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Even when a single factor is to be measured, it may occur in the context of blind random item selection that individuals work on items that are not based on exactly the same factor. Therefore, we explore the consequences of presenting items from different populations measuring different factors across and within individuals. We found that item inter-correlations can be substantial in the total population of individuals even when – in subpopulations of individuals – items are drawn from populations based on different, even uncorrelated factors. In order to address this challenge for convergent validity, we propose a method that helps to detect whether the correlation between items is due to the same common factor measured by the items across all individuals. Based on the analytical results and results from a simulation study, we provide recommendations for the detection of subpopulations of individuals responding to items from different item populations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Aims/BackgroundDesign/MethodsResultsConclusionIn recent years, there has been an increased interested in the contribution of fathers to childbirth. In the present paper we explore whether the father’s birth-related mindset (being either more natural or more medical) can predict the mother’s labour and birth outcomes and whether the father’s experience and evaluation of the birth can predict his psychological well-being after the birth.We conducted a longitudinal study (N = 304 expecting fathers) spanning the first trimester of pregnancy up to six months after birth.The study results could be integrated into a Single Indicator Model. They indicated that a father’s more natural birth-related mindset predicted low-intervention birth for the mother. A low-intervention birth led to a more positive birth experience for the father, and in turn to better postpartum adjustment, fewer symptoms of postpartum paternal depression eight weeks after birth, and more secure bonding to the infant six months after birthThe study results indicate that fathers do not take a merely passive role in childbirth. On the contrary, analogous to pregnant mothers, fathers’ views about childbirth might contribute to the course of birth, and their own postpartum psychological well-being. The results of the present study, thus, highlight the importance of fathers for childbirth and in a broader sense, the results also contribute to a better understanding of the psychological framework of birth and represents a valuable starting point for further research. [ABSTRACT FROM AUTHOR]

In this paper we explore whether mental representations about birth (birth‐related mindsets) assessed during pregnancy can predict labour and birth in the sense that the perception of birth as a natural (rather than a medical) event increases the probability of a low‐intervention birth. Birth, in turn, might affect short‐term and long‐term psychological well‐being. These assumptions were tested in a longitudinal study (N = 311), spanning the first half of pregnancy and up to 6 months after birth. The results of a single indicator model displayed a sequential process: women who held a more natural mindset prenatally were more likely to have low‐intervention births, which resulted in a more positive evaluation of the birth experience, which in turn predicted well‐being in the first weeks after birth (measured with ecological momentary assessment), and subsequently postpartum depression and post‐traumatic stress symptoms 8 weeks after birth as well as mother‐infant bonding 6 months after birth. The study demonstrates the relevance of psychological factors for childbirth. The construct of a birth‐related mindset could contribute to a better understanding of childbirth and help to make women's birth experiences safer and more satisfying and to improve the transition to motherhood. [ABSTRACT FROM AUTHOR]

Background: The aim of the present paper was to explore the role of partners for the stressful life events of birth and the transition to parenthood. Methods: In a first prospective longitudinal study (N = 304 dyads) we tested whether relationship quality positively predicted fewer interventions during labor and birth, a more positive birth experience, and better well-being during the first six weeks after birth. In a second study we surveyed mothers (N = 980; retrospective quasi-experimental design) who had given birth during the first lockdown of the COVID-19 pandemic in spring 2020 – some in the absence of their partners – to test the assumption that regardless of relationship quality, the presence of the partner was positively related to low-intervention births and the birth experience. Results: The results of the longitudinal study (Study 1) could be integrated into a Single Indicator model. They revealed that a high relationship quality assessed between week 5 and week 25 of pregnancy had a positive effect on birth experience for the mother and on psychological well-being during the transition to parenthood for both mothers and fathers. Results of the retrospective quasi-experimental field study (Study 2) revealed that the continuous presence of the partner was associated with a higher probability of a low-intervention birth and a more positive birth experience. Presence of a partner for only part of the birth did not positively predict labor and birth, but did positively predict the birth experience. The effects were independent of relationship quality. Conclusion: The results of both studies highlight the importance of partners for psychological well-being during labor and birth and the transition to parenthood. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the context of Bayesian factor analysis, it is possible to compute plausible values, which might be used as covariates or predictors or to provide individual scores for the Bayesian latent variables. Previous simulation studies ascertained the validity of mean plausible values by the mean squared difference of the mean plausible values and the generating factor scores. However, the mean correlation of sets of single plausible values of different factors were shown to be an adequate estimator of the correlation between factors. Using sets of single plausible values to compute a mean prediction in secondary analysis implies that their determinacy should be known. Therefore, a plausible value-based determinacy coefficient allowing for estimation of the determinacy of single plausible values was proposed and evaluated by means of two simulation studies. The first simulation study demonstrated that the plausible value-based determinacy coefficient is an adequate estimate of the correlation of single plausible values with the population factor. It is also shown that the plausible value-based determinacy coefficient of mean plausible values approaches the conventional, model parameter-based determinacy coefficient with increasing number of imputations. The second simulation study revealed that the plausible value-based determinacy coefficient and the model parameter-based determinacy coefficient yield similar results even for misspecified models in small samples. It also revealed that for small sample sizes and a small salient loading size, the coefficients of determinacy overestimate the validity, so that it is recommended to report the determinacy coefficients together with a bias-correction to estimate the validity of plausible values in empirical settings. [ABSTRACT FROM AUTHOR]

Methods for optimal factor rotation of two-facet loading matrices have recently been proposed. However, the problem of the correct number of factors to retain for rotation of two-facet loading matrices has rarely been addressed in the context of exploratory factor analysis. Most previous studies were based on the observation that two-facet loading matrices may be rank deficient when the salient loadings of each factor have the same sign. It was shown here that full-rank two-facet loading matrices are, in principle, possible, when some factors have positive and negative salient loadings. Accordingly, the current simulation study on the number of factors to extract for two-facet models was based on rank-deficient and full-rank two-facet population models. The number of factors to extract was estimated from traditional parallel analysis based on the mean of the unreduced eigenvalues as well as from nine other rather traditional versions of parallel analysis (based on the 95th percentile of eigenvalues, based on reduced eigenvalues, based on eigenvalue differences). Parallel analysis based on the mean eigenvalues of the correlation matrix with the squared multiple correlations of each variable with the remaining variables inserted in the main diagonal had the highest detection rates for most of the two-facet factor models. Recommendations for the identification of the correct number of factors are based on the simulation results, on the results of an empirical example data set, and on the conditions for approximately rank-deficient and full-rank two-facet models. [ABSTRACT FROM AUTHOR]

Unit-weighted scales are often computed in order to estimate the scores of individuals on common factors. Previous work has shown that unit-weighted scales imply a model that can be evaluated. The algebraic framework for this approach was presented here. The relationship between the fit of the model implied by unit-weighted scales, the scale length, and the item inter-correlations was evaluated algebraically and by means of a simulation study. The standardized root mean square residual (SRMR) excluding the elements in the main-diagonal of the item inter-correlation matrix can be used for the fit evaluation of the unit-weighted scale model. [ABSTRACT FROM AUTHOR]

The 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 fromparameters based onmaximum 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. [ABSTRACT FROM AUTHOR]

The 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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

A letter to the editor related to the need of diagnostic accuracy of critical flicker frequency (CFF) during comparison of different diagnosis tests of hepatic encephalopathy is presented.

Background/objectives l -Ornithine l -Aspartate (LOLA) is a mixture of two endogenous amino acids with the capacity to fix ammonia in the form of urea and/or glutamine. Its' efficacy for the treatment of Hepatic Encephalopathy (HE), a known hyperammonemic disorder, remains the subject of debate. This study quantitatively analyzed the efficacy of LOLA in patients with cirrhosis and HE. Methods Efficacy was defined as the extent of lowering of blood ammonia and improvement of mental state assessed in clinically overt HE (OHE) by Westhaven criteria or psychometric testing for assessment of Minimal HE (MHE). Appropriate keywords were used for electronic and/or manual searches of databases to identify RCTs for inclusion. Study quality and risk of bias were assessed using the Jadad Composite Scale together with The Cochrane Scoring Tool. Random Effects Models were used to express pooled Risk Ratio (RR) or Mean Difference (MD) with associated 95% Confidence Intervals (CI). Results 10 RCTs (884 patients) were included. Regression analysis showed no evidence of publication bias or other small study effects. Eight RCTs had low risk of bias by Jadad/Cochrane criteria. Comparison with placebo/no intervention controls revealed that LOLA was significantly more effective for improvement of mental state in all types of HE (RR 1.36 (95% CI 1.10–1.69), p = 0.005), OHE (RR: 1.19, 95% CI of 1.01–1.39, test for overall effect: Z = 2.14, p = 0.03), MHE (RR: 2.15 (1.48–3.14), p < 0.0001) and for lowering of blood ammonia (MD: −17.50 μmol/l (−27.73 to (−7.26)), p = 0.0008). Improvement of mental state was greater in trials with low risk of bias. Heterogeneity was reduced in trials from Europe or with >100 participants. Oral LOLA appeared particularly effective for the treatment of MHE. Conclusion LOLA appears to improve mental state and lower ammonia in patients with HE or MHE. Further studies are required in some subgroups of HE and in the era of HE reclassification. [ABSTRACT FROM AUTHOR]

Primitive neuroectodermal tumors (PNETs) such as human medulloblastomas are genetically heterogeneous and therefore poorly understood. In a rat model the SV40 large T antigen was used to induce neoplasms with characteristic features of PNETs. Tumor development requires a latency period of 8–11 months implicating secondary genetic alterations. To identify such secondary alterations we performed comparative analyses of two phenotypically identical PNET-derived cell lines. Indeed, these cell lines displayed distinct high-level amplification sites. Using a combination of subtractive cDNA analysis and radiation hybrid mapping we have now identified genes in the amplicon regions of the two cell lines. Interestingly, one of these genes encodes the rat homolog of a cytosolic branched chain aminotransferase (BCATC) previously shown to be amplified in a mouse teratocarcinoma cell line. We propose that this simple cloning strategy may serve as a powerful tool for the isolation of genes implicated in known chromosomal aberrations in primary tumors and tumor cell lines. Oncogene (2001) 20, 2023–2031. [ABSTRACT FROM AUTHOR]