

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So the example
that we have here--

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and you'll notice I already have
the correlation.sav data set

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opened--

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this is the example
from the textbook.

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We wanted to calculate a
correlation between someone's

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level of communication
apprehension

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and their heart rate change
when asked to give a speech.

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Now, Communication Apprehension
is an interval variable,

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and Heart Rate Change
is a ratio variable.

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So for those purposes, we can
definitely run your Pearson

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product-moment correlation.

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So to do that, we're going to
come up here to Regression.

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And we're going to
click on the one that

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says Correlation Matrix.

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So at this point,
all we have to do

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is take the variables that we
are interested in correlating

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with one another.

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And I'm going to
select the first one,

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and I'm going to click it over.

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And then I'm going to
select the second one,

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and I'm going to click it over.

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And you're going to
notice right off the bat

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that it's going to show up in
our relationships over here.

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So the first one is, again,
Communication Apprehension

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with Heart Rate Change.

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So Communication Apprehension
with Heart Rate Change,

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Pearson's r-value-- which you'll
notice, it's right there--

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is 0.906 or 0.91.

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And our p-value is
less than 0.001.

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So you'd notice that there's
these lines right here.

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And this is just
letting you know

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that Communication Apprehension,
if correlated with itself,

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would be a perfect score.

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And one of the nice
things that JASP does

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is it only reports one
side of this set of lines.

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And so instead of
reporting Heart Rate

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Change with Communication
Apprehension over here, which

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would be the same number,
it only reports it once,

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which does make it
easier to understand.

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So to help us
understand what that's

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going to look like
in a larger one,

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I'm going to come
over here, and I'm

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going to open up the
Textbook Data Shortened

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and look at a larger
correlation matrix.

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OK.

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So to run a larger correlation
matrix, what I want to do

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is I'm going to come up
here again to Regression.

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I'm going to go to
Correlation Matrix.

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And I'm going to scroll
down and find a few.

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And I'm going to make
this a little bit larger,

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but it's not going to let me.

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So I'm going to go ahead and
find the variables I know,

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like Big CA.

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We have an Assertiveness
and Responsiveness.

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And I'm going to come down here
to Big WTC or Big Willingness

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to Communicate.

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And again, one of the things
you'll notice right off

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the bat is that you have that
diagonal line of things that

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don't get reported,
and then you have

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a blank side, which is great.

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It makes life a lot easier
when it comes to reporting.

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So one of the things
that you will notice

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is that here we have
the very first one.

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It's Big CA with
Assertiveness, and it's

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a negative relationship.

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And it's, again, the
p-value is 0.001.

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The next one is Big CA
with Responsiveness.

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And you'll see that the
p-value is listed as 0.07.

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That is greater than 0.05.

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So that is a non-statistically
significant relationship.

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If you look right next
to it, Assertiveness

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with Responsiveness, it is
a very small relationship

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at 0.11.

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It's a minimal relationship.

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But then you have the actual
p-value, which is 0.004.

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So that would be what you
would actually report in SPSS,

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is p is equal to 0.004.

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So again, one of the
things to watch for

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is if they're greater
than 0.05 and if they

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are a different p-value.

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Other than that, it's
going to tell you exactly

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that p is less than 0.001.

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And that's how you can tell
when your relationships are

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statistically
significant in JASP.

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