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In this article the author discusses the challenges regarding the measurement and psychometric assumptions. He discusses professor Stephen Humphry's work regarding the description of an item response theory (IRT) model. He discusses Humphry's measurement assumptions and his views regarding the units underlying the scales and to develop definitions for researchers in the social sciences that mirror definitions used in the physical sciences.

The international take-up and rapid development of environmental assessment (EA) systems has been driven by the introduction of EA legal requirements and by the advancement of methodological aspects published in the EA literature. However, despite the existence of common legal frameworks and methodological guidelines, the way in which EA is practised and conceived within a multidisciplinary context varies. There are a range of interpretations defining the way in which EA should be used and how EA fits within different disciplines; in Europe, this variation is reflected in the formative and educational programmes offered by universities in EU Member States. This paper identifies existing trends in EA education by providing an overview of the ways in which EA is taught in Europe. A comparative analysis of 64 EA-related Master programmes representing nine EU Member States is conducted. Furthermore, the paper aims to set the basis for a discussion on the different social science and physical science understandings of EA revealed by the research findings. We recommend that interlinkages between these two understandings be improved and suggest that this could occur through the introduction of formal EA education, enhanced collaboration between different types of departments, and holistic approaches to teaching and learning. [ABSTRACT FROM AUTHOR]

The article presents challenges associated with understanding complex systems in science education. Complex systems possess hierarchical nature with various interacting levels. The aggregate nature of the complex system is unpredictable from isolated components and occurs by the interaction of multiple components. As the learning of complex systems involves several difficulties, these systems are basis of many areas of learning, having the ability to integrate across various learning disciplines. There is an integration of biology, chemistry, physical science and social science while learning aquatic systems. According to researchers, the ability of students to learn about complex systems as well as other challenging topics depends to a greater extent on the disposal of such processes to plan, sustain and reflect on the complex mechanisms underlying learning about complex systems. Complex systems can also be studied well with the help of computers. Computer-assisted learning environments can help students to learn complex systems efficiently.

This guide, for beginning catalogers with humanities or social sciences backgrounds, provides assistance in subject analysis (based on Library of Congress Subject Headings) of theses and dissertations (T/Ds) that are produced by graduate students in university departments in the hard sciences (physical sciences and engineering). It is aimed at those who have had little or no experience in cataloging, especially of this type of material, and for those who desire to supplement local mentoring resources for subject analysis in the hard sciences. Theses and dissertations from these departments present a special challenge because they are the results of current research representing specific new concepts with which the cataloger may not be familiar. In fact, subject headings often have not yet been created for the specific concept(s) being researched. Additionally, T/D authors often use jargon/terminology specific to their department. Catalogers often have many other duties in addition to subject analysis of T/Ds in the hard sciences, yet they desire to provide optimal access through accurate, thorough subject analysis. Tips are provided for determining the content of the T/D, strategic searches on WorldCat for possible subject headings, evaluating the relevancy of these subject headings for final selection, and selecting appropriate subdivisions where needed. Lists of basic reference resources are also provided. [ABSTRACT FROM AUTHOR]

Despite the recent wave of interest in the social and physical sciences regarding "complexity," relatively title attention has been given to the logical foundation of complexity measurement. With this in mind, a number of fairly simple, "reasonable" axioms for the measurement of network complexity are here presented, and some of the implications of these axioms are considered. It is shown that the only family of graph complexity measures satisfying the "reasonable" axioms is of limited theoretical utility, and hence that those seeking more interesting measures of complexity must be willing to sacrifice at least one intuitively reasonable constraint. Several existing complexity measures are also described, and are differentiated from one another on an axiomatic basis. Finally, some suggestions are offered regarding future efforts at measuring graph complexity. [ABSTRACT FROM AUTHOR]

In this article the author discusses professor Stephen Humphry's model of using metrology and physical science analogies to reformulate the relationship between discrimination and the unit. He reports on the metrology program developed by Humphry for humans and social science to follow. He states that Humphry's metrology program require the abandonment of descriptive Rasch models in favor of an explicit causal interpretation of the regression.

The article presents information about the areas covered in the upcoming issues of the "Journal of the American Statistical Association." In the year 2000, the journal will publish a series of short articles under four subject areas, namely, life and medical sciences, social science and business, physical sciences and engineering, and theory and methods. One subject area will be featured in the Review Section of each of the four issues in 2000. Each of these vignette collections will highlight some of the most important statistical advances in the given area, as well as outline potentially fruitful topics of research.