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Amos is a user-friendly tool for the specification, estimation and evaluation of structural equation models. The graphical user interface allows you to draw your model, to modify it and to visualize it in presentation quality. The program is an excellent must have especially for analyzing data from surveys, long-term studies or panel surveys. Resulting from complex relationships, your structural equation model (SEM) often contains a large number of different variables and dependencies. Amos takes this into account and builds your models more quickly and accurately, thereby fostering trust in your decision. Make comprehensive models by discovering hidden information in observed data and define latent variables. Thus, for example, “brand loyalty” can be discovered by combining the variables "buying frequency" and "sold items".
SPSS AMOS - The Structural Modeling Software
Structural Equation Modeling to Test Relationships
Amos provides you with powerful and easy-to-use structural equation modeling (SEM) software. Create more realistic models than if you used standard multivariate statistics or multiple regression models alone. Using Amos, you specify, estimate, assess, and present your model in an intuitive path diagram to show hypothesized relationships among variables. This enables you to test and confirm the validity of claims such as "value drives loyalty" in minutes, not hours.
Gain new insights using observed and latent variables
Amos enables you to build models that more realistically reflect complex relationships with the ability to use observed variables such as survey data or latent variables like “satisfaction” to predict any other numeric variable. Structural equation modeling, sometimes called path analysis, helps you gain additional insight into causal models and the strength of variable relationships. Apply Amos to a variety of research applications, for example:
- 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
- Institutional research—Study how work-related issues affect job satisfaction
|Operating System||Windows XP, Vista, 7, 8, 10 (32/64-Bit)|
|Min. CPU||Intel/AMD Processor 1 GHz, or higher|
|Min. RAM||1 GB RAM|
|Disk Space||800 MB|
- Quickly build graphical models using drag-and-drop drawing and editing tools.
- Create models that realistically reflect complex relationships.
- Use any numeric value, whether observed or latent, to predict any other numeric value.
- Use non-graphical scripting capabilities to run large, complicated models quickly and to generate similar models that differ slightly.
- Take advantage of multivariate analysis to extend standard methods such as regression, factor analysis, correlation and analysis of variance.
Uses Bayesian analysis
- Improve estimates by specifying an informative prior distribution.
- Take advantage of the underlying Markov chain Monte Carlo (MCMC) computational method, which is fast and can be adjusted automatically.
- Perform estimation with ordered categorical and censored data.
- Specify user-defined estimands using a simplified technique.
- Create models based on non-numerical data without having to assign numerical scores to the data.
- Work with censored data without having to make assumptions other than normality.
Offers various data imputation methods
- Use regression imputation to create a single, completed data set.
- Use stochastic regression imputation or Bayesian imputation to create multiple imputed data sets.
- You can also impute missing values or latent variable scores.