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8/11/2019 Sem in Amos and Mplus
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SEM: Step by Step
In AMOS and Mplus
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Data Management
In this tutorial, data will be in an SPSS format
Data will be transferred into an Mplus fileusing N2Mplus 1.0.37
N2Mplus 1.0.37 has an error in the coding inthat it leaves off the last participant in a datafile.
You want to check your descriptive statistics inSPSS and Mplus to make sure they agreebefore you do any analyses in Mplus
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Data Management
Locate your file inN2Mplus and thenhit Go.
This will create anMplus data file in thesame location as theoriginal SPSS file.
It will also give youthe Mplus syntax touse the data.
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Descriptives
Run descriptive statistics in SPSS and also in
Mplus.
Select the variable of interest from the dataset
(GPA, SDT, ITI, MSLSS, and Teacher).
The syntax for basic descriptive statistics is
shown below.
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Descriptives
The two important sections of information are
posted below.
Note the number of observations, and the
means.
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Descriptives
Compare these numbers to the SPSS
descriptive statistics with the same data.
Note that theres one missing participant and
the means are off slightly.
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Descriptives
To correct for this, simply add one participant
to the end of the data set in SPSS.
The values for the variables do not matter, as
long as there are values in there, since
N2Mplus kicks out the last one anyways.
Save the new file labeled as a different name.
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Descriptives
Run the data through N2Mplus again.
Run the descriptives again, but this time with
the new dataset.
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Descriptives
The new descriptive statistics should align
with the original SPSS file.
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Model
The next step is to build the model.
In AMOS, a visual for the model is given.
In Mplus, only syntax for the model is written.
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AMOS Model
These buttons will serve as your main
model building buttons for AMOS.
Single arrowheads represent regression paths.
Double arrowheads represent covariances
between variables.
Squares represent observed variables.
Ovals represent latent variables.
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AMOS Model
Below is the model we will be examining.
Note that the dependent variables have an
error term with them (labeled e1e3).
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AMOS Output
Before you run the AMOS model, there are a
few special output settings we need to
include.
View -> Analysis Properties
In the Output tab, check of Modification
Indices and Standardized Estimates
These will help us later if the model is not a
good fit.
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AMOS Output
Go to Analyze -> Calculate Estimates
View -> Text Output
In the text output we need to look at two tabsfor determining model fit:
Notes for the Model
This has the Chi Square statistic
Model Fit
This has the CFI, TLI, and RMSEA
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AMOS Output
In the Notes for Model page we can see the
Chi Square statistic
For a good model fit, we want the Chi Square
statistic to be not significant.
In this case the model was significant.
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AMOS Output
In the Model Fit page, we can see the CFI, TLI,
and RMSEA values.
For CFI, we want values > .95
For TLI, we want values > .90
For RMSEA, we want values < .08
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AMOS Output
Since the RMSEA was not a good fit, we
should further examine the model for ways to
improve the fit.
Examine the Modification Indices
This will show some potential way to improve
the model empirically.
Whatever changes made should make
theoretical sense.
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AMOS Output
In this case, it suggests a regression path from
ITI to GPA.
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AMOS Output
Once the path is added, the model can be re-
run.
It is important to keep track of any and all
changes to the model that are made.
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AMOS Output
The new model has a good fit for the chi
square
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AMOS Output
The CFI, TLI and RMSEA values show a good
model fit.
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AMOS Output
What is left to do is the interpretation of the
paths.
In the Estimates page of the output, you will
find the following table:
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AMOS Output
From the table you can see significant
regression paths. (*** indicates p < .001)
These paths can be interpreted just like a
normal linear regression
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Mplus Model
In Mplus, only the syntax is written.
Model: should be written first.
Y ON X is the format you use to have variable
Y being predicted by variable X.
Y BY X1 X2 X3 is the format you use if you havea latent variable Y made up of observed
variables X1, X2, and X3. Y WITH X is the format you use to indicate the
variables are correlated together.
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Mplus Model
To the right isthe model in
AMOS
To the left is the
syntax for the
model in Mplus
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Mplus Output
Mplus only has one tab of output, and it is
rather simple to find the numbers we need.
Again, we do not quite have a good model fit.
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Mplus Output
From the modification indices, the best choice
would be adding GPA ON ITI to achieve a
better fit.
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Mplus Output
Add ITI to the GPA ON set of variables.
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Mplus Output
Looking at the output, the model is a good fit.
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Mplus Output
Regression weights can be examined now