Adaptive Bayes sum test for the equality of two nonparametric functions

The statistical difference among massive data sets or signals is of interest to many diverse fields including neurophysiology, imaging, engineering, and other related fields. However, such data often have nonlinear curves, depending on spatial patterns, and have non-white noise that leads to difficulties in testing the significant differences between them. In this paper, we propose an adaptive Bayes sum test that can test the significance between two nonlinear curves by taking into account spatial dependence and by reducing the effect of non-white noise. Our approach is developed by adapting the Bayes sum test statistic by Hart [13 J.D. Hart, Frequentist-Bayes lack-of-fit tests based on Laplace approximations, J. Stat. Theory Practice 3 (2009), pp. 681–704. doi: 10.1080/15598608.2009.10411954[Taylor &; Francis Online] , [Google Scholar]]. The spatial pattern is treated through Fourier transformation. Resampling techniques are employed to construct the empirical distribution of test statistic to reduce the effect of non-white noise. A simulation study suggests that our approach performs better than the alternative method, the adaptive Neyman test by Fan and Lin [9 J. Fan and S. Lin, Test of significance when data are curves, J. Amer. Math. Soc. 93 (1997), pp. 1007–1021.[Web of Science ®] , [Google Scholar]]. The usefulness of our approach is demonstrated with an application in the identification of electronic chips as well as an application to test the change of pattern of precipitations.     

Expanding the role of long-term care social workers: assessment and intervention related to urinary incontinence

Urinary incontinence (UI) is a common and stigmatizing problem faced by long-term care (LTC) residents. It is typically addressed by medical professionals, with social work rarely involved. The purpose of this article is to illustrate how social workers can address the psychosocial implications of UI while working with residents and their family members as part of an interdisciplinary team. Using a case example and the NASW objectives for LTC, recommendations on how the role of the LTC social workers can be expanded to better address both the needs of residents, families, and the larger LTC system are provided.     

Does higher voter turnout among the poor lead to more equal policy representation?

The belief that elected officials are most responsive to the opinions of the wealthiest members of society is often assumed but has only recently begun to be tested. This paper examines a common explanation for why this disparity in political representation occurs: wealthy citizens vote at much higher rates than citizens with low incomes. Utilizing variation across states in voter turnout levels among the rich and poor, there is little evidence that increased voting among citizens with low incomes improves representation of their political opinions in the Senate. These findings cast doubt on the proposition that increased voter turnout among the poor is an avenue for promoting greater political equality in the United States.     

COI Policies: Tax Dollars Should Not Be Used to Fund U.S. Institutions Not Making the Grade

Over two billion dollars was awarded by the U.S. Department of Health and Human Services (HHS) in research funding from 2010 to March 2012 to institutions receiving a grade of "C," "D," or "F" on their conflict of interest policies, as determined by the American Medical Student Association's scorecard on conflict of interest policies. More institutional oversight is needed with regard to assuring conflict of interest policies at U.S. research institutions are adequate. As stewards of public funds, HHS should require a minimum standard which institutional conflict of interest policies should meet, beyond current regulatory requirements, before granting funding.     

The Coexistence of Competition and Cooperation between Networks: Implications from Two Taiwanese Healthcare Networks*

The issue of coopetition, the coexistence of cooperation and competition, has recently come to the fore in the strategic management field. Previous coopetition research has focused on the intra‐organizational level, inter‐organizational level and triad level, but less attention has been paid to coopetition at the network level. The purpose of this paper is to address the coexistence of competition and cooperation between networks, and to depict how networks with different structures interact with each other. Drawing from a detailed case study of two healthcare networks in Taiwan, we demonstrate how they first initiated competition, followed by cooperation and then coopetition. From our analysis of this example of network coopetition, we develop three propositions that address the forces driving competition and cooperation and the different structures that allow competition and cooperation to coexist. We found that two organizations will compete and cooperate simultaneously when each organization has complementary but distinctly different sets of resources and when the field of competition is distinctly separate from the field of cooperation. In addition, two networks will find it easier to balance competition and cooperation when each network has compatible but distinctly different structures. We argue that the simultaneous existence of cooperation and competition is not dependent on closeness to the customer, as previously suggested in the literature, but on the balance between the forces for cooperation and for competition. We suggest from this research that networks can maintain the balance between competition and cooperation when they act using different structures. Finally, we discuss the implications of the value of competition and then coopetition at multiple levels of analysis and the implications for future research from a practical perspective.     

Estimating individual-level interaction effects in multilevel models: a Monte Carlo simulation study with application*

Moderated multiple regression provides a useful framework for understanding moderator variables. These variables can also be examined within multilevel datasets, although the literature is not clear on the best way to assess data for significant moderating effects, particularly within a multilevel modeling framework. This study explores potential ways to test moderation at the individual level (level one) within a 2-level multilevel modeling framework, with varying effect sizes, cluster sizes, and numbers of clusters. The study examines five potential methods for testing interaction effects: the Wald test, F-test, likelihood ratio test, Bayesian information criterion (BIC), and Akaike information criterion (AIC). For each method, the simulation study examines Type I error rates and power. Following the simulation study, an applied study uses real data to assess interaction effects using the same five methods. Results indicate that the Wald test, F-test, and likelihood ratio test all perform similarly in terms of Type I error rates and power. Type I error rates for the AIC are more liberal, and for the BIC typically more conservative. A four-step procedure for applied researchers interested in examining interaction effects in multi-level models is provided.     

Minimizing Social Desirability Bias in Measuring Sensitive Topics: The Use of Forgiving Language in Item Development

Abstract

Social science research has long been concerned with attitudes, beliefs, and behaviors that are potentially objectionable, immoral, or illegal. These types of topics include, for example, racism, ableism, cheating, and stealing, among others. Referred to as “sensitive topics,” their investigation usually involves questions that require respondents to admit to attitudes, beliefs, and behaviors that violate social norms, making their assessment susceptible to error due to social desirability bias. This article describes an empirical investigation of an approach to minimize this bias, the use of “forgiving language” in survey item development and the effect on item variability. Using secondary data initially collected as part of a measurement development study of mental health providers’ stigmatization of service users, 15 pairs of similar, thematically targeted items, varying with respect to wording approach were tested using a purposive sample of mental health providers (N?=?220). Findings indicate that items crafted in a forgiving manner were not significantly influenced by social desirability bias, in contrast to items developed in more traditional language. Additionally, forgiving language-items produced higher levels of agreement, on average, when compared to those written in more traditional language. More research is indicated, including systematic variation of wording approaches, but these results seem promising.