LGM模型中缺失数据处理方法的比较:ML方法与Diggle-Kenward选择模型

追踪研究中缺失数据十分常见。本文通过Monte Carlo模拟研究,考察基于不同前提假设的Diggle-Kenward选择模型和ML方法对增长参数估计精度的差异,并考虑样本量、缺失比例、目标变量分布形态以及不同缺失机制的影响。结果表明:(1)缺失机制对基于MAR的ML方法有较大的影响,在MNAR缺失机制下,基于MAR的ML方法对LGM模型中截距均值和斜率均值的估计不具有稳健性。(2)DiggleKenward选择模型更容易受到目标变量分布偏态程度的影响,样本量与偏态程度存在交互作用,样本量较大时,偏态程度的影响会减弱。而ML方法仅在MNAR机制下轻微受到偏态程度的影响。     

追踪研究中缺失数据处理方法及应用现状分析

追踪研究中普遍存在缺失数据, 缺失数据处理方法的选择影响统计推断的精度及研究结果的有效性。首先, 阐述缺失机制及判断方法, 比较追踪研究中主要的缺失数据处理方法的特点、及实际应用中的缺失处理方法的选择和软件实现。其次, 对国内心理学中92篇追踪研究文献进行分析, 发现有59篇(64.13%)报告不同程度缺失, 其中仅39篇报告了处理方法且均为删除法。未来研究应深入探讨现有缺失数据处理方法的有效性, 进一步规范应用研究中缺失数据的处理。     

家庭功能对青少年情绪问题的作用机制

本研究以探索家庭功能发挥过程和发挥结果之间的关系并检验两者对青少年情绪问题的作用大小和机制为目的,采用Skinner等人编制的家庭评价量表(FAM,测量家庭功能发挥过程)和Olson编制的家庭适应性、亲密度量表(FACESⅢ,测量家庭功能发挥结果)、流调中心抑郁量表(CES-D)和焦虑自评量表(SAS)对422名中学生进行匿名问卷施测,通过相关分析、分层回归和结构方程模型统计分析发现:(1)总体上青少年的情绪处于良好状态;(2)家庭功能发挥过程和发挥结果各变量之间呈显著相关;(3)家庭功能发挥过程比家庭功能发挥结果对青少年情绪问题预测作用更大;(4)家庭功能发挥结果是家庭功能发挥过程与青少年情绪问题的部分中介变量。     

聚焦“缺失”对当下幸福体验的影响

人们的幸福体验与其关注的焦点密切相关。研究1采用图形聚焦范式,结果发现个体在情绪体验中出现聚焦缺失的聚焦错觉现象,聚焦缺失组个体的总体幸福体验低于非聚焦缺失组;研究2通过启动范式操纵被试的聚焦缺失程度,结果发现,高聚焦缺失个体的情绪幸福比低聚焦缺失的个体更低。2个研究结果发现:聚焦缺失是导致人们低幸福体验的根源,聚焦缺失使人们高估缺失所占整个生活比例,出现"缺失放大"现象。研究将聚焦错觉从"情感预测偏差"领域拓展至"当下幸福体验"领域,同时从聚焦"实物类匮乏"跨越到聚焦"抽象性缺失感",并初步揭示出"缺失放大"机制,未来或可进一步探讨聚焦缺失影响幸福体验的"缺失放大"机制。     

人际吸引机制探索:主我分享中情绪的作用

研究以计算机仿真交流平台为工具,采用模拟情景方法,探讨了人际互动中相似性对人际吸引的影响和人际吸引机制。采用2×2组间设计,共32名被试。结果显示:(1)主我分享是态度相似的重要组成部分;(2)在人际交流与互动中,主我分享是影响人际吸引的重要因素;(3)情绪是主我分享影响人际吸引的中介变量,起部分中介作用;(4)人际互动情境模拟是研究人际交往的一种有效途径。     

分类精确性指数Entropy在潜剖面分析中的表现:一项蒙特卡罗模拟研究

本研究通过蒙特卡洛模拟考查了分类精确性指数Entropy及其变式受样本量、潜类别数目、类别距离和指标个数及其组合的影响情况。研究结果表明:(1)尽管Entropy值与分类精确性高相关,但其值随类别数、样本量和指标数的变化而变化,很难确定唯一的临界值;(2)其他条件不变的情况下,样本量越大,Entropy的值越小,分类精确性越差;(3)类别距离对分类精确性的影响具有跨样本量和跨类别数的一致性;(4)小样本(N=50~100)的情况下,指标数越多,Entropy的结果越好;(5)在各种条件下Entropy对分类错误率比其它变式更灵敏。     

社会支持对老年人社会幸福感的影响机制:希望与孤独感的中介作用

基于积极心理学,采用问卷调查法,对859名老年人进行调查,探讨社会支持对老年人社会幸福感的影响机制,并检验希望与孤独感的中介效应模型及其模型适用性,借此构建四个变量之间的关系模型,探索促进老年人心理健康发展的内在机制。结果表明:(1)社会支持、希望、孤独感与社会幸福感之间两两相关显著,社会支持、希望和孤独感是影响老年人社会幸福感的重要因素;(2)社会支持通过希望对社会幸福感影响的中介效应显著;(3)社会支持通过孤独感对社会幸福感影响的中介效应显著;(4)在同时检验希望和孤独感在社会支持影响社会幸福感的多重中介效应时,二者在社会支持和社会幸福感间均起部分中介作用。研究结果证实了社会支持影响社会幸福感及希望和孤独感部分中介作用的心理机制,为提高老年人心理健康水平提供了参考建议。     

工作场所无礼行为的溢出和交叉效应:超越职场范围的负性作用机制

以往研究通常考查工作场所无礼行为对接收者个人和工作场所内相关结果的影响。而近来研究指出,无礼行为会超出工作场所的界限产生溢出效应,且工作场所无礼行为会对接收者之外的他人产生消极作用,即工作场所无礼行为的交叉效应。在综述国内外相关文献的基础上,总结出了溢出和交叉效应的产生机制,包括消极情绪和情感在无礼行为溢出效应中的中介机制,以及压力传递在无礼行为引发的消极情绪和行为反应与工作-家庭冲突中的中介角色;深入探讨了共情在无礼行为交叉效应中的调节机制和社会阻抑在无礼行为交叉效应中的中介机制;并建议未来相关研究选取新的结果变量视角、探究新的中介机制和边界条件以及进一步整合无礼行为的溢出和交叉的连锁效应机制     

幼儿教师共情的负面效应：一个有调节的中介模型

为探讨共情负面效应的作用机制及条件,采用共情量表、Maslach职业倦怠问卷教育版、情绪劳动策略量表和领悟社会支持量表对253名幼儿教师进行调查。结果发现：(1)幼儿教师的情绪共情正向影响情绪枯竭,该影响作用可以通过情绪劳动这一中介实现;(2)幼儿教师的认知共情负向影响情绪枯竭;(3)社会支持调节了幼儿教师情绪劳动对情绪共情与情绪枯竭关系的中介过程的后半段路径。研究结果对幼儿教师心理健康有重要意义。     

抑制和加工速度与液态智力的年老化

研究抑制能力和加工速度在液态智力年老化过程中的相对作用。被试为142名18~85岁健康成年人,完成包括抑制优势反应、加工速度和液态智力共13项测验。分层回归分析显示,抑制能力与年龄相关的特异性变异,在控制简单的速度变量时较控制复杂的速度变量时更大;结构方程分析表明,对液态智力年老化的中介作用,当选取较复杂的速度变量时加工速度的作用更大,而当选取较简单的速度变量时抑制能力的作用更大。研究结果表明,在液态智力的年老化过程中,除了加工速度的作用之外,抑制能力也起重要的中介作用。该结果为认知年老化的抑制理论和执行衰退假说提供了认知行为学研究的证据,并且对加工速度理论作了有益的补充和修正     

The role of collateral information about examinees in item parameter estimation

Standard procedures for estimating item parameters in item response theory (IRT) ignore collateral information that may be available about examinees, such as their standing on demographic and educational variables. This paper describes circumstances under which collateral information about examineesmay be used to make inferences about item parameters more precise, and circumstances under which itmust be used to obtain correct inferences.This work was supported by Contract No. N00014-85-K-0683, project designation NR 150-539, from the Cognitive Science Program, Cognitive and Neural Sciences Division, Office of Naval Research. Reproduction in whole or in part is permitted for any purpose of the United States Government. We are indebted to Tim Davey, Eugene Johnson, and three anonymous referees for their comments on earlier versions of the paper.     

Estimating correlations with missing data,a bayesian approach

The posterior distribution of the bivariate correlation is analytically derived given a data set wherex is completely observed buty is missing at random for a portion of the sample. Interval estimates of the correlation are then constructed from the posterior distribution in terms of highest density regions (HDRs). Various choices for the form of the prior distribution are explored. For each of these priors, the resulting Bayesian HDRs are compared with each other and with intervals derived from maximum likelihood theory.     

Estimation for the multiple factor model when data are missing

A maximum likelihood method of estimating the parameters of the multiple factor model when data are missing from the sample is presented. A Monte Carlo study compares the method with 5 heuristic methods of dealing with the problem. The present method shows some advantage in accuracy of estimation over the heuristic methods but is considerably more costly computationally.This paper is based on the author's doctoral dissertation at the Department of Psychology, University of Illinois at Urbana-Champaign. The author gratefully acknowledges the aid of Drs. Robert Bohrer, Charles Lewis, Robert Linn, Maurice Tatsuoka, and Ledyard Tucker.     

Nominal scale agreement among observers

Experiments are considered where each of a sample of subjects is assigned to one of C categories separately by each of a fixed or varying group of observers. Building on earlier publications, general procedures are proposed to analyze agreements and disagreements among observers. In the case of a varying group of observers, it is shown that it is not necessary to demand a constant number of observers per subject. In the case of a fixed group of observers, the problem of missing data is considered.The procedures are illustrated within the context of two clinical diagnosis examples. In the first example it is investigated which categories are relatively hard to distinguish from one another; a new theorem is applied that shows a useful property of the statistic kappa. In the second example it is investigated if a subgroup of observers can be found with a significantly higher degree of interobserver agreement.The author gratefully acknowledges the valuable suggestions by W. Molenaar, R. van Strik, R. Popping and the referees.     

2PL模型的两种马尔可夫蒙特卡洛缺失数据处理方法比较

马尔科夫蒙特卡洛（MCMC）是项目反应理论中处理缺失数据的一种典型方法。文章通过模拟研究比较了在不同被试人数,项目数,缺失比例下两种MCMC方法（M-H within Gibbs和DA-T Gibbs）参数估计的精确性,并结合了实证研究。研究结果表明,两种方法是有差异的,项目参数估计均受被试人数影响很大,受缺失比例影响相对更小。在样本较大缺失比例较小时,M-H within Gibbs参数估计的均方误差（RMSE）相对略小,随着样本数的减少或缺失比例的增加,DA-T Gibbs方法逐渐优于M-H within Gibbs方法     

Using Response Times for Joint Modeling of Response and Omission Behavior

Abstract

For adequate modeling of missing responses, a thorough understanding of the nonresponse mechanisms is vital. As a large number of major testing programs are in the process or already have been moving to computer-based assessment, a rich body of additional data on examinee behavior becomes easily accessible. These additional data may contain valuable information on the processes associated with nonresponse. Bringing together research on item omissions with approaches for modeling response time data, we propose a framework for simultaneously modeling response behavior and omission behavior utilizing timing information for both. As such, the proposed model allows (a) to gain a deeper understanding of response and nonresponse behavior in general and, in particular, of the processes underlying item omissions in LSAs, (b) to model the processes determining the time examinees require to generate a response or to omit an item, and (c) to account for nonignorable item omissions. Parameter recovery of the proposed model is studied within a simulation study. An illustration of the model by means of an application to real data is provided.     

Analysis of structural equation model with ignorable missing continuous and polytomous data

The main purpose of this article is to develop a Bayesian approach for structural equation models with ignorable missing continuous and polytomous data. Joint Bayesian estimates of thresholds, structural parameters and latent factor scores are obtained simultaneously. The idea of data augmentation is used to solve the computational difficulties involved. In the posterior analysis, in addition to the real missing data, latent variables and latent continuous measurements underlying the polytomous data are treated as hypothetical missing data. An algorithm that embeds the Metropolis-Hastings algorithm within the Gibbs sampler is implemented to produce the Bayesian estimates. A goodness-of-fit statistic for testing the posited model is presented. It is shown that the proposed approach is not sensitive to prior distributions and can handle situations with a large number of missing patterns whose underlying sample sizes may be small. Computational efficiency of the proposed procedure is illustrated by simulation studies and a real example.The work described in this paper was fully supported by a grant from the Research Grants Council of the HKSAR (Project No. CUHK 4088/99H). The authors are greatly indebted to the Editor and anonymous reviewers for valuable comments in improving the paper; and also to D. E. Morisky and J.A. Stein for the use of their AIDS data set.     

The dimensional effects of missing information on choice processing

This paper demonstrates that choice processing may be different in missing information situations than in full information situations depending on whether inferences are used to fill in missing values and the overlap of the missing information itself. It is shown that when individuals do not form inferences to fill in missing values, fewer full attribute-based processes and more processes which accommodate for missing attribute values, alternative-based or given-dimension attribute-based, are used. It is also shown that when a processing shift due to missing information does occur, the overlap of the missing values will affect the type of shift that takes place. If overlap is high, a shift to given-dimension attribute-based processing is more likely, and when overlap is low, a shift to alternative-based processing is more likely. When individuals do form inferences to fill in missing values, processing is more similar to that in full information situations. Finally, it is shown that individuals will often partially fill in missing information, thus moderating the proposed effects.