A social identity model of prosocial behaviors within nonprofit organizations

This study draws on social identity theory, while developing and testing the Social Identification Model of Prosocial Behavior among voluntary participants in nonprofit organizations. This model posits relationships between organization identification, commitment, satisfaction, and prosocial behavior among nonprofit volunteers. Path analysis results indicate successful identification of the model. Specifically, when volunteers identified with their nonprofit, they had higher levels of prosocial behaviors, commitment, and satisfaction. This study provides several important extensions to social identity theory and nonprofit literatures while highlighting the need for further research examining motivations behind prosocial behaviors within nonprofits.     

Type I error rates from likelihood‐based repeated measures analyses of incomplete longitudinal data

The last observation carried forward (LOCF) approach is commonly utilized to handle missing values in the primary analysis of clinical trials. However, recent evidence suggests that likelihood‐based analyses developed under the missing at random (MAR) framework are sensible alternatives. The objective of this study was to assess the Type I error rates from a likelihood‐based MAR approach – mixed‐model repeated measures (MMRM) – compared with LOCF when estimating treatment contrasts for mean change from baseline to endpoint (Δ). Data emulating neuropsychiatric clinical trials were simulated in a 4 × 4 factorial arrangement of scenarios, using four patterns of mean changes over time and four strategies for deleting data to generate subject dropout via an MAR mechanism. In data with no dropout, estimates of Δ and SE_Δ from MMRM and LOCF were identical. In data with dropout, the Type I error rates (averaged across all scenarios) for MMRM and LOCF were 5.49% and 16.76%, respectively. In 11 of the 16 scenarios, the Type I error rate from MMRM was at least 1.00% closer to the expected rate of 5.00% than the corresponding rate from LOCF. In no scenario did LOCF yield a Type I error rate that was at least 1.00% closer to the expected rate than the corresponding rate from MMRM. The average estimate of SE_Δ from MMRM was greater in data with dropout than in complete data, whereas the average estimate of SE_Δ from LOCF was smaller in data with dropout than in complete data, suggesting that standard errors from MMRM better reflected the uncertainty in the data. The results from this investigation support those from previous studies, which found that MMRM provided reasonable control of Type I error even in the presence of MNAR missingness. No universally best approach to analysis of longitudinal data exists. However, likelihood‐based MAR approaches have been shown to perform well in a variety of situations and are a sensible alternative to the LOCF approach. MNAR methods can be used within a sensitivity analysis framework to test the potential presence and impact of MNAR data, thereby assessing robustness of results from an MAR method. Copyright © 2004 John Wiley & Sons, Ltd.     

A semi-parametric approach to robust parameter design

Parameter design or robust parameter design (RPD) is an engineering methodology intended as a cost-effective approach for improving the quality of products and processes. The goal of parameter design is to choose the levels of the control variables that optimize a defined quality characteristic. An essential component of RPD involves the assumption of well estimated models for the process mean and variance. Traditionally, the modeling of the mean and variance has been done parametrically. It is often the case, particularly when modeling the variance, that nonparametric techniques are more appropriate due to the nature of the curvature in the underlying function. Most response surface experiments involve sparse data. In sparse data situations with unusual curvature in the underlying function, nonparametric techniques often result in estimates with problematic variation whereas their parametric counterparts may result in estimates with problematic bias. We propose the use of semi-parametric modeling within the robust design setting, combining parametric and nonparametric functions to improve the quality of both mean and variance model estimation. The proposed method will be illustrated with an example and simulations.     

A Risk-Based Method for Modeling Traffic Fatalities

We describe a risk-based analytical framework for estimating traffic fatalities that combines the probability of a crash and the probability of fatality in the event of a crash. As an illustrative application, we use the methodology to explore the role of vehicle mix and vehicle prevalence on long-run fatality trends for a range of transportation growth scenarios that may be relevant to developing societies. We assume crash rates between different road users are proportional to their roadway use and estimate case fatality ratios (CFRs) for the different vehicle-vehicle and vehicle-pedestrian combinations. We find that in the absence of road safety interventions, the historical trend of initially rising and then falling fatalities observed in industrialized nations occurred only if motorization was through car ownership. In all other cases studied (scenarios dominated by scooter use, bus use, and mixed use), traffic fatalities rose monotonically. Fatalities per vehicle had a falling trend similar to that observed in historical data from industrialized nations. Regional adaptations of the model validated with local data can be used to evaluate the impacts of transportation planning and safety interventions, such as helmets, seat belts, and enforcement of traffic laws, on traffic fatalities.     

Refined Inference on Long Memory in Realized Volatility

There is an emerging consensus in empirical finance that realized volatility series typically display long range dependence with a memory parameter (d) around 0.4 (Andersen et al., 2001; Martens et al., 2004). The present article provides some illustrative analysis of how long memory may arise from the accumulative process underlying realized volatility. The article also uses results in Lieberman and Phillips (2004, 2005) to refine statistical inference about d by higher order theory. Standard asymptotic theory has an O(n^-1/2) error rate for error rejection probabilities, and the theory used here refines the approximation to an error rate of o(n^-1/2). The new formula is independent of unknown parameters, is simple to calculate and user-friendly. The method is applied to test whether the reported long memory parameter estimates of Andersen et al. (2001) and Martens et al. (2004) differ significantly from the lower boundary (d = 0.5) of nonstationary long memory, and generally confirms earlier findings.     

The Gendered Face of Terrorism

What does sociology have to contribute to our understanding of terrorism? As scholars of gender and the Far Right, we believe that we have much to offer the current debates. In this article, we focus both on contemporary cases of terrorism, the attacks of September 11 and the Oklahoma City bombing, and terrorist movements as gendered: as enactments of masculinity. In particular, we focus on the contemporary white supremacist movement, the most dangerous domestic terrorist threat faced by the USA. We suggest that the ideology and organization of many terrorist groups are saturated with gendered meanings, both as the analytic prism through which they view their situation, and also as a means of political mobilization.     

Dementia Problem Behavior and the Production of Informal Caregiving Services

Informal caregivers of individuals with Alzheimer's disease spend a considerable amount of time providing care. In this paper, we use Grossman's health production and Becker's time allocation models to develop a model of informal care provision to elderly dementia patients. In our model, time inputs produce caregiving services, which provides utility to the caregiver, but reduces leisure. We assume that time is less productive of services on the margin as the disease progresses. In this framework, an increase in patients' disease severity does not necessarily increase informal caregiver time input. The cost of formal care establishes a reservation price for informal caregiving. When the costs of informal caregiving rise above this reservation level, the patient is institutionalized. We test empirically the effect of deterioration in the patients' condition, proxied by both disease severity and dementia problem behavior, on informal caregiving time. We find that dementia-related problem behaviors and functional limitations significantly increase inputs of informal caregiving time. Patients' problem behavior exerts a modifying effect on functional limitations, and patients' comorbidities have no effect on informal caregiving time.     

On the Evolution of Competitive Balance: The Impact of an Increasing Global Search

The Coase theorem maintains that where free-market precepts exist, the allocation of property rights does not impact the distribution of resources. An application to Major League Baseball suggests that institutions such as free agency and the reverse-order amateur draft would not impact player distributions and therefore would not impact competitive balance. The present study finds that the distribution of wins is generally consistent with the precepts of the Coase theorem and therefore suggests a course for those who wish to alter the level of competitive balance: Major League Baseball should increase its focus on expanding the size of its labor pool. (JEL O15 , L83 , C22 )