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Evaluation Purposes (evaluation + purpose)
Selected AbstractsConsequences of No Child Left Behind on evaluation purpose, design, and impactNEW DIRECTIONS FOR EVALUATION, Issue 117 2008Linda Mabry As an outgrowth of No Child Left Behind's narrow definition of scientifically based research, the priority given to certain quantitative evaluation designs has sparked debate among those in the evaluation community. Federal mandates for particular evaluation methodologies run counter to evaluation practice and to the direction of most evaluation theorists, who advocate for flexibility and adaptability in methods choices. The impact of this mandate for randomized clinical trials as the sine qua non of evaluation methods is not yet discernible, but the potential impact is explored through an analogous example involving the World Bank. © Wiley Periodicals, Inc. [source] Hybrid expert,fuzzy approach for evaluation of complex systemsEXPERT SYSTEMS, Issue 3 2009Veysi Öztürk Abstract: Evaluation of complex systems is generally complicated and time consuming. Evaluation is needed for nearly all engineering tasks and the obstacles related to evaluation increase in proportion to complexity. New techniques can be used to automate manual evaluation and to overcome the obstacles related to evaluation that cannot be solved (or can only be solved with great difficulty) with conventional computing. In this study, a methodology was developed to handle the heuristic knowledge of experts for evaluation purposes. In this method, knowledge was represented as a reference model of evaluation objectives, production rules, measures, methods and parameters. A ,common evaluation process' and ,common evaluation model', which simplify and speed up the evaluation process and decrease evaluation cost, were proposed and developed. A hybrid expert,fuzzy system, called ,intelligent evaluation system' (INES), which can be used for evaluation of complex systems was developed. To define a process and develop a system that simplifies and speeds up evaluation can save time, decrease cost and provide reusability. As the evaluation of complex systems includes uncertainty in some aspects, fuzzy logic was incorporated with an expert system for reasoning. INES was implemented successfully for the evaluation of an air defence system, which is a complex system used to protect a region from all air threats. [source] An assessment of hydraulic design of trickle laterals considering effect of minor losses,IRRIGATION AND DRAINAGE, Issue 4 2007Gürol Y canalisations d'eau; canalisations tertiaires; conception hydraulique; analyse; perte de charge Abstract The accurate design of trickle irrigation laterals needs to determine the total energy losses that includes the pipe friction losses along the lateral line and the local pressure losses, sometimes called minor losses, due to the protrusion of emitter barbs into the flow. Evaluation of energy losses is usually carried out by assuming the hypothesis that minor losses can be neglected, even if previous experimental studies indicated that minor losses can become a significant percentage of total energy losses as a consequence of the high number of emitters installed along the lateral line. In this study, a simple analytical procedure is presented to evaluate the effect of minor losses which is characterized by a coefficient ,i, expressing the amount of minor head losses as a fraction of the kinetic head, on trickle lateral design. According to both the design cases of without and including minor losses, the dimensionless design curves were developed for both the various lateral diameters and lengths. The results of two practical examples for designing either the diameter or the length indicated that, in some design cases, neglecting minor losses may lead to erroneous designs of the lateral diameter and length. This method is simple and easily adaptable to solve lateral hydraulic problems but sufficiently precise in comparison with the alternative procedures. The proposed equations are useful when applied for design and evaluation purposes and offer a practical field solution for laterals used in irrigation systems. Copyright © 2007 John Wiley & Sons, Ltd. La conception précise des canalisations tertiaires dans un système d'irrigation au goutte à goutte doit déterminer le total des pertes de charge qui viennent du frottement dans la canalisation elle-même mais aussi des pertes locales, parfois appelées pertes mineures, dues à la protubérance de barbes au niveau des goutteurs. Cette évaluation est normalement réalisée en considérant comme négligeables les pertes mineures, même si des études expérimentales ont montré que ces pertes mineures pouvaient constituer un pourcentage significatif de la perte de charge totale du fait du grand nombre de goutteurs installés le long de la canalisation tertiaire. Dans cette étude, une procédure analytique simple est proposée pour évaluer l'effet des pertes mineures sur la conception des canalisations tertiaires grâce à un coefficient ,i, exprimant le montant de ces pertes en une fraction de la charge cinétique. En fonction des diverses options de conception prenant ou non en compte les pertes mineures, des courbes de conception non dimensionnelles ont été tracées pour les diamètres et les longueurs des canalisations. Les résultats de deux exemples concrets montrent que, dans certains cas de conception, le fait de négliger les pertes mineures peut conduire à des conceptions de diamètre et de longueur erronées. Cette méthode est simple et facilement adaptable à la résolution de problèmes hydrauliques au niveau des tertiaires mais suffisamment précise par rapport des méthodes alternatives. Les équations proposées sont utiles pour la conception et l'évaluation et offrent une solution de terrain concrète pour les canalisations tertiaires utilisées dans des systèmes d'irrigation (au goutte à goutte). Copyright © 2007 John Wiley & Sons, Ltd. [source] SOME PRACTICAL GUIDANCE FOR THE IMPLEMENTATION OF PROPENSITY SCORE MATCHINGJOURNAL OF ECONOMIC SURVEYS, Issue 1 2008Marco Caliendo Abstract Propensity score matching (PSM) has become a popular approach to estimate causal treatment effects. It is widely applied when evaluating labour market policies, but empirical examples can be found in very diverse fields of study. Once the researcher has decided to use PSM, he is confronted with a lot of questions regarding its implementation. To begin with, a first decision has to be made concerning the estimation of the propensity score. Following that one has to decide which matching algorithm to choose and determine the region of common support. Subsequently, the matching quality has to be assessed and treatment effects and their standard errors have to be estimated. Furthermore, questions like ,what to do if there is choice-based sampling?' or ,when to measure effects?' can be important in empirical studies. Finally, one might also want to test the sensitivity of estimated treatment effects with respect to unobserved heterogeneity or failure of the common support condition. Each implementation step involves a lot of decisions and different approaches can be thought of. The aim of this paper is to discuss these implementation issues and give some guidance to researchers who want to use PSM for evaluation purposes. [source] Are there better indices for evaluation purposes than the h index?JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE AND TECHNOLOGY, Issue 5 2008A comparison of nine different variants of the h index using data from biomedicine In this study, we examined empirical results on the h index and its most important variants in order to determine whether the variants developed are associated with an incremental contribution for evaluation purposes. The results of a factor analysis using bibliographic data on postdoctoral researchers in biomedicine indicate that regarding the h index and its variants, we are dealing with two types of indices that load on one factor each. One type describes the most productive core of a scientist's output and gives the number of papers in that core. The other type of indices describes the impact of the papers in the core. Because an index for evaluative purposes is a useful yardstick for comparison among scientists if the index corresponds strongly with peer assessments, we calculated a logistic regression analysis with the two factors resulting from the factor analysis as independent variables and peer assessment of the postdoctoral researchers as the dependent variable. The results of the regression analysis show that peer assessments can be predicted better using the factor ,impact of the productive core' than using the factor ,quantity of the productive core.' [source] | ||||||||||