A general model for preferential and triadic choice in terms of centralF distribution functions

A model for preferential and triadic choice is derived in terms of weighted sums of centralF distribution functions. This model is a probabilistic generalization of Coombs' (1964) unfolding model and special cases, such as the model of Zinnes and Griggs (1974), can be derived easily from it. This new form extends previous work by Mullen and Ennis (1991) and provides more insight into the same problem that they discussed.     

Evaluation of job candidates with missing information: Effects of attribute importance and interattribute correlation

How do people make evaluations when important items of information are missing? In the context of personnel evaluations or product evaluations, researchers have proposed that decision makers may predict the missing attribute based on its assumed relationship to attributes that are present. In addition, some researchers have suggested that there is a penalty for missing information. An experiment was conducted to investigate the influence of the importance of the missing attribute on the hypothesized inference effect. Hypothetical job candidates were evaluated based on one or two attributes. The correlation between the attributes was varied between groups of subjects as was the importance of one of the attributes. The pattern of ratings of candidates with missing information varied significantly with the correlation condition when the missing attribute was very important, but did not vary as much when the missing attribute was less important. The results were generally consistent with the predictions of the Inferred Information Model (Johnson and Levin, 1985). On average, candidates with missing information were rated lower than comparable candidates with complete information and the missing attribute at an average level.     

Caveats on a process-tracing measure and a remedy

In process-tracing studies, a frequently used index describes whether information selection behavior is attribute — or alternativewise. The performance of this index is investigated under the assumption that subjects select information in a random fashion. The results show that the index may lead to inaccurate conclusions regarding the information-acquisition strategy of a subject. In addition, a Monte Carlo study is conducted that examines the sensitivity of the index to strategy changes. An alternative index is derived and a latent-class model is proposed for a parsimonious representation of individual differences in information acquisition.     

高校心理咨询师多重关系情境与决策类型的研究

为探索高校咨询师面临的多重关系伦理情境、决策过程,对访谈17名高校心理咨询师的资料进行分析。结果：常见情境主要包括接送礼物承载的新关系、来访者和咨询师除咨询关系外的师生关系、咨询师与和来访者有关的第三人有关系、咨询师在咨询室之外的场所偶遇来访者、来访者有咨询师的联系方式、来访者和咨询师有身体接触等。决策表现为两种：经验主导型,决策时未意识到处于伦理情境只凭经验决策;伦理主导型,决策时意识到处在伦理情境。结论：高校心理咨询存在一些特殊多重关系,心理咨询师需增加具有伦理意识的决策。     

What if I am the one? Measuring individual differences in emotional sensitivity to probability and emotional reactivity to possibility

Current theories of risk perception point to the powerful role of emotion and the neglect of probabilistic information in the face of risk, but these tendencies differ across individuals. We propose a method for measuring individuals' emotional sensitivity to probability to assess how feelings about probabilities, rather than the probabilities themselves, influence decisions. Participants gave affective ratings (worry or excitement) to 14 risky events, each with a specified probability ranging from 1 in 10 to 1 in 10,000,000. For each participant, we regressed these emotional responses against item probabilities, estimating a slope (the degree to which emotional responses change with probability) and an intercept (the emotional reaction to an event with a fixed probability). These two parameters were treated as individual difference scores and included in models predicting reactions to several health risk scenarios. Both emotional sensitivity to probability (slope) and emotional reactivity to possibility (intercept) significantly predicted responses to these scenarios, above and beyond the predictive power of other well‐established individual difference measures.     

The Influence of Perceived Deservingness on Policy Decisions Regarding Aid to the Poor

In the current climate of welfare reform, it is important to understand how perceptions of the poor affect policy decisions. This paper examines how people distinguish between the undeserving poor and the deserving poor, and how this differentiation affects policy decisions. Survey respondents rated each policy in a set of hypothetical policies on a liberal-conservative continuum. Analyses were then conducted to explore differences in the respondents' likelihood of recommending the most liberal and the most conservative of these policies. Study 1 demonstrated that liberal policies were more likely to be recommended and conservative policies were less likely to be recommended when the target group was perceived to be deserving rather than undeserving. Study 2 replicated this effect of perceived deservingness and demonstrated an effect of attribution of responsibility. That is, liberal policies were more likely to be recommended and conservative policies were less likely to be recommended when the responsibility for the target's poverty was attributed to society rather than to the individual.     

Random error reduction in analytic hierarchies: a comparison of holistic and decompositional decision strategies

The principle of ‘divide and conquer’ (DAC) suggests that complex decision problems should be decomposed into smaller, more manageable parts, and that these parts should be logically aggregated to derive an overall value for each alternative. Decompositional procedures have been contrasted with holistic evaluations that require decision makers to simultaneously consider all the relevant attributes of the alternatives under consideration (Fischer, 1977 ). One area where decompositional procedures have a clear advantage over holistic procedures is in the reduction of random error (Ravinder, 1992 ; Ravinder and Kleinmuntz, 1991 ; Kleinmuntz, 1990 ). Adopting the framework originally developed by Ravinder and colleagues, this paper details the results of a study of the random error variances associated with another popular multi‐criteria decision‐making technique, the Analytic Hierarchy Process (AHP); (Saaty, 1977 , 1980 ), as well as the random error variances of a holistic version of the Analytic Hierarchy Process (Jensen, 1983 ). In addition, data concerning various psychometric properties (e.g. the convergent validity and temporal stability) and values of AHP inconsistency are reported for both the decompositional and holistic evaluations. The results of the study show that the Ravinder and Kleinmuntz ( 1991 ) error‐propagation framework extends to the AHP and decompositional AHP judgments are more consistent than their holistic counterparts. Copyright © 2001 John Wiley & Sons, Ltd.     

认知行为实验研究中最佳素材容量的选择与确定：多元概化理论应用

心理学实验首先必须保证结果的有效性和可靠性。实验素材容量是影响实验结果的关键因素之一。本文的目的是通过运用多元概化理论分析几个典型认知行为实验的素材容量对实验结果精度的影响, 从而探讨最佳的实验素材容量的确定问题。研究结果发现, 在IAT测验上, 相容与不相容任务的素材容量为50时最佳, 可靠性指数为0.92; 在图-词干扰范式中, 素材容量以48时为最佳, 可靠性指数为0.95; 对线索提示范式, 50%有效提示中, 有效与无效提示最佳素材容量为35, 可靠性指数是0.97。研究表明, 多元概化理论可以很好地用于确定认知行为实验中素材的最佳容量。     

高校教师教学水平评价多元概化理论权重效应分析

探讨高校教师教学水平评价多元概化理论合成信度的权重效应。使用"高校教师教学水平评价问卷",要求534名学生对16名教师进行评价,对收集到的数据作嵌套设计的多元概化理论分析。结果表明：（1）在高校教师教学水平评价中,多元概化理论合成信度估计权重、先验权重和效果权重产生不同效应;（2）结合高校教师教学水平评价,在充分分析三种权重对合成信度影响的基础上,提出了多元概化理论"合成信度三种权重效应分析模式图",能为正确使用多元概化理论权重提供科学参考。