Table 24.2, 24.12, ... Person principal components/contrast analysis of residuals
Please do not interpret this as a usual factor analysis. These plots show contrasts between opposing factors, not loadings on one factor. For more discussion, see dimensionality and contrasts.
This Table decomposes the matrix of person correlations based on residuals to identify possible other contrasts (dimensions) that may be affecting response patterns. Specify PRCOMP=S or =R or =L to obtain this Table.
Table 24.0 Variance components scree plot for persons
Table 24.1, 24.11 Principal components plots of person loadings
Table 24.2, 24.12 Person Principal components analysis/contrast of residuals
Table 24.3, 24.13 Person contrast by items
Table 24.4, 24.14 Person contrast loadings sorted by measure
Table 24.5, 24.15 Person contrast loadings sorted by entry number
Table 24.99 Largest residual correlations for persons
Prior to this first contrast, the Rasch dimension has been extracted from the data. Residuals are those parts of the observations not explained by the Rasch dimension. According to Rasch specifications, these should be random and show no structure. The contrasts show conflicting local patterns in inter-item (or inter-person) correlations based on residuals or their transformations. Letters "E", "b", etc. relate persons to their unstandardized "raw" loadings on the first contrast.
In this Table 24.3, Landman and Rossner contrast with Chazelle and Ernst. Since Landman and Rossner misfit, they load on a second dimension in the data.
The loading is that on the first PCA contrast. It is unstandardized. In the factor analysis literature, values of ±.4 or more extreme are considered substantive. To standardize the loading, divide the loadings by their root-mean-square.
The measures and mean-square statistics are the same as those reported in Table 6 etc.
The letters under "ENTRY NUMBER" refer to the plots in Table 24.2.
The "cluster number" indicates a statistical clustering of the loadings (useful for splitting these persons into two or more unidimensional sub-samples). The clusters are obtained by doing a cluster-analysis of the loadings.
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