The potential role of perceived support for reduction of special education teachers’ burnout

Teacher burnout is conceived as a general concern in special education because of the emotionally demanding work context. This study explored the potential role of perceived support for reduction of burnout in a sample of 276 special education teachers working in lower (n=130) and higher (n=146) secondary schools. Participants completed the Maslach Burnout Inventory - Educators Survey (MBI-ES) and a scale on the perceived collaboration and support from general education teachers. To explore the association between perceived support and each burnout measure considered (emotional exhaustion, depersonalisation, and personal accomplishment), correlation analyses were performed. Then, in order to check the robustness of our empirical findings, multilevel regression models (at teacher-school-administrative area level) were used controlling for several variables (teacher socio-demographic characteristics, teacher training and professional background, inclusive teaching practice and school context). Results suggest the potential role of perceived support in reducing emotional exhaustion and improving personal accomplishment in all the models considered. Instead, the relationship between perceived support and depersonalisation seems to be quite controversial, moreover when adding controls related to teacher’s training and professional background

Selecting the W Matrix. Parametric vs Nonparametric Approaches

In spatial econometrics, it is customary to specify a weighting matrix, the so-called W matrix, just choosing one matrix from the different types of matrices a user is considering (Anselin, 2002). In general, this selection is made a priori, depending on the user’s judgment. This decision is extremely important because if matrix W is miss-specified in some way, parameter estimates are likely to be biased and they will be inconsistent in models that contain some spatial lag. Also, for models without spatial lags but where the random terms are spatially autocorrelated, the obtaining of robust standard estimates of the errors will be incorrect if W is miss-specified. Goodness-of-fit tests may be used to chose between alternative specifications of W. Although, in practice, most users impose a certain W matrix without testing for the restrictions that the selected spatial operator implies. In this paper, we aim to establish a nonparametric procedure where the chosen by objective criteria. Our proposal is directly related with the Theory of Information. Specifically, the selection criterion that we propose is based on objective information existing in the data, which does not depend on the investigator’s subjectivity: it is a measure of conditional entropy. We compare the performance of our criteria against some other alternative like the J test of Davidson and McKinnon or a likelihood ratio obtained in a maximum likelihood framework.

Do CEOs Ever Lose? Fairness Perspective on the Allocation of Residuals Between CEOs and Shareholders

In this study we introduce a justice perspective to examining the result of bargaining between CEOs and boards over the allocation of firm residuals that ultimately determines CEO compensation. Framing CEO pay as the result of bargaining between CEOs and boards focuses attention on the power of CEOs to increase their share of firm residuals in the form of increased compensation, and the diligence of boards of directors to constrain CEO opportunism. Framing this negotiation through a theory of justice offers an alternative perspective to the search for pay-performance sensitivity. We predict and find that as board diligence in controlling opportunism declines and CEO power increases, CEOs are increasingly able to capture a larger portion of firm residuals relative to shareholders. This finding supports critics who charge that CEO pay violates norms of distributive and procedural justice

Local weighting or the necessity of flexibility

The local estimation algorithms are well-known techniques in the current spatial econometric literature. The Geographically Weighted Regressions are very popular to estimate, locally, static models, whereas the SALE or the Zoom approaches are useful solutions in the case of dynamic models. These techniques are well founded from a methodological point of view and present interesting properties. However, Farber and Paez (2008) detect some inconsistencies in the behavior of some of these algorithms that claim for a further analysis. The point that we want to study in this paper refers to the role of the bandwith. This measure defines how many neighbors will be used in the estimation of the local parameters corresponding to each observation. The cross-validation is the most popular criteria to fix the bandwith, although there are several other criteria in the literature. We think that there is a basic problem with this approach. The objective of these algorithms is to relax the restriction of homogeneity of the parameters of the model allowing for local peculiarities; however the definition of local neighborhood is the same. It does not matter if the observation corresponds to an isolated and poorly communicated region or it belongs to a central and highly connected point. According to our view, this is a very restrictive decision that should be avoided. Specifically, we discuss the procedure of specifying the sequence of local weighting matrices that will be used in the analysis. Our purpose is to achieve that these matrices also reflect the local surrounding of each observation. We examine two different strategies in order to construct the local weighting matrices. The first is a parametric approach which involves the J test, as presented by Kelejian (2008), and the second is a nonparametric approach that uses the guidance of the symbolic entropy measures. The first part of the paper presents the overall problem, including a review of the literature; we discuss the solutions in the second part and the third part consists of a Monte Carlo simulation.

Semantic variation operators for multidimensional genetic programming

Multidimensional genetic programming represents candidate solutions as sets of programs, and thereby provides an interesting framework for exploiting building block identification. Towards this goal, we investigate the use of machine learning as a way to bias which components of programs are promoted, and propose two semantic operators to choose where useful building blocks are placed during crossover. A forward stagewise crossover operator we propose leads to significant improvements on a set of regression problems, and produces state-of-the-art results in a large benchmark study. We discuss this architecture and others in terms of their propensity for allowing heuristic search to utilize information during the evolutionary process. Finally, we look at the collinearity and complexity of the data representations that result from these architectures, with a view towards disentangling factors of variation in application.Comment: 9 pages, 8 figures, GECCO 201

Catalan government popularity. An example of economic effects on sub-national government support

The aim of this essay is to deal with economic voting in contexts of multilevel governance and to be a contribution to the debate on attribution of responsibilities in popularity functions literature. We use aggregate and individual data from Catalonia in order to analyse the relation between the state of the economy and the support for a sub-state government. The empirical analysis shows that the responsibility hypothesis works in regional governments without explicit macroeconomic competencies. We have also considered the evaluations of government performance on certain specific policies in order to clarify and determine the factors that drive Catalan government support. The article considers the implications of the findings for future attempts to model party support in a context of the European Union.Economic vote, popularity functions, attribution of responsabilities, grievance asymmetry