when it comes to data analysis pivot tables or pivoting is a powerful data
analysis tool it is sometimes referred to as OLAP cubes or online analytical
processing and basically what you can do with OLAP is take a data set that has
multiple dimensions and you can analyze the data on the fly in our case our
target variable is evaluation and we would like to estimate the average
teaching evaluation and the corresponding standard deviation for age
and for teaching evaluations beauty and age but at the same time we want to add
further dimensions to it such as gender tenure English language proficiency and
the visible minority status and the two key statistics we are interested in our
mean and standard deviation so using the OLAP cubes you can see that you can do
the analysis on the fly let us repeat this analysis using SPSS so in SPSS we
pick and analyze repose and then click on OLAP cubes and then we select
assembly variables that we are interested in age beauty score in
teaching evaluation we select the for categorical variables as grouping
variables within statistics we select number of cases mean and standard
deviation and then press ok and the output should appear in the output
window here you go you have the age normalized beauty school teaching
evaluation and the somebody statistics for the three and you could see that the
results are similar as to one we obtained before the primary advantage of
using OLAP cubes or pivoting is the analytics on the flyer that it allows
you to estimate the numbers using various choices and i'll show you right
now if i were to double click on this table it will open up the the pivoting
option and we can even have a formatting bar that comes with it so within the
gender or female variable let's say we select females who are not visible
minority and who speak english as a native speaker and are tenured and the
numbers are changing accordingly for untenured female professors who are
not visible minority and speak english as native the numbers are as follows the
average age is 49.8 and for ten eight forty two point oh five let's get not a
native speaker of english language and you see the numbers change drastically
so the strength of OLAP cubes is that you can make selections right here and
the numbers are updated in your table and once you got the numbers that you
like you just click here and this is your output the pivoting option is very
interesting let's say we take the tenured variable and drag it and drop it
here and all of a sudden our table is updated here you could see that now we
have added a new dimension and not only this we can actually transpose our table
for transposing I simply click the statistics bring them here and move
these over here variables and tenured status and if I unselect I'll just go
back to output you could see that my results have been transposed let's say
I'm interested in making a chart from this so I can select for example mean
value what is the average age of tenured and untenured profs I highlighted
these two numbers right clicked on it and I said create a graph and let's say
create a bar chart from it and right away I get the bar chart output with the
non tenured profs being slightly older than the tenured profs so essentially
pivoting is analytics on the fly you can create tables you can manipulate the
results and at the same time you can graph the or chart the relationships of
interest this way you don't have to subset data you don't have to write if
then statements you simply manipulate data on the go and get the results there