since we have adopted a hands-on approach to training it means that you
will have to use a statistical software with real-life datasets to learn the
concepts that we will illustrate it requires us to pick a software for the
courses and I have picked SPSS there are several reasons for my choice but the
most compelling reason is that C's SPSS is one of the easiest if not the easiest
statistical software to learn one may ask from an arguments sake why not use
spreadsheets almost every computer is loaded with one so why not use them for
statistical analysis I think spreadsheets are tremendously important
and useful tools I use spreadsheets on a daily basis but not for statistical
analysis spreadsheets are good for a whole variety of analytics but not
necessarily for statistical analysis and the chances of making a mistake are much
higher with spreadsheets than with tools designed specifically for statistical
analysis and it's not just the novices who would make mistakes with
spreadsheets even the world's top economists have been known to have made
mistakes with spreadsheets resulting in embarrassing situations with
spreadsheets you don't have reproducibility you do an analysis with
point-and-click environment and there's nothing to trace back your steps if you
were to find an error later you will also have a restricted tool set still as
statistics and analytics are discovering new modes and new methods everyday
spreadsheets are pretty much confined to the basic tools that they introduced
some ten fifteen years ago and that that new tools have have not been added at
the same pace that they are being created and then again if you have a
large data set your odds of making a mistake increase with the size of the
data set because the very logistics of handling big data in spreadsheet
environment and creates opportunities for unintended mistakes so how do you go
and move beyond spreadsheets you work with SPSS
or a software that is designed for statistical analysis you can work with
big data or bigger datasets and you would have at your disposal a very
comprehensive set of tools that are state-of-the-art and and then are easily
deployable so methods like neural networks and logistic regression which
are not available in spreadsheets but are available in SPSS and other similar
software and in fact a large amount of effort is dedicated just to data
management and data cleaning and then once the data are ready to be deployed
for analytics at that analytical part takes less time and effort the other
more important element of using SPSS is your ability to reproduce your research
using scripts the point-and-click environment is complemented with
scripting that is every command you execute will generate a script and that
script will be handily readily available to you at a later date if you
were to reproduce your research in a nutshell SPSS is easy to use easy to
learn has amazing data management and manipulation capabilities it has
ready set of advanced statistical tools it offers the flexibility
to use either the point-and-click environment or to create a script for
you in analytics it has tremendously powerful tabulation tools and reporting
tools and it has some state-of-the-art data visualization tools built into it
let me illustrate with examples why a tool like SPSS is more suitable than
spreadsheets because we can do things with these tools that are not possible
with spreadsheets so going back to the same data set where we looked at 463
courses let's generate some data visualizations to see why tools like
SPSS are preferred here we are looking at a histogram that shows the
distribution of teaching evaluation scores which averaged around four and
then you could see that some even had five where the few people had five more
most likely was the four was the most commonly devoted teaching evaluation but
there's a spread around it this is something that you can do with histogram
this histogram you can generate with a spreadsheet as well so how do you go
beyond it let's have a look in this example we are looking at the same data
but now we are creating we have created four histograms and we they are
differentiated by gender one for female and one for male and also differentiated
by ten year those who are not tenured profs was it those who attended prof and
you could see just quickly how easy it is for you to recognize that not only
that there are differences between male and female professors in terms of their
teaching evaluations the distributions are quite different but also their
distributions are different by by their tenure status another way of looking at
the same data is to put male and female district teaching evaluation
distributions side-by-side if you were to look at advanced data mining
techniques such as classification trees you could see how teaching evaluation is
different between males and females and then after that how purely scores of the
perception of their looks by students affect their teaching evaluation scores
and this is coming right out of SPSS again something that is not possible
with spreadsheets