Trichotomous preferences for gambles

Let G_X be the set of all gambles on a set X of decision outcomes. Trichotomous preferences divide G_X into three disjoint subsets, viz., the set P⁺ of preferable gambles, the set P⁰ of indifferent gambles, and the set P^- of unpreferable gambles. Those preference comparisons are made against the status quo, i.e., the do-nothing alternative. This paper presents and discusses dyadic representations of such trichotomous preferences and identifies necessary and sufficient axioms for the existence of those representations.     

FOK研究中一种新的标准测验——过度重复学习

提出FOK研究中一种新的标准测验形式——过度重复学习,并验证过度重复学习作为FOK研究的标准测验的可行性;另外还比较了作为标准测验,过度重复学习与再认的差别。同时,还探索了FOK判断中存在的两种错误类型——替代性错误和忽略性错误在FOK判断等级和标准测验成绩方面的差异。两个实验结果表明：FOK判断在预期随后的标准测验(过度重复学习和再认)成绩上是有准确性的;过度重复学习可以作为一种标准测验。实验结果还显示：替代性错误比忽略性错误的FOK判断等级要高;且无论是用再认还是用过度重复学习做标准测验,都能检验出两种错误类型项目在记忆激活强度上的差异。     

Statistical inference for multiple choice tests

Finite sample inference procedures are considered for analyzing the observed scores on a multiple choice test with several items, where, for example, the items are dissimilar, or the item responses are correlated. A discrete p-parameter exponential family model leads to a generalized linear model framework and, in a special case, a convenient regression of true score upon observed score. Techniques based upon the likelihood function, Akaike's information criteria (AIC), an approximate Bayesian marginalization procedure based on conditional maximization (BCM), and simulations for exact posterior densities (importance sampling) are used to facilitate finite sample investigations of the average true score, individual true scores, and various probabilities of interest. A simulation study suggests that, when the examinees come from two different populations, the exponential family can adequately generalize Duncan's beta-binomial model. Extensions to regression models, the classical test theory model, and empirical Bayes estimation problems are mentioned. The Duncan, Keats, and Matsumura data sets are used to illustrate potential advantages and flexibility of the exponential family model, and the BCM technique.The authors wish to thank Ella Mae Matsumura for her data set and helpful comments, Frank Baker for his advice on item response theory, Hirotugu Akaike and Taskin Atilgan, for helpful discussions regarding AIC, Graham Wood for his advice concerning the class of all binomial mixture models, Yiu Ming Chiu for providing useful references and information on tetrachoric models, and the Editor and two referees for suggesting several references and alternative approaches.     

论三段论推理过程中结论正确性的两种判断标准

人们进行三段论推理加工时是根据什么标准来判定推理结论的正确性的？实验结果支持下述观点：人们在进行三段论推理过程中,在判定推理结论的正误时,存在两种判定标准：一种是“形式标准”,另一种是“内容标准”;两种判定标准对于人们判定结论的正确性都有影响,哪个判定标准的影响力更大依赖于人们已掌握的知识结构。     

Latent variable selection in multidimensional item response theory models using the expectation model selection algorithm

The aim of latent variable selection in multidimensional item response theory (MIRT) models is to identify latent traits probed by test items of a multidimensional test. In this paper the expectation model selection (EMS) algorithm proposed by Jiang et al. (2015) is applied to minimize the Bayesian information criterion (BIC) for latent variable selection in MIRT models with a known number of latent traits. Under mild assumptions, we prove the numerical convergence of the EMS algorithm for model selection by minimizing the BIC of observed data in the presence of missing data. For the identification of MIRT models, we assume that the variances of all latent traits are unity and each latent trait has an item that is only related to it. Under this identifiability assumption, the convergence of the EMS algorithm for latent variable selection in the multidimensional two-parameter logistic (M2PL) models can be verified. We give an efficient implementation of the EMS for the M2PL models. Simulation studies show that the EMS outperforms the EM-based L₁ regularization in terms of correctly selected latent variables and computation time. The EMS algorithm is applied to a real data set related to the Eysenck Personality Questionnaire.     

DSM‐5 and Bereavement: The Loss of Normal Grief?

The mood disorder work group has proposed to eliminate the bereavement exclusion criterion from the diagnosis of major depression in the 5th edition of the American Psychiatric Association's (2012) Diagnostic and Statistical Manual of Mental Disorders. The proposal would break tradition with the long‐held distinction between depression and normal bereavement. This article reviews the development of the bereavement exclusion, discusses evidence for and against the proposal, and offers some relevant implications for counselors in light of the research on depression and bereavement.     

Transcending Lindemann's predictions for the melting temperatures of metals and for the long-wavelength limit of their liquid structure factors

Lindemann's melting criterion remains useful. However, one prediction it makes for liquid metals (our focus here) is that the long-wavelength limit of the structure factor S(q) at freezing, S _{T m} (0), where T _m is the melting temperature, is a universal constant. For 34 metals we have calculated S _{T m} (0) from input data, which is essentially the measured T _m and the surface thickness L, defined near freezing as the product of isothermal compressibility and surface tension. To complete the characterization of S _{T m} (0) we fit to one metal, chosen as Rb, for which S _{T m} (0) is well established experimentally. For a wide variety of metals considered, S _{T m} (0) is then found to vary by a factor of 10.     

Anchoring Effects in Simulated Academic Promotion Decisions: How the Promotion Criterion Affects Ratings and the Decision to Support an Application

Six experiments investigated the effect of the promotion criterion in simulated academic promotion decisions. In total, 547 undergraduate students and 33 university faculty members rated a promotion application, and some also indicated their decisions to support or to reject it. Performance ratings were reliably affected by the criterion, with a high criterion resulting in higher ratings than a low criterion, and this criterion effect was found regardless of the evaluator's expertise, whether he or she took the role of an independent assessor or the line manager to the applicant, or whether the criterion was provided by the experimenter or randomly generated by the participant. The criterion also affected the level of support for a candidate when the position applied for was perceived to be extremely competitive, or when a lesser position was considered at a later time. These results provide evidence that the use of a criterion, a fairly common practice in decision‐making processes, may bias performance evaluations, which in turn may have ripple effects that affect the outcome of a chain of events. Our results also shed light on the possible mechanisms that underlie the rating biases in performance appraisal. Copyright © 2014 John Wiley & Sons, Ltd.     

Assessment of Multiple Membership Multilevel Models: An Application to Interviewer Effects on Nonresponse

Multilevel multiple membership models account for situations where lower level units are nested within multiple higher level units from the same classification. Not accounting correctly for such multiple membership structures leads to biased results. The use of a multiple membership model requires selection of weights reflecting the hypothesized contribution of each level two unit and their relationship to the level one outcome. The Deviance Information Criterion (DIC) has been proposed to identify such weights. For the case of logistic regression, this study assesses, through simulation, the model identification rates of the DIC to detect the correct multiple membership weights, and the properties of model variance estimators for different weight specifications across a range of scenarios. The study is motivated by analyzing interviewer effects across waves in a longitudinal study. Interviewers can substantially influence the behavior of sample survey respondents, including their decision to participate in the survey. In the case of a longitudinal survey several interviewers may contact sample members to participate across different waves. Multilevel multiple membership models are suitable to account for the inclusion of higher-level random effects for interviewers at various waves, and to assess, for example, the relative importance of previous and current wave interviewers on current wave nonresponse. To illustrate the application, multiple membership models are applied to the UK Family and Children Survey to identify interviewer effects in a longitudinal study. The paper takes a critical view on the substantive interpretation of the model weights and provides practical guidance to statistical modelers. The main recommendation is that it is best to specify the weights in a multiple membership model by exploring different weight specifications based on the DIC, rather than prespecifying the weights.