now let's look at the situation where our two primary variables of interest
are continuous variables and we would like to explore relationship between the
two while we can add further categorical variables as additional dimension using
the teaching evaluation data we ask this question does age affect teaching
evaluations and we add two additional dimensions which are gender & tenure so
our data set consists of age and tenure these are the two primary variables of
interest that happen to be continuous and we add two dimensions ie gender &
tenure and there's these are the two categorical variables so age is on the
x-axis and teaching evaluation score is on the y-axis and the green color
circles represent males and the blue color circles represent females the
bottom panel is for tenured professors and the top panel is for untenured
instructors just by looking at the graphic we cannot make out any trend so
we need to add additional details here you see that I have removed the tenure
dimensions and I've kept the gender dimension the bottom panel is male the
top panel is female I change the circles from hollow circles to fill circles
gray-colored filled circles and more importantly I have added lowest lines
which are sort of regression type lines to see that for female instructors we
see sort of a downward sloping curve of teaching evaluations as age increases
but we don't see the same declining trend for male the sort of them the
rising trend with age then it sort of levels off and then eventually it
declines in this particular graphic we have the same type of information but we
have changed the age variable from being a continuous variable to an ordinal type
of variable where age is now in different brackets less than 28 years of
age 29 to 39 years of age 40 to 51 52 to 62 and
63 plus in addition the color of the circles is not representing males or
females but in fact it is representing two different variables the blue color
is the average teaching evaluations by age and the green color circles are the
normalized beauty score you could see that the normalized beauty score
prepared to decline with age and the teaching evaluations do not have the
same downward sloping impression here since we did not have any instructors
under the age of 28 we have a redundant category in the in the graphic if you
were to be using scripts which is an option available with SPSS we can
actually remove the first category which I have done here and redrawn the same
graphics and now you see here that the less than 28 years of age category has
been removed from the graph and there is no redundant information in the graphic
in SPSS we click on graph chart builder and select scatter second template draw
it to the canvas and now we will populate this template we put the age on
the x-axis teaching evaluations score on the y-axis the coloring is set by gender
and we add the additional dimension of tenure to the panel and click OK and
here's the resulting graphic to add the regression line we double click on the
chart and then click on regression line and pick Loess option and deselect
attached label to lined and click on apply and close the editor and we have
the lines drawn we can change the color by double clicking on the line to remove
tenured we go back to chart builder dialog and remove the row and panel
variable and remove the grouping stacking variable and instead select the
gender as the panel and click OK and now we double click on it to edit this
graphic we select first these circles and click on fill and select grey color
and for border a darker shade of grey see apply and then click on regression
line click on Loess deselect attach label to line click apply to change the
color of the line we double click on line and select color and select a
different color see apply close the dialog box and and here's the scatter
plot between age of the instructors and teaching evaluations with a
regression type line fit to it now let us try to generate average teaching
evaluation and normalized beauty score in the same graphic and instead of using
a continuous age variable we will use a categorical variable for H so we click
on chart builder and we click on scatter dot plot and this is the rightmost option
which is summary point plot data that we want to be drag it up there and on the
x-axis we will put the variable age which is bin different ages bend into
different categories and we now select the two continuous variables being the
normalized beauty score and the teaching evaluations score and drag them
to the y axis and we get this notice from SPSS and we just say ok and it's
pretty much it we click OK and the output should compare notice this
redundant category at the end which you can remove using the script option