Factor Score Predictors Research Articles

How Does the Elimination of Group Mean-Differences Affect Factor Score Determinacy?

The present study investigates to what degree the common variance of the factor score predictor with the original factor, i.e., the determinacy coefficient or the validity of the factor score predictor, depends on the mean-difference between groups. When mean-differences between groups in the factor score predictor are eliminated by means of covariance analysis, regression, or group specific norms, this may reduce the covariance of the factor score predictor with the common factor. It is shown that in a one-factor model with the same group mean-difference on all observed variables, the common factor cannot be distinguished from a common factor representing the group mean-difference. It is also shown that for common factor loadings equal or larger than .60, the elimination of a d = .50 mean-difference between two groups in the factor score predictor leads to only small decreases of the determinacy coefficient. A compensation-factor k is proposed allowing for the estimation of the number of additional observed variables necessary to recover the size of the determinacy coefficient before elimination of a group mean-difference. It turns out that for factor loadings equal or larger than .60 only a few additional items are needed in order to recover the initial determinacy coefficient after the elimination of moderate or large group mean-differences.

The Trade-Off Between Factor Score Determinacy and the Preservation of Inter-Factor Correlations.

Regression factor score predictors have the maximum factor score determinacy, that is, the maximum correlation with the corresponding factor, but they do not have the same inter-correlations as the factors. As it might be useful to compute factor score predictors that have the same inter-correlations as the factors, correlation-preserving factor score predictors have been proposed. However, correlation-preserving factor score predictors have smaller correlations with the corresponding factors (factor score determinacy) than regression factor score predictors. Thus, higher factor score determinacy goes along with bias of the inter-correlations and unbiased inter-correlations go along with lower factor score determinacy. The aim of the present study was therefore to investigate the size and conditions of the trade-off between factor score determinacy and bias of inter-correlations by means of algebraic considerations and a simulation study. It turns out that under several conditions very small gains of factor score determinacy of the regression factor score predictor go along with a large bias of inter-correlations. Instead of using the regression factor score predictor by default, it is proposed to check whether substantial bias of inter-correlations can be avoided without substantial loss of factor score determinacy using a correlation-preserving factor score predictor. A syntax that allows to compute correlation-preserving factor score predictors from regression factor score predictors, and to compare factor score determinacy and inter-correlations of the factor score predictors is given in the Appendix.

Score predictor factor analysis as a tool for the identification of single-item indicators

Score Predictor Factor Analysis (SPFA) was introduced as a method to compute factor score predictors that are – under some conditions – more highly correlated with the common factors resulting from factor analysis than the factor score predictors computed from the factor model. In the present study, we investigate SPFA as a model in its own rights. In order to provide a basis for this, the properties and the utility of SPFA factor score predictors and the possibility to identify single-item indicators in SPFA loading matrices were investigated. Regarding the factor score predictors, the main result is that the best linear predictor of the SPFA has not only perfect determinacy but is also correlation preserving. Regarding the SPFA loadings it was found in a simulation study that five or more population factors that are represented by only one variable with a rather substantial loading can more accurately be identified by means of SPFA than with factor analysis. Moreover, the percentage of correctly identified single-item indicators was substantially larger for SPFA than for the factor model. It is proposed that SPFA is a tool that can be especially helpful when short scales or single-item indicators are to be identified.

Score Predictor Factor Analysis: Reproducing Observed Covariances by Means of Factor Score Predictors.

The non-diagonal elements of the observed covariances are more exactly reproduced by the factor loadings than by the model implied by the corresponding factor score predictors. This is a limitation to the validity of factor score predictors. It is therefore investigated whether it is possible to estimate factor loadings for which the model implied by the factor score predictors optimally reproduces the non-diagonal elements of the observed covariance matrix. Accordingly, loading estimates are proposed for which the model implied by the factor score predictors allows for a least-squares approximation of the non-diagonal elements of the observed covariance matrix. This estimation method is termed score predictor factor analysis and algebraically compared with Minres factor analysis as well as principal component analysis. A population-based and a sample-based simulation study was performed in order to compare score predictor factor analysis, Minres factor analysis, and principal component analysis. It turns out that the non-diagonal elements of the observed covariance matrix can more exactly be reproduced from the factor score predictors computed from score predictor factor analysis than from the factor score predictors computed from Minres factor analysis and from principal components.

A Schmid-Leiman-based transformation resulting in perfect inter-correlations of three types of factor score predictors

Factor score predictors are computed when individual factor scores are of interest. Conditions for a perfect inter-correlation of the best linear factor score predictor, the best linear conditionally unbiased predictor, and the determinant best linear correlation-preserving predictor are presented. A transformation resulting in perfect correlations of the three predictors is proposed.

On the bias of factor score determinacy coefficients based on different estimation methods of the exploratory factor model

ABSTRACTThe factor score determinacy coefficient represents the common variance of the factor score predictor with the corresponding factor. The aim of the present simulation study was to compare the bias of determinacy coefficients based on different estimation methods of the exploratory factor model. Overall, determinacy coefficients computed from parameters based on maximum likelihood estimation, unweighted least squares estimation, and principal axis factoring were more precise than determinacy coefficients based on generalized least squares estimation and alpha factoring.

The determinacy of the regression factor score predictor based on continuous parameter estimates from categorical variables

ABSTRACTThe estimation of population parameters of the continuous common factor model from categorical observed variables is meanwhile regularly performed. It is shown that the formula for the calculation of the determinacy of the regression factor score predictor from the estimated model parameters has to be adapted under these conditions. A method for the calculation of this determinacy from the model parameters of the continuous population factor model based on categorical variables is proposed and evaluated by means of simulated population data. It turns out that using the uncorrected formula can lead to serious overestimation of determinacy for categorical variables.

Extending the Debate Between Spearman and Wilson 1929: When do Single Variables Optimally Reproduce the Common Part of the Observed Covariances?

The covariances of observed variables reproduced from conventional factor score predictors are generally not the same as the covariances reproduced from the common factors. We sought to find a factor score predictor that optimally reproduces the common part of the observed covariances. It was found algebraically that—under some conditions—the single observed variable with highest loading on a factor reproduces the non-diagonal elements of the observed covariance matrix more exactly than the conventional factor score predictors. This finding is linked to Spearman's and Wilson's 1929 debate on the use of single variables as factor score predictors. A population-based and a sample-based simulation study confirmed the algebraic result that taking a single variable can outperform conventional factor score predictors in reproducing the non-diagonal covariances when the nonzero loading size and the number of nonzero loadings per factor are small. The results indicated that a weighted aggregation of variables does not necessarily lead to an improvement of the score over the variable with the highest loading.

A Note on Equality and Inequality of Some Factor Score Predictors

Anderson and Rubin (1956), as well as Takeuchi et al. (1982) finally Schneeweiss and Mathes (1995) proposed factor score predictors that were regarded as orthogonal. Anderson and Rubin's and Takeuchi et al.'s factor score predictors are shown to be identical for non zero unique variances, although they have never been claimed being identical. It is shown that the Schneeweiss and Mathe's factor score predictor is not equal to McDonald's factor score predictor, although it has been claimed that these predictors are identical. It is, moreover, shown that the Schneeweiss and Mathe's factor score predictor is orthogonal only for the canonical orthogonal factor model.

Retained-Components Factor Transformation: Factor Loadings and Factor Score Predictors in the Column Space of Retained Components

Retained-Components Factor Transformation: Factor Loadings and Factor Score Predictors in the Column Space of Retained Components

On the Correlation of Common Factors with Variance Not Accounted for by the Factor Model

The case that the factor model does not account for all the covariances of the observed variables is considered. It is shown that principal components representing covariances not accounted for by the factor model can have a nonzero correlation with the common factors of the factor model. The substantial correlations of components representing variance not accounted for by the factor model with common factors are demonstrated in a simulation study comprising model error. Based on these results, a new version of Harman's factor score predictor minimizing the correlation with residual components is proposed.

Absolute coronary artery calcium score is the best predictor of non-calcified plaque involvement in patients with low calcium scores (1–100)

ObjectiveWe sought to identify the predictors of non-calcified plaque (NCP) burden in patients with low coronary artery calcium (CAC) scores of 1–100. MethodsWe studied 920 consecutive patients clinically referred for coronary CT angiography (CCTA) with concomitant CAC scoring. The 276 patients with CAC 1–100 were divided into four groups based on the CAC score: CAC = 0, 1–10, 11–50, and 51–100. Univariate and multivariate linear regression analyses were performed for the demographic, risk factor, and CAC score predictors of number of coronary segments with NCP. ResultsMean age was 55 ± 11 years and 56% were women. Demographics and risk factors failed to identify NCP involvement in univariate models. The lone predictor of NCP burden was the absolute CAC score, which was persistently associated with NCP in multivariable models (CAC 51–100 vs. CAC 1–10, β-coefficient 0.35, p = 0.03). ConclusionsAbsolute CAC score is the lone robust predictor of NCP burden when CAC is 1–100. Risk within this mild coronary calcification group is likely heterogeneous, driven by the absolute CAC score.

Taking the Error Term of the Factor Model Into Account

The problem of factor score indeterminacy implies that the factor and the error scores cannot be completely disentangled in the factor model. It is therefore proposed to compute Harman’s factor score predictor that contains an additive combination of factor and error variance. This additive combination is discussed in the framework of classical test theory. On this basis, a definition of reliability, standard error of measurement, and confidence intervals for the factor score predictor are proposed. It is argued that factor score predictor intervals should be used instead of single score predictors to account for the error term in the factor model. The calculation of reliabilities and factor score predictor intervals is illustrated by means of a small simulation study and an empirical example.

Model‐related factor score predictors for confirmatory factor analysis

The present paper introduces model-related (MR) factor score predictors, which reflect specific aspects of confirmatory factor models. The development is mainly based on Schönemann and Steiger's regression score components, but it can also be applied to the factor score coefficients. It is shown that the rotation of factor score predictors has no impact on the covariance matrix reproduced from the corresponding regression component patterns. Thus, regression score components or factor score coefficients can be rotated in order to obtain the required properties. This idea is the basis for MR factor score predictors, which are computed by means of a partial Procrustes rotation towards a target pattern representing the interesting properties of a confirmatory factor model. Two examples demonstrate the construction of MR factor score predictors reflecting specific constraints of a factor model.

Some Results on Mean Square Error for Factor Score Prediction.

For the confirmatory factor model a series of inequalities is given with respect to the mean square error (MSE) of three main factor score predictors. The eigenvalues of these MSE matrices are a monotonic function of the eigenvalues of the matrix Γ p = Φ 1/2 Λ p 'Ψ p-1 Λ p Φ 1/2. This matrix increases with the number of observable variables p. A necessary and sufficient condition for mean square convergence of predictors is divergence of the smallest eigenvalue of Γ p or, equivalently, divergence of signal-to-noise (Schneeweiss & Mathes, 1995). The same condition is necessary and sufficient for convergence to zero of the positive definite MSE differences of factor predictors, convergence to zero of the distance between factor predictors, and convergence to the unit value of the relative efficiencies of predictors. Various illustrations and examples of the convergence are given as well as explicit recommendations on the problem of choosing between the three main factor score predictors.