SOC
206
Spring
07
Second
Assignment
Part One:
In Part One
you will
estimate a path model of your choice without latent variables.
You will
have to
prepare the data in SPSS. This will involve running the regression
first in
SPSS to be able to check your AMOS run, then running the bivariate
correlation to see the total associations, and finally, creating a
smaller file
that has only the variables you want to work with and only the cases
that have
no missing value on any of these variables. I provide an example that
you can
adapt to your variables.
*Getting
the data
file.
get file 'filename.sav'.
*Recodes if necessary, make
sure to recode all missing as sysmis.
recode reliten
(lo thru 0 = sysmis) (4 thru hi=sysmis).
recode absingle
(2=1) (1=2).
*Selecting cases with no
missing values on any of the variables included in the model THIS IS
NECESSARY ONLY
BECAUE WE ARE USING AMOS LATER.
*The following transformations
will each create a dummy which takes 1 when the case is system missing
or
missing on the variable and 0 otherwise.
comp xeduc=sysmis(educ).
comp xxeduc=missing(educ).
comp xpremar=sysmis(premarsx).
comp xxpremar=missing(premarsx).
comp xreliten=sysmis( reliten).
comp xxreliten=missing(reliten).
comp xabsing=sysmis(absingle).
comp xxabsing=missing(absingle).
*To check these new dummies.
freq educ
xeduc xxeduc premarsx xpremar xxpremar reliten xreliten xxreliten absingle xabsing xxabsing.
*Selection of the non-missing
cases.
select if (xeduc
ne 1 and xxeduc
ne 1 and xpremar
ne 1 and xxpremar
ne 1 and xreliten ne 1 and xxreliten ne 1 and xxabsing ne 1 and xabsing ne 1).
*Check for the selection.
freq educ
premarsx reliten
absingle.
*Regression to check against
later.
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA
/CRITERIA=PIN(.05)
POUT(.10)
/NOORIGIN
/DEPENDENT absingle
/METHOD=ENTER reliten
premarsx educ.
*Correlation
matrix to see the total associations.
CORRELATIONS
/VARIABLES=absingle premarsx educ reliten
/PRINT=TWOTAIL
SIG
/MISSING=PAIRWISE .
*Save
the file with only the variables you need.
save outfile 'newfile.sav' /keep=educ
premarsx reliten
absingle.
To run AMOS you have to click on Amos
Graphic
in the the Program Menu. Getting the output will involve three steps.
Step A.
you will have to specify your data file. Step B. You will specify the
model by
drawing the path diagram. Step C. you will have to estimate the model. .
A.
Specifying the data file
Click on
File on the
main toolbar. From the pull-down menu select �Data Files�. You will see
a
window. Click on �File name� and choose your data file (newfile.sav).
You will see in the internal window Group number 1, the name of the
file you
chose and the number or cases
usable/number of
cases overall. Click OK.
B. Drawing the path diagram
On the
left, you
will find a window with various tool icons. Click on the
rectangular icon to create the rectangles for each observed
variable. Use the photocopy icon to make
rectangles of the same size. If you need to move
a rectangle, change its size or delete it, right click on the
mouse and
choose the right command from the pull-down menu (you can also click on
the
right icon in the tool-icons window). .
Remember
that each
endogenous variable has an error term. The error term is not observed
directly
(it does not exist outside the particular model), but calculated and is
thus an
"unobserved" variable. Click on the oval shape icon
in the tool-icons window Create the ovals for each
unobserved (error) variable. Use the photocopy icon to make ovals of the same size. If you need to
move an oval,
change its size or delete it, right click on the mouse and choose the
right
command from the pull-down menu (you can also click on the right icon
in the
tool-icons window). Another way of moving an item is by using the
little
truck from the tool window.
Shortcuts. In the tool window, there is an icon for the error term and
also one
for unobserved variables with observed indicators..
Double
click on one of the rectangles or
ovals . You will see a
window pop up. Choose text and write the
variable
name for that variable in the �Variable Name� window (the names must be
identical to the ones you have in the new SPSS file!). Alternatively,
to
specify the names of the variables in your rectangulars,
you can click on View/Set in the main toolbar. From the pull-down menu
select
�Variables in Dataset�. This will open a window with all the variables
in the
data set that you have specified in step A. Drag each of these
variable-names to its rectangular. . (The unobserved variables can be
called error1,
error2 etc., or anything else, that makes sense to you).
Click on the arrow shape icon in the tool-icons
window . Click
on the independent variable and while holding the mouse button down
extend the
line to the dependent variable and release button. Don't forget to draw
a path
from each (unobserved) error variable to its own dependent variable
pointing in
the right direction (from error to dep.var.!).
Click on
the
double-headed arrow shape icon in the tool-icons window Point at the
exogenous
variable lower on the chart and extend the curve to the exogenous
variable
above it.
Double
click on one
of the path from the error to the dependent variable. You will see a
window pop
up. Choose parameters and write �1� in the �Regression weight� window.
Close
the window. In your path diagram you will see the number �1� assigned
to the
path that goes from the oval variable.
NOW YOU ARE READY TO ESTIMATE YOUR
MODEL.
C. Estimating the model.
i.
Prepare the output
Click on
�View/Set�
on the main toolbar. From the pull-down menu select �Analysis
Properties�. A
window will pop-out. Choose �Output�. Specify all the choices on the
right
column. Specify the first five choices on the left.
iiRun the estimation.
Click
on Model-Fit. From the
pull-down
menu select Calculate Estimates. It will ask you for a filename with
the .amw extension. This is where the
content of your path
diagram will be saved. The output will be written into a file with the
same
name you give here, except with an extension .amo.
iii Get
the standardized coefficients on your diagram.
Amos finishes running the estimations
when
the icon with the point-up arrow in the left upper corner is
highlighted in red
Click on this icon to get the coefficients on your diagram. These are
the unstandardized coefficient. To get the
standardized coefficient click on �standardized estimates� in the small
window
at the left side of the path diagram. Printing your output
Make sure
you have
your model with the right coefficient on the top. Click on Edit on the
main
toolbar. From the pull-down menu select Fit to Page. Then click on File
on the
main toolbar. From the pull-down menu select Print.
Then print the standardized output. You can also copy the diagram and
past it
to another document (e.g. Word) by selecting Edit/Copy to Clipboard.
Your text output will be in a file
ending in
.amo. To open the
Output file click
on View/Set. From the pull-down menu select �Text Output�.
Choose
File from the top left corner and select �Print�.
Save your files before you exit.
There will
be four new files created by AMOS. *.amw
will be the
file that you need to call up if you want to redo the analysis. It is a
command
file with that includes your diagram. *.amo
is your
output file. *.ami or *.amj
is the command file that you would have written had you not used point
and
click. *.amp is your technical output
Look at your output and compare it
with the
regression output from SPSS.
Then redo the analysis but now
constrain all
non-significant parameters (with C.R. less than 1.96) to 0 (the way you
constrained the errors to have metric coefficients of 1). See 3. A. vi. Instead of setting the regression weight to
1, click on
the desired path and set the regression weight to 0. If all your
parameters are
significant, choose the one with the lowest C.R. and constrain that to
0.
Answer the following questions:
HAND IN YOUR OUTPUT FROM SPSS AND AMOS with your answers..
Part Two
In Part Two
you do a
latent variable analysis.
For this you will need to download a data set from the
The question we want to answer is how
happiness is influenced by individualist ideology. Are people who
subscribe to
a more individualistic ideology happier? Both happiness and
individualistic
ideology are hard to capture and will be modeled as a latent variable.
There is
a third, control variable, gender.
happiness, will have three indicators. Each scored on
a scale
from 1-10 where 1= completely dissatisfied and 10=perfectly satisfied.
:
HAPHOME (V180) Overall, how satisfied
are you
with your home life?
HAPFIN (V132) How satisfied are you with the financial situation of your
household?
HAPLIFE (V96) All
things considered, how satisfied are you with your life as a whole?
individualism, will have four indicators. Each is scored
on a ten
point scale of agreement.
1
2
3
4
5
6
7
8
9
10
PRIVOWN (V251)
Government ownership
of
Private
ownership of
business and industry should be
increased
business and industry should be increased
INDRESP (V252)
The state should take
more
responsibility
Individuals
should take more responsibility
to
ensure that everyone is provided
for
to
ensure that everyone is provided for
COMPETE (V254)
Competition is harmful. It brings out
the
Competition
is good it stimulates people to
worst
in
people
work
hard and develop new ideas
HARDWRK (V255)
Hard work doesn't generally bring
success
In the long run, hard work usually brings a
-- it is
more a
matter of luck and
connections
better life
GENDER
1=Male and 2=Female
Create the proper model. A few hints:
Select the icon for the latent
variable in
the tools-icon window. Drew oval shapes for the two latent
variables or
factors (Happiness and Individualism). To draw the indicators, click on each of the ovals as many times as
many
indicators it has.
Don't forget that since Individualism
and
Gender are causes and Happiness is effect, there is an error term in
the factor
Happiness, that you must draw as a latent
variable
with a path pointing from the error term to the factor with its
parameter
constrained to 1. The same goes for Individualism that is causally
influenced
by Gender.
Moreover, you will notice that one of
the
indicators will have a path from the latent variable that is fixed to
1. This
is necessary, because this fixes the metric of this underlying, latent
variable
and with that it also decides whether a high value means e.g.,
happiness or
unhappiness. If the indicator with the fixed path is scored such that
large
values indicated happiness and low values unhappiness, the latent
variable will
be scored accordingly. While the metric disappears in the standardized
solution, the direction of the scaling remains consequential.
Don't forget to name all variables
(observed
and unobserved, including the errors).
What you need to hand in is the print
out of
the diagram with the standardized coefficients. Using the usual
asterisk
notation, indicate which paths are significant at the .05, .01 or .001
level.
(You may use the infinity row of the t table in your book and treat
C.R. as
t-values.) Also hand in the overall fit of the model (Chi-square and
its degree
of freedom).
Answer the following questions: