let us begin with the fundamentals of visualization what you see on the right
side is a pictograph or a bar chart prepared by students in the kindergarten
class four year olds at the same Catholic school in Mississauga the smart
presents the frequency for transportation modes the kids have used
to come to school some have been dropped to school by car others by bus and two
students walk to school in the brave big world of big data we can see that
children are being trained at the earliest possible age with data and data
science the most important thing to realize is that the type of
visualization you will use depends upon the type of variables you're trying to
analyze for instance if you're working with categorical variables such as
gender you have to rely on a certain type of charting tools then if you were
to be working with continuous variables such as age and income in one case you
may be using bar charts in another case you will be required to use scatter
plots I would like to draw your attention to the extreme presentation
method developed by dr. Andrew abella essentially it lays out the possible
ways of depicting data based on what kind of variables you have at your
disposal this visualization this graphic is developed by dr. Abela that shows
that if you are interested in comparing variables or demonstrating their
distribution or composition or the relationship between two variables or
more you have to rely on specific type of graph if you're comparing items with
few categories you can use bar charts or column charts if you are comparing
behaviors over time and if you have the time periods running in two months
several months you may have to use a line chart and if the time periods are
not that many then you can use columns and other approaches if you're trying to
depict relationships between then to continue variables your choice is a
scatter plot the bubble chart will depict two variables on X and Y axis and
the third very people will be depicted by the size of
the circle so you can essentially have three variables if you're interested in
depicting the distribution of the data set you can use the histogram which
could be a bar chart type of histogram or a line histogram and the distribution
could also be shown through scatter plots if you would like to show the
composition then if it's a static data you can use pie charts if you're showing
the composition that changes over time for few periods you can use stacked
columns and if you have several periods you can use the stack area charts when
you're working with spss and the very first time during a session you would
like to create a new chart the first dialog box you encounter is in fact an
information or sort of a warning and it says that before you use this dialog
that is a chart builder dialog make sure that the measurement lever should be set
properly for each variable in your chart that is if it's a continuous variable it
must be scale or should be set at scale if it's a ordinal variable where
categories are in a particular order for example number of children per household
0 1 2 3 or 4 could be the possible values the variable has to be defined as
an ordinary ordinal variable and in case if you have a categorical variables such
as gender being male and female and in SPSS if it's coded numerically as 1 or 0
then those categories have to be labeled properly so when the software displays
these graphics instead of showing 1 and 0 for the variable gender it should
depict the male versus female if you were to recall the data set we have been
using so far and this is the teaching evaluation data from University of Texas
where we have attributes of the instructors and the teaching evaluation
scores and other relevant information about the courses notice that the
variables name for example if a person or instructor is a visible minority
individual is given and also the type if the string type which means that it's
not a numeric variable it is quoted as yes and no as alphabets rather than 1 or
0 and notice that the same variable minority I recoded it as
vismin called it visible minority of is men and coded it numerically so it's
1 or 0 and then I labeled it as visible minority and then I give it give it
values 0 and 1 I did the same with gender which was originally a string or
a character variable and then I recorded as female one being female 0 being males
and instead of being strained I converted it into a numeric variable
gave it a value label and then I coded this variable at 0 and 1 and proper
attributes and labels attached to those categories in both instances the
variable remains a nominal variable either as recorded as numeric or
recorded as character it is a nominal variable I would suggest that you pay
special attention to type and measure and define your variables or code them
under type in a correct fashion and then define them correctly under measure and
if you were to do this in advance the effort you will require to generate your
visualizations would be far less