Mónica Benito

Improving quality assessment of composite indicators in university rankings: a case study of French and German universities of excellence

Composite indicators play an essential role for benchmarking higher education institutions. One of the main sources of uncertainty building composite indicators and, undoubtedly, the most debated problem in building composite indicators is the weighting schemes (assigning weights to the simple indicators or subindicators) together with the aggregation schemes (final composite indicator formula). Except the ideal situation where weights are provided by the theory, there clearly is a need for improving quality assessment of the final rank linked with a fixed vector of weights. We propose to use simulation techniques to generate random perturbations around any initial vector of weights to obtain robust and reliable ranks allowing to rank universities in a range bracket. The proposed methodology is general enough to be applied no matter the weighting scheme used for the composite indicator. The immediate benefit achieved is a reduction of the uncertainty associated with the assessment of a specific rank which is not representative of the real performance of the university, and an improvement of the quality assessment of composite indicators used to rank. To illustrate the proposed methodology we rank the French and the German universities involved in their respective 2008 Excellence Initiatives.

A fast approach for dimensionality reduction with image data

An important objective in image analysis is dimensionality reduction. The most often used data-exploratory technique with this objective is principal component analysis, which performs a singular value decomposition on a data matrix of vectorized images. When considering an array data or tensor instead of a matrix, the high-order generalization of PCA for computing principal components offers multiple ways to decompose tensors orthogonally. As an alternative, we propose a new method based on the projection of the images as matrices and show that it leads to a better reconstruction of images than previous approaches.

Dimensionality Reduction with Image Data

A common objective in image analysis is dimensionality reduction. The most often used data-exploratory technique with this objective is principal component analysis. We propose a new method based on the projection of the images as matrices after a Procrustes rotation and show that it leads to a better reconstruction of images.

Detecting defects with image data

Quality control using continuous monitoring from images is emerging as an active research area. These applications require adaptive statistical techniques in order to detect and isolate process abnormalities. A novel approach is introduced for monitoring schemes in the setting of image data when the quality is associated with uniform pixel gray-scales. The proposed approach requires the definition of a statistic which takes into account both the spatial dependency and the changes in local variability. An application on paper surface demonstrates how the monitoring scheme performs in practical applications.

Modeling the enrollment demand of masters programs for the Spanish public university system

Recent studies have suggested that a causal link exists between the reputation of the institution and the subsequent demand indicators. However, it is unclear how these effects vary across institutional characteristics or whether these effects persist when considering other factors that affects demand outcomes. On the other hand, student demand studies have almost always focused on the demand side of the equilibrium but not the supply side, although both demand and supply equations relate quantity to price. Although the supply is clearly a driver of demand, there are other variables that significantly influence the demand rates. Spanish public university system shows particular features not considered in the mentioned studies. This paper has two objectives. The first one is to modelize the demand for Masters Programs in the Spanish public university system. We propose a panel methodology to estimate the behavior of the demand of Masters Programs based on the data provided by the seventeen Spanish Autonomous Communities. Disaggregated analysis are presented for domestic demand and international demand. We conclude that the offer is a powerful attractor of demand for domestic and international students, and therefore actions of supply reduction should be carefully applied and always according to strategic university policy criteria. The second aim of the article is to analyze the Masters Programs in the Spanish public university system and to provide a benchmark of the current situation of supply (number of programs) and demand (enrollment) at regional level (Spanish Autonomous Communities) and in relation to European scenarios.