Build structural equation models with more accuracy than standard 
multivariate statistics models using intuitive drag-and-drop 
functionality

IBM® SPSS® Amos gives you the power to easily perform structural 
equation modeling (SEM). Using SEM, you can quickly create models to 
test hypotheses and confirm relationships among observed and latent 
variables - moving beyond regression to gain additional insight.
Structural equation modeling (SEM) can take your research to the next 
level

When you conduct research, you're probably already using factor and 
regression analyses in your work. Structural equation modeling 
(sometimes called path analysis) can help you gain additional insight 
into causal models and explore the interaction effects and pathways 
between variables. SEM lets you more rigorously test whether your 
data supports your hypothesis. You create more precise models - 
setting your research apart and increasing your chances of getting 
published.

IBM SPSS Amos is the perfect modeling tool for a variety of purposes, 
including:

• Psychology - Develop models to understand how drug, clinical, and 
art therapies affect mood

• Medical and 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 and political engagement

• Educational research - Evaluate training program outcomes to 
determine impact on classroom effectiveness

• Market research - Model how customer behavior impacts new product 
sales or analyze customer satisfaction and brand loyalty

• Institutional research - Study how work-related issues affect job 
satisfaction

• Business planning - Create econometric and financial models and 
analyze factors affecting workplace job attainment

• Program evaluation - Evaluate program outcomes or behavioral models 
using SEM to replace traditional stepwise regression