Some Results on Mean Square Error for Factor Score Prediction

Non-parametric Regression Among Factor Scores: Motivation and Diagnostics for Nonlinear Structural Equation Models

We provide a framework for motivating and diagnosing the functional form in the structural part of nonlinear or linear structural equation models when the measurement model is a correctly specified linear confirmatory factor model. A mathematical population-based analysis provides asymptotic identification results for conditional expectations of a coordinate of an endogenous latent variable given exogenous and possibly other endogenous latent variables, and theoretically well-founded estimates of this conditional expectation are suggested. Simulation studies show that these estimators behave well compared to presently available alternatives. Practically, we recommend the estimator using Bartlett factor scores as input to classical non-parametric regression methods.

Using Electronic Handgrip Dynamometry and Accelerometry to Examine Multiple Aspects of Handgrip Function in Master Endurance Athletes: A Pilot Study

ABSTRACT

Klawitter, LA, Hackney, KJ, Christensen, BK, Hamm, JM, Hanson, M, and McGrath, R. Using electronic handgrip dynamometry and accelerometry to examine multiple aspects of handgrip function in master endurance athletes: A Pilot Study. J Strength Cond Res 37(9): 1777-1782, 2023-Electronic handgrip dynamometry and accelerometry may provide novel opportunities to comprehensively measure muscle function for human performance, especially for master athletes. This investigation sought to determine the multivariate relationships between maximal strength, asymmetry, rate of force development, fatigability, submaximal force control, bimanual coordination, and neuromuscular steadiness to derive one or more handgrip principal components in master-aged endurance athletes. We included n = 31 cyclists and triathletes aged 35-70 years. Maximal strength, asymmetry, rate of force development, fatigability, submaximal force control, bimanual coordination, and neuromuscular steadiness were measured twice on each hand using electronic handgrip dynamometry and accelerometry. The highest performing measures were included in the analyses. A principal component analysis was conducted to derive a new collection of uncorrelated variables from the collected handgrip measurements. Principal components with eigenvalues >1.0 were kept, and individual measures with a factor loading of |>0.40| were retained in each principal component. There were 3 principal components retained with eigenvalues of 2.46, 1.31, and 1.17. The first principal component, "robust strength," contained maximal strength, rate of force development, submaximal force control, and neuromuscular steadiness. The second principal component, "bilateral synergy," contained asymmetry and bimanual coordination, whereas the third principal component, "muscle conditioning," contained fatigability. Principal components 1, 2, and 3 explained 44.0, 31.6, and 24.4% of the variance, respectively. Different dimensions of muscle function emerged from our findings, suggesting the potential of a muscle function battery. Further research examining how these measures are associated with appropriate human performance metrics and lower extremity correlates is warranted.

Measurement invariance of the Liebowitz Social Anxiety Scale-Self-Report

OBJECTIVE

The Liebowitz Social Anxiety Scale-Self Report (LSAS-SR) is a self-report measure of social anxiety (SA), which has shown adequate psychometric properties across cultures. However, no study has systematically evaluated its measurement invariance (MI) between (a) individuals with and without a diagnosis of social anxiety disorder (SAD) and (b) males and females. The current study addresses this issue.

METHODS

We collected data on 257 (158 females) Italian individuals diagnosed with SAD and 356 (232 females) community-dwelling adults.

RESULTS

We initially found support for the unidimensionality of the Italian LSAS-SR measurement model in all samples. Using the Graded Response Model, we obtained evidence of partial MI and differential item functioning between community-dwelling and SAD-diagnosed individuals and evidence of strong MI between male and female participants.

CONCLUSIONS

The results of this study suggest that the Italian LSAS-SR measures the same trait in the same way across the symptom continuum and sexes, making it a psychometrically sound tool for assessment, screening, and research purposes.

Examining Additional Aspects of Muscle Function with a Digital Handgrip Dynamometer and Accelerometer in Older Adults: A Pilot Study

Background: Maximal handgrip strength (HGS) could be an incomplete and unidimensional measure of muscle function. This pilot study sought to examine the relationships between maximal HGS, radial and ulnar digit grip strength, submaximal HGS force control, HGS fatigability, neuromuscular HGS steadiness, and HGS asymmetry in older adults. Methods: A digital handgrip dynamometer and accelerometer was used to collect several HGS measurements from 13 adults aged 70.9 ± 4.0 years: maximal strength, radial and ulnar digit grip strength, submaximal force control, fatigability, neuromuscular steadiness, and asymmetry. Pearson correlations determined the relationships between individual HGS measurements. A principal component analysis was used to derive a collection of new uncorrelated variables from the HGS measures we examined. Results: The individual HGS measurements were differentially correlated. Maximal strength (maximal HGS, radial digit strength, ulnar digits strength), contractile steadiness (maximal HGS steadiness, ulnar digit grip strength steadiness), and functional strength (submaximal HGS force control, HGS fatigability, HGS asymmetry, HGS fatigability steadiness) emerged as dimensions from the HGS measurements that we evaluated. Conclusion: Our findings suggest that these additional measures of muscle function may differ from maximal HGS alone. Continued research is warranted for improving how we assess muscle function with more modern technologies, including handgrip dynamometry and accelerometry.

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.

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 metabolomic approach identifies differences in maternal serum in third trimester pregnancies that end in poor perinatal outcome

Metabolomics offers a powerful holistic approach to examine the metabolite composition of biofluids to identify disruptions present in disease. We used ultra performance liquid chromatography-mass spectroscopy on the maternal serum obtained in the third trimester to address the hypothesis that pregnancies ending in poor outcomes (small for gestational age infant, preterm birth, or neonatal intensive care admission, n = 40) would have a different maternal serum metabolic profiles to matched healthy pregnancies (n = 40). Ninety-eight identified metabolic features differed between normal and poor pregnancy outcomes. Classes of metabolites perturbed included free fatty acids, glycerolipids, progesterone metabolites, sterol lipids, vitamin D metabolites, and sphingolipids; these highlight potential molecular mechanisms associated with pregnancy complications in the third trimester linked by placental dysfunction. In this clinical setting, metabolomics has the potential to describe differences in fetoplacental and maternal metabolites in pregnancies with poor pregnancy outcomes compared with controls.