复杂问题解决中计算机模拟情境的逻辑框架

计算机模拟情境摆脱了实验室研究和现场研究的不足,满足了复杂问题解决的复杂性、动态性和模糊性等特征,受到研究者们的青睐.近年来,各种模拟情境在复杂问题解决研究中得到运用,其内部逻辑结构主要有线性结构方程和有限状态自动化,线性结构方程适用于等距数据,而有限状态自动化适用于称名数据.当前,对复杂问题解决的测量注重结果,而相对忽视过程.未来的模拟情境可以从问题特征、任务逻辑和测量方式来提高信度和效度.     

贝叶斯结构方程模型及其研究现状

在心理学研究中结构方程模型(Structural Equation Modeling, SEM)被广泛用于检验潜变量间的因果效应, 其估计方法有频率学方法(如, 极大似然估计)和贝叶斯方法两类。近年来由于贝叶斯统计的流行及其在结构方程建模中易于处理小样本、缺失数据及复杂模型等方面的优势, 贝叶斯结构方程模型发展迅速, 但其在国内心理学领域的应用不足。主要介绍了贝叶斯结构方程模型的方法基础和优良特性, 及几类常用的贝叶斯结构方程模型及其应用现状, 旨在为应用研究者介绍新的研究工具。     

检验的临界值：真伪差距多大才能辨别？ ——评《不同条件下拟合指数的表现及临界值的选择》

Hu和Bentler（1998,1999）通过模拟研究推荐结构方程模型拟合指数临界值后,受到不少批评和质疑。此后有关拟合指数的研究重点不再是推出新的临界值标准。郭庆科等人的文章《不同条件下拟合指数的表现及临界值的选择》,仿照Hu和Bentler的做法,通过模拟研究推荐新的拟合指数临界值标准。本文旨在揭示这种做法的错误所在。用简单的Z检验,说明检验的临界值是不能通过模拟研究确定的。通过将一个特定真模型的众多错误模型分类,说明结构方程分析中真模型与错误模型差距的多样性,无法通过模拟一对真伪模型来代表。讨论了统计检验的本质和确定临界值的逻辑,还谈到应当从哪些角度检验和评价结构方程模型     

在亚健康状态研究中运用结构方程模型的合理性

亚健康状态是处于健康与疾病之间的一种“非病但非健康”的中间状态,主要表现为躯体、心理和社会适应等方面的不适,目前尚没有公认的概念和测量诊断工具。由于亚健康状态研究数据中含有潜变量、测量误差以及变量间关系需要确定等因素,不能使用传统的统计学方法进行处理。结构方程模型能够同时处理观测变量、潜变量、测量误差及变量间的关系,应用结构方程模型验证亚健康状况测量量表的结构效度和亚健康状态理论模型是非常合理的。     

探索性结构方程建模(ESEM):EFA和CFA的整合

探索性结构方程建模(ESEM)是在测量模型部分使用了类似于EFA模型的SEM.作为一种高级统计方法,ESEM整合了EFA和CFA两种因子分析方法的功能和优点.通过ESEM,研究者既可以灵活地探索因子结构,又可以系统地验证因子模型,为潜变量的关系分析提供更适宜的测量模型.ESEM已经在某些社科领域的研究中得到应用,是一种值得推介的因子分析方法.ESEM的具体应用问题,例如因子旋转方法的选用、测验信度评价等,仍有待探讨.     

结构方程建模中的题目打包策略

结构方程建模中题目打包法的优缺点包括：指标数据质量变好、模型拟合程度提高; 估计偏差不大, 可校正; 估计稳定, 但降低了敏感性与可证伪性。打包法的前提条件是单维、同质, 适合结构模型分析, 不适合测量模型分析。对于单维测验, 给出了一个打包流程。对于通常的多个子量表(多维结构)测验, 推荐在子量表内打包, 每个子量表打包成1个指标或者3个指标, 用于结构方程建模。     

测量等价性的概念及其判定条件

测量等价性指的是,应用量表进行测量时,当观测变量和潜在特质之间的关系在相比较的各个组之间等同时,就称该量表具备测量等价性。特别地,来自不同群体但在潜在特质上得分相等的个体,他们观测变量的得分也应该相等。测量工具满足测量等价性的要求是进行组间差异比较的前提条件。该文首先明确了测量等价性的概念及其研究历史,然后阐述了测量等价性的重要性以及对测量等价性分析的必要性,进而讨论了在结构方程模型中测量等价性所要满足的5个条件,最后列举了模型优劣判定的拟合度指数     

bootstrap法在合成分数信度区间估计中的应用

在介绍bootstrap法原理的基础上,本文以一个同质测量模式的模拟数据为例,对结构方程模型下使用bootstrap法对合成分数信度的区间估计的应用中进行了演示。     

新世纪20年国内结构方程模型方法研究与模型发展

新世纪前20年, 国内结构方程模型(SEM)方法研究主要涉及5个主题：模型发展、参数估计、模型评价、测量不变性及特殊数据处理, 特别是模型发展方面(即SEM的各种变式)有较多成果。对每个主题, 在简述背景知识的基础上, 系统总结了方法学研究发展及成果。最后也讨论了SEM的国外方法学研究进展和未来研究方向。     

人格维度、自我和谐及行为抑制与心身症状的关系

研究旨在通过实证研究建立人格维度、自我和谐、行为抑制和心身症状之间关系的结构方程模型。对600余名被试问卷测量的相关、回归和结构方程模型分析表明,人格维度可以直接和/或通过行为抑制和自我和谐对心身症状有着正向或负向的预测作用,而且自我与经验的不和谐、行为抑制和心身症状本身都可以作为心理健康的指标,它们之间又存在密切的相互关系     

Invariance in Measurement and Prediction Revisited

Borsboom (Psychometrika, 71:425–440, 2006) noted that recent work on measurement invariance (MI) and predictive invariance (PI) has had little impact on the practice of measurement in psychology. To understand this contention, the definitions of MI and PI are reviewed, followed by results on the consistency between the two forms of invariance in the general case. The special parametric cases of factor analysis (strict factorial invariance) and linear regression analyses (strong regression invariance) are then described, along with findings on the inconsistency between the two forms of invariance in this context. Two numerical examples of inconsistency are reviewed in detail. The impact of violations of MI on accuracy of selection is illustrated. Finally, reasons for the slow dissemination of work on invariance are discussed, and the prospects for altering this situation are weighed. This paper is based on the Presidential Address given at the International Meeting of the Psychometric Society in Tokyo, Japan, on July 11, 2007. This research was supported by National Institute of Mental Health grants 1P30 MH 068685-01A1 and RO1 MH64707-01.     

Using SAS PROC TCALIS for multigroup structural equation modelling with mean structures

Multigroup structural equation modelling (SEM) is a technique frequently used to evaluate measurement invariance in social and behavioural science research. Before version 9.2, SAS was incapable of handling multigroup SEM. However, this limitation is resolved in PROC TCALIS in SAS 9.2. For the purpose of illustration, this paper provides a step-by-step guide to programming the tests of measurement invariance and partial invariance using PROC TCALIS for multigroup SEM with mean structures. Fit indices and parameter estimates are validated, thus providing an alternative tool for researchers conducting both applied and simulated studies. Other new features (e.g., different types of modelling languages and estimation methods) and limitations (e.g., ordered-categorical SEM and multilevel SEM) of the TCALIS procedure are also briefly discussed.     

二阶因素模型的测量等价性检验——以大学生网络利他行为量表为例

与一阶因素模型相比,二阶因素模型具有较多优点,但二阶因素模型的测量等价性检验要更复杂,它需要依次进行七个不同水平的检验：形等价、一阶弱等价、二阶弱等价、一阶强等价、二阶强等价、二阶严等价和一阶严等价。低水平的等价性满足之后,才能进行更为严格的高一水平的等价性检验。运用均值和协方差结构（MACS）模型对大学生网络利他行为量表（IABSU）进行二阶因素模型的测量等价性检验,结果表明,IABSU具有跨地域的完全一阶、二阶严等价性。     

On the misuse of manifest variables in the detection of measurement bias

Measurement invariance (lack of bias) of a manifest variableY with respect to a latent variableW is defined as invariance of the conditional distribution ofY givenW over selected subpopulations. Invariance is commonly assessed by studying subpopulation differences in the conditional distribution ofY given a manifest variableZ, chosen to substitute forW. A unified treatment of conditions that may allow the detection of measurement bias using statistical procedures involving only observed or manifest variables is presented. Theorems are provided that give conditions for measurement invariance, and for invariance of the conditional distribution ofY givenZ. Additional theorems and examples explore the Bayes sufficiency ofZ, stochastic ordering inW, local independence ofY andZ, exponential families, and the reliability ofZ. It is shown that when Bayes sufficiency ofZ fails, the two forms of invariance will often not be equivalent in practice. Bayes sufficiency holds under Rasch model assumptions, and in long tests under certain conditions. It is concluded that bias detection procedures that rely strictly on observed variables are not in general diagnostic of measurement bias, or the lack of bias.Preparation of this article was supported in part by PSC-CUNY grant #661282 to Roger E. Millsap.     

Measurement Invariance,Predictive Invariance,and the Duality Paradox

The statistical literature on bias in psychological testing distinguishes at least two forms of bias: measurement bias and predictive bias. Measurement bias concerns group differences in the relationship between a test and the latent variable to be measured. Predictive bias concerns group differences in the relationship between a test and an external criterion. How are these two forms of bias related? For example. if a test is unbiased in the predictive sense, does this fact support the hypothesis that the test is unbiased in the measurement sense? A theorem is given that describes the conditions under which measurement invariance (lack of bias) is consistent with predictive invariance for the linear case. Paradoxically, these two forms of invariance are shown to be inconsistent under realistic conditions. This duality or inconsistency is illustrated in simulated data. The implications of the duality for group differences research are illustrated in real data involving gender and ethnic differences on the SAT. The phenomenon of duality may force a reinterpretation of common empirical findings of test criterion regression slope invariance. and of invariance in test validities. Other implications are discussed.     

Measurement invariance,factor analysis and factorial invariance

Several concepts are introduced and defined: measurement invariance, structural bias, weak measurement invariance, strong factorial invariance, and strict factorial invariance. It is shown that factorial invariance has implications for (weak) measurement invariance. Definitions of fairness in employment/admissions testing and salary equity are provided and it is argued that strict factorial invariance is required for fairness/equity to exist. Implications for item and test bias are developed and it is argued that item or test bias probably depends on the existence of latent variables that are irrelevant to the primary goal of test constructers.Presidential address delivered at the Annual Meeting of the Psychometric Society in Berkeley, California, June 18–20, 1993.     

网络测验和纸笔测验的测量不变性研究：以生活满意度量表为例

以生活满意度量表为例,运用实证性因素分析,考察在中国文化下网络测验和传统纸笔测验之间的测量不变性。结果显示,网络测验和纸笔测验之间存在弱不变性,即网络测验和纸笔测验有着相同的测量单位;但网络测验和纸笔测验只存在部分的强不变性和部分的严格不变性,测验实施环境对结果的影响不可忽视。该研究表明,恰当设计的网络测验是可靠的,同时还提示,当一个测验在不同情境下运用时,检验测量不变性十分必要     

Dyadic measurement invariance and its importance for replicability in romantic relationship science

Comparisons of group means, variances, correlations, and/or regression slopes involving psychological variables rely on an assumption of measurement invariance—that the latent variables under investigation have equivalent meaning and measurement across group. When measures are noninvariant, replicability suffers, as comparisons are either conceptually meaningless, or hindered by inflated Type I error rates. We propose that the failure to account for interdependence among dyad members when testing measurement invariance may be a potential source of unreplicable findings in relationship research. We developed fully dyadic versions of invariance models, created an R package (dySEM) to make specifying dyadic invariance models easier and reporting more reproducible, and executed a Registered Report for gauging the extent of dyadic (non)invariance in romantic relationship research across measures of relationship well‐being, personality, and sexuality in a sample of 282 heterosexual couples. We found that although a number of popular measures display good evidence of dyadic invariance, a few display concerning levels and interesting patterns of noninvariance, while others appeared either noninvariant or poorly fitting for both men and women. We discuss our findings in terms of their meaning for the replicability dyadic close relationship research. We close by arguing that increased theorizing and research on dyadic invariance, and inclusive methods for analyzing invariance with indistinguishable dyads, are needed to capitalize on the opportunity to advance our field's understanding of dyadic constructions of relational concepts.     

贝叶斯多组比较——渐近测量不变性

在行为科学研究领域中,检验测量工具的测量不变性是进行群体差异比较的前提。目前,多组验证性因子分析(多组CFA)方法被广泛用于检验测量不变性,但是它对跨组等值的限制过于严格,在实际应用中常常存在大量局限。贝叶斯渐近测量不变性方法基于贝叶斯思想的优良特性,放宽了传统多组CFA方法对跨组差异的严格限制,避免了传统方法的问题,具有较高的应用价值。文章详细介绍了贝叶斯渐近测量不变性方法的原理及优势,同时通过实例展示了渐近测量不变性方法在Mplus软件中的具体分析过程。