电子资源在中东研究中的基本使用途径探索

查找文献资料是国际问题研究的基础性工作,中东研究也是如此。在信息技术日新月异的今天,如能熟练使用电子资源,就会达到事半功倍的效应。电子资源有着快捷、高效的特点,克服了查找纸质资源费时、费力的不足。本文从如何检索国内外已有的研究论著、查阅国内外第一手资料以及利用档案馆、图书馆和科研机构的网络资源等方面入手,探讨与中东研究相关的电子资源的基本使用途径。     

教材建设·师资培养·教法研究——文献检索课建设的核心

文献检索课在培养学生的创新精神和实践能力方面有着得天独厚的优势,要提高该课的教学质量,亟待解决教材建设、师资培养与教法研究三方面的问题.     

Child welfare and the challenge of causal inference

Causal inference refers to the assessment of cause and effect relationships in observational data—i.e., in situations where random assignment is impossible or impractical. Choices involving children in the child welfare system evoke core elements of causal inference—manipulation and the counterfactual. How would a child's circumstances differ if we changed her environment? This article begins with one mathematical approach to framing causal inference, the potential outcomes framework. This methodology is a cornerstone of newer approaches to causal inference. This framework makes clear the identification problem inherent in causal inference and highlights a key assumption often used to identify the model (ignorability or no unobserved confounding). The article then outlines the various approaches to causal inference and organizes them around whether they assume ignorability as well as other key features of each approach. The article then provides guidelines for producing good causal inference. These guidelines emerge from a review of methodological literature as broad ranging as epidemiology, statistics, economics, and policy analysis. These steps will be illustrated using an example from child welfare. The article concludes with suggestions for how the field could apply these newer methods.     

Comparison of Generalized Lambda Distribution (GLD) and Response Modeling Methodology (RMM) as General Platforms for Distribution Fitting

Distribution fitting is widely practiced in all branches of engineering and applied science, yet only a few studies have examined the relative capability of various parameter-rich families of distributions to represent a wide spectrum of diversely shaped distributions. In this article, two such families of distributions, Generalized Lambda Distribution (GLD) and Response Modeling Methodology (RMM), are compared. For a sample of some commonly used distributions, each family is fitted to each distribution, using two methods: fitting by minimization of the L ₂ norm (minimizing density function distance) and nonlinear regression applied to a sample of exact quantile values (minimizing quantile function distance). The resultant goodness-of-fit is assessed by four criteria: the optimized value of the L ₂ norm, and three additional criteria, relating to quantile function matching. Results show that RMM is uniformly better than GLD. An additional study includes Shore's quantile function (QF) and again RMM is the best performer, followed by Shore's QF and then GLD.     

Methodologic challenges of e-health research

In this article, research-based strategies used in a web-based study to assess the impact of participation in cancer-related mailing lists on cancer survivors and caregivers are highlighted. The paper presents alternative methods of measuring response rates in Internet surveys. Response rate estimation, based on American Association for Public Opinion Research guidelines, ranged between 15% and 24.9% for the study. Several strategies were implemented to increase survey participation and reduce item nonresponse. Few participants opted for a telephone survey as an alternative to a web-administered design (mixed-mode administration). Using time-stamp data to diagnose places in the questionnaire where respondents prematurely exited or broke off survey participation, we were able to decrease premature break offs. A lottery was also implemented to increase response rates. The experience of obtaining institutional review board approval for the lottery is also described. Preliminary data indicate slight increases in response rates subsequent to implementing the lottery.     

Determining Optimal Advertising Strategies: A Markov Decision Model Approach*

This study falls in the class of models in which advertising wearout and the differences between the learning and forgetting of advertisements are explicitly included. A discrete time Markov decision modeling approach is used to obtain optimal control limit policies, and an algorithm is provided to identify these policies. A control limit policy specifies whether or not to advertise in a specific time period on the basis of the level of awareness in that time period. Thus, the duration for which advertising is not done is determined endogenously, and the algorithm helps determine this duration for a given set of parameters. This is a particularly desirable feature, since advertising practitioners are interested in determining the optimal duration of advertising pulses. Computational experience suggests that the algorithm is very fast and easy to implement. Also, conditions on model parameters indicating the relative efficacy of pulsing versus uniform advertising are provided.     

Replication of Measures in Information Systems Research: The Case of IS SERVQUAL*

The Information Systems (IS) discipline should recognize potential pitfalls in replication of research studies. This response to a note appearing in this issue by Van Dyke, Prybutok, and Kappelman (1999) aims to serve two purposes. First, this note will address the problems we have with the Van Dyke et al. (1999) attempted replication of our own previous work. Second, we hope it serves to establish more generally an awareness in the IS field for the potential to create and accept the “illusion of replicability” when this has not really been established.     

A Multiple Objective Embedded Network Model for Human Resource Planning and an Implementation of the Tchebycheff Method

A multiple objective embedded network model is proposed to model a variety of human resource planning problems including executive succession planning, compensation planning, training program design, diversity management and human systems design. The Tchebycheff Method, an interactive multiple objective programming solution procedure developed by Steuer and Choo [32], is implemented using NETSIDE, a computer routine for solving network problems with side constraints developed by Kennington and Whisman [17]. This paper demonstrates how the network structure common to many types of human resource planning problems can be exploited to improve solution efficiency, and how our approach extends the use of network models in human resource planning by including multiple objectives and extranetwork constraints. An illustrative example demonstrating the modeling and solution approach is presented, and the potential applications of these approaches in two specific areas of human resource planning are discussed.     

A Heuristic Algorithm for the Capacitated Multiple Supplier Inventory Grouping Problem

This paper presents and solves a model for the multiple supplier inventory grouping problem, which involves the minimization of logistics costs for a firm that has multiple suppliers with capacity limitations. The costs included in the model are purchasing, transportation, ordering, and inventory holding, while the firm's objective is to determine the optimal flows and groups of commodities from each supplier. We present an algorithm, which combines subgradient optimization and a primal heuristic, to quickly solve the multiple supplier inventory grouping problem. Our algorithm is tested extensively on problems of various sizes and structures, and its performance is compared to that of OSL, a state-of-the-art integer programming code. The computational results indicate that our approach is extremely efficient for solving the multiple supplier inventory grouping problem.