Transcript SEM: Step by Step - Statistics Solutions

SEM: Step by Step
In AMOS and Mplus
Data Management
• In this tutorial, data will be in an SPSS format
• Data will be transferred into an Mplus file
using N2Mplus 1.0.37
• N2Mplus 1.0.37 has an error in the coding in
that it leaves off the last participant in a data
file.
• You want to check your descriptive statistics in
SPSS and Mplus to make sure they agree
before you do any analyses in Mplus
Data Management
• Locate your file in
N2Mplus and then
hit Go.
• This will create an
Mplus data file in the
same location as the
original SPSS file.
• It will also give you
the Mplus syntax to
use the data.
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.
Descriptives
• The two important sections of information are
posted below.
• Note the number of observations, and the
means.
Descriptives
• Compare these numbers to the SPSS
descriptive statistics with the same data.
• Note that there’s one missing participant and
the means are off slightly.
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.
Descriptives
• Run the data through N2Mplus again.
• Run the descriptives again, but this time with
the new dataset.
Descriptives
• The new descriptive statistics should align
with the original SPSS file.
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.
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.
AMOS Model
• Below is the model we will be examining.
• Note that the dependent variables have an
error term with them (labeled e1 – e3).
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.
AMOS Output
• Go to Analyze -> Calculate Estimates
• View -> Text Output
• In the text output we need to look at two tabs
for determining model fit:
– Notes for the Model
• This has the Chi Square statistic
– Model Fit
• This has the CFI, TLI, and RMSEA
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.
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
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.
AMOS Output
• In this case, it suggests a regression path from
ITI to GPA.
AMOS Output
• Once the path is added, the model can be rerun.
• It is important to keep track of any and all
changes to the model that are made.
AMOS Output
• The new model has a good fit for the chi
square
AMOS Output
• The CFI, TLI and RMSEA values show a good
model fit.
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:
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
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 have
a 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.
Mplus Model
• To the right is
the model in
AMOS

To the left is the
syntax for the
model in Mplus
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.
Mplus Output
• From the modification indices, the best choice
would be adding “GPA ON ITI” to achieve a
better fit.
Mplus Output
• Add ITI to the “GPA ON …” set of variables.
Mplus Output
• Looking at the output, the model is a good fit.
Mplus Output
• Regression weights can be examined now