预测视角下双因子模型与高阶模型的模拟比较

双因子模型和高阶因子模型,作为既有全局因子又有局部因子的两个竞争模型,在研究中得到了广泛应用。本文采用Monte Carlo模拟方法,在模型拟合比较的基础上,比较了效标分别为外显变量和内潜变量时,两个模型在各种负荷水平下预测准确度的差异。结果发现,两种模型在拟合效果方面无显著差异;但在预测效度方面,当效标为显变量时,两个模型的结构系数估计值皆为无偏估计;而效标为潜变量时,高阶因子模型表现优于双因子模型:高阶因子模型的结构系数为无偏估计,双因子模型的结构系数估计值则在50%左右的情况下存在偏差。     

双因子模型：多维构念测量的新视角

双因子模型是一种既有全局因子又有局部因子的模型,近年来有了许多应用。本文讨论了双因子模型和高阶因子模型在数学模型、参数之间的关系,概念上和应用上的差异;概述了双因子模型在信度研究、平衡量表、探索性因子分析和项目反应理论中的应用。作为例子,在Rosenberg自尊量表结构的研究中,通过双因子模型分析了自尊特质效应与项目表述方法效应。     

作答时间与反应依赖关系建模：基于双因子模型视角

在心理与教育测验中,测验的计算机化越来越普遍,使得被试作答的过程性数据的搜集也越来越便利。分层模型的提出为作答时间与反应的联合分析提供了一个基本的建模框架,且逐渐成为当前最流行的方法。虽然分层模型被广泛使用,但仅仅通过参数间的关系还不能很好地解释作答时间和反应之间的关系。因此,一些研究者提出了一系列改进模型,但仍然存在一些不足。基于双因子模型的新视角,文中将测验的作答时间与反应分别视为测量被试速度和能力的两个局部因子,而作答时间与反应又视为综合测量了被试的速度与准确率权衡的一般能力或全局因子。基于此,文中提出双因子分层模型,以探讨作答时间与反应的依赖关系。模拟研究发现Mplus程序能有效估计双因子分层模型的各参数,而忽视作答时间与反应依赖关系的分层模型的参数估计结果存在明显的偏差。在实例数据分析中,相较于分层模型,双因子分层模型的各模型拟合指数表现更好。此外,不同被试在不同项目上的作答时间与反应存在不同的依赖关系,从而对被试的作答准确率与时间产生不同的影响。     

双因子模型MCAT中多级评分项目选题策略的比较

双因子模型假设测验考察一个一般因子和多个组因子,符合很多教育和心理测验的因素结构。“维度缩减”方法将参数估计中多维积分计算化简为多个迭代二维积分,是双因子模型的重要特征。本文针对考察多级评分项目的计算机化自适应测验,首先推导双因子等级反应模型下Fisher信息量的计算,然后推导“维度缩减”方法在项目选择方法中的应用,最后在低、中、高双因子模式题库中比较D-优化方法、后验加权Fisher信息D优化方法(PDO)、后验加权Kullback-Leibler方法(PKL)、连续熵(CEM)和互信息(MI)方法在能力估计的相关、均方根误差、绝对值偏差和欧氏距离的表现。模拟研究表明：(1)双因子模式越强,即一般因子和组因子在项目上的区分度的差异越小,一般因子估计精度降低,组因子估计精度增加,整体能力的估计精度提高;(2)相同实验条件下,连续熵方法的测量精度最高,PKL方法的能力估计精度最低,其它方法的测量精度没有显著差异。     

基于双因子模型的测验总分和维度分的合成方法

双因子模型可以同时包含一个全局因子和多个局部因子,在描述多维测验结构时有其独特优势,近些年应用越来越广泛。文章基于双因子模型,提出了4种合成总分和维度分的方法,分别是:原始分法,加和法,全局题目加权加和法和局部题目加权加和法,并采用模拟的方法,在样本量、测验长度、维度间相关变化的条件下考察了这些方法与传统多维IRT方法的表现。最后,通过实证研究对结果进行了验证。结果显示:(1)全局加权加和法和局部加权加和法,尤其是局部加权加和法合成的总分和维度分与真值最接近、信度最高。(2)在维度间相关较高,测验长度较长的条件下,局部加权加和法的结果较好,部分条件下甚至优于多维IRT法。(3)仅有局部加权加和法合成的维度分能够反应维度间真实的相关关系。     

多维测验分数的报告与解释:基于双因子模型的视角

在心理、行为、管理和教育等社科领域,经常使用多维测验。本文评介并比较了各种多维测验的测量模型;总结了基于双因子模型计算得到的统计指标;根据不同研究目的,提出了两个兼顾简洁性和精确性的多维测验分析流程。作为例子,在马基雅维利主义人格量表的研究中,通过双因子模型分析了如何报告、解释多维测验分数以及如何利用多维建模进行后续分析。     

项目间多维测验作答时间数据分析：潜在特质速度间效应建模

随着计算机测验使用的普及化,被试在心理与教育测验上的作答反应时的获取也越发便利。为了充分利用项目反应时信息,单维与多维的反应时模型相继被提出。然后,在项目间多维反应时数据中,潜在特质速度之间可能存在共同关系（比如,层阶关系）,此时现有的反应时模型并不能适用。基于此,本研究提出了高阶对数正态反应时模型与双因子对数正态反应时模型。在模拟研究中,高阶对数正态反应时模型与双因子对数正态反应时模型的各参数都能被准确估计。在瑞文标准推理测验的三组测验项目的反应时数据中,双因子对数正态反应时模型表现出更为优秀的拟合效果,同时基于多个统计量说明了局部与全局潜在特质速度同时存在的必要性。因此,在项目间多维测验反应时数据分析中,非常有必要考虑多维潜在特质速度之间的共同效应。     

因子混合模型:潜在类别分析与因子分析的整合

因子混合模型(FMM)是考虑了群体潜在异质性后的因子分析模型,它将潜在类别分析(LCA)与传统的因子分析(FA)整合在同一框架内,既保留了两种分析技术的优点,同时又展现出独特优势。FMM的应用主要包括描述变量的潜在结构、对被试进行分组以及探测社会称许偏差等。我们建议分别采用FA、LCA与FMM三种模型拟合数据,参考拟合指数和模型可解释性选择最优模型。总结了FMM的分析步骤以及软件使用,并用于探讨大学生社会面子意识的测量模型。未来研究应关注FMM分析过程的简化,继续深化对拟合指数等方面的探讨。     

中小学生心理素质双因子结构的构建

尝试构建双因子模型来验证中小学生心理素质结构的理论构想。采用整群抽样法先后两次选取被试,第一次选取重庆、四川、广东各一所小学4-6年级共1607名学生,重庆、四川、湖北、江西、浙江5个省市初一到高三共2106名中学生,第二次选取重庆市潼南区五所小学4-6年级共1334名学生、北碚区两所中学初一到高三共1057名学生,尝试构建中小学生心理素质的双因子结构,同时比较双因子模型与传统维度结构模型拟合中小学生心理素质的优劣。结果表明:相比传统维度结构模型,双因子结构模型拟合数据最优。结论:双因子模型更适用于解释中小学生心理素质的结构。     

中庸思维方式的一般性与特殊性：基于双因子模型的视角

分层整群抽取2867名大学生,采用双因子模型探讨中庸思维方式量表（ZYTSC）的一般性与特殊性。结果表明,相较于局部因子,全局因子占主导地位,但多方思考与和谐性分别造成了23%、26%的方差变异,表明多方思考与和谐性等特殊成分的存在;中庸思维方式双因子模型的跨性别和跨年级强等值性得到支持。中庸思维方式量表同时测量了中庸思维方式的共同性和特异性成分,研究对理解和正确使用中庸思维方式量表具有一定的启示。     

项目表述效应对人格测验信度和效标效度的影响——以核心自我评价量表(CSES)为例

项目表述效应是指由项目表述方式的差异引起的与测量内容无关的系统变异,项目表述效应模型的统计本质是一种双因子模型。本研究以核心自我评价量表(CSES)为例,探讨项目表述效应对人格测验信效度的影响。采用核心自我评价量表、生活满意度量表和积极情感消极情感量表对340名“蚁族”进行测查。结果表明,CSES在核心自我评价特质以外,还存在一个反向题项目表述效应因子;忽视项目表述效应对CSES的同质性信度和效标关联效度有重要影响：高估CSES的同质性信度,低估核心自我评价与生活满意度、积极情感的正相关,高估核心自我评价与消极情感的负相关。     

Quantifying the Strength of General Factors in Psychopathology: A Comparison of CFA with Maximum Likelihood Estimation,BSEM, and ESEM/EFA Bifactor Approaches

Whether or not importance should be placed on an all-encompassing general factor of psychopathology (or p factor) in classifying, researching, diagnosing, and treating psychiatric disorders depends (among other issues) on the extent to which comorbidity is symptom-general rather than staying largely within the confines of narrower transdiagnostic factors such as internalizing and externalizing. In this study, we compared three methods of estimating p factor strength. We compared omega hierarchical and explained common variance calculated from confirmatory factor analysis (CFA) bifactor models with maximum likelihood (ML) estimation, from exploratory structural equation modeling/exploratory factor analysis models with a bifactor rotation, and from Bayesian structural equation modeling (BSEM) bifactor models. Our simulation results suggested that BSEM with small variance priors on secondary loadings might be the preferred option. However, CFA with ML also performed well provided secondary loadings were modeled. We provide two empirical examples of applying the three methodologies using a normative sample of youth (z-proso, n = 1,286) and a university counseling sample (n = 359).     

Irritability and Emotional Impulsivity as Core Feature of ADHD and ODD in Children

The categorical approach of diagnosing mental disorders entails the problem of frequently occurring comorbidities, suggesting a more parsimonious structure of psychopathology. In this study, we therefore aim to assess how affective dysregulation (AD) is associated with attention-deficit/hyperactivity disorder (ADHD) and oppositional defiant disorder (ODD) in children. To assess AD in children aged 8–12 years (n?=?391), we employed the parent version of a newly constructed parent rating scale. Following item reduction, we conducted exploratory and confirmatory factor analyses to establish a factorial structure of AD. One core dimension was identified, comprising irritability and emotional impulsivity, and two smaller dimensions, comprising positive emotionality and exuberance. Subsequently, we examined five different latent factor models – a unidimensional model, a first-order correlated factor model, a second-order correlated factor model, a traditional bifactor model, and a bifactor S-1 model, in which the first-order factor AD-Irritability/Emotional Impulsivity (II) was modeled as the general reference factor. A bifactor S-1 model with the a priori defined general reference domain AD-II provided the best fit to our data and was straightforward to interpret. This model showed excellent model fit and no anomalous factor loadings. This still held true, when comparing it to bifactor S-1 models with ADHD/ODD-related reference factors. Differential correlations with emotion regulation skills and the established Parent Proxy Anger Scale validate the interpretation of the different dimensions. Our results suggest that irritability/emotional impulsivity might be a common core feature of ADHD and ODD.

     

Bifactor models and rotations: exploring the extent to which multidimensional data yield univocal scale scores

The application of psychological measures often results in item response data that arguably are consistent with both unidimensional (a single common factor) and multidimensional latent structures (typically caused by parcels of items that tap similar content domains). As such, structural ambiguity leads to seemingly endless "confirmatory" factor analytic studies in which the research question is whether scale scores can be interpreted as reflecting variation on a single trait. An alternative to the more commonly observed unidimensional, correlated traits, or second-order representations of a measure's latent structure is a bifactor model. Bifactor structures, however, are not well understood in the personality assessment community and thus rarely are applied. To address this, herein we (a) describe issues that arise in conceptualizing and modeling multidimensionality, (b) describe exploratory (including Schmid-Leiman [Schmid & Leiman, 1957] and target bifactor rotations) and confirmatory bifactor modeling, (c) differentiate between bifactor and second-order models, and (d) suggest contexts where bifactor analysis is particularly valuable (e.g., for evaluating the plausibility of subscales, determining the extent to which scores reflect a single variable even when the data are multidimensional, and evaluating the feasibility of applying a unidimensional item response theory (IRT) measurement model). We emphasize that the determination of dimensionality is a related but distinct question from either determining the extent to which scores reflect a single individual difference variable or determining the effect of multidimensionality on IRT item parameter estimates. Indeed, we suggest that in many contexts, multidimensional data can yield interpretable scale scores and be appropriately fitted to unidimensional IRT models.     

Random intercept item factor analysis

The common factor model assumes that the linear coefficients (intercepts and factor loadings) linking the observed variables to the latent factors are fixed coefficients (i.e., common for all participants). When the observed variables are participants' observed responses to stimuli, such as their responses to the items of a questionnaire, the assumption of common linear coefficients may be too restrictive. For instance, this may occur if participants consistently use the response scale idiosyncratically. To account for this phenomenon, the authors partially relax the fixed coefficients assumption by allowing the intercepts in the factor model to change across participants. The model is attractive when m factors are expected on the basis of substantive theory but m + 1 factors are needed in practice to adequately reproduce the data. Also, this model for single-level data can be fitted with conventional software for structural equation modeling. The authors demonstrate the use of this model with an empirical data set on optimism in which they compare it with competing models such as the bifactor and the correlated trait-correlated method minus 1 models.     

新世纪20年国内心理统计方法研究回顾

新世纪头20年, 国内心理学11本专业期刊一共发表了213篇统计方法研究论文。研究范围主要包括以下10类(按论文篇数排序)：结构方程模型、测验信度、中介效应、效应量与检验力、纵向研究、调节效应、探索性因子分析、潜在类别模型、共同方法偏差和多层线性模型。对各类做了简单的回顾与梳理。结果发现, 国内心理统计方法研究的广度和深度都不断增加, 研究热点在相互融合中共同发展; 但综述类论文比例较大, 原创性研究论文比例有待提高, 研究力量也有待加强。     

Improvement in Detection of Differential Item Functioning Using a Mixture Item Response Theory Model

Usually, methods for detection of differential item functioning (DIF) compare the functioning of items across manifest groups. However, the manifest groups with respect to which the items function differentially may not necessarily coincide with the true source of the bias. It is expected that DIF detection under a model that includes a latent DIF variable is more sensitive to this source of bias. In a simulation study, it is shown that a mixture item response theory model, which includes a latent grouping variable, performs better in identifying DIF items than DIF detection methods using manifest variables only. The difference between manifest and latent DIF detection increases as the correlation between the manifest variable and the true source of the DIF becomes smaller. Different sample sizes, relative group sizes, and significance levels are studied. Finally, an empirical example demonstrates the detection of heterogeneity in a minority sample using a latent grouping variable. Manifest and latent DIF detection methods are applied to a Vocabulary test of the General Aptitude Test Battery (GATB).     

问卷数据建模的前传

问卷法是一种常见的实证研究方法。问卷数据建模的前期工作,就像是一栋大楼的奠基工程,基础是否扎实,影响后续的工程质量。本文专门讨论统计模型建立之前要做的事情（重点是量表评价）,内容包括：处理缺失值、评价量表的结构效度和题目删除的适当性、多维量表需要合成总分时检验同质性并计算合成信度、检验共同方法偏差和评价（变量）区分效度、题目打包、检验自变量的多重共线性,最后也涉及建模理据和无关变量控制等。     

Stochastic search item selection for factor analytic models

In this paper we implement a Markov chain Monte Carlo algorithm based on the stochastic search variable selection method of George and McCulloch (1993) for identifying promising subsets of manifest variables (items) for factor analysis models. The suggested algorithm is constructed by embedding in the usual factor analysis model a normal mixture prior for the model loadings with latent indicators used to identify not only which manifest variables should be included in the model but also how each manifest variable is associated with each factor. We further extend the suggested algorithm to allow for factor selection. We also develop a detailed procedure for the specification of the prior parameters values based on the practical significance of factor loadings using ideas from the original work of George and McCulloch (1993). A straightforward Gibbs sampler is used to simulate from the joint posterior distribution of all unknown parameters and the subset of variables with the highest posterior probability is selected. The proposed method is illustrated using real and simulated data sets.