Factor Views—717
Estimation Output
Select View/Estimation Output to display the main estimation output (unrotated loadings, communalities, uniquenesses, variance accounted for by factors, selected goodness-of-fit statistics). Alternately, you may click on the Stats toolbar button to display this view.
Rotation Results
Click View/Rotation Results to show the output table produced when performing a rotation (rotated loadings, factor correlation, factor rotation matrix, loading rotation matrix, and rotation objective function values).
Goodness-of-fit Summary
Select View/Goodness-of-fit Summary to display a table of goodness-of-fit statistics. For models estimated by ML or GLS, EViews computes a large number of absolute and relative fit measures. For details on these measures, see “Model Evaluation,” beginning on
page 741.
Matrix Views
You may display spreadsheet views of various matrices of interest. These matrix views are divided into four groups: matrices based on the observed dispersion matrix, matrices based on the reduced matrix, fitted matrices, and residual matrices.
Observed Covariances
You may examine the observed matrices by selecting View/Observed Covariance Matrix/ and the desired sub-matrix:
•The Covariance entry displays the original dispersion matrix, while the Scaled Covariance matrix scales the original matrix to have unit diagonals. In the case where the original matrix is a correlation, these two matrices will obviously be the same.
•Observations displays a matrix of the number of observations used in each pairwise comparison.
•If you select Anti-image Covariance, EViews will display the anti-image covariance of the original matrix. The anti-image covariance is computed by scaling the rows and columns of the inverse (or generalized inverse) of the original matrix by the inverse of its diagonals:
A= diag(S–1)–1S–1diag(S–1)–1
•Partial correlations will display the matrix of partial correlations, where every element represents the partial correlation of the variables conditional on the remaining variables. The partial correlations may be computed by scaling the anti-image covariance to unit diagonals and then performing a sign adjustment.
Factor Views—719
To display the loadings as a graph, select View/ Loadings/Loadings Graph... The dialog will prompt you for a set of indices for the factors you wish to plot. EViews will produce pairwise plots of the factors, with the loadings displayed as lines from the origin to the points labeled with the variable name.
By default, EViews will use the rotated solution if available; to override this choice, click on the Use unrotated solution checkbox.
The other settings allow you to control the handling of multiple graphs, and whether the graphs should be centered around the origin.
Scores
Select View/Scores... to compute estimates of factor score coefficients and to compute factor score values for observations. This view and the corresponding procedure are described in detail in “Estimating Scores,” on page 713.
Eigenvalues
One important class of factor model diagnostics is an examination of eigenvalues of the unreduced and the reduced matrices. In addition to being of independent interest, these eigenvalues are central to various methods for selecting the number of factors.
Select View/Eigenvalues... to open the Eigenvalue Display dialog. By default, EViews will display a table view containing a description of the eigenvalues of the observed dispersion matrix.
The dialog options allow you to control the output format and method of calculation:
•You may change the Output format to display a graph of the ordered eigenvalues. By default, EViews will display the resulting Scree plot along with a line representing the mean eigenvalue.
•To base calculations on the scaled observed, initial reduced or final reduced matrix, select the appropriate item in the Eigenvalues of combo.
•For table display, you may include the corresponding eigenvectors and dispersion matrix in the output by clicking on the appropriate Additional output checkbox.