L’engagement professionnel chez les nouveaux enseignants et la satisfaction des gestionnaires d’école à l’égard du travail effectué par les enseignants novices

Le monde actuel, marqué par une augmentation incessante des exigences professionnelles, requiert des enseignants une adaptation constante aux changements sociaux, culturels et économiques. Si, pour les enseignants expérimentés, l’accommodation à ces transformations est accompagnée de plusieurs défis, pour les nouveaux enseignants qui ne maîtrisent pas complètement tous les aspects de la profession, l’intégration au milieu scolaire peut être extrêmement difficile ou même insupportable, au point où certains quittent le métier. Néanmoins, à force de persévérance, un certain nombre des nouveaux enseignants franchissent les obstacles imposés par la profession. Dans leur cas, la satisfaction et l’engagement professionnel peuvent être des caractéristiques importantes qui les incitent à continuer à exercer leurs activités d’enseignement. Dans ce contexte, l’étude vise l’analyse des éléments liés à la construction de l’identité professionnelle des enseignants lors de leur insertion dans le métier, à partir des perceptions des nouveaux enseignants et de celles des gestionnaires des écoles primaires et secondaires. L’harmonie entre la perception de ces deux groupes d’acteurs scolaires peut constituer un important facteur du rendement des professionnels dans leur métier et de l’efficacité des institutions d’enseignement. Ainsi, du côté des nouveaux enseignants, l’étude examine les variables qui peuvent être liées à leur engagement professionnel et de celui des gestionnaires, elle vise à analyser les éléments qui peuvent être liés à leur satisfaction sur le travail effectué par les nouveaux enseignants. La présente étude, de type quantitatif, est constituée des analyses secondaires des données issues des enquêtes pancanadiennes auprès des directions et des enseignants d’écoles primaires et secondaires du Canada, menées en 2005 et 2006 par une équipe de professeurs de différentes universités canadiennes. Les analyses statistiques sont basées sur deux modèles théoriques : (1) l’engagement professionnel des nouveaux enseignants et (2) la satisfaction des gestionnaires sur le travail effectué par les nouveaux enseignants. Ces modèles sont examinés en suivant la théorie classique des tests (TCT) et celle des réponses aux items (TRI) afin de profiter des avantages de chacune des méthodes. Du côté de la TCT, des analyses de cheminement et des modélisations aux équations structurelles ont été effectuées pour examiner les modèles théoriques. Du côté de la TRI, des modélisations de Rasch ont été utilisées pour examiner les propriétés psychométriques des échelles utilisées par la recherche afin de vérifier si les données sont bien ajustées aux modèles et si les items se regroupent de façon logique pour expliquer les traits latents à l’étude. Les résultats mettent en évidence le rapport humain qui définit la profession enseignante. Autrement dit, pour les nouveaux enseignants, les émotions en classe, conséquence du processus d’interaction avec leurs élèves, sont le facteur majeur lié à l’engagement professionnel. Dans le même sens, la relation des nouveaux enseignants avec les divers membres de la communauté scolaire (parents des élèves, gestionnaires, personnel de l’école et autres enseignants) est un facteur-clé de la satisfaction des gestionnaires quant au travail des nouveaux enseignants. Les analyses indiquent également l’importance de la satisfaction au travail dans le modèle des nouveaux enseignants. Cette variable est un important déterminant de l’engagement professionnel et peut être associée à tous les autres éléments du modèle des nouveaux enseignants. Finalement, les résultats indiquent le besoin de construction des variables latentes avec un plus grand nombre d’items pour mieux positionner les personnes dans l’échelle de mesure. Ce résultat est plutôt important pour le modèle des gestionnaires qui indique de mauvais ajustements items-personnes.

Comparaison des réponses physiologiques lors d’un exercice incrémental maximal sur vélo immergé et sur terrain sec : aspects biomécaniques, cardiopulmonaires et hémodynamiques.

L’exercice en immersion dans l'eau peut générer des réponses hémodynamiques et cardiorespiratoires différentes à celles de l’exercice sur terraine sec. Cependant, aucune étude n’a comparé ces réponses sur vélo aquatique (VA) à celles sur vélo sur terrain sec (VS) à une même puissance mécanique externe (Pext). À cet égard, le premier travail de cette thèse visait, d’abord, à trouver les équivalences de Pext lors du pédalage sur VA en immersion à la poitrine par rapport au VS au laboratoire, en considérant que cela restait non déterminé à ce jour. Une équation de mécanique des fluides fut utilisée pour calculer la force déployée pour le système de pédalage (pales, leviers, pédales) et des jambes à chaque tour de pédale. Ensuite, cette force totale a été multipliée par la vitesse de pédalage pour estimer la Pext sur VA. Ayant trouvé les équivalences de Pext sur VA et VS, nous nous sommes fixés comme objectif dans la deuxième étude de comparer les réponses hémodynamiques et cardiorespiratoires lors d'un exercice maximal progressif sur VS par rapport au VA à une même Pext. Les résultats ont montré que le VO2 (p<0.0001) et la différence artério-veineuse (C(a-v)O2) (p<0.0001) étaient diminués lors de l’exercice sur VA comparativement à celui sur VS. Parmi les variables hémodynamiques, le volume d’éjection systolique (VES) (p˂0.05) et le débit cardiaque (Qc) (p˂0.05) étaient plus élevés sur VA. En plus, on nota une diminution significative de la fréquence cardiaque (FC) (p˂0.05). Étant donné qu’à une même Pext les réponses physiologiques sont différentes sur VA par rapport à celles sur VS, nous avons effectué une troisième étude pour établir la relation entre les différentes expressions de l'intensité relative de l'exercice (% du VO2max,% de la FCmax,% du VO2 de réserve (% de VO2R) et % de la FC réserve (% FCR)). Les résultats ont démontré que la relation % FCR vs % VO2R était la plus corrélée (régression linéaire) et la plus proche de la ligne d’identité. Ces résultats pourraient aider à mieux prescrire et contrôler l’intensité de l'exercice sur VA pour des sujets sains. Finalement, une dernière étude comparant la réactivation parasympathique après un exercice maximal incrémental effectué sur VA et VS en immersion au niveau de la poitrine a montré que la réactivation parasympathique à court terme était plus prédominante sur VA (i,e. t, delta 10 à delta 60 et T30, p<0.05). Cela suggérait, qu’après un exercice maximal sur VA, la réactivation parasympathique à court terme était accélérée par rapport à celle après l'effort maximal sur VS chez de jeunes sujets sains. En conclusion, nous proposons une méthode de calcul de la puissance mécanique externe sur VA en fonction de la cadence de pédalage. Nous avons démontré que pendant l’exercice sur VA les réponses hémodynamiques et cardiorespiratoires sont différentes de celles sur VS à une même Pext et nous proposons des équations pour le calcul du VO2 dans l’eau ainsi qu’une méthode pour la prescription et le contrôle de l’exercice sur VA. Finalement, la réactivation parasympathique à court terme s’est trouvée accélérée après un effort maximal incrémental sur VA comparativement à celle sur VS.

The Wage Rate and the Profit Rate in the Price of Production Equation: a New Solution to an Old Problem

The aim of this paper is to demonstrate that, even if Marx's solution to the transformation problem can be modified, his basic concusions remain valid.

Simulation-Based Finite and Large Sample Tests in Multivariate Regressions.

In the context of multivariate linear regression (MLR) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. In this paper, we propose a general method for constructing exact tests of possibly nonlinear hypotheses on the coefficients of MLR systems. For the case of uniform linear hypotheses, we present exact distributional invariance results concerning several standard test criteria. These include Wilks' likelihood ratio (LR) criterion as well as trace and maximum root criteria. The normality assumption is not necessary for most of the results to hold. Implications for inference are two-fold. First, invariance to nuisance parameters entails that the technique of Monte Carlo tests can be applied on all these statistics to obtain exact tests of uniform linear hypotheses. Second, the invariance property of the latter statistic is exploited to derive general nuisance-parameter-free bounds on the distribution of the LR statistic for arbitrary hypotheses. Even though it may be difficult to compute these bounds analytically, they can easily be simulated, hence yielding exact bounds Monte Carlo tests. Illustrative simulation experiments show that the bounds are sufficiently tight to provide conclusive results with a high probability. Our findings illustrate the value of the bounds as a tool to be used in conjunction with more traditional simulation-based test methods (e.g., the parametric bootstrap) which may be applied when the bounds are not conclusive.

Markovian Processes, Two-Sided Autoregressions and Finite-Sample Inference for Stationary and Nonstationary Autoregressive Processes.

In this paper, we develop finite-sample inference procedures for stationary and nonstationary autoregressive (AR) models. The method is based on special properties of Markov processes and a split-sample technique. The results on Markovian processes (intercalary independence and truncation) only require the existence of conditional densities. They are proved for possibly nonstationary and/or non-Gaussian multivariate Markov processes. In the context of a linear regression model with AR(1) errors, we show how these results can be used to simplify the distributional properties of the model by conditioning a subset of the data on the remaining observations. This transformation leads to a new model which has the form of a two-sided autoregression to which standard classical linear regression inference techniques can be applied. We show how to derive tests and confidence sets for the mean and/or autoregressive parameters of the model. We also develop a test on the order of an autoregression. We show that a combination of subsample-based inferences can improve the performance of the procedure. An application to U.S. domestic investment data illustrates the method.

A Representation Theorem for Domains with Discrete and Continuous Variables.

This paper proves a new representation theorem for domains with both discrete and continuous variables. The result generalizes Debreu's well-known representation theorem on connected domains. A strengthening of the standard continuity axiom is used in order to guarantee the existence of a representation. A generalization of the main theorem and an application of the more general result are also presented.

Computation and Analysis of Multiple Structural-Change Models

In a recent paper, Bai and Perron (1998) considered theoretical issues related to the limiting distribution of estimators and test statistics in the linear model with multiple structural changes. In this companion paper, we consider practical issues for the empirical applications of the procedures. We first address the problem of estimation of the break dates and present an efficient algorithm to obtain global minimizers of the sum of squared residuals. This algorithm is based on the principle of dynamic programming and requires at most least-squares operations of order O(T 2) for any number of breaks. Our method can be applied to both pure and partial structural-change models. Secondly, we consider the problem of forming confidence intervals for the break dates under various hypotheses about the structure of the data and the errors across segments. Third, we address the issue of testing for structural changes under very general conditions on the data and the errors. Fourth, we address the issue of estimating the number of breaks. We present simulation results pertaining to the behavior of the estimators and tests in finite samples. Finally, a few empirical applications are presented to illustrate the usefulness of the procedures. All methods discussed are implemented in a GAUSS program available upon request for non-profit academic use.

GLS Detrending, Efficient Unit Root Tests and Structural Change

We extend the class of M-tests for a unit root analyzed by Perron and Ng (1996) and Ng and Perron (1997) to the case where a change in the trend function is allowed to occur at an unknown time. These tests M(GLS) adopt the GLS detrending approach of Dufour and King (1991) and Elliott, Rothenberg and Stock (1996) (ERS). Following Perron (1989), we consider two models : one allowing for a change in slope and the other for both a change in intercept and slope. We derive the asymptotic distribution of the tests as well as that of the feasible point optimal tests PT(GLS) suggested by ERS. The asymptotic critical values of the tests are tabulated. Also, we compute the non-centrality parameter used for the local GLS detrending that permits the tests to have 50% asymptotic power at that value. We show that the M(GLS) and PT(GLS) tests have an asymptotic power function close to the power envelope. An extensive simulation study analyzes the size and power in finite samples under various methods to select the truncation lag for the autoregressive spectral density estimator. An empirical application is also provided.

Finite-Sample Inference Methods for Simultaneous Equations and Models with Unobserved and Generated Regressors

We propose finite sample tests and confidence sets for models with unobserved and generated regressors as well as various models estimated by instrumental variables methods. The validity of the procedures is unaffected by the presence of identification problems or \"weak instruments\", so no detection of such problems is required. We study two distinct approaches for various models considered by Pagan (1984). The first one is an instrument substitution method which generalizes an approach proposed by Anderson and Rubin (1949) and Fuller (1987) for different (although related) problems, while the second one is based on splitting the sample. The instrument substitution method uses the instruments directly, instead of generated regressors, in order to test hypotheses about the \"structural parameters\" of interest and build confidence sets. The second approach relies on \"generated regressors\", which allows a gain in degrees of freedom, and a sample split technique. For inference about general possibly nonlinear transformations of model parameters, projection techniques are proposed. A distributional theory is obtained under the assumptions of Gaussian errors and strictly exogenous regressors. We show that the various tests and confidence sets proposed are (locally) \"asymptotically valid\" under much weaker assumptions. The properties of the tests proposed are examined in simulation experiments. In general, they outperform the usual asymptotic inference methods in terms of both reliability and power. Finally, the techniques suggested are applied to a model of Tobin’s q and to a model of academic performance.

Simulation-Based Finite-and Large-sample Inference Methods in Multivariate Regressions and Seemingly Unrelated Regressions

In the context of multivariate regression (MLR) and seemingly unrelated regressions (SURE) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. in this paper, we propose finite-and large-sample likelihood-based test procedures for possibly non-linear hypotheses on the coefficients of MLR and SURE systems.

Exact Skewness-Kurtosis Tests for Multivariate Normality and Goodness-of-fit in Multivariate Regressions with Application to Asset Pricing Models

We study the problem of testing the error distribution in a multivariate linear regression (MLR) model. The tests are functions of appropriately standardized multivariate least squares residuals whose distribution is invariant to the unknown cross-equation error covariance matrix. Empirical multivariate skewness and kurtosis criteria are then compared to simulation-based estimate of their expected value under the hypothesized distribution. Special cases considered include testing multivariate normal, Student t; normal mixtures and stable error models. In the Gaussian case, finite-sample versions of the standard multivariate skewness and kurtosis tests are derived. To do this, we exploit simple, double and multi-stage Monte Carlo test methods. For non-Gaussian distribution families involving nuisance parameters, confidence sets are derived for the the nuisance parameters and the error distribution. The procedures considered are evaluated in a small simulation experi-ment. Finally, the tests are applied to an asset pricing model with observable risk-free rates, using monthly returns on New York Stock Exchange (NYSE) portfolios over five-year subperiods from 1926-1995.

Identification, Weak Instruments and Statistical Inference in Econometrics

We discuss statistical inference problems associated with identification and testability in econometrics, and we emphasize the common nature of the two issues. After reviewing the relevant statistical notions, we consider in turn inference in nonparametric models and recent developments on weakly identified models (or weak instruments). We point out that many hypotheses, for which test procedures are commonly proposed, are not testable at all, while some frequently used econometric methods are fundamentally inappropriate for the models considered. Such situations lead to ill-defined statistical problems and are often associated with a misguided use of asymptotic distributional results. Concerning nonparametric hypotheses, we discuss three basic problems for which such difficulties occur: (1) testing a mean (or a moment) under (too) weak distributional assumptions; (2) inference under heteroskedasticity of unknown form; (3) inference in dynamic models with an unlimited number of parameters. Concerning weakly identified models, we stress that valid inference should be based on proper pivotal functions —a condition not satisfied by standard Wald-type methods based on standard errors — and we discuss recent developments in this field, mainly from the viewpoint of building valid tests and confidence sets. The techniques discussed include alternative proposed statistics, bounds, projection, split-sampling, conditioning, Monte Carlo tests. The possibility of deriving a finite-sample distributional theory, robustness to the presence of weak instruments, and robustness to the specification of a model for endogenous explanatory variables are stressed as important criteria assessing alternative procedures.

Monte Carlo Tests with Nuisance Parameters: A General Approach to Finite-Sample Inference and Nonstandard Asymptotics

The technique of Monte Carlo (MC) tests [Dwass (1957), Barnard (1963)] provides an attractive method of building exact tests from statistics whose finite sample distribution is intractable but can be simulated (provided it does not involve nuisance parameters). We extend this method in two ways: first, by allowing for MC tests based on exchangeable possibly discrete test statistics; second, by generalizing the method to statistics whose null distributions involve nuisance parameters (maximized MC tests, MMC). Simplified asymptotically justified versions of the MMC method are also proposed and it is shown that they provide a simple way of improving standard asymptotics and dealing with nonstandard asymptotics (e.g., unit root asymptotics). Parametric bootstrap tests may be interpreted as a simplified version of the MMC method (without the general validity properties of the latter).

Finite-Sample Simulation-Based Inference in VAR Models with Applications to Order Selection and Causality Testing

Statistical tests in vector autoregressive (VAR) models are typically based on large-sample approximations, involving the use of asymptotic distributions or bootstrap techniques. After documenting that such methods can be very misleading even with fairly large samples, especially when the number of lags or the number of equations is not small, we propose a general simulation-based technique that allows one to control completely the level of tests in parametric VAR models. In particular, we show that maximized Monte Carlo tests [Dufour (2002)] can provide provably exact tests for such models, whether they are stationary or integrated. Applications to order selection and causality testing are considered as special cases. The technique developed is applied to quarterly and monthly VAR models of the U.S. economy, comprising income, money, interest rates and prices, over the period 1965-1996.

Inflation dynamics and the New Keynesian Phillips Curve: an identification robust econometric analysis

In this paper, we use identification-robust methods to assess the empirical adequacy of a New Keynesian Phillips Curve (NKPC) equation. We focus on the Gali and Gertler’s (1999) specification, on both U.S. and Canadian data. Two variants of the model are studied: one based on a rationalexpectations assumption, and a modification to the latter which consists in using survey data on inflation expectations. The results based on these two specifications exhibit sharp differences concerning: (i) identification difficulties, (ii) backward-looking behavior, and (ii) the frequency of price adjustments. Overall, we find that there is some support for the hybrid NKPC for the U.S., whereas the model is not suited to Canada. Our findings underscore the need for employing identificationrobust inference methods in the estimation of expectations-based dynamic macroeconomic relations.