FACTOR  
Configure Analysis  

Configure analysis The analysis is configured by clicking on Configure Analysis button in the main menu (see details). Note that before starting the computation the data must be read. This button opens the menu that helps to define the analysis. The menu is now shown ready to (a) compute polychoric correlations (with categories from 1 to 5), (b) compute parallel analysis, (c) retain two factors computed by Unweighted Least Squares factor analysis, (d) compute robust factor analysis, and (e) rotate the solution oblique Procuestean Rotation using the user provided target matrix. In addition, the output is stored in file output.txt. When computing the polychoric correlation, please note that:
Leastsquares exploratory factor analysis based on tetrachoric/polychoric correlations is a robust, defensible and widely used approach for performing item analysis. A relatively common problem in this scenario, however, is that the interitem correlation matrix might fail to be positive definite. In order to correct not positive definite correlation matrices, FACTOR implements smoothing methods. The basic principle in the smoothing corrections is to change the relative weight of the diagonal elements of the correlation matrix with respect to the nondiagonal elements. The challenge of the smoothing methods is to change the relative weight of the diagonal elements of the correlation matrix while destroying as little variance as possible in the process. In the present release of FACTOR, Ridge and Sweet smoothing methods have been implemented. It must be noted that Ridge Smoothing is a linear smoothing method that impacts all the variables in the correlation matrix. To prevent this from happening, we propose Sweet Smoothing: the aim of this nonlinear smoothing method is to focus the smoothing procedure only on the problematic variables while destroying as little variance as possible in the process. The researcher is allowed to choose between these two smoothing methods when analyzing a dataset. Different implementation of PA can be computed. This menu describes how PA can be configure: Hull method is a procedure to assess the number of factors to retain. This menu describes how Hull method can be configure: When robust factor analysis is selected (and bootstrap sampling has been allowed when reading the dataset from disk), the user can configure the different aspects shown in this menu: Advanced specifications of the model can be configured using this menu, that is shown ready to (a) display IRT parameterization; (b) compute factor scores and related indices and procedures (like DIANA); (c) compute continuous personfit indices; (d) compute bifactor model (PEBI); and (e) to compute different indices related to essential unidimensionality and the quality of the factor solutions: We implemented many methods to obtain simple structure. To configure the parameter values, the Configure rotation button opens the following menu: This menu shows the default parameter values of Normalized Direct Oblimin. These values are:
When a Target matrix is provided, the target can be refined using Objectively Refined Target Matrix (RETAM). When a target matrix is proposed by the user, RETAM helps to refine the target matrix allowing to free and to fixe elements of the target matrix. RETAM risks to capitalize a factor solution on chance (i.e., the factor model is fitted to the sample at hand, not to the population). In the RETAM crossvalidation study, the sample has been halfsplitted in two random subsamples: RETAM procedure is applied in the first subsample in order to obtain a refined target matrix; and the refined target matrix is then taken as a fixed target matrix (without further refinements) for the second subsample. If the rotated loading matrix in the second subsample is congruent with the rotated loading matrix in the first subsample, then the researcher must be confident that the final solution has not just been fitted to the sample data, but also to the population data. Here you have an example how to configure it: The default button sets the parameters of the rotation to the usual values in the literature. These values are the ones defined when the program starts. When simplimax rotation is used, a range of salient loading values (and a final number) must be specified. This is done during the computation itself (see details). Seneca Estimate is a procedure to estimate an optimal sample size. The proposal is based on an intensive simulation process in which the sample correlation matrix is used as a basis for generating datasets from a pseudopopulation in which the parent correlation holds exactly. And the criterion for determining the needed size is a threshold that quantifies the closeness between the pseudopopulation and the sample reproduced correlation matrices. 