Testing for Multiple Regression In Week 9, you completed your Part 1 for this Assignment. For this week, you will complete Part 2 where you will create a research question that can be answered through multiple regression using dummy variables. Part 2 To prepare for this Part 2 of your Assignment: Review Warner’s Chapter 12 and Chapter 2 of the Wagner course text and the media program found in this week’s Learning Resources and consider the use of dummy variables. Using the SPSS software, open the Afrobarometer dataset or the High School Longitudinal Study dataset (whichever you choose) found in this week’s Learning Resources. Consider the following: Create a research question with metric variables and one variable that requires dummy coding. Estimate the model and report results. Note: You are expected to perform regression diagnostics and report that as well. Once you perform your analysis, review Chapter 11 of the Wagner text to understand how to copy and paste your output into your Word document. For this Part 2 Assignment: Write a 2- to 3-page analysis of your multiple regression using dummy variables results for each research question. In your analysis, display the data for the output. Based on your results, provide an explanation of what the implications of social change might be. Use proper APA format, citations, and referencing for your analysis, research question, and display of output. HS_Long_Study_[student].sav GSS2014_student_8210_(6).sav Dummy Variables Dummy Variables Program Transcript DR. MATT JONES: Hi everybody, this is Dr. Matt Jones from the Center for Research Quality here to talk to you today about constructing dummy variables in SPSS. The purpose behind our conversation today is to show you how to construct these dummy variables to use as independent variables when you are fitting a multiple regression model or constructing a multiple regression model, And I have in front of us the Afro barometer data set. I've greatly simplified it for the purpose of this demonstration. You'll see, there are obviously only three variables in it, country in alphabetical order, country by region, and trust in government index. Now I might want to construct a variable or use a variable, country by region, that that might be relevant to my research question or might be an important controlling variable that I need to use in my multiple regression analysis. And it's very tempting just to throw it in as an independent variable as it is here. SPSS will allow me to do that. It will produce some output for that variable or coefficient so forth and associated p values. But the statistics generated really won't necessarily make any sense unless I'm creating a dummy variable or set of dummy variables from this original variable. So if I go and click on values here, you'll see that there are five 4 attributes or four groups to this variable country by region, West Africa, East Africa, Southern ...

1. Testing for Multiple Regression
In Week 9, you completed your Part 1 for this Assignment. For
this week, you will complete Part 2 where you will create a
research question that can be answered through multiple
regression using dummy variables.
Part 2 To prepare for this Part 2 of your Assignment:
Review Warner’s Chapter 12 and Chapter 2 of the Wagner
course text and the media program found in this week’s
Learning Resources and consider the use of dummy variables.
Using the SPSS software, open the Afrobarometer dataset or the
High School Longitudinal Study dataset (whichever you choose)
found in this week’s Learning Resources. Consider the
following:
Create a research question with metric variables and one
variable that requires dummy coding. Estimate the model and
report results. Note: You are expected to perform regression
diagnostics and report that as well.
Once you perform your analysis, review Chapter 11 of the
Wagner text to understand how to copy and paste your output
into your Word document.
For this Part 2 Assignment:
Write a 2- to 3-page analysis of your multiple regression using
dummy variables results for each research question. In your
analysis, display the data for the output. Based on your results,
provide an explanation of what the implications of social
change might be.
Use proper APA format, citations, and referencing for your
analysis, research question, and display of output.
HS_Long_Study_[student].sav

2. GSS2014_student_8210_(6).sav
Dummy Variables
Dummy Variables
Program Transcript
DR. MATT JONES: Hi everybody, this is Dr. Matt Jones from
the Center for
Research Quality here to talk to you today about constructing
dummy variables in
SPSS. The purpose behind our conversation today is to show

3. you how to
construct these dummy variables to use as independent variables
when you are
fitting a multiple regression model or constructing a multiple
regression model,
And I have in front of us the Afro barometer data set. I've
greatly simplified it for
the purpose of this demonstration. You'll see, there are
obviously only three
variables in it, country in alphabetical order, country by region,
and trust in
government index. Now I might want to construct a variable or
use a variable,
country by region, that that might be relevant to my research
question or might
be an important controlling variable that I need to use in my
multiple regression
analysis. And it's very tempting just to throw it in as an
independent variable as it
is here.
SPSS will allow me to do that. It will produce some output for
that variable or
coefficient so forth and associated p values. But the statistics
generated really
won't necessarily make any sense unless I'm creating a dummy
variable or set of
dummy variables from this original variable. So if I go and
click on values here,
you'll see that there are five 4 attributes or four groups to this
variable country by
region, West Africa, East Africa, Southern Africa, and North
Africa.
And the rule is for creating dummy variables is the number of

4. groups minus 1.
That is there four groups here, four attributes to this variable.
So I need to take 4
minus 1, obviously equals 3. I need to create three dummy
variables. One
variable is always left out if you will to service that reference
category. And just
again for the sake of simplicity, I'm going to leave number four,
North Africa, as
our reference category today.
What you pick as your reference category might be dependent
upon your
research question, some theory, what it is you're trying to find
out, again, very
context specific. But, again, for today, just going to sort of
randomly choose North
Africa as our reference category. Before you create your dummy
variable, you do
want to note the original coding on the original variable here
one, two, three, and
four and what those correspond to.
So let's go ahead and move out of here and create these
variables. Transform,
recode into different variables, this is how we're going to start
our process of
creating the dummy variables. Because we are creating new
variables. So SPSS
is asking, what's the input variable? You think of that as the
original variable,
country by region. Because we are going to pull this apart. So
country by region
is our original variable. Now let's create our first dummy
variable, we'll call it West
Africa.

5. © 2016 Laureate Education, Inc. 1
Dummy Variables
And for our label, we'll do the same. So I have to give it old and
new values. So
the old value for West Africa was 1, and the new value is going
to be 1. And for
dummy variables, they have to take on the attribute value of
either 1 or 0. 1 it has

6. a attribute, or 0 it doesn't. And hopefully that becomes just a
little bit clearer as
we walk through this process.
Now since I'm creating this dummy variable for West Africa,
I've already told
SPSS from that original variable, country by region, take all the
West Africa
cases and essentially flip that switch, turn them on to create this
new West Africa
dummy variable. All others turn off. Those are not West Africa.
So there are a couple different ways you can do that. I'm going
to show you the
what I would call quote, unquote the long way of doing this. So
if we have to,
remember we had three other groups to this variable. We had
the old value of 2,
which corresponded to East Africa, that's now going to be a 0.
We had 3 which
was southern Africa. Again, for our West Africa variable, it's
going to be 0. Don't
forget to hit add. In then four, North Africa, which is our
reference category, 0.
Add.
Now I could go ahead I could use the range. You know I could
have just done 2
through 4 equals 0. That would have worked. Or I could've also
done all other
values and then put the new value in of zero. So that would
have told SPSS, OK,
take 1 equals 1. All others 0. The only thing to be just a little
cautious about there
it is if you have user defined or system defined missing values.
They could

7. possibly be thrown in there. Again, it depends upon this specific
data set and
how things are coded in there.
So that's why I'm just showing you what I call quote unquote
the long way of
doing it today. Go ahead click continue. Be sure and hit this
radio icon change.
Once you hit that, you'll see the OK is sort of activated. We can
go ahead and
create that variable. We'll see we'll get some SPSS syntax
output here. And
there it is. It says our variables been created. OK, so here it is
West Africa.
Now we need to create two other dummy variables so that we
have our three
dummy variables here. So let's go ahead and see if we can
quickly do this. So
recode into different variables. One thing I always do is hit
reset because
everything you did before is still going to be in there, so I just
do this so I don't
confuse myself. Again, doing the same thing, country by region.
Except this one
I'm calling East Africa because I'm creating a separate dummy
variable for East
Africa.
OK, old and new value, so our old value for East Africa was 2.
Now the new
value is going to be 1. Remember dummy variables have to have
the value 1, it
has an attribute, or 0. So I'm saying, OK, all East Africa cases
now turn into 1 for
this dummy variable of the East Africa. Add. And 1, which was

8. West Africa, is
now going to be 0. I mean everything else is going to be 0, all
others 0.
© 2016 Laureate Education, Inc. 2
Dummy Variables
Again four our reference category, and our reference category is
going to be 0 in
each of them. Hit continue. Sure, again, hit change. If you don't
hit change, you'll

9. see that OK will not sort of light up, if you will, for you. You
have to go ahead, and
there we go. Hit OK. And there's our output, it's been created.
And one more.
Recode into different variables, reset, again, country by region,
our final variable,
which is Southern Africa.
Old and New value, so in the old value, the original country by
region that was
coded as a 3, and now it's going to be coded as 1 because we're
focusing on that
Southern Africa dummy variable, add. And all others 0. And
make sure we get
our reference North Africa in here, 0. Change. OK.
So now we can see we have West Africa, East Africa, and
Southern Africa set up
as dummy variables. And if we go click down here, if we go to
our data view,
you'll see our original variable, country by region, so 1,
remember 1, in the
original variable corresponded to West Africa. And if we go
over here and look at
our West Africa variable, you see that for those cases where the
original variable
was 1, coded West Africa, is now also 1 for the West Africa
dummy variable but 0
for East and Southern Africa.
And so if we go ahead and scroll down to East Africa, so here
we go. So for
cases where our original variable country by origin 2, remember
that equaled
East Africa, and still does in the original variable. But now if
we go over to West

10. Africa, 0, it's not West Africa, 1. It is East Africa. So for this
dummy variable those
things are matching up which is a good thing. And 0 for
Southern Africa. Again,
remember North Africa is serving as our reference category.
So now we've created dummy variables, we can go ahead and
use them in a
linear regression analysis. So let's just go ahead and run a very
quick linear
regression. So I've gone ahead and already entered those
variables in here. I'm
just going to use, we don't have many variables to work with, so
we're just going
to trust and government as our dependent variable. And only use
these, again,
just for the sake of demonstration these categorical dummy
variables as
independent variables. Click OK.
So by this point, you're probably very familiar with the output
around model
summary and the ANOVA, let's go ahead and look at the
coefficients. You'll see
here now that we have each of these dummy variables
represented. And so we
can go ahead and interpret these coefficients, these un-
standardized coefficients.
And what these un-standardized coefficients are representing is
a value in
reference to the reference category, to North Africa.
So for instance, West Africa, the un-standardized coefficient
there, 1.289, that is
saying compared to North Africa, there's a difference in West
Africa of 1.289

11. units or our dependent variable is trust in government measured
on this index.
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Dummy Variables
So there's a difference in trust in government of 1.289 units on
that index
compared to North Africa.
And we can go and an interpret the same for East Africa and
southern Africa,
those un-standardized coefficients are in comparison to the
North Africa means.
So it's the West Africa mean for trust in government being
compared to the North
Africa mean. So it's really what we're looking at here is the
difference between
those means of West Africa, North Africa, East Africa, and
North Africa, and
Southern Africa, and North Africa, again, the means. And that's
how you create
dummy variables in SPSS.
© 2016 Laureate Education, Inc. 4