Welcome to the seventh video in SPSS for Beginners from RStats Institute at
Missouri State University. In the last video, we learned how to compare
means when the same sample was measured twice. In this test, we are going to
measure one group (males) and then measure a second group (females) and then see if
the mean for males is statistically significantly different than the mean
for females. Two groups measured one time. Because each of the groups are
independent of each other, we will use an independent samples t-test. Using the
same data set that we created in the first video, let's look at the variables.
We need two groups. The variable "Gender" will work nicely for these groups. We
have males and females. We need to measure the groups on something that can
vary. We measured height and weight, so we can answer a question like: "is there a
significant difference in height between males and females." So let's do it. Go to
Analyze -> Compare Means -> Independent Samples t-Test. the first thing that we
need is the variable that we measured. So, we measured both height and weight. So
let's get crazy and examine both variables at the same time. We will move
both height and weight into the variables box. And, we want to compare
between groups, so we move gender into the grouping variables box. But notice
that okay is still not available? And we have these question marks? What else do
we need to do? SPSS is telling us that it does not know what
our coding scheme is. We need to tell SPSS what are the two groups. Now, you may
ask: "WHY do we need to tell SPSS about the groups? We only have two groups."
Yes, WE have two groups, but there may be times that you are using a categorical
variable coded for multiple groups, like freshman, sophomore, junior, senior. But we
only want to compare freshmen to seniors. So, ee have to be able to specify which
of the groups should be compared. Click on Define Groups. For Group 1, we
will assign "1", which is males; and for Group 2, we will assign "2" for females.
Now we have our two groups. Click Continue and you can now see that OK
is available. So we are ready to run this test. Click OK.
Just as before, we get one table with descriptive statistics. It tells the
sample size, the mean, the standard deviation, and the standard error of the
mean for each of our groups: males and females. Below that, we see the table
containing the inferential statistics. We can mostly focus on this middle part of
the table. We have the t-score, the degrees of freedom for our two groups,
and the p-value that corresponds to that t score at those degrees of freedom,
which we use to determine if there is a significant difference, or not. So I want
to mention that for each t-test there are two rows, each with different results
for the test. The top row is for equal variances assumed, and the bottom row is
for equal variances NOT assumed. You will learn all about what that means later.
For now it's okay to just stick with the top row.
Remember that there are
three ways to determine statistical significance. First,
we can compare our t-value to a critical value that we look up in a table called
Students t Table. So I did that, and for 8 degrees of freedom, the critical
value is 2.306. This will be the same critical value for both
of these t tests. For height, the t-value is 2.214, which is
smaller than our critical value of 2.306. But, the t-value for
weight is 3.413, which is larger than our critical value
of 2.306. When the t-value
exceeds the critical value, then
the means are different. We can also look at the probability value to see if it is
smaller than .05. The p value for height is .058, slightly
larger than .05, but the .009 for weight is
smaller than .05. And we can look at the confidence interval to see
if it crosses zero. For height, the lower value is negative and the upper value is
positive, so it crosses zero. Not different. For weight, both values are
positive, so it does not cross zero.
So what do you think? Were there any
significant differences for height or weight?
Height was NOT different, but
weight was different. We can check the actual means in the Descriptive
Statistics, for more clarity. Males weighed 142.8 pounds and
females weighed 123.2 on the average. So males weighed significantly
more than females, but their Heights were not statistically significantly
different. You know, I've been talking about "statistical significance" as if it
tells the whole story, but that is not actually correct. There is something else
that we should calculate in addition to the
p-value and the confidence interval. We should calculate the effect size. I have
another video in which I go into a great deal of explanation about effect sizes
and why they matter. For now, just know that an effect size is exactly what it
says on the tin: it is the size of the effect the difference between the means.
Check out the RStats Effect Size Calculator for t Tests, for a spreadsheet
that you can use to compute effect sizes directly from SPSS output. When you're
ready to do an independent samples t-test for real, check out these other
videos from RStats Institute that will teach you more about statistical
theory, setting up your t-test, interpreting the results, and writing up
your findings in APA style. Thanks for watching these videos about SPSS for
Beginners from RStats Institute at Missouri State University. We have one
more short video to wrap up this series. If you've liked these videos so far, be
sure to subscribe and have all of our videos available for your viewing when
you take your statistics course.