Welcome to the third video in SPSS for Beginners from the RStats Institute
at Missouri State University. So far, we've learned how to create variables in
SPSS, enter data, do a frequency count, and calculate measures of central tendency,
and measures of variability. Collectively, these are called
"Descriptive Statistics," because they describe what the set of numbers looks
like. The mean tells us what is the average height, and the standard
deviation tells us how spread out the heights are around that average. We are
now going to learn a little bit more about descriptive statistics and how to
convert them to z-scores.
Continuing with the data set that we created in the first video, we're going
to get some more descriptive statistics. Go to Analyze -> Descriptive Statistics and
choose Descriptives. When this window pops up, move over all of the variables
except for the random ID number. Now click on Options. Here we are presented
with all kinds of options for descriptive statistics. Some - like mean
and standard deviation - are already checked by default.
Others - like Range or Sum - are available to check, if we want them.
I think that the default settings are good, so let's just continue and click OK.
Here in the output, we see some useful information. For instance, we see the
number of valid scores for each variable. We also see the Valid N (listwise), which
is the number of complete cases with no missing data. For gender, we see that the
minimum is 1; the maximum is 2. That is kind-of-useful, at least for
checking that we do not have any data entry errors, but the mean and standard
deviation for gender is pointless. The average of 1.58 for male and female
does not really tell us anything; it just hints that there were a few more females
than males. On the other hand, the mean and standard deviation for height and
weight can be very useful. For example, the average weight was 133 pounds. The
standard deviation was 13.4 pounds, which tells us that about
two-thirds of our participants are going to be between 13.4 pounds
heavier and 13.4 pounds lighter, than the mean of 133 pounds.
Let me show you one more way to get descriptive statistics. This is going to
give you even more detail about each variable, and more options for plotting
and statistics. Let me show you how. Go to Analyze -> Descriptive Statistics -> Explore.
I'm going to do this twice; the first time we're going to focus just on the scale
variables. Move height and weight into the dependent list box. Now click on
Statistics. Let's really push it this time: click on Outliers and Percentiles,
and then Continue. Now click on Plots and choose Histogram.
We are really going all out, so click Continue and OK. Look at all of this
data! We have every kind of descriptive statistic that you could dream of. We
even have a special box for Percentiles. We have another one that would identify
if we have any outliers or extreme values. And there's our old friend the
histogram. Plus, a small stem-and-leaf plot.
Plus a new graph called a box plot. And those are for height only. We have a
second set of graphs for weight. So, you can see that we get a lot of information
here. But wait...there's more! Let's split all of this by gender.
Go to Analyze -> Descriptive Statistics -> Explore. We're going to keep the same
settings that we had previously. Simply move gender into the factor list,
and then click OK. Now we see the same descriptive statistics have now been
calculated separately for males and females. Remember this splitting
would work similarly if we had three groups, or four, or more. Notice that we
have separate histograms for males and females, separate stem-and-leaf plots, and
the box plots are now side-by-side, so that we can do comparisons. Knowing about
these options for descriptive statistics can help us to visualize our data,
depending upon the level of information that we need. If you need only the basics,
use the Descriptives command. If you want flexibility to choose exactly what
output you get, use Frequencies. And if you want to know the exquisite details
or to split the analysis by a categorical variable, use Explore.
But there is something else that I want to show you about these variables. Let's run
one more analysis and I will show you how
standardized values. Go to Analyze -> Descriptive Statistics -> Descriptives
First, I want to move gender out. We don't need that. Notice that there's a
little box down here that says "Save standardized values as variables." I'm
going to check that, and then click OK. In the output window, we see exactly the
same table that we had last time. But if you go back to your spreadsheet, you'll
notice two new variables. They are called "Z height" and "Z weight." You know what
those are? Those are the z-scores. SPSS converts each height measurement into a
standardized score that tells us how many standard deviation units this score
is away from the mean. Negative z-scores mean that a raw score is below average.
A positive z-score means that it is above average. So, that is a quick and easy way
to get z-scores in SPSS. This was just a quick introduction to z-scores. When
you are ready to learn more, check out these other videos from RStats
Institute and learn how to calculate z-scores by hand or how to do a z-test
using SPSS. Next, we are going to explore correlation and learn how to
measure the relationship between two scale variables.