AMOS

Computational Methods in AMOS

AMOS employs several sophisticated computational methods to estimate SEM coefficients, each suitable for different types of data and model specifications:

Expanding the Use of AMOS

Maximum Likelihood (ML): This is the most commonly used method in SEM for estimating model parameters. It assumes multivariate normality and provides robust results under a wide range of conditions.

Unweighted Least Squares (ULS): Ideal for situations where normality assumptions are violated, ULS is a non-parametric estimation method that does not rely on distributional assumptions.

Generalized Least Squares (GLS): A refinement of the ULS method, GLS adjusts for potential heteroskedasticity and autocorrelation in the data, providing more efficient and unbiased parameter estimates.

Browne’s Asymptotically Distribution-Free (ADF) Criterion: This method allows for SEM estimation without the assumption of normal distribution, making it suitable for data that deviate significantly from normality.

Scale-Free Least Squares: Designed to handle ordinal and categorical data, this method does not assume interval scaling, making it versatile for various types of data inputs.

AMOS’s visual approach to SEM not only simplifies the model-building process but also enhances the interpretability of results, allowing for immediate visual feedback and easy modification of models. This software is especially useful for researchers in psychology, social sciences, marketing, business, and any field where understanding the complex interplay between variables is crucial.

In educational settings, AMOS’s graphical interface serves as an excellent teaching tool, helping students grasp the fundamentals of SEM without being overwhelmed by complex mathematical formulas. Moreover, the software’s wide range of estimation methods ensures that users can tailor their analysis to the specific needs of their data, enhancing the precision and validity of their findings.

By combining ease of use with powerful analytical capabilities, AMOS democratizes access to advanced statistical modeling techniques, opening up new possibilities for research and data analysis across various disciplines.

Construction of model in AMOS:

First, we have to run AMOS. By clicking the “start” menu and selecting the “AMOS graphic” option, we can run the program. The moment AMOS starts running, a window appears called the “AMOS graphic.” In this window, we can manually draw our SEM model.

• Attaching Data: By selecting a file name from the data file option, we can attach data in AMOS for SEM analysis. This option also appears if we will click on the “select data” icon.
• Observed Variable: A rectangle icon is used to draw the observed variable.
• Unobserved Variable: A circle icon is used to draw the unobserved variable.
• Cause Effect Relationship: A single headed arrow in AMOS is used to draw the cause effect relationship between the observed and unobserved variables.
• Covariance: A double headed arrow is used to draw the covariance between variables.
• Error Term: In AMOS, the error term icon is next to the unobserved variable icon, and it is used to draw the latent variable.
• Naming the Variable: When we right click on a variable in a graphical window, the first option, “object properties,” is used to give the name of the variable in AMOS.

There are other icons as well, and these icons help in drawing the SEM model graphically. Icons such as erase icon, moving icon, calculate icon, view text, analysis properties, etc., help in drawing the SEM model graphically.

Understanding the text output in AMOS

After running the analysis, we can see the results on the graphic window. We can also see the text output. The graphic window will only show the standardized and unstandardized regressions and error term weights. All results will be shown in the text output.

AMOS will produce the following important output:

• Variable Summary: In AMOS and its text output variable summary, we can see how many variables and which variables are used for SEM analysis. We can see how many observed variables and how many unobserved variables were in the model.
• Accessing the Normality: In SEM model, data should be normally distributed. AMOS will give the text output, and Skewness, Kurtosis and Mahalanobis d-squared test will tell us about the normality of the data.
• Estimates: In AMOS text output, the estimate option will give the result for regression weight, standardized loading for factor, residual, correlation, covariance, direct effect, indirect effect, total effect, etc.
• Modification Index: In AMOS text output, the modification index result shows the reliability of the path drawn in the SEM model. If MI index value is large, then we can add more paths to the SEM model.
• Model Fit: In AMOS text output, model fit option will give the result for goodness of fit model statistics. It will show all the goodness of fit indexes, such as GFI, RMR, TLI, BIC, RMSER, etc.
• Error Message: If there is any problem, during the process of drawing the model (for example, if we forget to draw the error term or if we draw the covariance between two variables, or if missing data is present), then AMOS will either not calculate the result or it will give an error message.