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The purpose of this chapter is to discuss the conceptual, methodological, and statistical challenges in selecting appropriate peer institutions for comparative purposes. The authors' approach embraces a Western scientific tradition that physical things and phenomena can be "reduced into a set of key variables--identifiable parts--that make key contributions, and that the behavior of the whole--in this case institutions of higher education--can be understood by knowing the parts and how they contribute (behave)." Furthermore, a value is placed on "efficiency" and "effective (successful) outcomes." These assumptions have spawned a number of models that stress in varying degrees the larger environmental, "structural and/or contextual variables and 'inputs-processes-outputs.'" Input-processes-outputs models are central to comparative analyses in general and benchmarking in particular. A representation of a general benchmarking model is provided. This point of view frames the conceptual and methodological issues and the technique of principal components analysis the authors discuss in this chapter. (Contains 4 tables, 6 figures, and 4 notes.)

No methodology exists to aid the manager in empirically identifying a takeoff event in a realtime. This paper addresses this problem by developing generally applicable, simple and empirical tests for the determination of the takeoff point of a growth curve. The tests are applied to the adoption of wireless cellular telephony across 40 countries. The tests are shown to provide useful insight into the temporal development and dissemination of this technology. [ABSTRACT FROM AUTHOR]

This paper focuses on the general problem of forecasting the maximum value of a time series which by the nature of the data must approach an asymptotic value. Examples of such series include the growth of organisms, the concentration of a chemical reagent during a reaction occurring over time or the amount of a fossil fuel resource which has been discovered or produced as a function of time. The approach taken below differs from the usual models for this type of data in that it assumes that an unobserved time series is actually driving the process, and that the observed data series is a function of the unobserved process. In the case of fossil fuels the unobserved series might be a measure of the exploratory drilling, the number of production days in a given time period or even the amount of fiscal resources devoted to exploratory activities. A maximum likelihood method is developed for estimating the parameters of the process, especially the maximum S, and the covariance structure of the estimators is developed. The methodology is illustrated on an example of oil production. Finally, methods are developed for forecasting the data into the near future. [ABSTRACT FROM AUTHOR]

Mills' capture-recapture sampling method allows the estimation of the number of errors in a program by randomly inserting known errors and then testing the program for both inserted and indigenous errors. This correspondence shows how correct confidence limits and maximum likelihood estimates can be obtained from the test results. Both fixed sample size testing and sequential testing are considered. [ABSTRACT FROM AUTHOR]

Addresses issues discussed as commentaries on a study that used statistical methods in the evaluation of fossil fuels. Economics and statistical models criticized in the study; Response on the criticisms against the review of statistics used in the study; Clarification on the reported confidence intervals in the reserve estimates. [ABSTRACT FROM AUTHOR]