IBM SPSS Amos V24.0

IBM SPSS Amos, also known as structural Equation modeling tool, is a very practical graphical modeling software. The software is mainly used for various analysis of data, including regression analysis, correlation analysis, variance analysis, factor analysis, statistical analysis, etc.

IBM SPSS Amos V24.0 

 After analyzing the model, it gives detailed path diagrams, views and table views, allowing users to Have a clearer and more intuitive understanding of the model structure!

IBM SPSS Amos (Structural Equation Modeling):

A basic introduction to IBM SPSS Amos

IBM SPSS Amos is a perfect modeling tool for various purposes:

Psychology-development model to understand how medication, clinical and art therapies affect mood.

Healthcare research-confirming these three variables-confidence, savings, or research-the best prediction is the support of the doctor prescribing generic drugs.

Social Science-study how socio-economic status, organization membership and other factors influence differences in voting behavior and political participation.

Educational research-training evaluation program results to determine the impact of classroom effectiveness.

Market research-model how customer behavior affects new product sales or analyze customer satisfaction and brand loyalty.

Institutional Research-Study how job-related issues affect job satisfaction.

Business planning-establish econometric and financial models and analyze factors that affect workplace professionalism.

Program evaluation-use scanning electron microscopy to replace the traditional step-by-step regression to evaluate project results or behavior patterns

IBM SPSS Amos Software Features:

1- Estimate the mean of exogenous variables.

2- Estimate the intercept of the regression equation

3- Analytical capabilities and statistical functions.

4- Use Bollen and Stinebootstrap methods to evaluate the model.

5- Exploring whether there is an equivalent or better fitting model through random replacement test.

6- Calculate the percentile confidence interval and the percentile confidence interval of the modified deviation.

7- In the case of missing data, use the maximum likelihood method of all information to obtain a more effective and less biased estimate.

8- Use the fast bootstrap simulation method to obtain the approximate confidence interval of any parameter under any test distribution, including the standardized coefficient.

9- Multiple estimation methods, including maximum likelihood estimation, unweighted least squares, generalized least squares, Browne's asymptotic free distribution standard and free scale least squares.

10- Set the same label for two or more parameters on the path graph to achieve the constraint of equal parameters in the same group or different groups, including mean, intercept, regression weight, and covariance.

11- Perform bootstrapping on any parameter to give an approximate confidence interval for any model parameter under the assumption of a normal distribution, including the standardized coefficient estimated by Monte Carlo simulation.

12- Supported file types include: dBase(.dbf), Microsoft Excel(.xls), FoxPro(.dbf), Lotus(.wk1,.wk3,.wk4), Microsoft Access(.mdb), IBM SPSS Statistics(.sav) ), and text (.txt,.csv).

IBM SPSS Amos Software New features:

1. Provide SEM:

Create models that truly reflect complex relationships.

Use drag-and-drop drawing and editing tools to quickly build graphical models.

Use any observed or potential numeric value to predict any other numeric value.

Use multivariate analysis to extend standard methods such as regression, factor analysis, and correlation and analysis of differences.

Use non-graphical scripting capabilities to quickly run large and complex models and generate slightly different similar models.

2. Use Bayesian algorithm analysis:

Perform estimates with orderly classified data and review data.

Improve the estimation by specifying a rich prior distribution.

Use review data without making assumptions other than normal.

Create models based on non-digital data without assigning digital scores to the data.

Use the automatically adjustable bottom "Markov chain Monte Carlo (MCMC)" calculation method.

3. Provide various data attribution methods:

Use regression attribution to create a single complete data set.

You can also attribute missing values ​​or latent variable scores.

Use random regression attribution or Bayesian algorithm attribution to create multiple attribution data sets.




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