Development of an evidence-based conceptual framework for workplace assessment

One solution to the underutilization of workplace accommodations is to use teleconferencing technology to conduct remote assessments, effectively expanding the geographical area that experienced rehabilitation specialists can cover. However, such an effort requires a highly structured and comprehensive assessment protocol. This paper reports on the analysis of 53 existing assessments to develop a conceptual framework for assessment and the use of that framework to evaluate the applicability of 10 work-related assessment protocols for remote tele-assessment. While none of the 10 protocols were found to be sufficiently comprehensive for our purposes, the conceptual framework itself will serve as an important tool to structure a new comprehensive protocol that will be developed. Moreover, the framework can also be used by providers of workplace assessments to evaluate the suitability of any assessment protocol to meet the needs of any particular individual or workplace situation.     

Assessing Performance Budgeting at OMB: The Influence of Politics, Performance, and Program Size

This article investigates the impact of performance, as measuredby the OMB performance budgeting initiative called PerformanceAssessment Rating Tool (PART), on recommendations in the President'sbudget. In a multivariate analysis using data from the FY 2005budget, with appropriate controls for the political contentof programs, we find that the PART scores have a statisticallysignificant impact on budget decisions within OMB. We find thatPART scores have a larger impact on small and medium sized programsthan on large programs. We also find that the "results" componentof PART scores has a smaller impact on budget decisions thanthe "program purpose" component, a finding which tends to contradictthe goal of performance budgeting to redirect resources to programsthat produce results. The relative unimportance of the "results"component may be due to the lack of good outcome measures formost programs in PART.     

A case of mistaken identity? Debating the dilemmas of street-level bureaucracy with Musil et al.

Lipsky's Street-level Bureaucracy (Russell Sage Foundation, New York, 1980 Lipsky, M. 1980. Street-level Bureaucracy: The Dilemmas of Individuals in Public Service, New York: Russell Sage Foundation.  [Google Scholar]) has exerted a strong influence on the study of public service organisations. There has been a growing interest in using this perspective to understand the organisational context of social work and Musil et al.'s article in the EJSW (2004, ‘Do social workers avoid the dilemmas of work with clients?’, European Journal of Social Work, vol. 7, no. 3, pp. 305–319) reflects this interest. Musil et al. argue that it is possible to identify two forms of practitioner response to the constraints of street-level bureaucracies: adapting working practices in ways that disadvantage service users or challenging working conditions in order to achieve more professionally acceptable practice. Their contribution to the debate is helpful, particularly with regard to their identification of responses by practitioners that seek to advance the interests of service users. However, we view their approach as constrained by lack of consideration of the construction of social work roles within particular street-level bureaucracies. We suggest that Lipsky's work should be approached as a tentative analytic framework, rather than as a fixed model and we argue that a productive approach to research on social workers as street-level bureaucrats is conjunctural analysis. Such analysis examines the contexts, circumstances and statuses of practitioners and how these factors shape the specific forms of street-level practice that operate in particular organisational settings. We illustrate this in our discussion of the factors that are likely to have had a bearing on the two practice settings used as case studies by Musil et al.     

Elderly African Americans as intragenerational caregivers

A growing number of older Americans serve as caregiving resources for their elderly spouses, partners, relatives, and/or friends. Yet, little research has addressed the impact and implications of this trend among African Americans. This article describes the results of a qualitative study examining the intragenerational caregiving experiences and perspectives of two cohorts of African Americans (urban and rural, N = 129) between the ages of 58 and 89. Its findings reveal important differences and commonalities associated with elderly African American caregivers and their care giving in urban and rural settings. Additionally, the study suggests that volunteer caregivers can often fill the void left by partners and relatives who predecease or fail to assume responsibility for the care of their elderly relatives. Their efforts do not displace those of friends and family members, but rather address unmet needs in available care. The discussion focuses on the implications of the study findings for public policy and practice.     

Identifying Technology Spillovers and Product Market Rivalry

The impact of R&D on growth through spillovers has been a major topic of economic research over the last thirty years. A central problem in the literature is that firm performance is affected by two countervailing “spillovers” : a positive effect from technology (knowledge) spillovers and a negative business stealing effects from product market rivals. We develop a general framework incorporating these two types of spillovers and implement this model using measures of a firm's position in technology space and productmarket space. Using panel data on U.S. firms, we show that technology spillovers quantitatively dominate, so that the gross social returns to R&D are at least twice as high as the private returns. We identify the causal effect of R&D spillovers by using changes in federal and state tax incentives for R&D. We also find that smaller firms generate lower social returns to R&D because they operate more in technological niches. Finally, we detail the desirable properties of an ideal spillover measure and how existing approaches, including our new Mahalanobis measure, compare to these criteria.     

Genetic programming as a means for programming computers by natural selection

Many seemingly different problems in machine learning, artificial intelligence, and symbolic processing can be viewed as requiring the discovery of a computer program that produces some desired output for particular inputs. When viewed in this way, the process of solving these problems becomes equivalent to searching a space of possible computer programs for a highly fit individual computer program. The recently developed genetic programming paradigm described herein provides a way to search the space of possible computer programs for a highly fit individual computer program to solve (or approximately solve) a surprising variety of different problems from different fields. In genetic programming, populations of computer programs are genetically bred using the Darwinian principle of survival of the fittest and using a genetic crossover (sexual recombination) operator appropriate for genetically mating computer programs. Genetic programming is illustrated via an example of machine learning of the Boolean 11-multiplexer function and symbolic regression of the econometric exchange equation from noisy empirical data.Hierarchical automatic function definition enables genetic programming to define potentially useful functions automatically and dynamically during a run, much as a human programmer writing a complex computer program creates subroutines (procedures, functions) to perform groups of steps which must be performed with different instantiations of the dummy variables (formal parameters) in more than one place in the main program. Hierarchical automatic function definition is illustrated via the machine learning of the Boolean 11-parity function.     

Outsourcing to a Powerful Contract Manufacturer: The Effect of Learning‐by‐Doing

The contract manufacturing industry has grown rapidly in recent years as firms have increasingly outsourced production to reduce costs. This growth has created powerful contract manufacturers (CMs) in several industries. Achieving a competitive cost position is often a primary motive for outsourcing. Outsourcing influences both the original equipment manufacturer's (OEM) and the CM's production levels, and, therefore, through learning‐by‐doing renders future costs dependent on past outsourcing decisions. As such, outsourcing should not be viewed as a static decision that, once made, is not revisited. We address these considerations by analyzing a two‐period game between an OEM and a powerful CM wherein both firms can reduce their production costs through learning‐by‐doing. We find that partial outsourcing, wherein the OEM simultaneously outsources and produces in‐house, can be an optimal strategy. Also, we find that the OEM's outsourcing strategy may be dynamic—i.e., change from period to period. In addition, we find both that the OEM may engage in production for leverage (i.e., produce internally when at a cost disadvantage) and that the CM may engage in low balling. These and other findings in this paper demonstrate the importance of considering learning, the power of the CM, and future periods when making outsourcing decisions.     

Decomposition of Scatter Ratios Used in Monitoring Multivariate Process Variability

Wilks’ ratio statistic can be defined in terms of the ratio of the sample generalized variances of two non-independent estimators of the same covariance matrix. Recently this statistic has been proposed as a control statistic for monitoring changes in the covariance matrix of a multivariate normal process in a Phase II situation, particularly when the dimension is larger than the sample size. In this article we derive a technique for decomposing Wilks’ ratio statistic into the product of independent factors that can be associated with the components of the covariance matrix. With these results, we demonstrate that, when a signal is detected in a control procedure for the Phase II monitoring of process variability using the ratio statistic, the signaling value can be decomposed and the process variables contributing to the signal can be specifically identified.     

Combining an Internal Pilot with an Interim Analysis for Single Degree of Freedom Tests

An internal pilot with interim analysis (IPIA) design combines interim power analysis (an internal pilot) with interim data analysis (two-stage group sequential). We provide IPIA methods for single df hypotheses within the Gaussian general linear model, including one and two group t tests. The design allows early stopping for efficacy and futility while also re-estimating sample size based on an interim variance estimate. Study planning in small samples requires the exact and computable forms reported here. The formulation gives fast and accurate calculations of power, Type I error rate, and expected sample size.     

Detection and Interpretation of a Multivariate Signal Using Combined Charts

A control procedure is presented in this article that is based on jointly using two separate control statistics in the detection and interpretation of signals in a multivariate normal process. The procedure detects the following three situations: (i) a mean vector shift without a shift in the covariance matrix; (ii) a shift in process variation (covariance matrix) without a mean vector shift; and (iii) both a simultaneous shift in the mean vector and covariance matrix as the result of a change in the parameters of some key process variables. It is shown that, following the occurrence of a signal on either of the separate control charts, the values from both of the corresponding signaling statistics can be decomposed into interpretable elements. Viewing the two decompositions together helps one to specifically identify the individual components and associated variables that are being affected. These components may include individual means or variances of the process variables as well as the correlations between or among variables. An industrial data set is used to illustrate the procedure.