The Effects of Multitasking on Operations Scheduling

This study considers a typical scheduling environment that is influenced by the behavioral phenomenon of multitasking. Under multitasking, the processing of a selected job suffers from interruption by other jobs that are available but unfinished. This situation arises in a wide variety of applications; for example, administration, manufacturing, and process and project management. Several classical solution methods for scheduling problems no longer apply in the presence of multitasking. The solvability of any scheduling problem under multitasking is no easier than that of the corresponding classical problem. We develop optimal algorithms for some fundamental and practical single machine scheduling problems with multitasking. For other problems, we show that they are computationally intractable, even though in some cases the corresponding problem in classical scheduling is efficiently solvable. We also study the cost increase and value gained due to multitasking. This analysis informs companies about how much it would be worthwhile to invest in measures to reduce or encourage multitasking.     

A Comparison of Some Methods for the Selection of a Common Eigenvector Model for the Covariance Matrices of Two Groups

Two new nonparametric common principal component model selection procedures based on bootstrap distributions of the vector correlations of all combinations of the eigenvectors from two groups are proposed. The performance of these methods is compared in a simulation study to the two parametric methods previously suggested by Flury in 1988, as well as modified versions of two nonparametric methods proposed by Klingenberg in 1996 and then by Klingenberg and McIntyre in 1998. The proposed bootstrap vector correlation distribution (BVD) method is shown to outperform all of the existing methods in most of the simulated situations considered.     

A Seemingly Unrelated Nonparametric Additive Model with Autoregressive Errors

This article considers a nonparametric additive seemingly unrelated regression model with autoregressive errors, and develops estimation and inference procedures for this model. Our proposed method first estimates the unknown functions by combining polynomial spline series approximations with least squares, and then uses the fitted residuals together with the smoothly clipped absolute deviation (SCAD) penalty to identify the error structure and estimate the unknown autoregressive coefficients. Based on the polynomial spline series estimator and the fitted error structure, a two-stage local polynomial improved estimator for the unknown functions of the mean is further developed. Our procedure applies a prewhitening transformation of the dependent variable, and also takes into account the contemporaneous correlations across equations. We show that the resulting estimator possesses an oracle property, and is asymptotically more efficient than estimators that neglect the autocorrelation and/or contemporaneous correlations of errors. We investigate the small sample properties of the proposed procedure in a simulation study.     

Power analysis for clustered non-continuous responses in multicenter trials

Power analysis for multi-center randomized control trials is quite difficult to perform for non-continuous responses when site differences are modeled by random effects using the generalized linear mixed-effects model (GLMM). First, it is not possible to construct power functions analytically, because of the extreme complexity of the sampling distribution of parameter estimates. Second, Monte Carlo (MC) simulation, a popular option for estimating power for complex models, does not work within the current context because of a lack of methods and software packages that would provide reliable estimates for fitting such GLMMs. For example, even statistical packages from software giants like SAS do not provide reliable estimates at the time of writing. Another major limitation of MC simulation is the lengthy running time, especially for complex models such as GLMM, especially when estimating power for multiple scenarios of interest. We present a new approach to address such limitations. The proposed approach defines a marginal model to approximate the GLMM and estimates power without relying on MC simulation. The approach is illustrated with both real and simulated data, with the simulation study demonstrating good performance of the method.     

Matching in sexual experience for married, cohabitating, and dating couples

The present study examined heterosexual romantic partners' number of intercourse partners prior to the initiation of their relationship to determine if a significant positive correlation (matching) occurred between partners, and if this matching was associated with their level of love, satisfaction with, and commitment to the relationship. One hundred and six couples who were dating, cohabitating, or married participated in this study. Results indicated that, with the exception of cohabitating couples, romantic partners showed a significant level of matching in the prior number of intercourse partners. Further, among the married couples, a higher discrepancy between men's and women's number of previous intercourse partners was related to lower levels of love, satisfaction, and commitment in the relationship.     

Differences by gender, ethnicity, and acculturation in the efficacy of the keepin' it REAL model prevention program

This study examined whether the efficacy of keepin' it REAL, a model program for substance use prevention in schools, was moderated by gender, ethnicity, and acculturation. Gender differences in program efficacy may arise through boys' higher risk of drug use, inadequate attention to girls' developmental issues, or cultural factors like polarized gender expectations. Data came from a randomized trial in 35 Phoenix, Arizona, middle schools involving 4,622 mostly Latino 7th graders. Using multi-level mixed models and multiple imputation missing techniques, results for the total sample showed no gender differences in program effects on recent substance use, but the program was more effective in fostering boys' than girls' anti-drug norms. Subgroup analyses demonstrated several more beneficial program effects for boys than girls (less alcohol and cigarette use and stronger anti-drug norms), but only among less acculturated Latinos. There were no gender differences in program effects among more acculturated Latinos, nor among non-Latino whites.     

Examining the correlates of engagement and disengagement coping among help-seeking battered women

This study examined several potential correlates of engagement and disengagement coping, including abuse-related factors, socioeconomic and social coping resources, and childhood trauma variables among a sample of battered women (N = 388). Relationship abuse frequency, particularly psychological aggression, and peritraumatic dissociation were the strongest positive predictors of the use of disengagement coping. Social coping resources, including tangible support and appraisals of social support and belonging, were associated with higher engagement coping and lower disengagement coping. A positive association was also found between interparental domestic violence and disengagement coping, and negative associations were found between both childhood physical and sexual abuse and engagement coping. Results suggest that coping strategies used by battered women are multidetermined and deserve further exploration.     

Pearls and pitfalls in evaluating a Student Assistance Program: a five-year case study

This article presents data from a five-year evaluation-research case study of a large urban schools district's internal Student Assistance Program (SAP). The district employed specially trained and licensed school-based counselors to implement an internal SAP expanded to include tertiary prevention, and modeled after an employee assistance program established for adults. From the population of more than 10,000 students who received some type of program assistance for at-risk behaviors, a stratified random sample of 2,238 cases, over a five year period, were reviewed. Results indicate a statistically significant decline (p < .05) in students' drug use and a significant improvement in internal locus of control occurred each year. While not statistically significant, the average number of student discipline referrals was less than the year prior to program admission; however, there was no significant change in academic performance. A significant correlation between students' drug use and the amount of time in program, the type of assistance received, and the student's custody status was also found.     

Environmental changes to increase physical activity: perceptions of older urban ethnic-minority women

Despite the numerous benefits of physical activity, older adults continue to be more sedentary than their younger counterparts, and sedentary behavior is more prevalent among older racial and ethnic minorities than among Whites. This study used the nominal group technique (NGT) to examine participants' perceptions of what neighborhood environmental changes would encourage greater physical activity for older African American and Hispanic women. Participants age 50-75 years were recruited from 2 urban community health clinics. Nine NGT sessions (45 participants) were conducted. The women were asked what changes in their neighborhood environment would encourage them to become more physically active. Responses to the research question were tabulated, and qualitative analysis was used to identify themes and categories. Major categories were physical environment changes, safety, and activities/social support. Although the physical environment received the greatest number of points, concerns for personal safety cut across categories. Participants indicated the need for more facilities in which to be active.