Structural Equations: IBM SPSS Amos way

IBM SPSS Amos builds models that more realistically reflect complex relationships because any numeric variable, whether observed or latent and it  can be used to predict any other numeric variable. In Amos, you can specify, estimate, assess, and present your model in an intuitive path diagram to show hypothesized relationships among variables. This software also enables you to simultaneously analyze data from several populations, such as multiple ethnic groups. Increase the reliability of variables in your analysis by including multiple indicators. Impute missing values and latent scores, such as factor scores, with multiple imputations. You can also use SPSS Amos for longitudinal studies, multiple-group analysis, and reliability analysis. Researchers and graduate students who have observational, or non-experimental, data apply SPSS Amos in a variety of fields . Some of the Examples may include:
• Psychology – Develop models to understand how drug, clinical, and art therapies affect mood.
• Healthcare research – Confirm which of three variables – confidence, savings, or research – best predicts  a doctor’s support for prescribing generic drugs.
• Social sciences – Study how socioeconomic status, organizational membership, and other determinants influence differences in voting behavior
• Educational research – Evaluate training program outcomes to determine the impact on classroom effectiveness.
• Market research – Model how customer behavior impacts new product sales.
• Institutional research – Study how work-related issues affect job satisfaction.

After you fit a model, the SPSS Amos path diagram shows the strength of the relationship between variables. For example, when working with data from a product survey on condiments, you might initially assume that the variable, “satisfaction of taste,” is the best brand loyalty indicator. You can also fit multiple models in a single analysis. IBM SPSS Amos examines every pair of models where one model can be obtained by placing parameter restrictions on the other. IBM SPSS Amos even suggests how the model may be improved – for example, by adding an arrow to connect two variables. Graphs and statistics help you find an optimum trade-off between model simplicity and goodness of fit.