Concepts,Theory, and Techniques ROBUST METHODS: AN ALTERNATIVE APPROACH FOR ANALYZING DATA SETS CONTAINING INFLUENTIAL DATA POINTS

Recent advances in statistical estimation theory have resulted in the development of new procedures, called robust methods, that can be used to estimate the coefficients of a regression model. Because such methods take into account the impact of discrepant data points during the initial estimation process, they offer a number of advantages over ordinary least squares and other analytical procedures (such as the analysis of outliers or regression diagnostics). This paper describes the robust method of analysis and illustrates its potential usefulness by applying the technique to two data sets. The first application uses artificial data; the second uses a data set analyzed previously by Tufte [15] and, more recently, by Chatterjee and Wiseman [6].     

The supermarkets and the small shop: is there a real choice?

Have supermarkets driven out the small shopkeeper? Are high-streets dying? Has supermarket power doomed the convenience store? Do local planning decisions restrict high-street competition? Can only the giants survive? Frederick Wheeler explains how the Competition Commission tries to give buyers a choice.     

Standardizing the World Income Inequality Database*

Objective. Cross‐national research on the causes and consequences of income inequality has been hindered by the limitations of existing inequality data sets: greater coverage across countries and over time is available from these sources only at the cost of significantly reduced comparability across observations. The goal of the Standardized World Income Inequality Database (SWIID) is to overcome these limitations. Methods. A custom missing‐data algorithm was used to standardize the U.N. University's World Income Inequality Database; data collected by the Luxembourg Income Study served as the standard. Results. The SWIID provides comparable Gini indices of gross and net income inequality for 153 countries for as many years as possible from 1960 to the present, along with estimates of uncertainty in these statistics. Conclusions. By maximizing comparability for the largest possible sample of countries and years, the SWIID is better suited to broad cross‐national research on income inequality than previously available sources.     

The Role of Community in Disaster Response: Conceptual Models

We focus on the role that community plays in the continuum of disaster preparedness, response and recovery, and we explore where community fits in conceptual frameworks concerning disaster decision-making. We offer an overview of models developed in the literature as well as insights drawn from research related to Hurricane Katrina. Each model illustrates some aspect of the spectrum of disaster preparedness and recovery, beginning with risk perception and vulnerability assessments, and proceeding to notions of resiliency and capacity building. Concepts like social resilience are related to theories of “social capital,” which stress the importance of social networks, reciprocity, and interpersonal trust. These allow individuals and groups to accomplish greater things than they could by their isolated efforts. We trace two contrasting notions of community to Tocqueville. On the one hand, community is simply an aggregation of individual persons, that is, a population. As individuals, they have only limited capacity to act effectively or make decisions for themselves, and they are strongly subject to administrative decisions that authorities impose on them. On the other hand, community is an autonomous actor, with its own interests, preferences, resources, and capabilities. This definition of community has also been embraced by community-based participatory researchers and has been thought to offer an approach that is more active and advocacy oriented. We conclude with a discussion of the strengths and weaknesses of community in disaster response and in disaster research.     

Questions and Answers

In addition to his contributions to biostatistics and clinical trials, Paul Meier had a long-term interest in the legal applications of statistics. As part of this, he had extensive experience as a statistical consultant. Legal consulting can be a minefield, but as a result of his background, Paul had excellent advice to give to those starting out on how to function successfully in this environment.