结构方程模型应用中模型选择的原理和方法

随着结构方程模型在心理学研究中的应用,针对研究问题建构理论模型成为研究者们所重视的问题。该文探讨了心理学研究中模型选择的一般原则和基本方法,希望对广大研究者有所帮助。     

对变化/分割模型的检验(Ⅱ)

在前一报告的基础上设计了两个实验,对变化／分割模型做进一步的检验,实验３控制目标时距和该时距内的填充数字系列的分割段数以及段的持续时间,操纵段内的项目数;结果显示,数字系列的分割段数明显影响延迟操作条件下的时距估计,而段内的项目数则不影响对时距的估计,实验４控制目标时距和该时距内填充数字系列的分割段数以及段内的项目数,操纵的持续时间,结果显示,对时距的估计取决于被试所傅物时间标尺,四个实验结果表明     

积极与消极情绪关系的理论及研究

积极与消极情绪的关系一直是情绪领域中最引人注目的焦点之一。该文介绍了探讨积极与消极情绪关系的模型及相关的证据,包括以下4个方面的内容：（1）情绪的双极模型及相关证据;（2）情绪的双变量模型及相关证据;（3）情绪的混合模型及相关证据;（4）解决各种模型争论的新视角：条件决定论。文章最后分析了该领域研究的不足,并提出了未来研究的方向     

现代测量理论下四大认知诊断模型述评

该文介绍并比较了现代测量理论下四大认知诊断模型的思想方法、模型结构及各自的特点性能等。LLTM是一个较早的认知诊断模型,它实现了认知与测量的结合;规则空间模型实现了对认知结构的诊断,并创造性地提出了Q矩阵理论;统一模型与融合模型是同一类模型：两者均沿用了规则空间模型的Q矩阵方法,但克服了规则空间模型中的一些不足;融合模型被认为是二十一世纪初创立的一个很成功的认知诊断模型。     

对变化/分割模型的检验(Ⅱ)

在前一报告的基础上设计了两个实验,对变化／分割模型做进一步的检验。实验3控制目标时距和该时距内的填充数字系列的分割段数以及段的持续时间,操纵段内的项目数;结果显示,数字系列的分割段数明显影响延迟操作条件下的时距估计,而段内的项目数则不影响对时距的估计。实验4控制目标时距和该时距内填充数字系列的分割段数以及段内的项目数,操纵段的持续时间;结果显示,对时距的估计取决于被试所使用的时间标尺。四个实验结果表明,变化／分割模型只有较高的预测效度,但也有不足之处。为了更好地从认知的观点解释时距估计的影响因素,作者对已提出的分段综合模型做了进一步的论证,并认为分段综合模型是一个能较完整解释影响人类时距估计的各种因素的理论模型。     

美国临床心理学培养模式的演进及展望

受到社会、组织及自身发展因素的影响, 美国临床心理学在不同时期存在着不同的培养模式。文章选取“科学家－实践者”模式、“实践者－学者”模式、“临床心理科学家”模式、“当下的临床科学家”模式及“循证实践者”模式等五种最具代表性的经典模式, 介绍了1949年以来美国临床心理学培养模式的特点及发展规律。认为“循证实践者”模式修正了前面四种培养模式的个体主义倾向, 将单个的“科学家－实践者”, 分化成“科学家”与“实践者”两个集团, 强调实践者遵循科学家提供的最佳证据, 考虑病人的特征、文化与价值观, 在管理者协调下开展实践。这种模式代表着当代临床心理学培养模式的发展方向。     

An Introduction to Network Psychometrics: Relating Ising Network Models to Item Response Theory Models

In recent years, network models have been proposed as an alternative representation of psychometric constructs such as depression. In such models, the covariance between observables (e.g., symptoms like depressed mood, feelings of worthlessness, and guilt) is explained in terms of a pattern of causal interactions between these observables, which contrasts with classical interpretations in which the observables are conceptualized as the effects of a reflective latent variable. However, few investigations have been directed at the question how these different models relate to each other. To shed light on this issue, the current paper explores the relation between one of the most important network models—the Ising model from physics—and one of the most important latent variable models—the Item Response Theory (IRT) model from psychometrics. The Ising model describes the interaction between states of particles that are connected in a network, whereas the IRT model describes the probability distribution associated with item responses in a psychometric test as a function of a latent variable. Despite the divergent backgrounds of the models, we show a broad equivalence between them and also illustrate several opportunities that arise from this connection.     

当代西方语篇阅读信息加工理论模型的演进

在探讨阅读过程的信息加工活动的过程中产生了多种理论与模式．当代西方语篇阅读研究的建构一整合模型、建构主义模型和记忆基础模型沿着Kintsch提出的语篇表征三个层次理沦．从各自不同的角度提出了对语篇阅读信息加工过程的看法,建构整合模型主要考察了语篇表征中课文基础表征建构的信息活动过程．建构主义模型和记忆基础模型主要探讨了语篇表征中情境模型的建构和更新过程。     

A Note on “Constant Latent Odds-Ratios Models and the Mantel–Haenszel Null Hypothesis” Hessen, 2005

In a recent paper, Hessen (Psychometrika 70(3):497–516, 2005) introduces the class of constant latent odds-ratios models as an extension of the Rasch model for which the sum score is still the sufficient statistic for ability. In this paper the relation between both the general and the general parametric constant latent odds-ratios model and the Rasch model is considered.     

Marginal maximum likelihood estimation for the one-parameter logistic model

Two algorithms are described for marginal maximum likelihood estimation for the one-parameter logistic model. The more efficient of the two algorithms is extended to estimation for the linear logistic model. Numerical examples of both procedures are presented. Portions of this research were presented at the meeting of the Psychometric Society in Chapel Hill, N.C. in May, 1981. Thanks to R. Darrell Bock, Gerhard Fischer, and Paul Holland for helpful comments in the course of this research.     

不同类型考试焦虑影响考试成绩的差异模式

调查了440名高中生,以不同类型考试焦虑为预测变量,以掌握目标、情绪性、应对反应、考试自我效能感、成绩-接近目标、成绩-回避目标、担忧、学习和考试技能、认知干扰为中介变量,期末数学考试成绩为最终结果变量,探讨不同类型考试焦虑影响考试成绩的模式。结果表明:不同类型考试焦虑具有不同的影响考试成绩的模式,即存在认知主导型模式(简称C模式)、生理唤醒主导型模式(简称P模式)、技能缺乏主导型模式(S模式)等三种不同的模式。担忧是C模式中的主要中介变量,P模式的主要特征是担忧对情绪性存在显著影响,S模式的特点是中介变量较少,学习和考试技能变量受考试焦虑和考试自我效能感双重影响并作用于考试成绩。     

前瞻记忆的理论模型综述

前瞻记忆是相对于回溯记忆提出的一种特殊的长时记忆,是指记住在将来某个恰当的时间执行先前意向的行为。本文介绍了近年来研究者提出的前瞻记忆认知和提取机制的理论模型,并对其做了评述。最后,本文针对目前研究中的薄弱环节尝试性地提出了今后前瞻记忆研究进一步发展的方向。     

Estimation of nonlinear mixed-effects continuous-time models using the continuous-discrete extended Kalman filter

Many intensive longitudinal measurements are collected at irregularly spaced time intervals, and involve complex, possibly nonlinear and heterogeneous patterns of change. Effective modelling of such change processes requires continuous-time differential equation models that may be nonlinear and include mixed effects in the parameters. One approach of fitting such models is to define random effect variables as additional latent variables in a stochastic differential equation (SDE) model of choice, and use estimation algorithms designed for fitting SDE models, such as the continuous-discrete extended Kalman filter (CDEKF) approach implemented in the dynr R package, to estimate the random effect variables as latent variables. However, this approach's efficacy and identification constraints in handling mixed-effects SDE models have not been investigated. In the current study, we analytically inspect the identification constraints of using the CDEKF approach to fit nonlinear mixed-effects SDE models; extend a published model of emotions to a nonlinear mixed-effects SDE model as an example, and fit it to a set of irregularly spaced ecological momentary assessment data; and evaluate the feasibility of the proposed approach to fit the model through a Monte Carlo simulation study. Results show that the proposed approach produces reasonable parameter and standard error estimates when some identification constraint is met. We address the effects of sample size, process noise variance, and data spacing conditions on estimation results.     

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。     

Rasch-representable reaction time distributions

This article investigates properties of a representation based on the Rasch test model for reaction times (RT) that was proposed by Micko. Necessary and sufficient conditions for a set of RT distributions to be Rasch-representable are derived. It is shown that independent serial and independent parallel processing models cannot be reconciled with the representation. However, random extreme models compatible with the Reasch-representation exist that assume RT is determined by the longest or he shortest processing time of a random number of independent paraloel channels. Nonparametric properties of Rasch-representable distributions are derived that can be used for testing the model and for estimating its parameters. Conditions are presented for Rasch-representable distributions to form a scale family. Finally, Rasch-represent-able distributions are characterized interms of their hazard functions.For helpful discussions, we are grateful to Hans Irtel, Christoph Micko, Hartmann Scheiblechner, and Hans-Henning Schultz.     

A relation between a between-item multidimensional IRT model and the mixture rasch model

Two generalizations of the Rasch model are compared: the between-item multidimensional model (Adams, Wilson, and Wang, 1997), and the mixture Rasch model (Mislevy & Verhelst, 1990; Rost, 1990). It is shown that the between-item multidimensional model is formally equivalent with a continuous mixture of Rasch models for which, within each class of the mixture, the item parameters are equal to the item parameters of the multidimensional model up to a shift parameter that is specific for the dimension an item belongs to in the multidimensional model. In a simulation study, the relation between both types of models also holds when the number of classes of the mixture is as small as two. The relation is illustrated with a study on verbal aggression. Frank Rijmen was supported by the Fund for Scientific Research Flanders (FWO). This research is also funded by the GOA/2000/02 granted from the KU Leuven. We would like to thank Kristof Vansteelandt for providing the data of the study on verbal aggression.     

图表加工的理论模型

图表作为一种以图形和表格记录、展示和传递信息的表征方式已得到广泛应用。图表加工是指人们对图表所呈现信息的认知加工。文章首先说明并简要评述图表加工的规则模型、认知过程模型和计算模型,然后介绍2004年提出的层次框架模型,最后进行小结并指出未来的研究方向     

A preliminary investigation of fixed and repetitive models during object imitation training

Empirically based guidelines for imitation training for children with Autism Spectrum Disorder (ASD) are limited and there is no existing evidence about what types of imitative models foster faster acquisition of imitation in children with ASD. We compared rates of acquisition for two different methods for presenting the imitative model (i.e., repetitive, fixed) in simple (Experiment 1) and conditional (Experiment 2) discrimination arrangements. The results suggest that some children with ASD may acquire imitation more rapidly when repetitive models, rather than fixed models are used to present the target skill. In Experiment 3, we investigated the features of object imitation models that might influence acquisition. The results of Experiment 3 suggest that the dynamic nature of repetitive models might be responsible for the differential acquisition we observed in the earlier two Experiments. Additionally, the presence of an outcome (e.g., stacked blocks) during training does not enhance acquisition.     

拥挤效应的特征加工机制

拥挤效应指对外周视野内目标刺激的识别受到该刺激周围无关刺激干扰的现象。大部分研究者认为拥挤效应发生在特征加工水平, 是由刺激特征间的过度整合引起的, 并基于此提出多种理论假设, 包括特征整合、重心解释、强制平均以及相应的量化模型, 但这些模型还缺乏直接的支持性证据。未来的研究应该结合ERP技术, 采用多种刺激(如运动刺激等等), 考察时间和空间维度上的拥挤效应的产生机制。