sem in amos and mplus

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

sem in amos and mplus

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