References of "Magis, David"
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See detailUne solution numérique au test de Cattell pour déterminer le nombre de composantes principales à retenir
Raîche, Gilles; Magis, David ULg; Walls, Ted et al

Conference (2009, May)

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See detailLocal equating methods in the NEAT design
Janssen, Rianne; Magis, David ULg; San Martin, Ernesto et al

Conference (2009, April)

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See detailLa correction du résultat d’un étudiant en présence de tentatives de fraudes
Raîche, Gilles; Magis, David ULg; Béland, Sébastien

Scientific conference (2009, January)

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See detailLa détection des patrons de réponses problématiques dans le contexte des tests informatisés
Blais, Jean-Guy; Raîche, Gilles; Magis, David ULg

in Blais, Jean-Guy (Ed.) Evaluation des apprentissages et technologies de l’information et de la communication : Enjeux, applications et modèles de mesure (2009)

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See detailCrossed random effects models for the detection of differential item functioning in psychometrics
Magis, David ULg; Frederickx, Sofie; Tuerlinckx, Francis et al

Conference (2008, October)

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See detailDetection of differential item functioning: crossed random-effects models and robust diagnostics tools
Magis, David ULg; De Boeck, Paul

Scientific conference (2008, September)

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See detailA crossed random effects model to detect differential item functioning
Frederickx, Sofie; Magis, David ULg; Tuerlinckx, Francis et al

Conference (2008, June)

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See detailL’utilisation des simulations informatisées pour la recherche en éducation
Raîche, Gilles; Sodoké, Komi; Blais, Jean-Guy et al

Conference (2008, May)

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See detailSome score-equating methods in psychometrics and education
Magis, David ULg

Scientific conference (2008, February)

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See detailAdaptive Bayesian estimation of proficiency level in computerized adaptive testing
Magis, David ULg

Scientific conference (2008, February)

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See detailAlteration of Right Ventricular-Pulmonary Vascular Coupling in a Porcine Model of Progressive Pressure Overloading
Ghuysen, Alexandre ULg; Lambermont, Bernard ULg; Kolh, Philippe ULg et al

in Shock (Augusta, Ga.) (2008), 29(2), 197-204

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 ... [more ▼]

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). Afterload 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. Eaincreased progressively after embolization, from 0.26 +/- 0.04 to 2.2 +/- 0.7 mmHg mL (P < 0.05). Ees increased from 1.01 +/-0.07 to 2.35 +/- 0.27 mmHg mL (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. [less ▲]

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See detailAn efficient method to generate data and compute exact P-values in goodness-of-fit testing
Magis, David ULg

in Communications in Statistics : Simulation & Computation (2008), 37

In this article, we use a characterization of the set of sample counts that do not match with the null hypothesis of the test of goodness of fit. Two direct applications arise: first, to instantaneously ... [more ▼]

In this article, we use a characterization of the set of sample counts that do not match with the null hypothesis of the test of goodness of fit. Two direct applications arise: first, to instantaneously generate data sets whose corresponding asymptotic P-values belong to a certain pre-defined range; and second, to compute exact P-values for this test in an efficient way. We present both issues before illustrating them by analyzing a couple of data sets. Method’s efficiency is also assessed by means of simulations. We focus on Pearson’s X2 statistic but the case of likelihood-ratio statistic is also discussed. [less ▲]

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See detailEquating the Chilean university entrance exam
Magis, David ULg; Janssen, Rianne; San Martin, Ernesto et al

Conference (2007, November)

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See detailEst-ce qu'il est possible d'obtenir le résultat véritable d'un élève même si celui-ci tente de tricher ?
Raîche, Gilles; Blais, Jean-Guy; Magis, David ULg

Conference (2007, October)

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See detailQuick data set generation and exact P-value computation in goodness-of-fit testing
Magis, David ULg

Conference (2007, October)

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See detailAdaptive estimators of proficiency in adaptive testing
Blais, Jean-Guy; Raîche, Gilles; Magis, David ULg

Conference (2007, June)

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See detailAdaptive estimators of trait level in adaptive testing: Some proposals
Raîche, Gilles; Blais, Jean-Guy; Magis, David ULg

in Weiss, David (Ed.) Proceedings of the 2007 GMAC Conference on Computerized Adaptive Testing (2007, June)

In a computerized adaptive test (CAT), we seek an acceptably accurate trait (θ) level estimate using an optimal number of items. Bayesian estimation methods like MAP and EAP are often used to compute that ... [more ▼]

In a computerized adaptive test (CAT), we seek an acceptably accurate trait (θ) level estimate using an optimal number of items. Bayesian estimation methods like MAP and EAP are often used to compute that estimate. Unfortunately, with such methods, decreasing the number of items generates bias whenever the true θ level differs significantly from the a priori estimate. Adaptive versions of the maximum a posteriori and expected a posteriori estimation methods are proposed to reduce this bias. These AMAP and AEAP methods adapt the a priori values used in the estimation function according to the previously computed θ estimate obtained from the previous administered item. The performance of AMAP and AEAP is evaluated in a CAT context. [less ▲]

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See detailInfluence, information and item response theory in discrete data analysis
Magis, David ULg

Doctoral thesis (2007)

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See detailOn the detection of influential subsets of categories in goodness of fit testing
Magis, David ULg

in Statistical Methodology (2007), 4

In this paper we focus on the well-known test of goodness of fit for comparing observed counts to expected values under some null hypothesis. When the latter is rejected, we propose a simple method for ... [more ▼]

In this paper we focus on the well-known test of goodness of fit for comparing observed counts to expected values under some null hypothesis. When the latter is rejected, we propose a simple method for detecting which subset(s) of category counts provoke(s) that rejection. The approach aims at building intervals iteratively and drawing appropriate conclusions on that basis.We discuss this method with respect to other classical approaches. We illustrate our purpose by treating some examples. [less ▲]

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