WEIGHTED SUMS OF NEGATIVELY ASSOCIATED RANDOM VARIABLES

In this paper, we establish strong laws for weighted sums of negatively associated (NA) random variables which have a higher‐order moment condition. Some results of Bai Z.D. & Cheng P.E. (2000) [Marcinkiewicz strong laws for linear statistics. Statist. and Probab. Lett. 43, 105–112,] and Sung S.K. (2001) [Strong laws for weighted sums of i.i.d. random variables, Statist. and Probab. Lett. 52, 413–419] are sharpened and extended from the independent identically distributed case to the NA setting. Also, one of the results of Li D.L. et al. (1995) [Complete convergence and almost sure convergence of weighted sums of random variables. J. Theoret. Probab. 8, 49–76,] is complemented and extended.     

Why do participants in voluntary organizations leave? Exploring the relationship between value congruence and length of stay

Recognizing the importance of values and the specific characteristics of participants and situations in voluntary organizations, we examine how value congruence—the fit or compatibility of values between participants and the organization, or among participants—interacts with personal and situational factors to predict participants' length of stay in their organizations. Introducing the case of a voluntary organization that offers shared housing, we measure value congruence through textual similarity in the self‐introduction documents of 49 participants and the organization's mission statement. This approach differs from the self‐reported measures based on participants' perceptions or recalled interactions used in previous studies. In line with expectation‐disconfirmation theory, participants with the strongest beliefs in organizational values had shorter lengths of stay. The amount of intraorganizational communication also moderated the relationship between value congruence and length of stay. This study provides theoretical and methodological implications for nonprofit management by considering personal and situational factors and evaluating value congruence by textual similarity.     

Predictive factors of acculturation attitudes and social support among Asian immigrants in the USA

This study examines acculturation attitude and its relation to social support among Asian immigrants. The data were collected from 242 Korean, Indian and Filipino immigrants in the USA using the Acculturation Attitude Scale and the Social Support Index. Findings showed that Koreans had a less positive attitude toward acculturation than did Indian and Filipino participants. Acculturation attitude was positively correlated with educational level and English fluency. This study also found a negative correlation between acculturation attitude and social support. Social support from friends and English fluency were identified as significant predictor variables in determining the level of acculturation attitude. These findings not only contribute to social work education and practice, but also increase cultural sensitivity and awareness in working with these populations.     

Minority comparison model: Effects of Whites’ multiracial evaluation on symbolic racism and racialized policy preferences

Most Racial Studies primarily focus on African Americans without paying attention to nonblack minorities, and it fails to capture recent increase in racial diversity. Based on previous theories and empirical findings, we propose a new model, minority comparison model, which accounts for theoretical shortcomings in Racial Studies. This model (1) captures psychological processes that compare blacks and nonblacks, and (2) explores the effects of whites’ multiracial evaluation on racialized policy preferences. Drawing on a 2008 national representative sample, this study finds that whites who have positive stereotype of nonblacks (e.g., Hispanics and/or Asians) but negative stereotype of blacks show substantially higher symbolic racism and stronger opposition to Affirmative Action, whereas whites who have positive stereotype of blacks but negative stereotypes of nonblacks have stronger opposition to expansive immigration policy. Our study offers new ways of understanding and accounting for symbolic racism in modern context, and shows how whites’ preferences in racialized policies are influenced by multiracial evaluation.     

The Kumaraswamy distribution: median-dispersion re-parameterizations for regression modeling and simulation-based estimation

The Kumaraswamy distribution is very similar to the Beta distribution, but has the important advantage of an invertible closed form cumulative distribution function. The parameterization of the distribution in terms of shape parameters and the lack of simple expressions for its mean and variance hinder, however, its utilization with modeling purposes. The paper presents two median-dispersion re-parameterizations of the Kumaraswamy distribution aimed at facilitating its use in regression models in which both the location and the dispersion parameters are functions of their own distinct sets of covariates, and in latent-variable and other models estimated through simulation-based methods. In both re-parameterizations the dispersion parameter establishes a quantile-spread order among Kumaraswamy distributions with the same median and support. The study also describes the behavior of the re-parameterized distributions, determines some of their limiting distributions, and discusses the potential comparative advantages of using them in the context of regression modeling and simulation-based estimation.     

Estimation of parameters in heavy-tailed distribution when its second order tail parameter is known

Estimating parameters in heavy-tailed distribution plays a central role in extreme value theory. It is well known that classical estimators based on the first order asymptotics such as the Hill, rank-based and QQ estimators are seriously biased under finer second order regular variation framework. To reduce the bias, many authors proposed the so-called second order reduced bias estimators for both first and second order tail parameters. In this work, estimation of parameters in heavy-tailed distributions are studied under the second order regular variation framework when the second order parameter in the distribution tail is known. This is motivated in large part by a recent work by the authors showing that the second order tail parameter is known for a large class of popular random difference equations (for example, ARCH models). The focus is on least squares estimators that generalize rank-based and QQ estimators. Though other possible estimators are also briefly discussed, the least squares estimators are most simple to use and perform best for finite samples in Monte Carlo simulations.     

Changing the default setting for information privacy protection: What and whose personal information can be better protected?

With Internet service providers (ISPs) increasingly demanding personal information to develop personalized services, people have become more vulnerable to privacy infringement. As a way to protect individuals’ privacy, industrialized countries have implemented a “notice-and-consent” requirement, meaning an ISP must obtain users’ consent to collect personal information in the course of the ISP's business. Drawing on prospect theory and earlier work on information privacy and behavioral science, in this study, we administered an online survey experiment to test whether the giving of consent differs between ‘opt-in’ and ‘opt-out’ frames. The framing effect was found to be moderated by personal information type, people's attitudes toward privacy, and people's privacy infringement experience. The results indicate that the opt-in frame better protects users’ information privacy, and the framing effect is magnified when the targeted information concerns online activities, when users have weakly held privacy attitudes, and when users have less experience of privacy infringement.