主观概率判断的支持理论

支持理论是一个关于主观概率判断的非外延性理论,它有以下几个主要观点：1.主观概率判断受到描述的影响,具有描述的依赖性;2.主观概率判断的结果是判断者对中心假设的相对支持的反映;3.主观概率在二元判断中表现出二元互补性,在多元判断中表现出次可加性;4.主观概率判断存在分解效应和促进效应     

Decision-Making About Reproductive Choices Among Individuals At-Risk for Huntington's Disease

We explored how individuals at-risk for HD who have or have not been tested make reproductive decisions and what factors are involved. We interviewed 21 individuals (8 with and 4 without the mutation, and 9 un-tested) in-depth for 2 hours each. At-risk individuals faced a difficult series of dilemmas of whether to: get pregnant and deliver, have fetal testing, have pre-implantation genetic diagnosis, adopt, or have no children. These individuals weighed competing desires and concerns: their own desires vs. those of spouses vs. broader moral concerns (e.g., to end the disease; and/or follow dictates against abortion) vs. perceptions of the interests of current or future offspring. Quandaries arose of how much and to whom to feel responsible. Some changed their perspectives over time (e.g., first “gambling,” then being more cautious). These data have critical implications for genetic counselors and other health care workers and future research, particularly as more genetic tests become available.     

A variable neighborhood search method for generalized blockmodeling of two-mode binary matrices

The clustering of two-mode proximity matrices is a challenging combinatorial optimization problem that has important applications in the quantitative social sciences. We focus on one particular type of problem related to the clustering of a two-mode binary matrix, which is relevant to the establishment of generalized blockmodels for social networks. In this context, clusters for the rows of the two-mode matrix intersect with clusters of the columns to form blocks, which should ideally be either complete (all 1s) or null (all 0s). A new procedure based on variable neighborhood search is presented and compared to an existing two-mode K-means clustering algorithm. The new procedure generally provided slightly greater explained variation; however, both methods yielded exceptional recovery of cluster structure.     

A two-stage logistic regression model for analyzing inter-rater agreement

Studies of agreement commonly occur in psychiatric research. For example, researchers are often interested in the agreement among radiologists in their review of brain scans of elderly patients with dementia or in the agreement among multiple informant reports of psychopathology in children. In this paper, we consider the agreement between two raters when rating a dichotomous outcome (e.g., presence or absence of psychopathology). In particular, we consider logistic regression models that allow agreement to depend on both rater- and subject-level covariates. Logistic regression has been proposed as a simple method for identifying covariates that are predictive of agreement (Coughlin et al., 1992). However, this approach is problematic since it does not take account of agreement due to chance alone. As a result, a spurious association between the probability (or odds) of agreement and a covariate could arise due entirely to chance agreement. That is, if the prevalence of the dichotomous outcome varies among subgroups of the population, then covariates that identify the subgroups may appear to be predictive of agreement. In this paper we propose a modification to the standard logistic regression model in order to take proper account of chance agreement. An attractive feature of the proposed method is that it can be easily implemented using existing statistical software for logistic regression. The proposed method is motivated by data from the Connecticut Child Study (Zahner et al., 1992) on the agreement among parent and teacher reports of psychopathology in children. In this study, parents and teachers provide dichotomous assessments of a child's psychopathology and it is of interest to examine whether agreement among the parent and teacher reports is related to the age and gender of the child and to the time elapsed between parent and teacher assessments of the child.The authors thank the Associate Editor and the referees for their helpful comments and suggestions. We also thank Gwen Zahner for use of data from the Connecticut Child Study, which was conducted under contract to the Connecticut Department of Children and Youth Services. This research was supported by grants HL 69800, AHRQ 10871, HL52329, HL61769, GM 29745, MH 54693 and MH 17119 from the National Institutes of Health.     

Hierarchical classes models for three-way three-mode binary data: interrelations and model selection

Several hierarchical classes models can be considered for the modeling of three-way three-mode binary data, including the INDCLAS model (Leenen, Van Mechelen, De Boeck, and Rosenberg, 1999), the Tucker3-HICLAS model (Ceulemans, Van Mechelen, and Leenen, 2003), the Tucker2-HICLAS model (Ceulemans and Van Mechelen, 2004), and the Tucker1-HICLAS model that is introduced in this paper. Two questions then may be raised: (1) how are these models interrelated, and (2) given a specific data set, which of these models should be selected, and in which rank? In the present paper, we deal with these questions by (1) showing that the distinct hierarchical classes models for three-way three-mode binary data can be organized into a partially ordered hierarchy, and (2) by presenting model selection strategies based on extensions of the well-known scree test and on the Akaike information criterion. The latter strategies are evaluated by means of an extensive simulation study and are illustrated with an application to interpersonal emotion data. Finally, the presented hierarchy and model selection strategies are related to corresponding work by Kiers (1991) for principal component models for three-way three-mode real-valued data.     

A Data Analysis Method for Using Longitudinal Binary Outcome Data from a SMART to Compare Adaptive Interventions

Sequential multiple assignment randomized trials (SMARTs) are a useful and increasingly popular approach for gathering information to inform the construction of adaptive interventions to treat psychological and behavioral health conditions. Until recently, analysis methods for data from SMART designs considered only a single measurement of the outcome of interest when comparing the efficacy of adaptive interventions. Lu et al. proposed a method for considering repeated outcome measurements to incorporate information about the longitudinal trajectory of change. While their proposed method can be applied to many kinds of outcome variables, they focused mainly on linear models for normally distributed outcomes. Practical guidelines and extensions are required to implement this methodology with other types of repeated outcome measures common in behavioral research. In this article, we discuss implementation of this method with repeated binary outcomes. We explain how to compare adaptive interventions in terms of various summaries of repeated binary outcome measures, including average outcome (area under the curve) and delayed effects. The method is illustrated using an empirical example from a SMART study to develop an adaptive intervention for engaging alcohol- and cocaine-dependent patients in treatment. Monte Carlo simulations are provided to demonstrate the good performance of the proposed technique.     

Factors Impacting on Career Choices of Technikon Students From Previously Disadvantaged High Schools

Career development is a socially constructed process involving complex interactions among different structures, forces, and systems all constituting spheres of influence. The purpose of this study was to investigate factors that influence career decisions. Eighty respondents (40 males and 40 females with mean age of 22 years) participated in the study. Results revealed that family variables of parental high expectations of children and appropriate communications within the family, as well as factors such as prestige statuses of some occupations, school curricular subjects, academic performance, teacher influences, and peer pressure had strong positive influences on career choices. Barriers to career choices identified included finance, lack of appropriate information, poor academic performance, and unsatisfactory career counseling services. Results suggest need for a better approach to career education in schools and counseling services that are adaptive to social, economic, and cultural contexts.     

人之将尽,消费国货？死亡信息的暴露增加国货选择的现象、中介和边界条件解析

死亡信息无处无时不在。恐惧管理理论认为,死亡暴露会促使个体采取两种防御机制：支持文化世界观和提升自尊。来自西方发达国家消费者的数据显示,死亡暴露会促使这些消费者偏爱国货、支持文化世界观,内在机制是爱国主义情绪的唤醒。但对将购买外国货视为追求高自尊和高身份的发展中国家消费者而言,尚没有学者检验死亡暴露会促使他们继续购买外国货以提升自尊,还是转而购买国货以支持文化世界观。本文用实验检验了这两种竞争预测,发现死亡暴露会增加发展中国家消费者的国货选择,但机制是补偿个人控制感的缺失。同时,已有探讨死亡暴露与国货偏好的研究并没有检验何时死亡暴露将不再增加国货偏好,本文发现启动亲密的人际归属感是边界条件。     

Mood effects on dominated choices: Positive mood induces departures from logical rules

In two studies, we investigated the role of mood states in dominated behavioral choices. Past research has shown that mood effects on judgment and decision‐making can be pervasive. Yet, the role of mood in dominated choices has so far been neglected. The present research represents a first empirical examination of mood effects on dominated choices. We measured (Study 1) or manipulated (Study 2) mood states in participants who made a series of choices in a gambling game. In this choice task without trade‐offs, participants were provided with information about the outcomes and probabilities associated with each choice option. The strategy to maximize the expected mean outcome implied the application of a straightforward and logical rule: Always choose the dominant option with the highest expected value. It has been argued in the literature that mood should have little or no impact when preferences are clear or strong. Still, we expected that mood states would affect even these dominated choices, building on previous work that showed that positive mood states enhance flexibility, creativity, and explorative behavior. The results showed that decisions made in a happier mood were less often in accordance with the logical rule than decisions made in a sadder mood. To conclude, happier mood states are associated to a lesser extent with decisions in accordance with a rule‐based strategy that maximizes expected mean outcomes in dominated choices. Copyright © 2010 John Wiley & Sons, Ltd.     

Trait impulsivity and callous-unemotional traits as predictors of inhibitory control and risky choices among high-risk adolescents

Trait impulsivity and callous-unemotional traits are associated with behavioural problems among high-risk adolescents. While both are known to influence behaviour, the nature of their expression in high-risk behaviours, particularly those related to inhibitory control, is not well understood. In the current, preliminary study, we examined whether and how these traits predicted deficits in behaviour driven by bottom-up, automatic versus deliberate, top-down inhibitory processes among high-risk adolescents. Two go/no-go task variants, emotional and non-emotional, were used to assess reactive response inhibition, and the Balloon Analogue Risk Task was used to assess the ability to resist deliberate risky choices. The results showed that the two types of self-reported trait measures were differentially associated with performance on the two types of behavioural inhibition tasks. Trait impulsivity predicted non-emotional inhibitory control whereas callous-unemotional traits predicted risky choices. The results also showed that the emotions task elicited slower reaction times and higher false alarm rates than did the letters task, and that participants had greater difficulty inhibiting responses to negatively than to positively valenced no-go stimuli. While preliminary, the results suggest that the interplay between trait impulsivity and callous-unemotional traits is an important determinant of inhibitory behaviour in this high-risk adolescents.