Most of the strategies that have been proposed to determine the number of components that account for the most variation in a principal components analysis of a correlation matrix rely on the analysis of the eigenvalues and on numerical solutions. The Cattell’s scree test is a graphical strategy with a nonnumerical solution to determine the number of components to retain. Like Kaiser’s rule, this test is one of the most frequently used strategies for determining the number of components to retain. However, the graphical nature of the scree test does not definitively establish the number of components to retain. To circumvent this issue, some numerical solutions are proposed, one in the spirit of Cattell’s work and dealing with the scree part of the eigenvalues plot, and one focusing on the elbow part of this plot. A simulation study compares the efficiency of these solutions to those of other previously proposed methods. Extensions to factor analysis are possible and may be particularly useful with many low-dimensional components.

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This paper outlines a computerized adaptive testing (CAT) framework and presents an R package for the simulation of response patterns under CAT procedures. This package, called catR, requires a bank of items, previously calibrated according to the four-parameter logistic (4PL) model or any simpler logistic model. The package proposes several methods to select the early test items, several methods for next item selection, different estimators of ability (maximum likelihood, Bayes modal, expected a posteriori, weighted likelihood), and three stopping rules (based on the test length, the precision of ability estimates or the classification of the examinee). After a short description of the different steps of a CAT process, the commands and options of the catR package are presented and practically illustrated.

This article proposes a novel approach to detect differential item functioning (DIF) among dichotomously scored items. Unlike standard DIF methods that perform an item-by-item analysis, we propose the ''LR lasso DIF method'': logistic regression (LR) model is formulated for all item responses. The model contains item-specific intercepts, an effect of the sum score, and item-group interaction (i.e., DIF) effects, with a lasso penalty on all DIF parameters. Optimal penalty parameter selection is investigated through several known information criteria (Akaike information criterion, Bayesian information criterion, and cross validation) as well as through a newly developed alternative. A simulation study was conducted to compare the global performance of the suggested LR lasso DIF method to the LR and Mantel-Haenszel methods (in terms of false alarm and hit rates). It is concluded that for small samples, the LR lasso DIF approach globally outperforms the LR method, and also the Mantel-Haenszel method, especially in the presence of item impact, while it yields similar results with larger samples.

In the very early phase of endotoxinic shock, right ventricular-vascular coupling is preserved by an increase in RV contractility. Later, myocardial oxygen consumption and energetic cost of RV contractility are increased, as evidenced by the decrease in RV efficiency, and right ventricular-vascular uncoupling occurs. Therefore, therapies aiming at restoring right ventricular-vascular coupling in endotoxic shock should attempt to increase RV contractility and to decrease RV afterload but also to preserve RV mechanical efficiency.

The purpose of this paper is to list the recent updates of the R package catR. This package allows for generating response patterns under a computerized adaptive testing (CAT) framework with underlying item response theory (IRT) models. Among the most important updates, well-known polytomous IRT models are now supported by catR; several item selection rules have been added; and it is now possible to perform post-hoc simulations. Some functions were also rewritten or withdrawn to improve the usefulness and performances of the package.

In acute pulmonary embolism, right ventricular (RV) failure may result from exceeding myocardial contractile resources with respect to the state of vascular afterload. We investigated the adaptation of RV performance in a porcine model of progressive pulmonary embolism. Twelve anesthetized pigs were randomly divided into two groups: gradual pulmonary arterial pressure increases by three injections of autologous blood clot (n=6) or sham-operated controls (n=6). Right ventricular pressure-volume (PV) loops were recorded using a conductance catheter. Right ventricular contractility was estimated by the slope of the end-systolic PV relationship (Ees). After load was referred to as pulmonary arterial elastance (Ea) and assessed using a four-element Windkessel model. Right ventricular-arterial coupling (Ees/Ea) and efficiency of energy transfer (from PV area to external mechanical work [stroke work]) were assessed at baseline and every 30 min for 4 h. Ea increased progressively after embolization, from 0.26+/-0.04 to 2.2+/-0.7 mmHg mL(-1) (P<0.05). Ees increased from 1.01+/-0.07 to 2.35+/-0.27 mmHg mL(-1) (P<0.05) after the first two injections but failed to increase any further. As a result, Ees/Ea initially decreased to values associated with optimal SW, but the last injection was responsible for Ees/Ea values less than 1, decreased stroke volume, and RV dilation. Stroke work/PV area consistently decreased with each injection from 79%+/-3% to 39%+/-11% (P<0.05). In response to gradual increases in afterload, RV contractility reserve was recruited to a point of optimal coupling but submaximal efficiency. Further afterload increases led to RV-vascular uncoupling and failure.

This paper focuses on two likelihood-based indices of person fit, the index lz and the Snijders’s modified index lz *. The first one is commonly used in practical assessment of person fit, although its asymptotic standard normal distribution is not valid when true abilities are replaced by sample ability estimates. The lz * index is a generalization of lz , which corrects for this sampling variability. Surprisingly, it is not yet popular in the psychometric and educational assessment community. Moreover, there is some ambiguity about which type of item response model and ability estimation method can be used to compute the lz * index. The purpose of this article is to present the index lz * in a simple and didactic approach. Starting from the relationship between lz and lz *, we develop the framework according to the type of logistic item response theory (IRT) model and the likelihood-based estimators of ability. The practical calculation of lz * is illustrated by analyzing a real data set about language skill assessment.