A Modest Proposal: A New Standard for the Normal

The extensive research literature in many areas of behavioral, medical, and social sciences has led some reviewers to the use of quantitative methods for research synthesis. Typically, these analyses have involved estimating the effect magnitude from each of a series of studies and averaging the estimates to obtain a single index of effect magnitude. This article provides some statistical methods for estimating and testing linear models for effect magnitude and can be used for determining the effect of variations in experimental conditions. These methods can be used for synthesizing the results of studies where the index of effect magnitude is a correlation coefficient or standardized mean difference. A natural test for model specification is also given.     

The Analysis of Survey Data with Framing Effects

A well-known difficulty in survey research is that respondents’ answers to questions can depend on arbitrary features of a survey’s design, such as the wording of questions or the ordering of answer choices. In this paper, we describe a novel set of tools for analyzing survey data characterized by such framing effects. We show that the conventional approach to analyzing data with framing effects—randomizing survey-takers across frames and pooling the responses—generally does not identify a useful parameter. In its place, we propose an alternative approach and provide conditions under which it identifies the responses that are unaffected by framing. We also present several results for shedding light on the population distribution of the individual characteristic the survey is designed to measure.     

An Introduction to Practical Sequential Inferences via Single-Arm Binary Response Studies Using the binseqtest R Package

We review sequential designs, including group sequential and two-stage designs, for testing or estimating a single binary parameter. We use this simple case to introduce ideas common to many sequential designs, which in this case can be explained without explicitly using stochastic processes. We focus on methods provided by our newly developed R package, binseqtest, which exactly bound the Type I error rate of tests and exactly maintain proper coverage of confidence intervals. Within this framework, we review some allowable practical adaptations of the sequential design. We explore issues such as the following: How should the design be modified if no assessment was made at one of the planned sequential stopping times? How should the parameter be estimated if the study needs to be stopped early? What reasons for stopping early are allowed? How should inferences be made when the study is stopped for crossing the boundary, but later information is collected about responses of subjects that had enrolled before the decision to stop but had not responded by that time? Answers to these questions are demonstrated using basic methods that are available in our binseqtest R package. Supplementary materials for this article are available online.     

Bayesian Estimation of the Two-Parameter Gamma Distribution

In this article, we present and discuss an original price index being a special case of a general formula for price indices. We show that the discussed formula satisfies most postulates coming from the axiomatic price index theory. We compare this index to other known and popular price indices in a simulation study.     

金融发展、城镇化与贫困减缓--基于系统GMM的估计

采用1978-2010年中国省际面板数据,运用系统GMM估计对金融发展（金融规模和金融效率）、城镇化与贫困减缓的关系进行经验检验。研究结果表明：城镇化与贫困减缓均具有时间的积累和持续性;金融发展与城镇化、贫困减缓具有显著的正相关关系,随着经济的增长变化,金融发展能够显著提高城镇化进程,进而促进贫困减缓。与金融规模相比较,金融效率的城镇化和贫困减缓效应偏小。     

中国银行业全面对外开放水平的基本判断与分析

正确测度、判断中国银行业的对外开放水平,是确保我国银行业乃至整个金融业在对外开放过程中发展、稳定与安全的前提和基础。为达此目的,本文首先给出了中国银行业对外开放的评价指标体系,在此基础上构建了能全面、准确衡量中国银行业对外开放水平的测度模型;借助于该模型,本文对中国银行业对外开放水平进行了测度与判断。研究结果表明:与自身相比,中国银行业的对外开放水平一直处于上升状态,且在入世过渡期间上升幅度迅速加大;而与成熟的美国银行业相比,中国银行业全面对外开放水平还不高,仍有较大差距。最后,基于本文对中国银行业全面对外开放水平的判断,提出了相应的对策建议。     

Policy Learning for Flood Mitigation: A Longitudinal Assessment of the Community Rating System in Florida

Floods continue to inflict the most damage upon human communities among all natural hazards in the United States. Because localized flooding tends to be spatially repetitive over time, local decisionmakers often have an opportunity to learn from previous events and make proactive policy adjustments to reduce the adverse effects of a subsequent storm. Despite the importance of understanding the degree to which local jurisdictions learn from flood risks and under what circumstances, little if any empirical, longitudinal research has been conducted along these lines. This article addresses the research gap by examining the change in local flood mitigation policies in Florida from 1999 to 2005. We track 18 different mitigation activities organized into four series of activities under the Federal Emergency Management Agency's (FEMA) Community Rating System (CRS) for every local jurisdiction in Florida participating in the FEMA program on a yearly time step. We then identify the major factors contributing to policy changes based on CRS scores over the seven-year study period. Using multivariate statistical models to analyze both natural and social science data, we isolate the effects of several variables categorized into the following groups: hydrologic conditions, flood disaster history, socioeconomic and human capital controls. Results indicate that local jurisdictions do in fact learn from histories of flood risk and this process is expedited under specific conditions.     

A Performance Measure for Software Reuse Projects

The efficient development of software has become a critical factor to the success of many organizations. Significant competitive advantage can be realized by reducing the time to complete software development, while maintaining or improving quality levels. Project management activities, including the measurement of performance at the level of the project, can help the organization to both control projects and learn from experience. Traditionally, the productivity of a software development project has been measured as the complexity of code produced per unit of labor involved. An increasing number of organizations, however, are adopting the practice of software reuse, and under such conditions, such a traditional measure of productivity does not fully capture all the relevant elements of the reuse-driven development environment. This research develops a new performance measure that considers four major components of performance: productivity associated with development of new code, productivity associated with the reuse of previously written code, quality of the reuse decision, and relative value of reuse to the company. To illustrate the feasibility of the measure, a real-world productivity assessment is presented for an example company, and related implementation issues are discussed.     

Axiomatic Justification for a Geometric Quality Aggregation Function

A central issue in many decision-making situations is the need to consider multiple factors. A special case of multifactor modeling is the quality aggregation problem, which is to derive an overall quality measurement from a set of component quality measurements. Although much research has been published regarding the components of a quality construct, alternative methods for aggregating quality components have been largely ignored. The function generally used for this aggregation is an arithmetic weighted average. This article proposes four axioms that are intuitively desirable in a quality aggregation function. Concepts are drawn from related disciplines such as utility theory, decision theory, and microeconomics. Empirical evidence is presented to support the axioms. The arithmetic weighted average function is found to be inconsistent with all four axioms. A geometric, or multiplicative, function form is presented as a superior alternative, consistent with the four axioms. Model specification issues and other implications are discussed.     

Multiple Conceptualizations of Small Business Web Use and Benefit*

Small businesses play an important role in the U.S. economy and there is anecdotal evidence that use of the Web is beneficial to such businesses. There is, however, little systematic analysis of the conditions that lead to successful use of and thereby benefits from the Web for small businesses. Based on the innovation adoption, organizations, and information systems (IS) implementation literature, we identify a set of variables that are related to adoption, use, and benefits of information technology (IT), with particular emphasis on small businesses. These variables are reflective of an organization's contextual characteristics, its IT infrastructure, Web use, and Web benefits. Since the extant research does not suggest a single theoretical model for Web use and benefits in the context of small businesses, we adopt a modeling approach and explore the relationships between “context‐IT‐use‐benefit” (CIUB) through three models—partial‐mediator, reduced partial‐mediator, and mediator. These models posit that the extent of Web use by small businesses and the associated benefits are driven by organizations’ contextual characteristics and their IT infrastructure. They differ in the endogeneity/exogeneity of the extent of IT sophistication, and in the direct/mediated effects of organizational context. We examine whether the relationships between variables identified in the literature hold within the context of these models using two samples of small businesses with national coverage, including various sizes, and representing several industry sectors. The results show that the evidence for patterns of relationships is similar across the two independent samples for two of these models. We highlight the relationships within the reduced partial‐mediator and mediator models for which conclusive evidence are given by both samples. Implications for small business managers and providers of Web‐based technologies are discussed.