Bayesian Model Selection and Model Averaging

This paper reviews the Bayesian approach to model selection and model averaging. In this review, I emphasize objective Bayesian methods based on noninformative priors. I will also discuss implementation details, approximations, and relationships to other methods. Copyright 2000 Academic Press.     

自我同一性研究的新模型——双环模型

自Erikson提出自我同一性的概念以来,它一直是发展心理学的重要课题,这方面的实证研究主要基于Marcia提出的同一性状态模型。随后的研究者Luyckx等人不断整合和改进状态模型,并结合自我关注领域对反思型自我关注和沉浸型自我关注的区分,提出了双环模型的理论。双环模型理论认为,广度探索、深度探索、沉浸探索、做出承诺和认同承诺五个维度构成了自我同一性形成和发展的两个环——"承诺形成环"和"承诺评价环",两个环之间不断地发生着动态的交互作用,其结果造成了自我同一性形成和发展的6种状态:达成,早闭,沉浸延缓,轻松混淆,扩散混淆和未分化。     

Model modification

An analysis of empirical data often leads to a rejection of a hypothesized model, even if the researcher has spent considerable efforts in including all available information in the formulation of the model. Thus, the researcher must reformulate the model in some way, but in most instances there is, at least theoretically, an overwhelming number of possible actions that could be taken. In this paper a modification index will be discussed which should serve as a guide in the search for a better model. In statistical terms, the index measures how much we will be able to reduce the discrepancy between model and data, as defined by a general fit function, when one parameter is added or freed or when one equality constraint is relaxed. The modification index discussed in this paper is an improvement of the one incorporated in the LISREL V computer program in that it takes into account changes in all the parameters of the model when one particular parameter is freed.The research reported in this paper has been supported by The Swedish Council for Research in the Humanities and Social Sciences under Research Program Multivariate Statistical Analysis, Project Director Karl G Jöreskog.     

试论现代医学模式所要求的系统思维方式

深入研究医学模式转变的主客观基础,找准关键,从而促进医学模式的转变,是摆在我们面前的重要课题.本文尝试性地围绕转变医学模式的主观基础--思维方式展开探讨.     

Janet Taylor Spence: A Model Role Model

The article summarizes and reflects on the contributions of Janet Taylor Spence in the areas of editing, service to her institutions, and service to her profession. It is based on both the objective record and on the personal experience and observations of the author, a long-time colleague. It reviews the many editorial positions Janet Taylor Spence held, including being editor of the Annual Review of Psychology and Contemporary Psychology, and the award she received from the National Academy of Sciences. It also sketches many of the leadership positions she held, including being president of the American Psychological Association and the first elected president of the Association for Psychological Science. It emphasizes both her outstanding professional and personal qualities.     

人格测量：从累积式模型到展开式模型

当前大多数人格测量都采用的是累积式反应模型方法,该模型假设被试在测验上的得分随其能力或特质提高而增加,但是随着人格测量技术的不断发展,这一模型的实施效果遭到了质疑,研究者们开始关注展开式模型,该模型认为被试的反应取决于被试能力和项目阈值的匹配程度,当被试能力与项目阈值完全匹配时,被试做出肯定回答的概率达到最高点,称之为“理想点”,展开式模型的目的就是找到被试的理想点,从而寻找其真正的态度强度或人格特质水平。GGUM作为一种比较成熟的展开式模型,已经开始应用于人格测量的各个领域,但仍需要进行大规模的试测,在评估和预测效度方面积累经验,建立业界认可的心理测量学标准,不断探讨和开发相应的心理测量理论和简便易行的统计程序。     

一种多级评分的认知诊断模型：P-DINA模型的开发

当前绝大多数认知诊断计量模型仅适用于0-1评分数据资料, 大大限制了认知诊断在实际中的应用, 也限制了认知诊断的进一步推广和发展。本文对具有较好发展前景的DINA模型进行拓展, 开发出适合多种评分(含0-1二级评分和多级评分)数据资料的P-DINA模型, 同时采用MCMC算法实现模型参数的估计, 并对该模型性能进行研究。结果表明：(1)本文开发的P-DINA模型无论是在无结构型属性层级关系下还是在结构型属性层级关系下, 参数估计的精度均较高, 参数估计的稳健性较强, 说明开发的P-DINA模型基本合理、可行。(2)P-DINA模型可采用MCMC算法实现参数估计, 且参数估计的精度较高。(3)整体来看, 无结构型属性层级关系和结构型属性层级关系下, P-DINA模型在项目参数的估计精度上两者基本相当; 但在被试属性判准率(MMR和PMR)上无结构型属性层级关系表现的稍差一些。(4)无结构型属性阶层关系下：模型诊断的属性个数越多, 参数 估计的精度越差、属性诊断的正确率(MMR和PMR)越低, 但参数 的估计精度越好; 若想保证属性模式判准率在80%以上, 建议诊断的属性个数不宜超过7个。总之, 本研究为拓展认知诊断在教育学和心理学中的应用提供了一种新方法、新模型。     

Model Comparisons and Model Selections Based on Generalization Criterion Methodology

The purpose of this article is to formalize the generalization criterion method for model comparison. The method has the potential to provide powerful comparisons of complex and nonnested models that may also differ in terms of numbers of parameters. The generalization criterion differs from the better known cross-validation criterion in the following critical procedure. Although both employ a calibration stage to estimate parameters, cross-validation employs a replication sample from the same design for the validation stage, whereas generalization employs a new design for the critical stage. Two examples of the generalization criterion method are presented that demonstrate its usefulness for selecting a model based on sound scientific principles out of a set that also contains models lacking sound scientific principles that are either overly complex or oversimplified. The main advantage of the generalization criterion is its reliance on extrapolations to new conditions. After all, accurate a priori predictions to new conditions are the hallmark of a good scientific theory. Copyright 2000 Academic Press.     

形成性测量模型：结构方程模型的新视角

形成性测量模型(Formative Model, FM)是指标变异导致潜变量变异的模型, 反映性测量模型(Reflective Model, RM)是潜变量变异导致指标变异的模型。FM在模型界定、识别和估计、信效度评价以及模型应用等方面均与RM存在极大的不同。模型界定错误会使参数估计发生偏差, 影响统计结论的有效性, 应当审慎考虑指标和潜变量之间的关系, 选择恰当的测量模型。进一步揭示两者的区别和误用带来的偏差, 完善FM的识别和估计、信效度评价方法、对变量含义的解释以及高阶FM的理论解释和模型估计是未来的研究方向。     

Frequentist Model Averaging in Structure Equation Model With Ordinal Data

Psychometrika - In practice, it is common that a best fitting structural equation model (SEM) is selected from a set of candidate SEMs and inference is conducted conditional on the selected model....     

Sensitivity of Fit Indices to Model Misspecification and Model Types

The search for cut-off criteria of fit indices for model fit evaluation (e.g., Hu &; Bentler, 1999 Hu, L. and Bentler, P. M. 1999. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling., 6: 1–55. [Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]) assumes that these fit indices are sensitive to model misspecification, but not to different types of models. If fit indices were sensitive to different types of models that are misspecified to the same degree, it would be very difficult to establish cut-off criteria that would be generally useful. The issue about SEM fit indices being sensitive to different types of models has not received sufficient attention, although there is some research suggesting that this might be the case (e.g., Kenny &; McCoach, 2003 Kenny, D. A. and McCoach, D. B. 2003. Effect of the number of variables on measures of fit in structural equation modeling. Structural Equation Modeling., 10: 333–351. [Taylor &; Francis Online], [Web of Science ®] , [Google Scholar]). This study examines if fit indices are sensitive to different types of models while controlling for the severity of model misspecification. The findings show that most fit indices, including some very popular ones (e.g., RMSEA), may be sensitive to different types of models that have the same degree of specification error. The findings suggest that, for most fit indices, it would be difficult to establish cut-off criteria that would be generally useful in SEM applications.     

Model Predictive Impedance Control: A Model for Joint Movement

Impedance control has been suggested as the strategy employed by the central nervous system to control human postures and movements. A realization of this strategy is presented that uses a model predictive control algorithm as a higher motor controller. External disturbances are explicitly included in the model. The combination of 3 key factors-joint impedance control, model predictive controller, and external disturbance input-forms the basis for the generality of this model. The model was applied to 3 different types of joint movements: a tracking movement with an unpredicted disturbance, a rhythmic movement, and an unstable biped model of human walking. Computer simulation results showed excellent performance of the model in all 3 cases for optimal values of active joint impedances and an exact match between the musculoskeletal system and the model internal to the model predictive controller. The controller was also able to maintain acceptable performance in the presence of a 25% mismatch between the musculoskeletal system and its internal model.     

阅读过程眼动控制理论模型:E-Z Reader

简要介绍了一个新近出现的阅读过程中的眼动控制理论模型——E-Z Reader模型,并对该模型的特点进行了深入讨论。文中还涉及了其它几个相关的理论模型．如Morrison(1984)模型和Mr．Chips：理想观察者模型等。最后就E-Z Reader在中文阅读过程认知研究领域中的应用前景进行了讨论。     

论道德榜样——对现代社会道德榜样的检视

在市场经济条件下,道德榜样遭遇到了一系列理论和实际问题.因此,对道德榜样进行理论审视,实为必要.在现代社会,掌握和控制社会道德资源者、对社会负有重大责任及具有重要影响者、成年人,是道德榜样的三大基本类型.道德榜样的先验论、需要论、发现论和实践论,从不同角度回答了道德榜样的产生机制问题.道德榜样的理想主义本质及其对道德普遍主义的诉求,构成了道德榜样的基础条件.道德榜样既具有外在功能,更具有内在功能,二者是有机统一的.     

Multilevel Model Prediction

Multilevel models are proven tools in social research for modeling complex, hierarchical systems. In multilevel modeling, statistical inference is based largely on quantification of random variables. This paper distinguishes among three types of random variables in multilevel modeling—model disturbances, random coefficients, and future response outcomes—and provides a unified procedure for predicting them. These predictors are best linear unbiased and are commonly known via the acronym BLUP; they are optimal in the sense of minimizing mean square error and are Bayesian under a diffuse prior. For parameter estimation purposes, a multilevel model can be written as a linear mixed-effects model. In this way, parameters of the many equations can be estimated simultaneously and hence efficiently. For prediction purposes, we show that it is more convenient to retain the multiple equation feature of multilevel models. In this way, the efficient BLUPs are easy to compute and retain their intuitively appealing recursive form. We also derive explicit equations for standard errors of these different types of predictors. Prediction in multilevel modeling is important in a wide range of applications. To demonstrate the applicability of our results, this paper discusses prediction in the context of a study of school effectiveness. This research was supported by a grant from the Graduate School at the University of Wisconsin at Madision and the National Science Foundation, Grant number SES-0436274. We are grateful to Norman Webb at Wisconsin Center for Education Research for making available the data used in the reported application.     

Adaptive Model Checking

We consider the case where inconsistencies are present betweena system and its corresponding model, used for automatic verification.Such inconsistencies can be the result of modeling errors orrecent modifications of the system. Despite such discrepancies,we can still attempt to perform automatic verification. In fact,as we show, we can sometimes exploit the verification resultsto assist in automatically learning the required updates tothe model. In a related previous work, we have suggested theidea of black box checking, where verification starts withoutany model, and the model is obtained while repeated verificationattempts are performed. Under the current assumptions, an existinginaccurate (but not completely obsolete) model is used to expeditethe updates. We use techniques from black box testing and machinelearning. We present an implementation of the proposed methodologycalled AMC (for Adaptive Model Checking). We discuss some experimentalresults, comparing various tactics of updating a model whiletrying to perform model checking.