Política de acceso abierto a la producción científica en la Facultad de CC Geológicas de la UCM

En el marc de les accions que està portant a terme la Biblioteca de la Universidad Complutense de Madrid per promocionar l'ús del " Archivo institucional de E-Prints Complutense" entre els seus professors i investigadors, la Biblioteca de la Facultat de Ciències Geològiques de la UCM ha posat en marxa una iniciativa, recolzada pels Serveis Centrals de la BUCM, destinada a dipositar a l'arxiu institucional de la universitat la producció científica de la Facultat. L'aspecte clau d'aquesta iniciativa consisteix a emfatitzar el pes institucional i patrimonial de la producció investigadora d'una institució i en demanar el suport de l'equip deganal del centre perquè sigui ell qui comuniqui als seus investigadors l'oportunitat de dipositar tot aquest patrimoni, fent-lo més visible i augmentant amb això el seu impacte, en el dipòsit que a l'efecte ha creat la universitat. La Biblioteca de la Facultat de Ciències Geològiques de la UCM està procedint, en conseqüència, a dipositar a l'arxiu institucional de la universitat tots aquells resultats de recerca del centre que, dins del respecte a la legalitat, sigui possible introduir sense que, fins al present, investigador algun d'aquest centre s'hagi manifestat contrari a la proposta deganal.

Aplicación web para gestionar el plan docente de la Facultad de Traducción e Interpretación

Aquest projecte va ser creat per organitzar el pla docent de la Facultat de Traducció i Interpretació, controlant de forma automàtica les hores dedicades d’un professor al llarg d’un curs. L’aplicació ajuda a emmagatzemar tota la informació necessària i útil referent a totes les funcions d’un professor dins d’una facultat. A més, permet relacionar i mostrar aquesta informació sense la necessitat de consultar altres fonts.

Key competences in the new ventures: a model for evaluating

This research studies from an internal view based on the Competency-Based Perspective (CBP), key organizational competencies developed for small new business. CBP is chosen in an attempt to explain the differences characterizing the closed companies from the consolidated ones. The main contribution of this paper is the definition of a set of key organizational competencies for new ventures from services and low technology based sectors. Using the classification proposed by [1] and a review of the entrepreneurship literature, the main competencies were defined and classified as: managerial, input-based, transformation-based, and output-based competencies. The proposed model for evaluating new ventures organizational competence is tested by means of Structural Equation

Scoring Methods for Ordinal Multidimensional Forced-Choice Items

In most psychological tests and questionnaires, a test score is obtained bytaking the sum of the item scores. In virtually all cases where the test orquestionnaire contains multidimensional forced-choice items, this traditionalscoring method is also applied. We argue that the summation of scores obtained with multidimensional forced-choice items produces uninterpretabletest scores. Therefore, we propose three alternative scoring methods: a weakand a strict rank preserving scoring method, which both allow an ordinalinterpretation of test scores; and a ratio preserving scoring method, whichallows a proportional interpretation of test scores. Each proposed scoringmethod yields an index for each respondent indicating the degree to whichthe response pattern is inconsistent. Analysis of real data showed that withrespect to rank preservation, the weak and strict rank preserving methodresulted in lower inconsistency indices than the traditional scoring method;with respect to ratio preservation, the ratio preserving scoring method resulted in lower inconsistency indices than the traditional scoring method

Robust Factor Analysis for Compositional Data

Factor analysis as frequent technique for multivariate data inspection is widely used also for compositional data analysis. The usual way is to use a centered logratio (clr)transformation to obtain the random vector y of dimension D. The factor model istheny = Λf + e (1)with the factors f of dimension k & D, the error term e, and the loadings matrix Λ.Using the usual model assumptions (see, e.g., Basilevsky, 1994), the factor analysismodel (1) can be written asCov(y) = ΛΛT + ψ (2)where ψ = Cov(e) has a diagonal form. The diagonal elements of ψ as well as theloadings matrix Λ are estimated from an estimation of Cov(y).Given observed clr transformed data Y as realizations of the random vectory. Outliers or deviations from the idealized model assumptions of factor analysiscan severely effect the parameter estimation. As a way out, robust estimation ofthe covariance matrix of Y will lead to robust estimates of Λ and ψ in (2), seePison et al. (2003). Well known robust covariance estimators with good statisticalproperties, like the MCD or the S-estimators (see, e.g. Maronna et al., 2006), relyon a full-rank data matrix Y which is not the case for clr transformed data (see,e.g., Aitchison, 1986).The isometric logratio (ilr) transformation (Egozcue et al., 2003) solves thissingularity problem. The data matrix Y is transformed to a matrix Z by usingan orthonormal basis of lower dimension. Using the ilr transformed data, a robustcovariance matrix C(Z) can be estimated. The result can be back-transformed tothe clr space byC(Y ) = V C(Z)V Twhere the matrix V with orthonormal columns comes from the relation betweenthe clr and the ilr transformation. Now the parameters in the model (2) can beestimated (Basilevsky, 1994) and the results have a direct interpretation since thelinks to the original variables are still preserved.The above procedure will be applied to data from geochemistry. Our specialinterest is on comparing the results with those of Reimann et al. (2002) for the Kolaproject data