Scale freeness in factor analysis

The notion of scale freeness does not seem to have been well understood in the factor analytic literature. It has been believed that if the loss function that is minimized to obtain estimates of the parameters in the factor model is scale invariant, then the estimates are scale free. It is shown that scale invariance of the loss function is neither a necessary nor a sufficient condition for scale freeness. A theorem that ensures scale freeness in the orthogonal factor model is given in this paper.The authors are grateful for the suggestions of the referees.     

知觉与行为分离现象在二维铁钦纳错觉实验中的验

利用二维铁钦纳错觉图片对单手及双手抓握情境下的知觉与行为分离现象进行实验验证,并探讨注意引导等因素对分离现象的影响。实验包括知觉任务和行为任务两项内容,前者主要测量被试的主观错觉量,后者则探讨分离现象是否存在及其影响因素。在行为任务中,记录被试在中心（目标）圆尺寸“实际相同”和“知觉相同”等不同情境下的行为,以拇指和食指抓握图片中心圆的最大抓握孔径为行为指标。结果发现：单手抓握时,左、右手的行为虽然在一定程度上受到了错觉的影响,但均显示出分离现象;双手抓握时,右手受错觉影响的程度减少,分离现象增强,而左手的结果则比较混淆。因此,分离现象是存在的,但它的出现可能受到注意引导、注意分配倾向或双手协调能力等因素的影响。     

Contingent approaches to making likelihood judgments about polychotomous cases: the influence of task factors

Two experiments tested the influence of three task factors on respondents' tendency to use normative, heuristic, and random approaches to making likelihood judgments about polychotomous cases (i.e., cases in which there is more than one alternative to a focal hypothesis). Participants estimated their likelihood of winning hypothetical raffles in which they and other players held various numbers of tickets. Responding on non‐numeric scales (vs. numeric ones) and responding under time pressure (vs. self‐paced) increased participants' use of a comparison‐heuristic approach, resulting in non‐normative judgment patterns. A manipulation of evidence representation (whether ticket quantities were represented by numbers or more graphically by bars) did not have reliably detectable effects on processing approaches to likelihood judgment. The authors discuss the implications of these findings for the further development of likelihood judgment theories, and they discuss parallels between contingent processing in choice and contingent processing in likelihood judgment. Copyright © 2005 John Wiley & Sons, Ltd.     

说服效应的理论模型、影响因素与应对策略

说服效应是指面临说服性信息时, 个体态度发生转变并影响其决策行为的一种现象, 该现象普遍存在于政治选举、生活消费及商业广告等领域, 具有重要的应用价值。说服效应的两个主要理论模型包括双加工模型和自我功效理论。信息源的可信度、情绪、信息框架、社会网络、事件卷入度等是影响说服效应的主要因素。说服效应的应对策略包括公开承诺和自我控制。未来的研究应该从说服效应的产生根源、特征成分及应用领域等方面进行深入地探讨。     

Mokken Scale Analysis for Dichotomous Items Using Marginal Models

Scalability coefficients play an important role in Mokken scale analysis. For a set of items, scalability coefficients have been defined for each pair of items, for each individual item, and for the entire scale. Hypothesis testing with respect to these scalability coefficients has not been fully developed. This study introduces marginal modelling as a framework to derive the standard errors for the scaling coefficients and test hypotheses about these coefficients. Several examples demonstrate the possibilities of marginal modelling in Mokken scale analysis. These possibilities include testing whether Mokken’s criteria for a scale are satisfied, testing whether scalability coefficients of different items are equal, and testing whether scalability coefficients are equal across different groups.     

Pooling data versus averaging model fits for some prototypical multinomial processing tree models

For a study with multinomial data where there are n_g individuals and with each person having n_r test trials, the question arises as to how to fit the parameters of a multinomial processing tree (MPT) model. Should each parameter be estimated for each individual and then averaged to obtain a group estimate, or should the frequencies in the multinomial categories be pooled so that the model is fit once for the entire group? This basic question is explored with a series of Monte Carlo simulations for some prototypical MPT models. There is a general finding of a pooling advantage for the case where there is a single experimental condition. Also when there are different experimental conditions, there is reduced bias for detecting condition differences for a method based on the pooled data. Although the focus of the paper is on multinomial models, a general theorem is advanced that establishes a basic condition that determines whether there is or is not a difference between the averaging of individual estimates and the estimate based on the pooled data.     

Rejoinder on the normalized maximum likelihood and Bayesian decision theory: Reply to Grünwald and Navarro (2009)

We provide a short reply to [Grünwald, P., & Navarro, D. (2009). NML, Bayes and true distributions: A comment on Karabatsos and Walker. Journal of Mathematical Psychology, in press (doi:10.1016/j.jmp.2008.11.005)] comment on the article by [Karabatsos, G., & Walker, S. (2006). On the normalized maximum likelihood and Bayesian decision theory. Journal of Mathematical Psychology, 50, 517-520].     

A Latent Class Multidimensional Scaling Model for Two-Way One-Mode Continuous Rating Dissimilarity Data

In this paper, we propose a cluster-MDS model for two-way one-mode continuous rating dissimilarity data. The model aims at partitioning the objects into classes and simultaneously representing the cluster centers in a low-dimensional space. Under the normal distribution assumption, a latent class model is developed in terms of the set of dissimilarities in a maximum likelihood framework. In each iteration, the probability that a dissimilarity belongs to each of the blocks conforming to a partition of the original dissimilarity matrix, and the rest of parameters, are estimated in a simulated annealing based algorithm. A model selection strategy is used to test the number of latent classes and the dimensionality of the problem. Both simulated and classical dissimilarity data are analyzed to illustrate the model.     

基于等级反应模型的属性层级方法

给出基于等级反应模型的属性层级方法（Attribute Hierarchy Method, AHM）,并简记为GRM-AHM,提出了相应的确定GRM-AHM的期望项目反应模式全集的方法和一种新的归类法LL。用蒙特卡洛模拟实验比较GRM-AHM的几种归类法的归准率（属性模式归准率和单个属性的平均判准率）。结果发现,新归类法的归准率与AHM中的方法A差不多,但比方法B高很多;随着被试作答失误率的提高,它们的归准率都有所下降。在归类精度和简单性方面,GRM-AHM都比Bolt等(2004)提出的多级评分融合模型（Fusion Model）好