Interpersonal Attachment and Patterns of Ideological Belief

We examine whether two general dimensions of sociopolitical belief—right-wing authoritarianism (RWA) and social dominance orientation (SDO)—are rooted in insecure psychological attachment. Based on an undergraduate sample ( N =  255), we model the relations among attachment styles, general worldviews, RWA, and SDO. A structural equation model indicated that anxious attachment led to RWA but not SDO and that this effect was mediated by the belief that the world is a dangerous place. In contrast, avoidant attachment led to SDO but not RWA, and this effect was mediated by the belief that the world is an uncaring, competitive jungle in which people are motivated to maximize personal utility. We discuss the implications of these findings for the nature and origins of political conservatism.     

Multilevel and Latent Variable Modeling with Composite Links and Exploded Likelihoods

Composite links and exploded likelihoods are powerful yet simple tools for specifying a wide range of latent variable models. Applications considered include survival or duration models, models for rankings, small area estimation with census information, models for ordinal responses, item response models with guessing, randomized response models, unfolding models, latent class models with random effects, multilevel latent class models, models with log-normal latent variables, and zero-inflated Poisson models with random effects. Some of the ideas are illustrated by estimating an unfolding model for attitudes to female work participation. We wish to thank The Research Council of Norway for a grant supporting our collaboration.     

Doubly Robust-Type Estimation for Covariate Adjustment in Latent Variable Modeling

Due to the difficulty in achieving a random assignment, a quasi-experimental or observational study design is frequently used in the behavioral and social sciences. If a nonrandom assignment depends on the covariates, multiple group structural equation modeling, that includes the regression function of the dependent variables on the covariates that determine the assignment, can provide reasonable estimates under the condition of correct specification of the regression function. However, it is usually difficult to specify the correct regression function because the dimensions of the dependent variables and covariates are typically large. Therefore, the propensity score adjustment methods have been proposed, since they do not require the specification of the regression function and have been applied to several applied studies. However, these methods produce biased estimates if the assignment mechanism is incorrectly specified. In order to make a more robust inference, it would be more useful to develop an estimation method that integrates the regression approach with the propensity score methodology. In this study we propose a doubly robust-type estimation method for marginal multiple group structural equation modeling. This method provides a consistent estimator if either the regression function or the assignment mechanism is correctly specified. A simulation study indicates that the proposed estimation method is more robust than the existing methods. This research was partially supported by the Ministry of Education, Science, Sports and Culture, Grant-in-Aid for Young Scientists (B), 187-30406.     

Multilevel Modeling with Correlated Effects

When there exist omitted effects, measurement error, and/or simultaneity in multilevel models, explanatory variables may be correlated with random components, and standard estimation methods do not provide consistent estimates of model parameters. This paper introduces estimators that are consistent under such conditions. By employing generalized method of moments (GMM) estimation techniques in multilevel modeling, the authors present a series of estimators along a robust to efficient continuum. This continuum depends on the assumptions that the analyst makes regarding the extent of the correlated effects. It is shown that the GMM approach provides an overarching framework that encompasses well-known estimators such as fixed and random effects estimators and also provides more options. These GMM estimators can be expressed as instrumental variable (IV) estimators which enhances their interpretability. Moreover, by exploiting the hierarchical structure of the data, the current technique does not require additional variables unlike traditional IV methods. Further, statistical tests are developed to compare the different estimators. A simulation study examines the finite sample properties of the estimators and tests and confirms the theoretical order of the estimators with respect to their robustness and efficiency. It further shows that not only are regression coefficients biased, but variance components may be severely underestimated in the presence of correlated effects. Empirical standard errors are employed as they are less sensitive to correlated effects when compared to model-based standard errors. An example using student achievement data shows that GMM estimators can be effectively used in a search for the most efficient among unbiased estimators. This research was supported by the National Academy of Education/Spencer Foundation and the National Science Foundation, grant number SES-0436274. We thank the editor, associate editor, and referees for detailed feedback that helped improve the paper.     

An analysis of reinforcement sensitivity theory and the five-factor model

Reinforcement Sensitivity Theory (RST) and the Five-Factor Model (FFM) are two prominent personality accounts that have emerged from different backgrounds. Although the two accounts are applied to similar research topics, there is limited empirical work examining the correspondence between them. The current study explored the relationship between RST-based personality traits and the FFM domains and facets in an undergraduate sample (n = 668). Regression analyses indicated that Sensitivity to Punishment (SP) was positively associated with Neuroticism and Agreeableness, and negatively associated with Extraversion, Openness, and Conscientiousness. In contrast, Sensitivity to Reward (SR) was positively associated with Extraversion and Neuroticism, and negatively associated with Agreeableness and Conscientiousness. Exploratory analyses at the facet level specified the relationship between SP, SR, and each domain. A factor analysis was also conducted to explore the higher-order factor structure of RST and the FFM domains. Three factors emerged, which we labelled SP, Stability-Impulsivity, and Sensation Seeking. Taken together, these findings suggest that there is substantial overlap between these two accounts of personality. Copyright © 2007 John Wiley & Sons, Ltd.     

浅论肝移植医师的理念和思维

随着医学的进步,在新的医疗形势下,肝移植是对器官移植科医师的巨大挑战,在实际临床工作中,应首先树立正确的器官移植医患关系观,不断提高自己的人文修养和职业素养,进一步完善与肝移植相关的新的知识结构,运用正确的哲学理念和逻辑方式来处理与肝移植相关的临床问题。使肝移植患者得到满意的治疗效果。     

变性术后患者心理状况的影响因素分析及对策

变性术后的患者是个特殊的弱势群体,心理问题非常突出,它是生理、个人、家庭、社会等多种因素交互影响的结果;因此,通过社会、家庭及个体的共同努力,帮助其建立良好的个体心理模式,稳定其心理状态,可促进其心理的健康发展,这对于变性手术的成功和帮助变性后患者重新走向社会有重要意义。     

情绪信息与注意偏向

情绪信息对注意的调节是情绪研究的热点问题之一。过去的研究多集中于注意偏向,研究范式的多样化、被试群体特质的差异性,导致对注意偏向机制的诸多争论,形成了注意成分说、图式说、注意资源说和PDP模型。注意成分说认为,威胁相关的刺激损害了注意解除能力而引起偏向。图式说认为,图式或知识结构的激活引发注意偏向。注意资源说认为,过多注意资源的消耗引起情绪Stroop实验中的注意偏向。PDP模型认为,特殊人群过高的输入单元激活水平引起注意偏向。     

焦虑和抑郁动物模型的研究方法和策略

焦虑和抑郁障碍是严重威胁人们健康的精神疾病。焦虑和抑郁动物模型是通过一定的心理社会应激使动物产生明显的焦虑和抑郁情绪,模拟人类焦虑和抑郁障碍,以探讨其细胞、分子和基因水平机制。情绪障碍动物模型通常通过表面效度、结构效度和预测效度进行评价。目前的动物模型虽然存在一定的缺点,但已经为临床和科研提供了巨大帮助。     

样例学习条件下的因果力估计

在逐个呈现因果样例的条件下,考察单一因果关系因果力估计的特点,同时检验联想解释,概率对比模型,权重DP模型,效力PC理论和pCI规则。实验让65名大学生被试估计不同化学药物影响动物基因变异的能力。实验结果表明：（1）对产生原因的因果力估计符合权重DP模型;（2）对预防原因的因果力估计较多符合效力PC理论;（3）因果力估计具有复杂多样性,难以用统一的模式加以描述和概括。