IBM SPSS Amos makes structural equation modeling (SEM) easy and accessible
Download IBM SPSS Amos - An application that provides users with the means to build and test relationships between attitudinal and behavioral models in a simple manner. The trial download is the FULL VERSION of SPSS AMOS once activated with your CSUF Authorized Software Code. NOTE: These instructions were done on a PC, however instructions on a Mac are similar. Minimum System Requirements. The latest version of AMOS is 3.1 on Mac Informer. It is a perfect match for IDE in the Developer Tools category. The app is developed by AMOS Team and its user rating is 3.4 out of 5.
IBM SPSS Amos builds models that more realistically reflect complex relationships because any numeric variable, whether observed (such as non-experimental data from a survey) or latent (such as satisfaction and loyalty) can be used to predict any other numeric variable.
Amos Student Version Mac, free amos student version mac software downloads, Page 3. Spss Amos For Mac Free Downloads - 2000 Shareware periodically updates software information and pricing of Spss Amos For Mac from the publisher, so some information may be slightly out-of-date. You should confirm all information before relying on it.
Its rich, visual framework lets you to easily compare, confirm and refine models.
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Quickly build graphical models using IBM SPSS Amos’ simple drag-and-drop drawing tools. Models that used to take days to create are just minutes away from completion. And once the model is finished, simply click your mouse and assess your model’s fit. Then make any modifications and print a presentation-quality graphic of your final model.
A non-graphical, programmatic approach, introduced with SPSS Amos 20, improves accessibility for those who can benefit by specifying models directly. Its scripting capabilities improve the productivity of users who need to run large, complicated models, and make it easy to generate many similar models that differ slightly.
SPSS Amos 20 introduces a non graphical method to specify models.
Its approach to multivariate analysis encompasses and extends standard methods – including regression, factor analysis, correlation and analysis of variance. New capabilities include bootstrapping of user-defined functions of the model parameter for increased model stability.
Obtain Bayesian estimates of model parameters and other quantities
Bayesian analysis enables you to apply your subject-area expertise or business insight to improve estimates by specifying an informative prior distribution. Markov chain Monte Carlo (MCMC) is the underlying computational method for Bayesian estimation. The MCMC algorithm is fast and the MCMC tuning parameter can be adjusted automatically.
Perform estimation with ordered categorical and censored data Create a model based on non-numerical data without having to assign numerical scores to the data. Or work with censored data without having to make assumptions other than the assumption of normality. You can also impute numerical values for ordered-categorical and censored data. The resulting dataset can be used as input to programs that require complete numerical data.
Impute missing values or latent variable scores
Amos Software
Choose from three data imputation methods: regression, stochastic regression, or Bayesian. Use regression imputation to create a single completed dataset. Use stochastic regression imputation or Bayesian imputation to create multiple imputed datasets. You can also impute missing values or latent variable scores.
See how easy it is to use IBM SPSS Amos1. Select a data file | Input data from a variety of file formats (IBM® SPSS® Statistics, Micosoft® Excel, text files, or many others). Select grouping variables and group values. IBM SPSS Amos also accepts data in a matrix format if you’ve computed a correlation or covariance matrix. |
2. Specify your model | Use drag-and-drop drawing tools to quickly specify your path diagram model. Click on objects in the path diagram to edit values, such as variable names and parameter values. Or simply drag variable names from the variable list to the object in the path diagram to specify variables in your model. |
3. Select analysis properties | Select the analysis properties you wish to examine, such as standardized estimates of parameters or squared multiple correlations. Constrain parameters for more precise models by directly specifying path coefficients |
4. View output | IBM SPSS Amos output provides standardized or un-standardized estimates of covariances and regression weights as well as a variety of model fit measures. Hotlinks in the help system link to explanations of the analysis in plain English. |
IBM SPSS Amos output provides standardized or un-standardized estimates of covariances and regression weights as well as a variety of model fit measures. Hotlinks in the help system link to explanations of the analysis in plain English. | |
5. Assess your model’s fit | Make any modifications to your model and print publication-quality output. |
About SEM Software
There are several main points to be made about the software programs that can be used for performing SEM.
(1) There are quite a few software programs currently available for performing structural equation modeling. The first of the widely used programs of this sort was LISREL, which as of this writing is still available. A number of other programs are also available including EQS, Amos, CALIS (a module of SAS), SEPATH (a module of Statistica), and Mplus. There are also two packages in R, lavaan and 'sem', which are of course available for free.
(2) New users of SEM inevitably wish to know which of these programs is best. One point in this regard is that most of these programs are updated fairly frequently, making any description I might give of the limitations of a program potentially out of date. Another point to make is that different individuals prefer different features. Some want the software that will allow them to get started most quickly, others want the software with the most capabilities, still others want the software that is freely available to them.
(3) My thoughts about software is that the different packages serve different users. Currently, in this website I try to provide information for multiple types of users and, therefore, provide examples in several different languages. Here I give a brief description of some different languages and their characteristics.
(6) Amos. Amos is a popular package with those getting started with SEM. I have often recommend folks start learning SEM using the free student version of Amos simply because it is such a good teaching tool. It has the most useful manual for beginning users of SEM as well. What it lacks at present: (1) limited capacity to work with categorical response variables (e.g. logistic or probit forms) and (2) a limited capacity for multi-level modeling. Amos does have a Bayesian component now, which is helpful. That said, at the moment, it is a pretty limited Bayesian implementation and leaves the more advanced options out.
(7) Lavaan. We are now well into what can be called the 'R-age' and it is, well, all the rage alright. R is transforming quantitative analysis. Its role will continue to grow at a dramatic rate for the foreseeable future. There are two R packages dedicated to second-generation SEM analyses ('classical sem', which involved the anaysis of covariance structures). At the moment, I choose the lavaan package to present at this site, which is not to say that the SEM R packages isn't just fine. As of 2015, a new R package for local estimation of models is available, aptly called 'piecewiseSEM'.
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(8) Bayesian Implementations. At present, I am starting to present winBUGS tutorials for Bayesian implementations. winBUGS is a free software package for Markov chain Monte Carlo (MCMC) simulation using Bayes theorem. More can be found about this advanced application method under the Bayesian tab.
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I am happy to receive any comments about your experiences with SEM software or about this material at jimgrace001@gmail.com.