We are continuing with our exploration
of scale variables. I've already showed
you how to get histograms, stem-and-leaf
plots, P-P plots, and box plots using
various descriptive statistics commands.
We are going to dive deeper into
exploring scale level data with charts
that you can build in the SPSS Chart Builder.
IBM SPSS Statistics software gives you
multiple ways to create and edit charts.
As we've already seen, you can edit and
create charts in the Frequencies dialog box.
For this example, we're going to use
the Chart Builder to create a more
complex chart. Last time, we used the
Means command to create a bar chart for
average income for different levels of
job satisfaction. Now, we will build the
same chart using the Chart Builder.
From the menus, choose Graphs -> Chart Builder
The Chart Builder is a dialog box with
interactive canvas that allows you to
preview how the chart will look when you
create it. Now, the chart that you see
will not look exactly like the one that
gets built, but it's a reasonable
approximation. This is important to note
if you are working in a classroom
setting. Although you are using exactly
the same variables, your canvas will not
look like the canvas of the person
working next to you. Your final graphs,
however, WILL be the same. In the Gallery,
we see the variety of charts that are
available. Bar charts are typically
selected by default. We see that there
are eight types of charts listed in the
category of bar charts. The icons give
you information about what the chart
will look like. If you need to know the
name of a particular chart, hover your
cursor over the icon to see its name.
Drag the icon for "simple bar chart" into
the Canvas. The Chart Builder will show
you a preview of what the chart might
look like, on the canvas. The preview
chart, however, is not ready to print and
the OK button is still unavailable.
We need to put variables into this graph.
Both the x axis and the y axis have drop
zones in which you can place variables.
You do not necessarily need a variable
for both axes, as we will see in a moment.
To create a bar chart,
we want to put our categories on the
x-axis, and the y-axis will show us how
much of something is in that category.
We're going to use job satisfaction on
the x-axis. This variable was measured on
a five-point Likert scale, which runs
from very dissatisfied to very satisfied.
For our purposes, we want to treat job
satisfaction as a categorical variable.
As you can see by the ruler icon,
job satisfaction has already been classified
as a scale variable. No problem. We can
change that here. Right-click on
job satisfaction in the variables list, and
choose "Ordinal." Job satisfaction is an
ordered category. Being "very dissatisfied"
is lower in this variable than being
"very satisfied." Now drag job satisfaction
into the x-axis drop zone.Tthe y-axis is
automatically set to the "Count" statistic.
This will tell us how many people are in
each of the five categories of job
satisfaction. You will also notice that
the OK button is now available, so click OK.
In the output window, we see the
number of people in each category.
The counts look fairly similar across
the categories. There is no real pattern.
Well, that's good, as far as it goes, but
honestly, we could have done the same
thing in the Frequencies dialog box.
What would be more interesting to know is the
average income for each category of job
satisfaction. Return to the Chart Builder.
As with previous analyses, our variables
are still in the Chart Builder, just as
we left them. This time, drag income from
the variables list to the y-axis drop zone.
We already have a categorical
variable on the x-axis. When we add a
scale variable to the y-axis,
the dropzone defaults to the "Mean" statistic.
Click OK. We can now see that
income is strongly related to job
satisfaction: those who are highly
satisfied, have a much higher mean income
than those who are highly dissatisfied.
This is a much more useful chart.
But there are still plenty of things we
could do to improve it. Go back to the
Chart Builder. We have a great many
options available to us in the element
properties window to improve on this chart.
Now the Element Properties window should
be open by default, but if it is not you
can open it by clicking here. The Element
Properties window allows you to change
the properties of various chart elements.
We can change the type of bars in a bar
chart, or the axes on the chart. We can
change elements for individual bars.
We can see that "Bar 1" is selected as being
available to edit. Bar 1 refers to the
first type of bar in the category, so in
this case, these are all part of Bar 1.
The Statistics drop down menu shows us
which statistics are available. Typically,
the same statistics will be available
for every type of chart. We could choose
median income, or maximum, or even sum.
This can be very useful in certain cases
where you want to add up totals within a
category. Then we have some other
options: we could display a "percentage
greater than" a given criteria, such as a
poverty line. Or we can work within
ranges. We can report on the variability
or the standard deviation. If you choose
one of the options with a question mark,
you get a new button called
"Set Parameters..." Here, you can set a value that
makes sense in your data. Now, we however,
are just going to leave it set for the
Mean. Another important feature in the
Element Properties window is the display
of error bars. Error bars give us
confidence
intervals around the mean, but they can
also be set to display the standard
error, or standard deviation. Sometimes
you want to use two standard deviations
as your comparison for whether two means
differ. You can set that here. Whenever
you make changes in the Element
Properties window, it is very important
that you "apply" those changes. If you
don't, they will not show up. So click "Apply".
Notice the changes in the canvas.
We can also edit the x-axis and the
y-axis here. Click on x-axis. Here, the
axis label is "job satisfaction"; perhaps
we would want to change that to
"Job satisfaction category". If our categories
were wrongly ordered, we could fix that
by changing their direction here, or even
change it on a granular level by
reordering answer options. Be sure to
click Apply. For the y-axis, the label is
"mean household income in thousands".
We could change that to "average income".
We could also rescale the data to present
the logarithmic mean, if you are into
that sort of thing. Again, click Apply.
Click on Footnote 1. We can make any
adjustments that we like. "Error bars show
a 95% confidence interval". Apply.
Those are some of the changes that we can make
with the Element Properties. We can also
add titles and footnotes to the chart.
Select the "Titles/Footnotes" tab.
We want to add a title. The title
appears in the canvas with a label "T 1".
In the Element Properties window select
Title 1 in the element properties list.
In the content text box, type "Income by
job satisfaction".
This is the text of the title that will
display. Click "Apply" to save the text.
You will not see the text displayed in the
canvas, but it will be on your finished
chart. Click OK to build the bar chart.
The bar chart shows us that participants
who are more satisfied with their jobs
tend to have higher household incomes.
We could also interpret that as: people who
have higher incomes like their jobs more.
Although this chart is useful just the
way that it is, we can improve upon its
appearance. For example, we could clean it
up to match APA style. To edit the chart,
double-click anywhere on the bar chart.
This will open the Chart Editor and the
accompanying properties window. APA style
requires that the background be white
and that we remove the box around the
entire chart .Click on the gray
background in the properties window.
Fill and Border should now be selected.
The color of the fill and the border can be
changed here. We want neither of them.
Click to change the fill to "None", and the
border to "None." The red slash means "none".
Click Appl. Now, that is a great start!
Bu,t we really need to do something about
those ugly, honey-mustard-colored bars.
Double-click on any one of the bars.
APA style calls for grayscale, rather than
color. We will change the bar color to a
medium grey. Click Apply.
We already know that the income is in
U.S. dollars. We could add a dollar sign
to each of these categories. Click on the
numbers, in the y axis, in the properties
window. Click on the Number Format tab.
For "Leading Characters" type a dollar
sign ($). Change the decimals to zero.
For trailing characters, type a capital "K".
This trailing characters box is also
where you could apply a % to percentages,
if that was what your values were
displaying. Click Apply. I can also make
changes to footnotes directly in the
Chart Editor. For instance, I know that
the comment about the error bars should
be included in a note beneath the table
figure in APA style, so I will remove it here.
I will also remove the title
because I think that should be included
in an APA style title beneath the table,
later on. And now that I think about it,
I think that I just want to label the
x-axis as "Job Satisfaction", After all.
I don't need the word "Category." There is no
Apply Button when you edit directly in
the Chart Editor. To save your changes,
just close the window. To get this chart
ready to use in a paper or on a poster,
I will export it to Microsoft Word.
Although you can create charts for data
screening and various descriptive
statistics commands, The Chart Builder
gives you dramatically more control over
the appearance of your chart or figure.
Regardless of what type of scale data
you have, you should explore your data
before using it, and you should strive to
display your data honestly, so that we
can all learn something valuable from them.