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Hierarchical Framework (hierarchical + framework)
Selected AbstractsTesting the Water: Practitioner Opinion of a Regional Credit Scheme (NICATS)HIGHER EDUCATION QUARTERLY, Issue 3 2001Anthony Cook The Northern Ireland Credit Accumulation and Transfer Scheme encompasses all levels from introductory to doctoral. It was designed to facilitate the progression of learners through both the Further and Higher Education structures in Northern Ireland and has provided the model for developments elsewhere. Part of its development included a consultative procedure that involved curriculum specialists liaising with a wide range of practitioners to identify strengths and problematic areas within the scheme. The consultation found that, at the time (1998), practitioner awareness of CATS schemes was generally poor. Most teachers of lower level courses felt that the scheme in general would add value to their courses since it would place them within a hierarchical framework and indicate to their students clear forward progression routes. Many teachers of multilevel courses (in particular degrees) felt that attempting to define levels within a course would result in a loss of teacher autonomy and a reduction in the flexibility with which courses could be offered. Many interviewees stressed the sequential nature of their subject's structure and the perception that this caused problems for student progression through a system of levels based on generic descriptors. It is concluded that although there was broad practitioner support for NICATS, many of its potential benefits will only be realized after substantial staff development. When implemented, it should result not only in a more transparent description of courses but also substantial development in the delivery of curricula and the assessment of student learning. [source] Hierarchical Logistic Regression: Accounting for Multilevel Data in DIF DetectionJOURNAL OF EDUCATIONAL MEASUREMENT, Issue 3 2010Brian F. French The purpose of this study was to examine the performance of differential item functioning (DIF) assessment in the presence of a multilevel structure that often underlies data from large-scale testing programs. Analyses were conducted using logistic regression (LR), a popular, flexible, and effective tool for DIF detection. Data were simulated using a hierarchical framework, such as might be seen when examinees are clustered in schools, for example. Both standard and hierarchical LR (accounting for multilevel data) approaches to DIF detection were employed. Results highlight the differences in DIF detection rates when the analytic strategy matches the data structure. Specifically, when the grouping variable was within clusters, LR and HLR performed similarly in terms of Type I error control and power. However, when the grouping variable was between clusters, LR failed to maintain the nominal Type I error rate of .05. HLR was able to maintain this rate. However, power for HLR tended to be low under many conditions in the between cluster variable case. [source] A hierarchical Bayesian model for predicting the functional consequences of amino-acid polymorphismsJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 1 2005Claudio J. Verzilli Summary., Genetic polymorphisms in deoxyribonucleic acid coding regions may have a phenotypic effect on the carrier, e.g. by influencing susceptibility to disease. Detection of deleterious mutations via association studies is hampered by the large number of candidate sites; therefore methods are needed to narrow down the search to the most promising sites. For this, a possible approach is to use structural and sequence-based information of the encoded protein to predict whether a mutation at a particular site is likely to disrupt the functionality of the protein itself. We propose a hierarchical Bayesian multivariate adaptive regression spline (BMARS) model for supervised learning in this context and assess its predictive performance by using data from mutagenesis experiments on lac repressor and lysozyme proteins. In these experiments, about 12 amino-acid substitutions were performed at each native amino-acid position and the effect on protein functionality was assessed. The training data thus consist of repeated observations at each position, which the hierarchical framework is needed to account for. The model is trained on the lac repressor data and tested on the lysozyme mutations and vice versa. In particular, we show that the hierarchical BMARS model, by allowing for the clustered nature of the data, yields lower out-of-sample misclassification rates compared with both a BMARS and a frequen-tist MARS model, a support vector machine classifier and an optimally pruned classification tree. [source] The evolution of the galaxy red sequence in simulated clusters and groupsMONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY, Issue 1 2008A. D. Romeo ABSTRACT N -body/hydrodynamical simulations of the formation and evolution of galaxy groups and clusters in a , cold dark matter (,CDM) cosmology are used in order to follow the building-up of the colour,magnitude relation in two clusters and in 12 groups. We have found that galaxies, starting from the more massive, move to the red sequence (RS) as they get aged over times and eventually set upon a ,dead sequence' (DS) once they have stopped their bulk star formation activity. Fainter galaxies keep having significant star formation out to very recent epochs and lie broader around the RS. Environment plays a role as galaxies in groups and cluster outskirts hold star formation activity longer than the central cluster regions. However, galaxies experiencing infall from the outskirts to the central parts keep star formation on until they settle on to the DS of the core galaxies. Merging contributes to mass assembly until z, 1, after which major events only involve the brightest cluster galaxies. The emerging scenario is that the evolution of the colour,magnitude properties of galaxies within the hierarchical framework is mainly driven by star formation activity during dark matter haloes assembly. Galaxies progressively quenching their star formation settle to a very sharp ,red and dead' sequence, which turns out to be universal, its slope and scatter being almost independent of the redshift (since at least z, 1.5) and environment. Differently from the DS, the operatively defined RS evolves more evidently with z, the epoch when it changes its slope being closely corresponding to that at which the passive galaxies population takes over the star-forming one: this goes from z, 1 in clusters down to 0.4 in normal groups. [source] A taxonomy of biological informationOIKOS, Issue 2 2010Richard H. Wagner Reproduction, and thus information transfer across generations, is the most essential process of life, yet biologists lack a consensus on terms to define biological information. Unfortunately, multiple definitions of the same terms and other disagreements have long inhibited the development of a general framework for integrating the various categories of biological information. Currently, the only consensus is over two general categories, genetic information, which is encoded in DNA, and non-genetic information, which is extracted from the environment. Non-genetic information is the key to understanding gene-environment interactions and is the raw material of fields such as developmental plasticity, behavior, communication, social learning and cultural evolution. In effect, differences in information possessed by individuals produce phenotypic variation. We thus define biological information as ,factors that can affect the phenotype in ways that may influence fitness'. This definition encompasses all information that is potentially relevant to organisms, which includes the physical environment. Biological information can be acquired passively from genes or via processes such as epigenetics, parental effects and habitat inheritance, or actively by organisms sensing facts about their environment. The confusion over definitions mainly concerns non-genetic information, which takes many more forms than genetic information. Much of the confusion derives from definitions based on how information is used rather than on the facts from which it is extracted. We recognize that a fact becomes information once it is detected. Information can thus be viewed analogously to energy in being either potential or realized. Another source of confusion is in the use of words outside their usual meanings. We therefore present intuitive definitions and classify them according to categories of facts in a hierarchical framework. Clarifying these concepts and terms may help researchers to manipulate facts, allowing a fuller study of biological information. [source] Ever since Clements: from succession to vegetation dynamics and understanding to interventionAPPLIED VEGETATION SCIENCE, Issue 1 2009S.T.A. Pickett Abstract Introduction: This paper surveys a framework for vegetation dynamics to provide conceptual background for a series of papers addressing the role of vegetation dynamics in restoration. Richness of the foundation: Classical succession theory provides key ingredients for contemporary process studies of vegetation dynamics. The contemporary framework incorporates processes identified by Gleason and other critics of Clements' theory. Multiple causality: The Clementsian causes, when expanded to include interaction and to clarify net effects, accommodate those now recognized in vegetation dynamics. A mature successional framework: A hierarchical framework has emerged to evaluate the causes of vegetation dynamics. The framework identifies the general causes as site availability, species availability, and species performance. Differentials as drivers: Differentials in any of the three general causes can drive change in plant communities. Each general cause consists of specific mechanisms. A model template: To predict vegetation dynamics trajectories, models are required. A model template is presented to operationalize the hierarchical framework. Outcomes are contingent on species pools and environmental contexts and may be progressive or retrogressive. Relationships of frameworks: Other contemporary frameworks in biology relate to vegetation dynamics. Application to restoration: The vegetation dynamics framework is relevant to restoration through linkages with landscape ecology, disturbance ecology, competition, invasion ecology, and community assembly. The differentials of site availability, species availability, and species performance suggest the processes and strategies available for restoration. Conclusions: A synthetic framework of vegetation brings together the mechanisms required for successful restoration. [source] | ||||||||||