Welcome to the second video in SPSS for Beginners from RStats Institute
at Missouri State University. In our first video, we learn how to create
variables in SPSS. The next step is to add some data, and we're going to begin
in Data View. Here in Data View these are the same four variables that we created
in the first video. So now we can add some numbers. Pause the video and enter
these same numbers into your SPSS spreadsheet.
Now that we have numbers, it's important to understand just what these data
represent. The first column is a random identification number. It stands in for
the names of the participants and keeps our data anonymous. This second variable
is gender. And these last two columns represent the height and the weight for
each participant. Even after you've named a variable, it's possible to change the
variable names. Double-click on a variable name to change it. When you do,
you will be taken to Variable View, which is where you actually will make the
changes. We set the measure for each variable previously. The ID variable is
nominal because it stands for a number. It stands in for a participant's name. The
variable "gender" is also nominal, and we are going to code gender as 1 and 2,
for Male and Female. When a categorical variable has only two categories, we call
it "dichotomous" So the 1 and the 2 are categories. You can be in one category or
the other. You can't be in both; you can't be in neither. These last two columns
represent height and weight. Height and weight are both quantitative variables,
not categorical. They are measuring something. They both have fixed intervals
between the scores, and they both have a meaningful zero. Both height and weight
are set to Scale because they are both ratio level. Before we begin analyzing
these numbers, there is one other thing that we should do. For a variable like
gender - where we did not code the 1 and the 2 - we don't want to get confused with
who was male who was female; what number stood for what. And so we are going to
assign value labels for each level of this categorical variable.
Click on Values. I'm going to tell SPSS to represent all of the 1's as Male and
all of the 2's as Female. Of course, I could make 2 = Female or
0 = Female; really any number that I wanted to, depending on the coding.
The 1 does not mean that males are "first place." The 2 does not mean that females
are twice as good the number is only a placeholder; it does not indicate an order
or a quantity. So, now click OK. In fact, when I return to Data View, you can
see the numbers, but watch this: you see this button? Click it and you can toggle
between numbers and value labels. Let's leave this set with the value labels on.
It's just easier that way. Now we can look at our data. The height of our
participant was measured in inches, and we have values between 60 and 70 inches,
which is between five and six feet tall (1.5 to 1.8 meters). Weight was measured in
pounds. Of course it might be easier to see the range if we sorted these data.
Ctrl-click on Mac or right-click on PC and choose "Sort Ascending." All of these
participants were between 116 and 153 pounds. Just for illustration, I'm going to
pretend that there were two participants for whom we did not get their height or
their weight; both of them female. Notice that when the numbers are toggled on, all
that I need to do is type "2"; however, when the value labels are toggled on,
I need to double-click and select "Female." So now I think we're ready to analyze
these data. One of the simplest things that we can do is to count up how often
things occur. For example, we want to know how many males and females were in
our sample. We want their frequencies. This is easy enough to do in SPSS. We're
going to use the Analyze menu. Whenever you
run an analysis in SPSS, you use the Analyze menu. We can see that there are lots of
options, each with their own sub-menus and sub-sub-menus. The one that we want
is Analyze -> Descriptive Statistics -> Frequencies. This window pops up and you
will see lots of windows of this type in SPSS. All of the variables that we have
in our dataset are on the left, and the variables that we want to analyze go on
the right. You can select a variable for analysis by clicking on its name and
then clicking on this arrow between the boxes. Alternatively, you can also
drag-and-drop, and in some cases you can double click. Let me show you just how
easy it is to use SPSS: click OK. What we are seeing now is the output window, and
here is something very important to know about SPSS, especially compared with
other types of statistical software: SPSS will give you copious amounts of output,
often more than you really need, and you need to know how to interpret that
output. In SPSS, it is easy to run an analysis, but it takes some education to
learn how to interpret the output. First, we see a summary of the variables in the
box labeled "Statistics." We have 12 valid scores for gender with no missing data,
but for height, we only have scores for 10 people, with 2 missing values.
The valid sample size is the number of participants for whom we actually have
scores. This first frequency table is for gender. The total tells us that we have
12 valid scores. We see that there are 5 males and 7 females.
Notice the columns for "Percent" and "Valid Percent." They are exactly the same. They are the
same because we have no missing values for gender. This second frequency table
is for height. Remember that we have missing values for height for two of our
participants, so we see the valid total is 10. Two values are missing in the data
set - called system missing - and the total is 12. We see that the Percent column is
different than the Valid Percent column. The Percent column is calculated based
on the total sample size of 12; the Valid Percent is calculated on the valid
n of 10 people for whom we actually have data. I recommend reporting the valid
percent column unless you have a specific reason why you need to report
Percent. Well, this is a good start, but we can do better. Let's make some pictures
of our data. I am going to run another analysis and I want you to see that you
do not need to go back to the data set. You can run a new analysis from the
output window, as well. Just click on Analyze -> Descriptive Statistics ->
-> Frequencies. You can see that our previous analysis is still in the window.
We could clear it by clicking on this Reset button, but let's just continue
with these data. So this time, click on Charts, and then under Chart Type, click
on Bar Charts. Let's change Chart Values to Percentages. Click Continue, but
before you click OK, let's turn off the frequency tables
because we already have those. Now, click OK.
In the output window, we see that the chart for gender looks really good.
We have two distinct bars, one for male one for female, and we can estimate the
percentages of each. But when we look at the bar chart for height, the options
just don't look as good. We can definitely do better. Let's run
another analysis. Click on Analyze -> Descriptive Statistics -> Frequencies.
This time, click on Charts, and then under Chart Type, click on Histogram. Let's also
choose "Show normal curve on histogram." Notice that the chart values are now
gray, because we don't need them. Click continue, but before clicking OK, let's do
one more thing. Click on Statistics. Here we can choose other options like the
mean, the standard deviatio,n the minimum, and maximum. We could also get the
variance, the standard error of the mean, and the sum is good, too. As you see, we
can pick as many of these options as we would like. If we change our mind, we can
unselect them, too. Click continue and then OK. In the output
window, we see all of the statistics that we asked for. For example, the average
height was 65.8 inches. The tallest person? 70 inches tall. The shortest? 62
inches tall. If we added up all of their heights, they would total 658 inches.
But notice this first histogram for gender. It just doesn't look good, not like it
did with the bar chart. The bars are connected, but gender is supposed to be
discrete categories. We no longer see the labels for males and females. And the
normal curve makes absolutely no sense. On the other hand, the histogram for
height is much improved. The bars touch, indicating that the data are connected,
and the superimposed normal curve makes sense with these data. We can see that
the shape of the data match reasonably well with a normal distribution. The
important thing to learn here is that you should choose the statistics and the
graphs that are appropriate to your data. A nominal variable like gender should be
reported with frequencies and a bar chart. Scale variables like height should
be reported with a mean, standard deviation, and a histogram. We know that
the average height for all participants is 65.8 inches, but
what if we want to split that by males and females? Let me show you how. Click on
Analyze, but instead of Descriptive Statistics, choose Compare Means and this
first option, simply labeled, "Means." Here we have the options for dependent
variables. "Layers" refers to the independent variable, or categorical
variable. We did not really assign people to the condition called gender, so gender
would really be what is called a "quasi independent variable." Still, we will use
gender as our independent variable. We want to examine differences in height, so
height will be the dependent variable. Now click OK. We can see the means and
the standard deviations from males and females separately and together. There
are 5 each for males and females, 10 total. We see that males were a few
inches taller on the average than females.
The total mean and standard deviation here are the same as the values that we
got earlier using the Frequencies command.
Overall frequency counts, charts, and descriptive statistics are a great way
to take a peek at your data and see just what you have. It's a good idea to do
this before running any other kind of analysis. In our next video, we will
look a little bit more at these descriptive statistics and how to
convert raw scores into z-scores. I'll see you then.