The Cronbach's alpha is the most widely used method for estimating internal consistency reliability. This procedure has proved very resistant to the passage of time, even if its limitations are well documented and although there are better options as omega coefficient or the different versions of glb, with obvious advantages especially for applied research in which the ítems differ in quality or have skewed distributions. In this paper, using Monte Carlo simulation, the performance of these reliability coefficients under a one-dimensional model is evaluated in terms of skewness and no tau-equivalence. The results show that omega coefficient is always better choice than alpha and in the presence of skew items is preferable to use omega glb coefficients even in small samples.

This study compares the performance of two approaches in analysing four-point Likert rating scales with a factorial model: the classical factor analysis (FA) and the item factor analysis (IFA). For FA, maximum likelihood and weighted least squares estimations using Pearson correlation matrices among items are compared. For IFA, diagonally weighted least squares and unweighted least squares estimations using items polychoric correlation matrices are compared. Two hundred and ten conditions were simulated in a Monte Carlo study considering: one to three factor structures (either, independent and correlated in two levels), medium or low quality of items, three different levels of item asymmetry and five sample sizes. Results showed that IFA procedures achieve equivalent and accurate parameter estimates; in contrast, FA procedures yielded biased parameter estimates. Therefore, we do not recommend classical FA under the conditions considered. Minimum requirements for achieving accurate results using IFA procedures are discussed.

Several medications commonly used for a number of medical conditions share a property of functional inhibition of acid sphingomyelinase (ASM), or FIASMA. Preclinical and clinical evidence suggest that the ASM/ceramide system may be central to severe acute respiratory syndrome‐coronavirus 2 (SARS‐CoV‐2) infection. We examined the potential usefulness of FIASMA use among patients hospitalized for severe coronavirus disease 2019 (COVID‐19) in an observational multicenter study conducted at Greater Paris University hospitals. Of 2,846 adult patients hospitalized for severe COVID‐19, 277 (9.7%) were taking an FIASMA medication at the time of their hospital admission. The primary end point was a composite of intubation and/or death. We compared this end point between patients taking vs. not taking an FIASMA medication in time‐to‐event analyses adjusted for sociodemographic characteristics and medical comorbidities. The primary analysis was a Cox regression model with inverse probability weighting (IPW). Over a mean follow‐up of 9.2 days (SD = 12.5), the primary end point occurred in 104 patients (37.5%) receiving an FIASMA medication, and 1,060 patients (41.4%) who did not. Despite being significantly and substantially associated with older age and greater medical severity, FIASMA medication use was significantly associated with reduced likelihood of intubation or death in both crude (hazard ratio (HR) = 0.71, 95% confidence interval (CI) = 0.58–0.87, P < 0.001) and primary IPW (HR = 0.58, 95%CI = 0.46–0.72, P < 0.001) analyses. This association remained significant in multiple sensitivity analyses and was not specific to one particular FIASMA class or medication. These results show the potential importance of the ASM/ceramide system in COVID‐19 and support the continuation of FIASMA medications in these patients. Double‐blind controlled randomized clinical trials of these medications for COVID‐19 are needed.

In the context of multidimensional structures, with the presence of a common factor and multiple specific or group factors, estimates of reliability require specific estimators. The use of classical procedures such as the alpha coefficient or omega total that ignore structural complexity are not appropriate, since they can lead to strongly biased estimates. Through a simulation study, the bias of six estimators of reliability in multidimensional measures was evaluated and compared. The study is complemented by an empirical illustration that exemplifies the procedure. Results showed that the estimators with the lowest bias in the estimation of the total reliability parameter are omega total, the two versions of greatest lower bound (GLB) and the alpha coefficient, which in turn are also those that produce the highest overestimation of the reliability of the general factor. Nevertheless, the most appropriate estimators, in that they produce less biased estimates of the reliability parameter of the general factor, are omega limit and omega hierarchical.

There is a series of conventions governing how Confirmatory Factor Analysis gets applied, from minimum sample size to the number of items representing each factor, to estimation of factor loadings so they may be interpreted. In their implementation, these rules sometimes lead to unjustified decisions, because they sideline important questions about a model’s practical significance and validity. Conducting a Monte Carlo simulation study, the present research shows the compensatory effects of sample size, number of items, and strength of factor loadings on the stability of parameter estimation when Confirmatory Factor Analysis is conducted. The results point to various scenarios in which bad decisions are easy to make and not detectable through goodness of fit evaluation. In light of the findings, these authors alert researchers to the possible consequences of arbitrary rule following while validating factor models. Before applying the rules, we recommend that the applied researcher conduct their own simulation studies, to determine what conditions would guarantee a stable solution for the particular factor model in question.

The increasing value of character strengths in the prediction of well-being and psychopathology, after the effects of functional social support and sociodemographic variables are accounted for, is examined. Participants were 1494 Spanish-speaking students between the ages of 18 and 68 (43.3% men and 56.7% women) who completed measures of character strengths, functional social support, subjective well-being, psychological well-being, and symptoms of psychopathology. Functional social support had predictive value in explaining the variability of each component of well-being and psychopathology. Regarding character strengths, theological strengths had the greatest predictive power for life satisfaction (β = 0.41), positive affect (β = 0.49), affect balance (β = 0.45), purpose in life (β = 0.60), self-acceptance (β = 0.50), environmental mastery (β = 0.47), and positive relations with others (β = 0.25). Emotional strengths made the strongest contribution to the variance explained (β = 0.41) of autonomy, and intellectual strengths were the strongest predictive variable for personal growth (β = 0.39). Strengths of restraint had the greatest predictive power for the global severity index of psychopathology (β = –0.27). Functional social support and character strengths have strong links to mental health. Positive interventions to develop these variables could contribute to enhance well-being and prevent psychological distress.