Politics,Pragmatism, and Departures from the U.S. Courts of Appeals, 1954–2004*

Objective. This article uses Cox proportional hazards models to explain departures from the U.S. Courts of Appeals from 1954–2004. I argue that reforms to pension eligibility and financial incentives in 1984 extended opportunities for voluntary departures and strategic behavior, and that reasons for quitting (whether political, personal, or institutional) differed among types of departures and over time. Methods. The data represent all judge‐years from 1954–2004. Both the full period and 1954–1983 and 1984–2004 subperiods are examined. Results. The effects of political factors are limited to partial retirements and suggest judges' recognition of contentious confirmation politics since the 1980s. Personal and institutional variables dominate outright departures. Conclusions. Explanations for departures from the circuit bench differ both by type and over time. Scholars and policymakers should refine arguments regarding judicial turnover and its causes.     

A conceptual framework for understanding the outsourcing decision

Outsourcing is an increasingly important initiative being pursued by corporations in North America and Europe in pursuit of improved efficiency. This paper provides a conceptual framework to assist managers in identifying and implementing outsourcing decisions. In particular, the framework suggests a way for managers to identify the pre- and post-contractual risks associated with outsourcing decisions along with strategies that can be implemented in the pre-contractual stage in order to mitigate those risks. Empirical findings from the ‘transactions cost' literature are referenced to illustrate elements of our conceptual framework.     

Measuring natural resource scarcity

One fundamental index of world social welfare is the availability of natural resources relative to population. In recent years, social, policy, and even physical scientists have been unable to reach consensus on whether natural resources are becoming increasingly scarce. The prevailing pessimistic view of the 1970s has been strongly challenged in the 1980s by resource-population optimists such as Julian Simon. In this paper, we argue that one source of failure to reach consensus is the fundamental ambiguity of available measures of natural resource scarcity. Surveying measures such as unit extraction cost, relative price, and the reserves-to-production ratio, we discuss difficulties of interpretation. Some of the problems identified may yield to further research, but others appear irremediable.     

Metachoice for strategic analysis

A necessary precursor to strategic choice is the selection of a strategic choice evaluation method — metachoice. Unfortunately, managers are unlikely to find much guidance on how to conduct metachoice analysis in the strategy literature. In light of this problem, a template is presented based on two questions that must be addressed by those engaged in strategic analysis generally and metachoice specifically: (1) what are the organization's strategic goals? (2) how willing are they to monetize their predictions and valuations? Four resulting choice methods are presented and discussed: Discounted Cash Flow Analysis (including variants based on real options), Profitability Analysis, Modified Discounted Cash Flow Analysis and Multi-Goal Analysis.     

Using Service‐Customer Matrices in Strategic Analysis of Nonprofits

This article demonstrates the value of service‐customer matrices (SCMs) for managers performing strategic analysis of nonprofit organizations (NPOs). Some of the benefits of using SCMs are understanding the portfolio of businesses; clarifying what sponsors and clients value; identifying rivals, key attributes, and revenues on a service‐customer segment basis; and understanding and formulating positioning strategy.     

Mean squared error of prediction approach to the analysis of a combined array

SUMMARY The combined array provides a powerful, more statistically rigorous alternative to Taguchi's crossed-array approach to robust parameter design. The combined array assumes a single linear model in the control and the noise factors. One may then find conditions for the control factors which will minimize an appropriate loss function that involves the noise factors. The most appropriate loss function is often simply the resulting process variance, recognizing that the noise factors are actually random effects in the process. Because the major focus of such an experiment is to optimize the estimated process variance, it is vital to understand the resulting prediction properties. This paper develops the mean squared error for the estimated process variance for the combined array approach, under the assumption that the model is correctly specified. Specific combined arrays are compared for robustness. A practical example outlines how this approach may be used to select appropriate combined arrays within a particular experimental situation.     

Challenges in Teaching Short Courses by an Academic Department: The University of Florida Experience

“A Week of Short Courses” is the title given by the Department of Statistics at the University of Florida to its offerings each spring. The offerings are short courses, each lasting 2 1/2 days. The program began in the spring of 1991 with four courses, and has grown to six courses in 1994. During this four-year period the Department of Statistics has faced many challenges that are addressed in this paper.     

Orthogonal blocking of response surface split-plot designs

When all experimental runs cannot be performed under homogeneous conditions, blocking can be used to increase the power for testing the treatment effects. Orthogonal blocking provides the same estimator of the polynomial effects as the one that would be obtained by ignoring the blocks. In many real-life design scenarios, there is at least one factor that is hard to change, leading to a split-plot structure. This paper shows that for a balanced ordinary least square–generalized least square equivalent split-plot design, orthogonal blocking can be achieved. Orthogonally blocked split-plot central composite designs are constructed and a catalog is provided.     

Modifying a central composite design to model the process mean and variance when there are hard-to-change factors

Summary.  An important question within industrial statistics is how to find operating conditions that achieve some goal for the mean of a characteristic of interest while simultaneously minimizing the characteristic's process variance. Often, people refer to this kind of situation as the robust parameter design problem. The robust parameter design literature is rich with ways to create separate models for the mean and variance from this type of experiment. Many times time and/or cost constraints force certain factors of interest to be much more difficult to change than others. An appropriate approach to such an experiment restricts the randomization, which leads to a split-plot structure. The paper modifies the central composite design to allow the estimation of separate models for the characteristic's mean and variances under a split-plot structure. The paper goes on to discuss an appropriate analysis of the experimental results. It illustrates the methodology with an industrial experiment involving a chemical vapour deposition process for the manufacture of silicon wafers. The methodology was used to achieve a silicon layer thickness value of 485 Å while minimizing the process variation.