算法拒绝的三维动机理论

算法拒绝意指尽管算法通常能比人类做出更准确的决策, 但人们依然更偏好人类决策的现象。算法拒绝的三维动机理论归纳了算法主体怀疑、道德地位缺失和人类特性湮没这三个主要原因, 分别对应信任、责任和掌控三种心理动机, 并对应改变算法拒绝的三种可行方案: 提高人类对算法的信任度, 强化算法的主体责任, 探索个性化算法设计以突显人对算法决策的控制力。未来研究可进一步以更社会性的视角探究算法拒绝的发生边界和其他可能动机。     

MIRT模型中多维能力及其相关矩阵估计的影响因素

多维项目反应理论因其模型本身的天然优势及其兼具因素分析与项目反应理论于一身的优点,而被广大研究者及应用者所重视.本研究在前人研究基础上,重点讨论MIRT多维能力及能力间相关矩阵的参数估计问题.研究采用Monte Carlo模拟方法进行,在三因素完全随机设计（4 ×3×3）下,使用MCMC算法,探讨测验维度数、维度间的相关大小和测验项目数三个因素对MIRT能力及其相关矩阵估计的影响.     

有中介的调节模型检验方法：甄别和整合*

在心理和教育研究中,经常遇到调节和中介效应。模型包含不止3个变量时,可能同时包含调节和中介变量,一种常见的模型是有中介的调节模型。本文评介了文献上可以找到的五种检验有中介的调节模型的方法,逐一甄别,指出其中一些不合理之处,并推荐较好的方法。整合其中主要方法,总结出检验有中介的调节模型的流程,用偏差校正的百分位 Bootstrap 法或马尔科夫链蒙特卡罗法检验其中的中介效应,并用一个实际例子演示如何用此流程检验有中介的调节模型。     

Approaching archetypes: reconsidering innateness

The question of innateness has hounded Jungian psychology since Jung originally postulated the archetype as an a priori structure within the psyche. During his life and after his death he was continually accused of Lamarckianism and criticized for his theory that the archetypes existed as prior structures. More recently, with the advent of genetic research and the human genome project, the idea that psychological structures can be innate has come under even harsher criticism even within Jungian thought. There appears to be a growing consensus that Jung's idea of innate psychological structures was misguided, and that perhaps the archetype-as-such should be abandoned for more developmental and 'emergent' theories of the psyche. The purpose of this essay is to question this conclusion, and introduce some literature on psychological innateness that appears relevant to this discussion.     

IRT模型参数估计的新方法——MCMC算法

本研究主要探讨MCMC算法在IRT模型参数估计中的实现及其估计精度.通过模拟多种实验条件(人少题少、人题适中、人多题多、被试数及其参数固定情况下项目数变化、项目数及其参数固定情况下人数变化),考察两参数和叁参数Logistic模型的MCMC算法对其参数估计的精度,并与国际通用测量程序-Bilog程序(E-M算法)进行比较研究.模拟实验研究表明,上述各种实验条件下,MCMC算法均可用于IRT模型参数估计,且其估计的精度均较Bilog程序(E-M算法)高,值得推广.     

缺失数据处理的期望-极大化算法与马尔可夫蒙特卡洛方法

缺失数据普遍存在于心理学研究中, 影响着统计推断。极大似然估计(MLE)与基于贝叶斯的多重借补(MI)是处理缺失数据的两类重要方法。期望-极大化算法(EM)是寻求MLE的一种强有力的方法。马尔可夫蒙特卡洛方法(MCMC)可以相对简易地实现MI, 而且可以适用于复杂情况下的缺失数据处理。结合研究的需要讨论了实现这两类方法的适用软件。     

Temporalising Tableaux

As a remedy for the bad computational behaviour of first-order temporal logic (FOTL), it has recently been proposed to restrict the application of temporal operators to formulas with at most one free variable thereby obtaining so-called monodic fragments of FOTL. In this paper, we are concerned with constructing tableau algorithms for monodic fragments based on decidable fragments of first-order logic like the two-variable fragment or the guarded fragment. We present a general framework that shows how existing decision procedures for first-order fragments can be used for constructing a tableau algorithm for the corresponding monodic fragment of FOTL. Some example instantiations of the framework are presented.     

An EM algorithm for fitting two-level structural equation models

Maximum likelihood is an important approach to analysis of two-level structural equation models. Different algorithms for this purpose have been available in the literature. In this paper, we present a new formulation of two-level structural equation models and develop an EM algorithm for fitting this formulation. This new formulation covers a variety of two-level structural equation models. As a result, the proposed EM algorithm is widely applicable in practice. A practical example illustrates the performance of the EM algorithm and the maximum likelihood statistic.We are thankful to the reviewers for their constructive comments that have led to significant improvement on the first version of this paper. Special thanks are due to the reviewer who suggested a comparison with the LISREL program in the saturated means model, and provided its setup and output. This work was supported by National Institute on Drug Abuse grants DA01070, DA00017, and a UNH 2002 Summer Faculty Fellowship.     

A marginal maximum likelihood method for the vector threshold model to analyze dichotomous choice data

Abstract: At least two types of models, the vector model and the unfolding model can be used for the analysis of dichotomous choice data taken from, for example, the pick any/ n method. The previous vector threshold models have a difficulty with estimation of the nuisance parameters such as the individual vectors and thresholds. This paper proposes a new probabilistic vector threshold model, where, unlike the former vector models, the angle that defines an individual vector is a random variable, and where the marginal maximum likelihood estimation method using the expectation-maximization algorithm is adopted to avoid incidental parameters. The paper also attempts to discuss which of the two models is more appropriate to account for dichotomous choice data. Two sets of dichotomous choice data are analyzed by the model.     

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。