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- IBM SPSS Amos 32 Structural Equation Modeling Software
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類別Statistical Analysis Software
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介紹IBM SPSS Amos is a professional Structural Equation Modeling (SEM) software solution designed for data analysis, statistical modeling, and causal relationship analysis. Based on advanced structural equation modeling techniques, the software helps users build and analyze complex models. Users can create structural equation model diagrams, define model parameters, evaluate model fit, and perform multi-group comparisons,IBM SPSS Amos supports a variety of analytical methods, including path analysis, causal relationship analysis, Confirmatory Factor Analysis (CFA), and multilevel m
IBM SPSS Amos 32
IBM SPSS Amos was designed as a tool for teaching structural equation modeling in a way that emphasizes the simplicity that underlies this powerful approach to data analysis. Every effort was made to see that it is easy to use. Amos integrates an easy-to-use graphical interface with an advanced computing engine for SEM. The publication-quality path diagrams of Amos provide a clear representation of models for students and fellow researchers. The numeric methods implemented in Amos are among the most effective and reliable available.
Graphical and non-graphical model specification
IBM SPSS Amos accepts a path diagram as a model specification and displays parameter estimates on a path diagram. Path diagrams used for model specification and those that display parameter estimates are of presentation quality. They can be printed directly or imported into other applications such as word processors, desktop publishing programs, and general-purpose graphics programs.
Full information estimation with missing data
When some data values are missing, Amos offers a choice between maximum likelihood estimation or Bayesian estimation instead of ad hoc methods like listwise or pairwise deletion.
Bootstrap
The program makes bootstrap standard errors and confidence intervals available for all parameter estimates, effect estimates, sample means, variances, covariances, and correlations. It also implements percentile intervals and bias-corrected percentile intervals (Stine, 1989), as well as Bollen and Stine’s (1992) bootstrap approach to model testing.
Model comparisons
Multiple models can be fitted in a single analysis. Amos examines every pair of models in which one model can be obtained by placing restrictions on the parameters of the other. The program reports several statistics appropriate for comparing such models.
Group comparisons
Amos can analyze data from several populations at once.
Bayesian estimation
Amos uses an MCMC (Markov chain Monte Carlo) algorithm to perform Bayesian estimation. Bayesian estimation can be used to to avoid negative variance estimates and other types of improper solutions. It can be used to estimate any arbitrary function of the model parameters.
Estimation of means and intercepts
Amos makes it easy to estimate means for exogenous variables and intercepts in regression equations.
Specification search (Exploratory SEM)
Amos's specification search provides a method for systematically specifying, fitting and evaluating a large number of candidate models. In a specification search, some single- and double- headed arrows in a path diagram are designated as optional. Amos fits the model both with and without each optional arrow, using every possible subset of the optional arrows. Tools are provided for choosing among the models on the basis of fit, parsimony, and interpretability.
Data imputation
If your dataset contains missing values, you can use regression imputation to create a new, completed dataset in which the missing values have been filled in with estimated numeric values.
In addition, if your dataset contains missing, censored, or ordered-categorical values, you can use either Bayesian imputation or stochastic regression imputation to create one or more completed datasets in which the missing, censored or ordered-categorical values have been filled in with estimated numeric values.
Analysis of censored data
Amos uses Bayesian estimation to fit models to datasets that contain censored values. It can estimate posterior predictive distributions for censored values, and perform imputation for censored values.
Analysis of ordered-categorical data
Amos uses Bayesian estimation to fit models to datasets that contain ordered-categorical variables. (For example, variables that are scored pass/fail or low/medium/high rather than numerically.) It can estimate the posterior predictive distribution of the numeric variable that underlies a categorical response, and can impute a numeric value for a categorical response.
Mixture modeling
Mixture modeling is appropriate when you have a model that is incorrect for an entire population, but where the population can be divided into subgroups in such a way that the model is correct in each subgroup. Amos allows (but does not require) you to assign some cases to groups before the analysis starts. Amos attempts to learn from any cases that are already classified and then to classify those cases that are not already classified.
User-defined estimands
Amos can estimate any function of the model parameters, complete with bootstrap standard errors, confidence intervals and significance tests.
Growth curve analysis
Amos can construct linear growth curve models.
Tests of assumptions
Amos provides a test of univariate normality for each observed variable as well as a test of multivariate normality. It attempts to detect outliers.
Annotated text output
Text output is annotated by popups that provide brief explanations of selected portions of the output.
Extensive online help.
Extensive context-sensitive help is supplied with the program in Microsoft's CHM format (the usual help format for Windows programs). The same help content is provided on this website in a more web-friendly format.
User's guide with many examples
39 examples in the user's guide show how to use Amos.
IBM SPSS Amos 32 System Requirements
Windows 10, Windows 11, Windows Server 2012 R2, 2016, 2019
CPU
Intel or AMD x86 processor running at 1 gigahertz (GHz) or higher
RAM
2 gigabyte (GB) of RAM or more.
HD
1 gigabytes (GB) of available hard-disk space.
Display
XGA (1024x768) or a higher-resolution monitor
IBM SPSS Amos 32 Structural Equation Modeling Software
IBM SPSS Amos was designed as a tool for teaching Structural Equation Modeling (SEM), emphasizing the simplicity underlying this powerful data analysis methodology. Every effort has been made to ensure ease of use. Amos integrates an intuitive graphical interface with an advanced computational engine for SEM. Its publication-quality path diagrams provide students and researchers with clear representations of their models. The numerical methods implemented in Amos are among the most efficient and reliable available.
Graphical and Non-Graphical Model Specification
IBM SPSS Amos accepts path diagrams as model specifications and displays parameter estimates directly on the diagrams. Both the path diagrams used for model specification and those used to display parameter estimates are publication quality. They can be printed directly or imported into other applications such as word processors, desktop publishing software, and general-purpose graphics programs.
Full Information Estimation with Missing Data
When some data values are missing, Amos allows users to choose between Maximum Likelihood Estimation and Bayesian Estimation rather than relying on ad hoc methods such as listwise or pairwise deletion.
Bootstrapping
The program provides bootstrap standard errors and confidence intervals for all parameter estimates, effect estimates, sample means, variances, covariances, and correlations. It also implements percentile intervals and bias-corrected percentile intervals (Stine, 1989), as well as the bootstrap method developed by Bollen and Stine (1992) for model testing.
Model Comparison
Multiple models can be fitted within a single analysis. Amos examines each pair of models to determine whether one model can be obtained by imposing parameter restrictions on the other. The program reports several statistics that are useful for comparing such models.
Group Comparison
Amos can analyze data from multiple populations within a single analysis.
Bayesian Estimation
Amos performs Bayesian estimation using MCMC (Markov Chain Monte Carlo) algorithms. Bayesian estimation can be used to avoid negative variance estimates and other types of improper solutions. It can also be used to estimate arbitrary functions of model parameters.
Estimation of Means and Intercepts
Amos makes it easy to estimate the means of exogenous variables and intercepts in regression equations.
Specification Search (Exploratory SEM)
Amos Specification Search provides a systematic method for specifying, fitting, and evaluating a large number of candidate models. During a specification search, selected single-headed and double-headed arrows in a path diagram can be designated as optional. Amos fits models using every possible subset of these optional arrows, with or without each optional path. Tools are provided to help users select among models based on goodness of fit, parsimony, and interpretability.
Data Imputation
If your dataset contains missing values, regression imputation can be used to create a new complete dataset in which missing values are replaced with estimated numerical values.
In addition, if your dataset contains missing, censored, or ordinal categorical values, Bayesian imputation or stochastic regression imputation can be used to create one or more complete datasets in which the missing, censored, or ordinal categorical values are replaced with estimated values.
Censored Data Analysis
Amos uses Bayesian estimation to fit models to datasets containing censored values. It can estimate posterior predictive distributions for censored values and perform imputation for those values.
Ordinal Categorical Data Analysis
Amos uses Bayesian estimation to fit models to datasets containing ordinal categorical variables, such as variables scored as pass/fail or low/medium/high rather than numerically. It can estimate posterior predictive distributions of the underlying numerical variables associated with categorical responses and can impute numerical values for categorical responses.
Mixture Modeling
Mixture modeling can be used when a single model is not appropriate for the entire population, but the population can be divided into subgroups for which separate models are valid. Before analysis begins, Amos allows, but does not require, some cases to be assigned to groups. Amos attempts to learn from the cases that have already been classified and then classify those that have not yet been assigned.
User-Defined Estimates
Amos can estimate any function of model parameters and provides bootstrap standard errors, confidence intervals, and significance tests.
Growth Curve Analysis
Amos can construct linear growth curve models.
Assumption Testing
Amos provides univariate normality tests for each observed variable as well as multivariate normality tests. It also attempts to detect outliers.
Annotated Text Output
Text output includes pop-up annotations that provide brief explanations of selected portions of the output.
Extensive Online Help
The program provides extensive context-sensitive help in Microsoft's CHM format, a commonly used help format for Windows applications. The same help content is also available on the website in a more web-friendly format.
User Guide with Numerous Examples
The User Guide includes 39 examples demonstrating how to use Amos.
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