Select the Advanced tab of
the Structural
Equation Modeling Results dialog box to access the options described
here.

Model summary. Click the Model summary button to open the model summary spreadsheet, which presents model output in a spreadsheet form convenient for analysis. A variety of information is given for each path in the model. For a description of the spreadsheet, see Model Summary Spreadsheet.

Basic summary statistics. Click the Basic summary statistics button to produce a spreadsheet containing some of the basic summary statistics also presented in the Summary box. See Statistics in the Structural Equation Modeling Results Summary Box for more details on the information available there.

Iteration history. Click the Iteration history button to create a spreadsheet with iteration results. During iteration, STATISTICA saves the output that is displayed in the Iteration Window for the last executed sequence of iterations. By opening this spreadsheet, you can review these results. You can also save them to a data file or analyze them graphically. The statistics in this spreadsheet are described in Iteration Results.

Goodness-of-Fit Indices. Use the options under Goodness-of-Fit Indices to generate several of the best-known and most useful indices of fit for evaluating structural equation models. There are, literally, dozens of such indices, and we present only those which we think are very widely used, especially valuable, or both.

Noncentrality-based indices. Click
the Noncentrality-based indices
button to produce a spreadsheet with the non-centrality parameter and
four goodness-of-fit indices. These indices are all based on the idea,
first proposed by Steiger and Lind (1980), of basing goodness-of-fit assessment
on an estimation of the population noncentrality parameter.

Other single sample indices. Click the Other single sample indices button to create a spreadsheet with a sampling of some of the better known single sample indices of fit, and some related measures. For more details see the Single Sample, Goodness-of-Fit Indices spreadsheet.

Lagrange multiplier statistics. Click
the Lagrange multiplier statistics
button to produce a spreadsheet with Lagrange Multiplier statistics for
each constraint required by the fitting procedure. In the Correlation
option of Data to Analyze (see
Analysis
Parameters), each endogenous manifest variable has a dummy latent
variable attached to it, with its variance constrained to one. If the
New standardization method is
employed, each endogenous latent variable has its variance constrained
to one, and STATISTICA gives
a Lagrange multiplier statistic for each variable.

The Lagrange Multiplier statistics should be zero, if the constraints serve (as they are meant to) solely as identification constraints. If any are non-zero, or significantly exceed the standard error, then the model has probably been miss-specified, or estimation did not converge properly.