In this video we'll take a look at coefficient alpha and how to run that in
SPSS. Coefficient alpha is also known as Cronbach's alpha and it's one way to
estimate the reliability of an instrument. And really when we're using
coefficient alpha,
we're estimating the internal consistency reliability.
And in this example we're going to go ahead and take a look at a very commonly
used scale, the Satisfaction with Life Scale,
which was created by Diener et al.
And on the scale there's five questions and you can see those here,
"In most ways my life is close to my ideal," "The conditions of my life are
excellent," and so on.
And what happens here is the respondents read each question and they answer on the
following seven point Likert scale, where they answer strongly disagree, disagree,
slightly disagree, and so on. So they choose one of those responses and they
get a 1 if they answer strongly disagree, a 2 for disagree, and, for example, a 7 for
strongly agree.
All of you probably taken a scale before, a Likert scale,
many of you have taken several of them I'm sure.
So what we want to do once again is find the reliability in SPSS as measured by
coefficient alpha. And it's important to keep in mind if there's five different
questions and each person responds on a 1 to 7 scale, then the total possible score
for an individual could be as low as 5,
that is if they got a one on each question or strongly disagree, they
would have a score of 5, all the way to 35 if
they answered 7 on every single question.
So it's good to keep that in mind when you're running these analyses in SPSS
to make sure that you never see any responses that are outside of those
ranges.
If you do, like you saw 3 or you saw a 38,
that's an indication that something's wrong. Something's going on there and we
want to take a look at that, there's a miskeyed value, or what have you.
And in this example we have 80 respondents who took the scale and I've
already entered those into SPSS, in this hypothetical example. But the results are
fairly consistent with what we find in the literature using the scale.
So let's go ahead and take a look at that.
OK here I've got the data and we have five different variables, Satisfaction
with Life Scale item 1, item 2, 3, 4, and 5. And then as I said before we have the
responses for 80 total people.
Alright, so let's go ahead and run the analysis. So we want to go to Analyze,
and then Scale, and then select Reliability Analysis.
And then here we have our five items. I'm going to press the Shift key, select the
last one, and that grabs them all.
Move them over to the Items box.
Notice under Model the default is Alpha and that's what we want.
And then I'll go ahead and give it a label, I'll call it
Satisfaction
with Life Scale.
And then let's go to Statistics.
And then here under
Descriptives for, all go ahead and select all of these and we'll talk about that in a
minute. And then everything else I'll just leave as is.
Go ahead and click Continue and then click OK. And here we get our results and
you can see Case Processing Summary we had 80 valid cases,
zero were excluded, so that means everybody had meaningful values that
were used in the estimation of the reliability coefficient here.
Next table, Reliability Statistics, this is the one we really are looking for
first and foremost, although we'll discuss the other tables as well and
they are important, but this is where our alpha is at and it's given under
Cronbach's
Alpha. We have a reliability rounded to two decimal places of .88.
And that's really very good. The highest possible reliability coefficient is 1.0.
But 0.88 is really quite good.
The highest ones I see without being a massively developed intelligence test or
what have you
is .91, .92, .93, sometimes a little higher, but that's the absolute best I typically
see and .88 is right up there. So I would consider the reliability as
measured by Cronbach's alpha for the Satisfaction with Life Scale to be very
good, and that's actually what we find in practice when the scale is used as well.
Next let's look at the item statistics.