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SEM: Step by Step

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.

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SEM: Step by Step

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  1. SEM: Step by Step In AMOS and Mplus

  2. 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

  3. 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.

  4. 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.

  5. Descriptives • The two important sections of information are posted below. • Note the number of observations, and the means.

  6. 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.

  7. 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.

  8. Descriptives • Run the data through N2Mplus again. • Run the descriptives again, but this time with the new dataset.

  9. Descriptives • The new descriptive statistics should align with the original SPSS file.

  10. 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.

  11. 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.

  12. AMOS Model • Below is the model we will be examining. • Note that the dependent variables have an error term with them (labeled e1 – e3).

  13. 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.

  14. 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

  15. 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.

  16. 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

  17. 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.

  18. AMOS Output • In this case, it suggests a regression path from ITI to GPA.

  19. 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.

  20. AMOS Output • The new model has a good fit for the chi square

  21. AMOS Output • The CFI, TLI and RMSEA values show a good model fit.

  22. 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:

  23. 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

  24. 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.

  25. Mplus Model • To the right is the model in AMOS • To the left is the syntax for the model in Mplus

  26. 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.

  27. Mplus Output • From the modification indices, the best choice would be adding “GPA ON ITI” to achieve a better fit.

  28. Mplus Output • Add ITI to the “GPA ON …” set of variables.

  29. Mplus Output • Looking at the output, the model is a good fit.

  30. Mplus Output • Regression weights can be examined now

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