IBM SPSS Amos Changelog

What's new in IBM SPSS Amos 23.0.0

Jul 13, 2015

In Amos 23 you can estimate multiple simple user-defined estimands in a single analysis. In previous releases, you could estimate only one simple user-defined estimand at a time. Simple user-defined estimands now have the following limitations:
1. Each estimand must be defined by a single expression.
2. In a multiple-group analysis, each group must have the same path diagram.
An alternative method for specifying user-defined estimands is completely general and not subject to the above limitations, but is more difficult to use.
Simple user-defined estimands are demonstrated in Examples 38 and 39 of the User's Guide. They are implemented using the CValueSimple class.
You can use any valid Visual Basic expression to specify a simple estimand. You can make use of Microsoft's .NET Framework in defining your estimands. For example, you can use the System.Math class to estimate the square root of a parameter named abc by entering the expression
xyz = Math.Sqrt(p.abc)
An estimate of xyz together with bootstrap standard errors and confidence intervals will then appear in Amos's output in the same way that built-in estimates do. If your model has no variable named abc, so that abc is unambiguously the name of a parameter, you can omit the "p." and just write
xyz = Math.Sqrt(abc)
Hint: When you type Math, be sure to use an upper case M. Then, type a period after Math and you will be able to choose Sqrt from a list of available functions.

New in IBM SPSS Amos 22.0.0 (Aug 14, 2013)

New in IBM SPSS Amos 21.0.0 (Jul 18, 2013)

New in IBM SPSS Amos 20.0.0 (Jul 18, 2013)

New in IBM SPSS Amos 19.0.0 (Jul 18, 2013)

New in IBM SPSS Amos 18.0.0 (Jul 18, 2013)

New
Improvements in the drawing of path diagrams:
The appearance of path diagrams is improved.
The drawing interface is faster and more responsive.
Objects in path diagrams can be translucent with color gradients.
When variables in a path diagram are moved, all connecting arrows move simultaneously.
The magnifier tool (previously called the loupe tool) is improved.
You can open the Object Properties dialog by double-clicking an object in the path diagram. (Amos 5 had this capability. Now it is back.)
Improvements to the Program Editor:
Classes and class members can be selected from dropdown lists.
Code completion in the Program Editor is improved.
The Program Editor displays helpful tooltips when the mouse is hovered over a token.
Changes in the drawing of path diagrams:
When path diagrams are copied to the clipboard, they are now copied as bitmaps, not as Windows metafiles. The bitmap format has the drawback that the image of a path diagram becomes degraded when you resize the image after pasting it into another application such as Microsoft Word.
In the list of path diagram files in the left pane of the path diagram window, you can click a file name to open its path diagram. (It used to be a double-click.)
Context-sensitive help is now accessed in a consistent way throughout Amos Graphics. To obtain help for an individual element (such as a button or a check box) of an Amos Graphics window, hold the mouse pointer over that element and press F1.
The FillStyle property is ignored. It is retained for syntactic compatibility with previous versions of Amos.
The four pen widths, very thin, thin, thick, very thick are no longer used.
The path diagram browser (formerly called the path diagram viewer) has been moved from the Windows Start menu to the Amos Graphics File menu.
The Customize item has been removed from the Tools menu in Amos Graphics.
Changes to the object model (for programmers):
Variables of type Single are now of type Double.
Arguments to the AboutToShowMsgBox Event have changed.
The pd WindowHandle method has been eliminated.
The pd Form method has been replaced by the Window method.
The PDElement Highlighted property has been renamed to IsHighlighted.
The PDElement Selected property has been renamed to IsSelected.

New in IBM SPSS Amos 17.0.0 (Jul 18, 2013)

New
Copy and paste path diagrams:
You can copy and paste a path diagram, or part of a path diagram, from one Amos Graphics window to another.
To copy a path diagram to the clipboard, click Edit -> Copy (to clipboard).
To paste a path diagram from the clipboard to the Amos Graphics window, click Edit -> Paste.
Convert a path diagram to a Visual Basic program:
You can convert a path diagram to an equivalent Visual Basic program.
To convert a path diagram to a Visual Basic program, select Tools®Write a Program from the Amos Graphics menu.
Enhanced growth curve plugin:
The growth curve plugin now automatically constrains parameters in a way that is appropriate for many growth curve models. The following parameter constraints are imposed.
The regression weights for the "intercept" latent variable are fixed at 1.
The regression weights for the "slope" latent variable are fixed at equally spaced intervals starting at 0 and ending at 1. For example, if measurements were made at 5 time points, the 5 regression weights are fixed at 0, 0.25, 0.50, 0.75 and 1.00.
The intercepts of the measured variables are fixed at zero.
The error variances are constrained to be equal by giving each error variance the same name, Var.
The error variables are required to be uncorrelated.
The means and variances of the "intercept" and "slope" latent variables are unconstrained.
The covariance between "intercept" and "slope" is unconstrained.
You can modify the parameter constraints after Amos draws the path diagram for the growth curve model. For example, you will want to change the regression weights for the "slope" latent variable if your time points are not equally spaced. If you want to remove the equality constraints on the error variances you can do so by deleting the parameter name, Var, which is automatically assigned to all of the error variances.
To use the growth curve plugin, click Plugins -> Growth Curve Model.
Specify a default value for the 'All groups' check box:
You can now specify a default value for the All groups check box. This allows you to choose whether the path diagram objects that you draw will start out with a check mark next to All groups, or with no check mark next to All groups. To specify a default value for the All groups check box:
In Amos Graphics, open or create a multiple-group model.
Draw a new object, say a rectangle.
Right-click the rectangle and then select Object Properties from the menu that pops up.
In the Object Properties dialog, click the Parameters tab.
Put a check mark next to All groups if you want the objects that you draw in the future to have All groups checked by default. Or make sure that there is not a check mark next to All groups if you want the objects that you draw in the future to have All groups unchecked by default.
Click the Set Default button.
In the Set Default Object Properties dialog, click OK.
New pd method, EditPaste:
Pastes a path diagram, or a part of a path diagram, from the Windows clipboard into the Amos Graphics window. This method is equivalent to the menu selection Edit -> Paste.
New pd method, ToolsWriteAProgram:
Converts a path diagram to an equivalent Visual Basic program. This method is equivalent to the menu selection Tools -> Write a Program.
Changes to the Program:
In multiple imputation, the variable in the completed data file that contains the imputation number was previously called ImputeNo. It is now called Imputation_.

New in IBM SPSS Amos 16.0.0 (Jul 18, 2013)

New
Mixture Modeling:
Amos performs 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. Mixture modeling is discussed in the context of structural equation modeling by Arminger, Stein & Wittenberg (1999), Hoshino (2001), Lee (2007, Chapter 11), Loken (2004), Vermunt & Magidson (2005), and Zhu & Lee (2001), among others.
Zhu, H. T., & Lee, S. Y. (2001). A Bayesian analysis of finite mixtures in the LISREL model. Psychometrika, 66(1), 133-152.
Vermunt, J. K., & Magidson, J. (2005). Structural equation models: Mixture models. In B. Everitt & D. Howell (Eds.), Encyclopedia of statistics in behavioral science (pp. 1922-1927). Chichester, UK: Wiley.
Loken, E. (2004). Using latent class analysis to model temperament types. Multivariate Behavioral Research, 39(4), 625-652.
Lee, S. Y. (2007). Structural equation modeling: A Bayesian approach. Chichester, UK: John Wiley and Sons.
Hoshino, T. (2001). Bayesian inference for finite mixtures in confirmatory factor analysis. Behaviormetrika, 28(1), 37-63.
Arminger, G., Stein, P., & Wittenberg, J. (1999). Mixtures of conditional mean- and covariance-structure models. Psychometrika, 64(4), 475-494.
Any model can be used in mixture modeling. Example 34 and Example 35 use a saturated model. These examples also demonstrate the fitting of latent structure analysis models, which require the observed variables to be independent (uncorrelated for multivariate normal variables). Example 36 employs a regression model. Factor analysis models have also been used in mixture modeling (Lubke & Muthén, 2005).
Lubke, G. H., & Muthén, B. (2005). Investigating population heterogeneity with factor mixture models. Psychological Methods, 10(1), 21-39.
Mixture modeling is often known as latent class analysis. In the terminology of Lazarsfeld (Lazarsfeld & Henry, 1968), the term latent class analysis is reserved for the variant of latent structure analysis in which all variables are categorical. Amos does not perform that type of latent class analysis.
Lazarsfeld, P. F., & Henry, N. W. (1968). Latent structure analysis. Boston: Houghton Mifflin.
Mixture Modeling, Clustering, and Discriminant Analysis:
One byproduct of the Bayesian approach to mixture modeling, as implemented in Amos, is the probabilistic assignment of individual cases to groups. Mixture modeling can thus be viewed as a form of clustering (Fraley & Raftery, 2002). As such, mixture modeling offers a model-based alternative to heuristic clustering methods such as k-means clustering.
Fraley, C., & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association, 97(458), 611-631.
In the Amos implementation, it is possible to assign some cases to groups in advance of the mixture modeling analysis. These cases provide a training set that assists in classifying the remaining cases. When used in this way, mixture modeling offers a model-based alternative to discriminant analysis.
The first example of mixture modeling (Example 34) in this User's Guide employs a dataset in which some cases are already classified. The mixture modeling analysis consists of classifying the remaining cases. Persons who have carried out multiple-group analyses using previous versions of Amos will find that practically no new learning is required for Example 34. In Amos, a mixture modeling analysis in which some cases are already classified is set up in almost the same way as an ordinary multiple-group analysis in which the group membership of every case is known in advance.
Changes to the Program:
On the Parameters tab of the Object Properties dialog box, the All Groups check box is initially selected (a check mark appears in the box for newly created objects; previously, the check box was initially empty for newly created objects). This change now means that when you assign a name or a value to a parameter in a multi-group analysis, the same name or value is assigned by default to the corresponding parameter in every other group. For example, if you set a regression weight to 0 in any group, that regression weight will simultaneously be set to 0 in all groups. If you want to name a parameter or give it a constant value in some groups but not others, remove (deselect) the check mark next to All Groups.

New in IBM SPSS Amos 7.0.0 (Jul 18, 2013)

New in IBM SPSS Amos 6.0.0 (Jul 18, 2013)

Bayesian Estimation:
Bayesian estimation combines data together with any prior beliefs or knowledge about model parameters that the analyst may have, to arrive at a posterior distribution that summarizes the updated state of knowledge about the parameters. Bayesian estimation offers a number of benefits to structural equation modelers. Among them are:
Explicit incorporation of any available prior information or beliefs about model parameters
Good performance in small samples
Avoidance of inadmissible model parameter values (e.g., negative variances) through the choice of an appropriate prior distribution
Estimation and hypothesis testing for any user-specified function of the model parameters
Fit Measures Window:
The Fit Measures window displays Bayesian measures of model fit.
Data Imputation:
The Data Imputation window is used to replace each missing value in a dataset by an estimate called an imputed value. Once each missing value has been replaced by an imputed value, the resulting completed dataset can be analyzed by data analysis methods that are designed for complete data. Amos provides three methods of data imputation.
In regression imputation, the model is first fitted using maximum likelihood. After that, model parameters are set equal to their maximum likelihood estimates and linear regression is used to predict the unobserved values for each case as a linear combination of the observed values for that same case. Predicted values are then plugged in for the missing values.
Stochastic regression imputation (Little & Rubin, 2002) imputes values for each case by drawing at random from the conditional distribution of the missing values given the observed values, with the unknown model parameters set equal to their maximum-likelihood estimates. Because of the random element in stochastic regression imputation, repeating the imputation process many times will produce a different completed dataset each time.
Bayesian imputation is like stochastic regression imputation, except that it takes into account the fact that the parameter values are only estimated and not known. For details on Bayesian imputation, see How Bayesian imputation works.
The Data Imputation window can be used to perform multiple imputation. In multiple imputation (Schafer, 1997) one of the nondeterministic imputation methods (either stochastic regression imputation or Bayesian imputation) is used to create multiple completed datasets. While the observed values never change, the imputed values vary from one completed dataset to the next. Special techniques are required to analyze the multiple completed datasets.
Schafer, J.L. (1997). Analysis of incomplete multivariate data. London, UK: Chapman and Hall.
Latent variables do not have a special status in any of the three imputation methods. A latent variable is treated as an extreme case of missing data in which every observation on the variable is missing.
Data files containing imputed values may be saved for subsequent analyses by Amos or any other statistical analysis programs.
Other New Features:
Print Preview for path diagrams
Improved zooming and scrolling
Drawing Path Diagrams
Copying path diagrams to the clipboard
Multiple Amos Graphics windows

New in IBM SPSS Amos 5.0.0 (Jul 18, 2013)

Specification search
Structural equation modeling (SEM) is an intrinsically confirmatory technique, but in practice it is often used in an exploratory way. Various tools have been developed for adapting this confirmatory technique to exploratory uses (MacCallum, 1986). These include the use of modification indices and Lagrange multiplier tests for selectively adding parameters to a model, and the use of z statistics (also called critical ratios) and Wald tests for selectively eliminating parameters (Bentler, 1989; Jöreskog & Sörbom, 1996).
Amos provides an alternative approach to exploratory SEM. In this approach, exploratory SEM is treated as a problem in model selection in which the number of candidate models is permitted to be large. Tools are provided for systematically fitting many candidate models and for choosing among them on the basis of fit, parsimony, and interpretability.
When you have data from multiple groups, you often start by asking if it is necessary to draw a separate path diagram for each group, or if the same path diagram will do for all groups. If you conclude that all the groups share the same path diagram, you can proceed to ask whether parameter values are invariant across groups. For example, if you are studying boys and girls, you might want to know whether boys and girls have the same regression weights, or if only certain regression weights are the same for boys and girls. Of course there are also variances and covariances as well as regression weights to consider. Because of the large number of possible cross-group constraints, it is necessary to have a strategy for deciding which cross-group constraints are worth testing and in what order to test them. Bollen (1989), Kline (1998), and others discuss such strategies. Amos implements an automatic procedure for generating a nested hierarchy of models in which cross-group constraints are introduced incrementally in a pre-chosen order.
No automatic procedure can anticipate the purpose of an individual study. If necessary, you can modify Amos's automatically generated cross-group constraints to suit the needs of an individual study. However, no such customization will be necessary in most cases. You also have the option of performing multiple-group analyses by imposing cross-group constraints manually, just as in Amos 4.
The content of the Amos 7 output file is the same as in Amos 4, but the new output viewer includes additional navigational aids, display options, and table formatting options.
Font, color, and other accessibility settings for Internet Explorer affect the Amos output viewer.
The online help has been expanded and extensively cross-referenced to provide you with the help you need, when you need it.
Amos allows you to easily show or hide all variable labels in a path diagram.