Contextualization and Context Theorizing in Teams Research: A Look Back and a Path Forward

In this paper we review recent field research on teams in the workplace with the objective to help advance research about teams and their external context. We investigate contextualization by reviewing the research settings in which teams have been studied, and we investigate context theorizing by reviewing work where external context variables have been explicitly modeled. We propose guidelines to improve contextualization and avenues to explore context theorizing.     

An Examination of Public Private Academic Partnerships: Does Program Success Enhance University Performance Outcomes?

In response to a steady decline in State funding, universities have become heavily involved in a variety of public private academic partnerships (PPAPs) in such areas as medical research, business incubation, technological commercialization, continuing professional education, and intercollegiate athletics. Despite this trend, research which examines if such partnerships actually enhance university funding is rare within the public organization literature. Given this, we focus on university intercollegiate athletics programs, which represent one of the largest PPAPs ever undertaken by universities, to examine whether PPAP success can potentially enhance university performance outcomes leading to increased funding levels. Such programs represent a PPAP because public, private, and academic entities all come together to form an organization (division 1 football at the bowl subdivision level) which seeks to benefit all three entities through sharing resources and responsibilities. Study results showed that athletic success positively influenced a variety of university performance outcome variables such as enrollments and endowments. While results indicate that PPAP success can enhance university funding, we also suggest modifications that should enhance the chances that PPAPs will be successful.     

A linear model-based test for the heterogeneity of conditional correlations

Current methods of testing the equality of conditional correlations of bivariate data on a third variable of interest (covariate) are limited due to discretizing of the covariate when it is continuous. In this study, we propose a linear model approach for estimation and hypothesis testing of the Pearson correlation coefficient, where the correlation itself can be modeled as a function of continuous covariates. The restricted maximum likelihood method is applied for parameter estimation, and the corrected likelihood ratio test is performed for hypothesis testing. This approach allows for flexible and robust inference and prediction of the conditional correlations based on the linear model. Simulation studies show that the proposed method is statistically more powerful and more flexible in accommodating complex covariate patterns than the existing methods. In addition, we illustrate the approach by analyzing the correlation between the physical component summary and the mental component summary of the MOS SF-36 form across a fair number of covariates in the national survey data.     

A two-sample empirical likelihood ratio test based on samples entropy

Powerful entropy-based tests for normality, uniformity and exponentiality have been well addressed in the statistical literature. The density-based empirical likelihood approach improves the performance of these tests for goodness-of-fit, forming them into approximate likelihood ratios. This method is extended to develop two-sample empirical likelihood approximations to optimal parametric likelihood ratios, resulting in an efficient test based on samples entropy. The proposed and examined distribution-free two-sample test is shown to be very competitive with well-known nonparametric tests. For example, the new test has high and stable power detecting a nonconstant shift in the two-sample problem, when Wilcoxon’s test may break down completely. This is partly due to the inherent structure developed within Neyman-Pearson type lemmas. The outputs of an extensive Monte Carlo analysis and real data example support our theoretical results. The Monte Carlo simulation study indicates that the proposed test compares favorably with the standard procedures, for a wide range of null and alternative distributions.     

A course for clinical trial personnel in clinical study designs, randomization, allocation schedules, and interactive response systems

This continuing education course for professionals involved in all areas of clinical trials integrates concepts related to the role of randomization in the scientific process. The course includes two interactive lecture and discussion sections and a workshop practicum. The first interactive lecture introduces basic clinical trial issues and statistical principles such as bias, blinding, randomization, control groups, and the importance of formulating clear and discriminating clinical and statistical hypotheses. It then focuses on the most commonly used clinical study designs and the corresponding patient randomization schemes. The second interactive lecture focuses on the implementation of randomization of patients and drug supply through allocation and component ID schedules. The workshop practicum, conducted in small groups, enables students to apply the lecture concepts to real clinical studies. Flexibility was built into the workshop practicum materials to allow the course content to be customized to specific audiences, and the interactive lecture sessions can be stretched to cover more advanced topics according to class interest and time availability.     

Reserving Capacity for Urgent Patients in Primary Care

This paper examines the effect of the common practice of reserving slots for urgent patients in a primary health care practice on two service quality measures: the average number of urgent patients that are not handled during normal hours (either handled as overtime, referred to other physicians, or referred to the emergency room) and the average queue of non‐urgent or routine patients. We formulate a stochastic model of appointment scheduling in a primary care practice. We conduct numerical experiments to optimize the performance of this system accounting for revenue and these two service quality measures as a function of the number of reserved slots for urgent patients. We compare traditional methods with the advanced‐access system advocated by some physicians, in which urgent slots are not reserved, and evaluate the conditions under which alternative appointment scheduling mechanisms are optimal. Finally, we demonstrate the importance of patient arrival dynamics to their relative performance finding that encouraging routine patients to call for same‐day appointments is a key ingredient for the success of advanced‐access.     

Unmet home care service needs of rural older adults with Alzheimer's disease: a perspective of informal caregivers

This study assessed unmet service needs of rural older adults with Alzheimer's disease (AD) and identified factors that were related to these needs. Data were collected from 109 informal caregivers of AD patients. Over half of the patients experienced unmet service needs in 1 or more areas of activities of daily functioning. Informal caregiver burden and patient's gender and functional status were significantly related to patients' unmet service needs. Patients' use of formal services was marginally related to their unmet service needs. To better address patients' service needs, a comprehensive needs assessment should be conducted with both patients and their caregivers.