Religion as a Meaning System: Policy Implications for the New Millennium

The development of social policy related to religion has received increasing focus in recent years, yet psychology continues to play a relatively minor role in this important domain. In the current article, religion's positive and negative influences as a meaning system on individual, community, and societal well-being are delineated. The challenges facing psychology in contributing to public policy development in the religious arena are examined, challenges that stem from profound differences in the meaning systems of religion, government, and psychology. These challenges notwithstanding, a number of different pathways in the domains of applied research, community practice, and policy development are delineated through which psychology can help to maximize positive, and minimize negative, outcomes in the religion and social policy arena.     

I'm OK, You're (Not) OK: The Private Welfare State in the United States

The American welfare state has been premised on the mixture of substantial private, tax‐subsidized benefits rather than on more universalistic public benefits. That pattern is in some ways being undermined as private pension plans are increasingly going bankrupt and firms reduce the benefits they provide employees. On the other hand, however, the current Bush administration is attempting to enhance the private sector role by allowing individuals to invest at least a part of their social insurance contributions into private savings plans. These changes may in the end increase the role of the public sector as a regulator and underwriter at the same time that attempts are made to minimize public sector involvement.     

Experiment-dependent priors in psychology and physics

Sometimes conducting an experiment to ascertain the state of a system changes the state of the system being measured. Kahneman & Tversky modelled this effect with support theory. Quantum physics models this effect with probability amplitude mechanics. As this paper shows, probability amplitude mechanics is similar to support theory. Additionally, Viscusi's proposed generalized expected utility model has an analogy in quantum mechanics.     

国际人类学与民族学联合会都市人类学委员会简介

都市人类学委员会(Commission on Urban Anthropology,简称 CUA)在1983年于温哥华召开的第11届国际人类学与民族学大会(International Congress of Anthropological and Ethnological Sciences,简称ICAES)上正式成立.CUA的成立是人们长久以来所认识到的"将涉足都市研究并激发了这一领域的进一步研究的国际学者聚集到一起"的需要的成功结果.自正式创建以来,委员会的主要目标是:(1)组织学术会议与研讨会;(2)建设一个国际网络,以促进世界范围内的都市人类学合作.     

A Simulation Study of Some Biasing Parameters for the Ridge Type Estimation of Poisson Regression

This article proposes several estimators for estimating the ridge parameter k based on Poisson ridge regression (RR) model. These estimators have been evaluated by means of Monte Carlo simulations. As performance criteria, we have calculated the mean squared error (MSE), the mean value, and the standard deviation of k. The first criterion is commonly used, while the other two have never been used when analyzing Poisson RR. However, these performance criteria are very informative because, if several estimators have an equal estimated MSE, then those with low average value and standard deviation of k should be preferred. Based on the simulated results, we may recommend some biasing parameters that may be useful for the practitioners in the field of health, social, and physical sciences.     

Heteroscedasticity and Distributional Assumptions in the Censored Regression Model

Data censoring causes ordinary least-square estimators of linear models to be biased and inconsistent. The Tobit estimator yields consistent estimators in the presence of data censoring if the errors are normally distributed. However, nonnormality or heteroscedasticity results in the Tobit estimators being inconsistent. Various estimators have been proposed for circumventing the normality assumption. Some of these estimators include censored least absolute deviations (CLAD), symmetrically censored least-square (SCLS), and partially adaptive estimators. CLAD and SCLS will be consistent in the presence of heteroscedasticity; however, SCLS performs poorly in the presence of asymmetric errors. This article extends the partially adaptive estimation approach to accommodate possible heteroscedasticity as well as nonnormality. A simulation study is used to investigate the estimators’ relative performance in these settings. The partially adaptive censored regression estimators have little efficiency loss for censored normal errors and appear to outperform the Tobit and semiparametric estimators for nonnormal error distributions and be less sensitive to the presence of heteroscedasticity. An empirical example is considered, which supports these results.     

Influence diagnostics for Student-t censored linear regression models

In this paper, we extend the censored linear regression model with normal errors to Student-t errors. A simple EM-type algorithm for iteratively computing maximum-likelihood estimates of the parameters is presented. To examine the performance of the proposed model, case-deletion and local influence techniques are developed to show its robust aspect against outlying and influential observations. This is done by the analysis of the sensitivity of the EM estimates under some usual perturbation schemes in the model or data and by inspecting some proposed diagnostic graphics. The efficacy of the method is verified through the analysis of simulated data sets and modelling a real data set first analysed under normal errors. The proposed algorithm and methods are implemented in the R package CensRegMod.