58 research outputs found
The determinacy of the regression factor score predictor based on continuous parameter estimates from categorical variables
Factor analysis as data matrix decomposition: a new approach for quasi-sphering in noisy ICA
In this paper, a new approach for quasi-sphering in noisy ICA by means of exploratory factor analysis (EFA) is introduced. The EFA model is considered as a novel form of data matrix decomposition. By factoring the data matrix, estimates for all EFA model parameters are obtained simultaneously. After the preprocessing, an existing ICA algorithm can be used to rotate the sphered factor scores towards independence. An application to climate data is presented to illustrate the proposed approach
Extending the Debate Between Spearman and Wilson 1929: When do Single Variables Optimally Reproduce the Common Part of the Observed Covariances?
On the construction of all factors of the model for factor analysis
indeterminacy, factor scores, confirmatory factor analysis, exploratory factor analysis, distance between factors,
Rotational uniqueness conditions under oblique factor correlation metric
In an addendum to his seminal 1969 article J\"{o}reskog stated two sets of
conditions for rotational identification of the oblique factor solution under
utilization of fixed zero elements in the factor loadings matrix. These
condition sets, formulated under factor correlation and factor covariance
metrics, respectively, were claimed to be equivalent and to lead to global
rotational uniqueness of the factor solution. It is shown here that the
conditions for the oblique factor correlation structure need to be amended for
global rotational uniqueness, and hence, that the condition sets are not
equivalent in terms of unicity of the solution.Comment: Postprint, 5 page
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