Softbox
AMOS
AMOS - Structural Equation Modeling to Test Relationships
Producator: IBM SPSS | Platforma: Windows | Modelare 3D
Build structural equation models with more accuracy than standard multivariate statistics models using intuitive drag-and-drop functionality.
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Link producator: IBM SPSS
- Comercial Destinate end-userilor persoane fizice sau companii / firme
- Academic Institutii de invatamant: scoli, licee, universitati
- Guvernamental Pentru administratii / autoritati de stat publice sau locale
| Part number | Denumire licenta | Platforma | Licenta | RON cu TVA | Cantitate | Cos |
|---|---|---|---|---|---|---|
| New - AMOS | ||||||
| AMOS-1 | AMOS |
|
1 User | 1.427,29 RON | ||
Prezentare AMOS
Build structural equation models with more accuracy than standard multivariate statistics models using intuitive drag-and-drop functionality.
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.
Expanded statistical options based on Bayesian estimation
With Amos, you can perform estimation with ordered-categorical and censored data, enabling you to:
• Create a model 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
You can also impute numerical values for ordered-categorical data or censored data, so you can create a complete numerical dataset when one is required. Or, impute values for missing values in the new dataset. You also have the option of estimating posterior predictive distributions to determine probable values for missing or partially missing data in a latent variable model.