Organizational Compatibility and Workplace Drug Testing: Modeling the Adoption of Innovative Social Control Practices

Few researchers have examined organizational variation in the adoption of workplace drug testing, but innovation theory suggests that adoption is more likely when it is compatible with an organization's values, previously introduced ideas, and needs. Using data from the 1997 National Employee Survey, this research models the effects of organizational compatibility, industry, and establishment size on the adoption of drug testing. The data reveal that compatibility, as measured by an organization's rules orientation, presence of an employee assistance program, and mechanization, is associated with the adoption of drug testing. As predicted, the adoption of drug testing varies across industries and by establishment size.     

Explaining Cooperation: How Resource Interdependence, Goal Congruence, and Trust Affect Joint Actions in Policy Implementation

A perennial problem when political decisions are to be implementedis how to make authorities work together. Previous researchshows that resource interdependence, goal congruence, and mutualtrust increase interorganizational cooperation. In this article,it is argued that interaction effects must also be consideredin order to fully understand how these variables affect cooperation.The study is based on 203 dyads of Swedish Public EmploymentService offices and municipalities in 2003. I find that mutualtrust is necessary if goal congruence is to increase cooperationbetween these agencies. Furthermore, mutual trust only has apositive effect if organizations have similar objectives. However,trust is not required for resource interdependence to affectcooperation, and the effect of trust is not dependent on theorganizations' mutual dependence. The results imply that trustand goal congruence must exist simultaneously in order to promotejoint actions. Thus, if a management strategy aimed at increasingcooperation only focuses on the organizations' objectives orthe level of trust between them, it will fail. An importantlesson for future research is that including interaction termsin the analysis improves our understanding of interorganizationalcooperation.     

The Development of Affect Specificity in Infants' Use of Emotion Cues

This study examined the emergence of affect specificity in infancy. In this study, infants received verbal and facial signals of 2 different, negatively valenced emotions (fear and sadness) as well as neutral affect via a television monitor to determine if they could make qualitative distinctions among emotions of the same valence. Twenty 12‐ to 14‐month‐olds and 20 16‐ to 18‐month‐olds were examined. Results suggested that younger infants showed no evidence of referential specificity, as they responded similarly to both the target and distracter toys, and showed no evidence of affect specificity, showing no difference in play between affect conditions. Older infants, in contrast, showed evidence both of referential and affect specificity. With respect to affect specificity, 16‐ to 18‐month‐olds touched the target toy less in the fear condition than in the sad condition and showed a larger proportion of negative facial expressions in the sad condition versus the fear condition. These findings suggest a developmental emergence after 15 months of age for affect specificity in relating emotional messages to objects.     

Some observations on the history of large philanthropic foundations in Britain and the United States

Large philanthropic foundations such as those which first developed in the United States in the late nineteenth and early twentieth century had four characteristics: (a) the aim of contributing to the public good; (b) applying science and scientific method to human affairs, interpreting science broadly; (c) using great wealth to pursue these purposes; and (d) seeking public recognition of their charitable status in doing so. Between 1870 and 1930 the large foundation emerged as a major social institution, and under the influence of their officers began to be major patrons of applied social science for public policy purposes. Critics of foundations have charged that they were private bodies without external accountability; they were secretive, undemocratic, unrepresentative and indeed pillars of the ruling class. Nevertheless, foundations have exercised a disproportionate influence upon public policy research and have sought to apply fundamental knowledge to tackling social problems. They represent the institutionalisation of knowledge-based social engineering.     

Analytical approximations for iterated bootstrap confidence intervals

Standard algorithms for the construction of iterated bootstrap confidence intervals are computationally very demanding, requiring nested levels of bootstrap resampling. We propose an alternative approach to constructing double bootstrap confidence intervals that involves replacing the inner level of resampling by an analytical approximation. This approximation is based on saddlepoint methods and a tail probability approximation of DiCiccio and Martin (1991). Our technique significantly reduces the computational expense of iterated bootstrap calculations. A formal algorithm for the construction of our approximate iterated bootstrap confidence intervals is presented, and some crucial practical issues arising in its implementation are discussed. Our procedure is illustrated in the case of constructing confidence intervals for ratios of means using both real and simulated data. We repeat an experiment of Schenker (1985) involving the construction of bootstrap confidence intervals for a variance and demonstrate that our technique makes feasible the construction of accurate bootstrap confidence intervals in that context. Finally, we investigate the use of our technique in a more complex setting, that of constructing confidence intervals for a correlation coefficient.     

Improved Monte Carlo inference for models with additive error

Some statistical models defined in terms of a generating stochastic mechanism have intractable distribution theory, which renders parameter estimation difficult. However, a Monte Carlo estimate of the log-likelihood surface for such a model can be obtained via computation of nonparametric density estimates from simulated realizations of the model. Unfortunately, the bias inherent in density estimation can cause bias in the resulting log-likelihood estimate that alters the location of its maximizer. In this paper a methodology for radically reducing this bias is developed for models with an additive error component. An illustrative example involving a stochastic model of molecular fragmentation and measurement is given.