What makes students satisfied? A discussion and analysis of the UK’s national student survey

This paper analyses data from the National Students Survey, determining which groups of students expressed the greatest levels of satisfaction. We find students registered on clinical degrees and those studying humanities to be the most satisfied, with those in general engineering and media studies the least. We also find contentment to be higher among part-time students, and significantly higher among Russell group and post-1992 universities. We further investigate the sub-areas that drive overall student satisfaction, finding teaching and course organisation to be the most important aspects, with resources and assessment and feedback far less relevant. We then develop a multi- attribute measure of satisfaction which we argue produces a more accurate and more stable reflection of overall student satisfaction than that based on a single question

Model-based Methods of Classification: Using the mclust Software in Chemometrics

Due to recent advances in methods and software for model-based clustering, and to the interpretability of the results, clustering procedures based on probability models are increasingly preferred over heuristic methods. The clustering process estimates a model for the data that allows for overlapping clusters, producing a probabilistic clustering that quantifies the uncertainty of observations belonging to components of the mixture. The resulting clustering model can also be used for some other important problems in multivariate analysis, including density estimation and discriminant analysis. Examples of the use of model-based clustering and classification techniques in chemometric studies include multivariate image analysis, magnetic resonance imaging, microarray image segmentation, statistical process control, and food authenticity. We review model-based clustering and related methods for density estimation and discriminant analysis, and show how the R package mclust can be applied in each instance.

Medieval property investors, ca. 1300-1500

This paper utilises a dataset of freehold land and property transactions from medieval England to highlight the growing commercialisation of the economy. By drawing on the legal records we are able to demonstrate that the medieval real estate market provided the opportunity for investors to profit. Careful analysis of the data provides evidence of group purchases, multiple transactions and investors buying outside of their own locality. The identification of these ‘investors’ and their buying behaviours, set within the context of the English medieval economy, contributes to the early commercialisation debate

Advance Contracts for the Sale of Wool in Medieval England; An Undeveloped and Inefficient Market?

While it is commonly believed that derivative instruments are a recent invention, we document the existence of forward contracts for the sale of wool in medieval England around 700 years ago. The contracts were generally entered into by English monasteries, who frequently sold their wool for up to twenty years in advance to mostly foreign and particularly Italian merchants. Employing a unique source of data collected by hand from the historical records, we determine the interest rates implied in these transactions and we also examine the efficiency of the forward and spot markets. The calculated interest rates average around 20%, in accordance with available information concerning the interest rates used in other types of transactions at that time. Perhaps surprisingly, we also find little evidence of informational inefficiencies in these markets.Wood market, forward contracts, market efficinecy, Medieval England, Interest rates

Leger est aprendre mes fort est arendre;: Wool, Debt and the Dispersal of Pipewell Abbey (1280 - 1330)

has long been known that English Cistercian monasteries often sold their wool in advance to foreign merchants in the late thirteenth century. The abbey of Pipewell in Northamptonshire features in a number of such contracts with Cahorsin merchants. This paper looks again at these contracts in the context of over 200 other such agreements found in the governmental records. Why did Pipewell descend into penury over this fifty year period? This case study demonstrates that the promise of ready cash for their most valuable commodity led such abbots to make ambitious agreements – taking on yet more debt to service existing creditors - that would lead to their eventual bankruptcy.

Cosmological baryonic and matter densities from 600,000 SDSS Luminous Red Galaxies with photometric redshifts

We analyze MegaZ-LRG, a photometric-redshift catalogue of Luminous Red Galaxies (LRGs) based on the imaging data of the Sloan Digital Sky Survey (SDSS) 4th Data Release. MegaZ-LRG, presented in a companion paper, contains 10^6 photometric redshifts derived with ANNz, an Artificial Neural Network method, constrained by a spectroscopic sub-sample of 13,000 galaxies obtained by the 2dF-SDSS LRG and Quasar (2SLAQ) survey. The catalogue spans the redshift range 0.4 < z < 0.7 with an r.m.s. redshift error ~ 0.03(1+z), covering 5,914 deg^2 to map out a total cosmic volume 2.5 h^-3 Gpc^3. In this study we use the most reliable 600,000 photometric redshifts to present the first cosmological parameter fits to galaxy angular power spectra from a photometric redshift survey. Combining the redshift slices with appropriate covariances, we determine best-fitting values for the matter and baryon densities of Omega_m h = 0.195 +/- 0.023 and Omega_b/Omega_m = 0.16 +/- 0.036 (with the Hubble parameter h = 0.75 and scalar index of primordial fluctuations n = 1 held fixed). These results are in agreement with and independent of the latest studies of the Cosmic Microwave Background radiation, and their precision is comparable to analyses of contemporary spectroscopic-redshift surveys. We perform an extensive series of tests which conclude that our power spectrum measurements are robust against potential systematic photometric errors in the catalogue. We conclude that photometric-redshift surveys are competitive with spectroscopic surveys for measuring cosmological parameters in the simplest vanilla models. Future deep imaging surveys have great potential for further improvement, provided that systematic errors can be controlled.Comment: 24 pages, 23 figures, MNRAS accepte

Default Priors and Predictive Performance in Bayesian Model Averaging, with Application to Growth Determinants

Economic growth has been a showcase of model uncertainty, given the many competing theories and candidate regressors that have been proposed to explain growth. Bayesian Model Averaging (BMA) addresses model uncertainty as part of the empirical strategy, but its implementation is subject to the choice of priors: the priors for the parameters in each model, and the prior over the model space. For a well-known growth dataset, we show that model choice can be sensitive to the prior specification, but that economic significance (model-averaged inference about regression coefficients) is quite robust to the choice of prior. We provide a procedure to assess priors in terms of their predictive performance. The Unit Information Prior, combined with a uniform model prior outperformed other popular priors in the growth dataset and in simulated data. It also identified the richest set of growth determinants, supporting several new growth theories. We also show that there is a tradeoff between model and parameter priors, so that the results of reducing prior expected model size and increasing prior parameter variance are similar. Our branch-and-bound algorithm for implementing BMA was faster than the alternative coin flip importance sampling and MC3 algorithms, and was also more successful in identifying the best model.