Well, friends, I thought I would
take just a minute to
spend a little more time on
factor analysis and working
with loadings.
We've almost made it through
this little sector of the
ladder, and you're
looking good.
It should be a blast.
Our analysis producer a rotated
component matrix for
each of three factors, and the
factor loadings, of course,
explain the interaction
of the variables with
each identified factor.
You will recall that we had
three factors who had
eigenvalues of above
1.0, and therefore
they were deemed important.
These loadings speak to each
of those three factors, and
these interactions provide
extensive insight into
important issues in
the data set.
Here's our rotated
component matrix.
We have the top, factor 1,
factor 2, and factor 3.
Here we have the variables that
we produced in that, the
percentage of disciplinary
placements, African American
and Spanish white student
enrollments, the economically
disadvantaged percentages, the
limited English proficiency,
[INAUDIBLE] and special ed.
In this first one, we note that
the largest players were
the economically disadvantaged
Hispanic, and limited English
proficiency, and at risk.
And then, of course, white.
When we examine the data set, we
notice that factor 1 might
be called ethnicity because we
have Hispanic and white having
a lot of impact upon
economically disadvantaged,
limited English proficiency,
and at risk.
And of course, when you go in
and exam the data set, as you
would suspect, rising
percentages of Hispanics
certainly do impact the
economically disadvantaged,
limited English proficiency,
and the
number of at rick students.
The next one we notice that the
most profound impacts are
with the percentage of
disciplinary placements.
Then we come down and we notice
that it is very much
impacted by special
ed and by the
percentage of at rick students.
I read this to say that if
you're a special ed student
and you're at risk, and you do
anything, your butt's going to
get at disciplinary placement.
You would be more likely to.
And we might call that
special needs.
The last of these, the most
important factor, is the
percentage of African Americans
which impact the
percentage of Hispanic, white.
It appears to me that the
African American percentages
in a school and the white
percentages are somewhat
related and opposed
to Hispanics.
In other words, as you get more
Hispanics in districts in
Texas across those with higher
ratios or percentages of
Hispanic, they have less
percentages of whites and
African Americans.
That makes sense to me, and we
might just simply call this
factor African American
enrollment.
Now we identified three
important factors.
Again, factor 1 could named
ethnicity, factor 2 could be
named special needs, and
factor 3 could be named
African American.
Interpretations of these
loadings inside the data set
provides powerful insight
into that data set.
That's why we might conduct a
factor analysis, to really
dissect a data set to try
to understand what is
going on within it.
As always, I want to thank you
very much for your support.
Live long and prosper.
Peace and long life.
You have a blessed day.
This is Dr. Dawg signing off.
