- [Instructor] You're looking at a cheat sheet
that has been provided to you as a PDF.
I want you to understand the context
behind the three distinct case studies
that we're gonna be doing over the next several videos
in this chapter.
There's three styles of doing regression.
And this whole idea of having different regression styles
might be new to you.
If that is, you probably are used
to doing regression as simultaneous.
That's what we can kind of think of as regular regression.
So let me briefly go through the three,
and then we'll have a case study of each.
So you're gonna do hierarchical
when you have a very strong theory
that allows you to meaningfully
order the variables in the sequence in which
those variables are entered into the regression.
So let's say that you want to better understand
childhood reading acquisition or vocabulary
and you want to measure the effect
of something like Head Start.
Someone might critique you and say,
"Wow, you really need to take into account
"parental education or parental income
"or something like hours read to at home."
So what you would do in hierarchical
is you would put those variables in first
followed by Head Start.
So you're sequencing either chronologically
or in importance.
It's a terribly important technique
and kind of considered the gold standard academically.
When you're doing simultaneous,
you have a less rich theory involved.
You're basically that you've assembled a list of variables
which you believe to be important variables,
but you don't have an additional basis
on which to rank or sequence those variables.
So essentially what you're using regression to do
is to help indicate to you
which of those variables are more important
and which of those variables are less important.
Again, this is the standard way of doing things.
The final approach is called stepwise,
and it's not hard to find an academic
that really doesn't like stepwise.
Sometimes in an academic setting,
you'll be told in no uncertain terms
do not do stepwise.
However, in a world where data science
and predictive analytics and data mining are popular,
this is actually very similar to this technique.
So stepwise, which is very widely found,
it's not found only in SPSS,
or things like regression trees
or other things associated with predictive analysis
are of a very different style.
So we're gonna be also seeing an example of this.
So once more, each of these will be covered
in its own case study.