1/4 de finale de championnat de France 2e serie qui a oppose l'ASL a l'equipe d'Ogeu le 4 juin 2006. Laurent Zimmermann a retrouve des photos sur un CD envoye par Yves Bergantin, le photographe d'un journal bearnais qui suivait le match. Les photos sont superbes... Nous les diffusons avec un petit retour sur le beau parcours des seniors cette annee la en championnat de France.

The pair comparison design for distinguishing between stimuli located on the same natural or hypothesized linear continuum is used both when the response is a personal preference and when it is an impersonal judgment. Appropriate models which complement the different responses have been proposed. However, the models most appropriate for impersonal judgments have also been described as modeling choice, which may imply personal preference. This leads to potential confusion in interpretation of scale estimates of the stimuli, in particular whether they reflect a substantive order on the variable or reflect a characteristic of the sample which is different from the substantive order on the variable. Using Thurstone's concept of a discriminal response when a person engages with each stimulus, this article explains the overlapping and distinctive relationships between models for pair comparison designs when used for preference and judgment. In doing so, it exploits the properties of the relatively new hyperbolic cosine model which is not only appropriate for modeling personal preferences but has an explicit mathematical relationship with models for impersonal judgments. The hyperbolic cosine model is shown to be a special case of a more general form, referred to in parallel with Thurstone's Law of Comparative Judgment, as a specific law of comparative preference. Analyses of two real data sets illustrate the differences between the models most appropriate for personal preferences and impersonal judgments in a pair comparison design.

This brief report derives the N in the penalty term of the Schwarz's (1978) Bayesian information criterion (BIC) for two-parameter logistic item response models. The results in this study show that the N is the number of persons for fixed item models, whereas it is the number of observations (the Number of Persons times the Number of Items) for random item models. Given these results, the authors recommend researchers to calculate the BIC or to validate the BIC value that shows in the output of software instead of accepting the output value without a further check of implicit assumptions made for the software.

As there is currently a marked increase in the use of both unidimensional (UCAT) and multidimensional computerized adaptive testing (MCAT) in psychological and health measurement, the main aim of the present study is to assess the incremental value of using MCAT rather than separate UCATs for each dimension. Simulations are based on empirical data that could be considered typical for health measurement: a large number of dimensions (4), strong correlations among dimensions (.77-.87), and polytomously scored response data. Both variable- (SE < .316, SE < .387) and fixed-length conditions (total test length of 12, 20, or 32 items) are studied. The item parameters and variance–covariance matrix Φ are estimated with the multidimensional graded response model (GRM). Outcome variables include computerized adaptive test (CAT) length, root mean square error (RMSE), and bias. Both simulated and empirical latent trait distributions are used to sample vectors of true scores. MCATs were generally more efficient (in terms of test length) and more accurate (in terms of RMSE) than their UCAT counterparts. Absolute average bias was highest for variable-length UCATs with termination rule SE < .387. Test length of variable-length MCATs was on average 20% to 25% shorter than test length across separate UCATs. This study showed that there are clear advantages of using MCAT rather than UCAT in a setting typical for health measurement.

It has been widely known that the Type I error rates of goodness-of-fit tests using full information test statistics, such as Pearson's test statistic χ2 and the likelihood ratio test statistic G2, are problematic when data are sparse. Under such conditions, the limited information goodness-of-fit test statistic M2 is recommended in model fit assessment for models with binary response data. A simulation study was conducted to investigate the power and Type I error rate of M2 in fitting unidimensional models to many different types of multidimensional data. As an additional interest, the behavior of RMSEA2 was also examined, which is the root mean square error approximation (RMSEA) based on M2. Findings from the current study showed that M2 and RMSEA2 are sensitive in detecting the misfits due to varying slope parameters, the bifactor structure, and the partially (or completely) simple structure for multidimensional data, but not the misfits due to the within-item multidimensional structures.

In standardized multiple-choice testing, examinees may change their answers for various reasons. The statistical analysis of answer changes (ACs) has uncovered multiple testing irregularities on large-scale assessments and is now routinely performed at many testing organizations. This article exploits a recent approach where the information about all previous answers is used only to partition administered items into two disjoint subtests: items where an AC occurred and items where an AC did not occur. Two optimal statistics are described, each measuring a difference in performance between these subtests, where the performance is estimated from the final responses. Answer-changing behavior was simulated, where realistic distributions of wrong-to-right, wrong-to-wrong, and right-to-wrong ACs were achieved under various conditions controlled by the following independent variables: type of test, amount of aberrancy, and amount of uncertainty. Results of computer simulations confirmed the theoretical constructs on the optimal power of both statistics and provided several recommendations for practitioners.

The purpose of this study was to examine the performance of the Metropolis-Hastings Robbins-Monro (MH-RM) algorithm in the estimation of multilevel multidimensional item response theory (ML-MIRT) models. The accuracy and efficiency of MH-RM in recovering item parameters, latent variances and covariances, as well as ability estimates within and between clusters (e.g., schools) were investigated in a simulation study, varying the number of dimensions, the intraclass correlation coefficient, the number of clusters, and cluster size, for a total of 24 conditions. Overall, MH-RM performed well in recovering the item, person, and group-level parameters of the model. Ratios of the empirical to analytical standard errors indicated that the analytical standard errors reported in flexMIRT were somewhat overestimated for the cluster-level ability estimates, a little too large for the person-level ability estimates, and essentially accurate for the other parameters. Limitations of the study, implications for educational measurement practice, and directions for future research are offered.

When tests consist of a small number of items, the use of latent trait estimates for secondary analyses is problematic. One area in particular where latent trait estimates have been problematic is when testing for item misfit. This article explores the use of plausible-value imputations to lessen the severity of the inherent measurement unreliability in shorter tests, and proposes a parametric bootstrap procedure to generate empirical sampling characteristics for null-hypothesis tests of item fit. Simulation results suggest that the proposed item-fit statistics provide conservative to nominal error detection rates. Power to detect item misfit tended to be less than Stone's item-fit statistic but higher than the statistic proposed by Orlando and Thissen, especially in tests with 20 or more dichotomously scored items.

Q-matrix validation is of increasing concern due to the significance and subjective tendency of Q-matrix construction in the modeling process. This research proposes a residual-based approach to empirically validate Q-matrix specification based on a combination of fit measures. The approach separates Q-matrix validation into four logical steps, including the test-level evaluation, possible distinction between attribute-level and item-level misspecifications, identification of the hit item, and fit information to aid in item adjustment. Through simulation studies and real-life examples, it is shown that the misspecified items can be detected as the hit item and adjusted sequentially when the misspecification occurs at the item level or at random. Adjustment can be based on the maximum reduction of the test-level measures. When adjustment of individual items tends to be useless, attribute-level misspecification is of concern. The approach can accommodate a variety of cognitive diagnosis models (CDMs) and be extended to cover other response formats.

Repeatedly using items in high-stake testing programs provides a chance for test takers to have knowledge of particular items in advance of test administrations. A predictive checking method is proposed to detect whether a person uses preknowledge on repeatedly used items (i.e., possibly compromised items) by using information from secure items that have zero or very low exposure rates. Responses on the secure items are first used to estimate a person's proficiency distribution, and then the corresponding predictive distribution for the person's responses on the possibly compromised items is constructed. The use of preknowledge is identified by comparing the observed responses to the predictive distribution. Different estimation methods for obtaining a person's proficiency distribution and different choices of test statistic in predictive checking are considered. A simulation study was conducted to evaluate the empirical Type I error and power rate of the proposed method. The simulation results suggested that the Type I error of this method is well controlled, and this method is effective in detecting preknowledge when a large proportion of items are compromised even with a short secure section. An empirical example is also presented to demonstrate its practical use.