A comparative evaluation of factor- and component-based structural equation modelling approaches under (in)correct construct representations

Structural equation modelling (SEM) has evolved into two domains, factor-based and component-based, dependent on whether constructs are statistically represented as common factors or components. The two SEM domains are conceptually distinct, each assuming their own population models with either of the statistical construct proxies, and statistical SEM approaches should be used for estimating models whose construct representations correspond to what they assume. However, SEM approaches have often been evaluated and compared only under population factor models, providing misleading conclusions about their relative performance. This is partly because population component models and their relationships have not been clearly formulated. Also, it is of fundamental importance to examine how robust SEM approaches can be to potential misrepresentation of constructs because researchers may often lack clear theories to determine whether a factor or component is more representative of a given construct. Addressing these issues, this study begins by clarifying several population component models and their relationships and then provides a comprehensive evaluation of four SEM approaches – the maximum likelihood approach and factor score regression for factor-based SEM as well as generalized structured component analysis (GSCA) and partial least squares path modelling (PLSPM) for component-based SEM – under various experimental conditions. We confirm that the factor-based SEM approaches should be preferred for estimating factor models, whereas the component-based SEM approaches should be chosen for component models. Importantly, the component-based approaches are generally more robust to construct misrepresentation than the factor-based ones. Of the component-based approaches, GSCA should be chosen over PLSPM, regardless of whether or not constructs are misrepresented.     

结构方程模型统计检验力分析：原理与方法

结构方程模型是心理学、管理学、社会学等学科中重要的统计工具之一。然而, 大量使用结构方程模型的研究忽视了对该方法的统计检验力进行必要的分析和报告, 在一定程度上降低了这些研究的结果的证明效力。结构方程模型的统计检验力分析方法主要有Satorra-Saris法、MacCallum法与Monte Carlo法三类。其中Satorra-Saris法适用于备择模型清晰、检验对象相对简单、检验方法基于χ²分布的情形; MacCallum法适用于基于χ²分布的模型拟合检验且备择模型不明的情形; Monte Carlo法适用于检验对象相对复杂、采用模拟或重抽样方法进行检验的情形。在实际应用中, 研究者应当首先判断检验的目的、方法以及是否有明确的备择模型, 并根据这些信息选择具体的分析方法。     

基于多层结构方程模型的情境效应分析—— 兼与多层线性模型比较

多层(嵌套)数据的变量关系研究, 必须借助多层模型来实现。两层模型中, 层一自变量Xij按组均值中心化, 并将组均值 置于层2截距方程式中, 可将Xij对因变量Yij的效应分解为组间和组内部分, 二者之差被称为情境效应, 称为情境变量。多层结构方程模型(MSEM)将多层线性模型(MLM)和结构方程模型(SEM)相结合, 通过设置潜变量和多指标的方法校正了MLM在情境效应分析中出现的抽样误差和测量误差, 同时解决了数据的多层(嵌套)结构和潜变量的估计问题。除了分析原理的说明, 还以班级平均竞争氛围对学生竞争表现的情境效应为例进行分析方法的示范, 并比较MSEM和MLM的异同, 随后展望了MSEM情境效应模型、情境效应无偏估计方法和情境变量研究的拓展方向。     

结构方程模型的应用及分析策略

差不多所有心理、教育、社会等概念,均难以直接准确测量,结构方程（SEM,Struc－tulalEquationModelling）提供一个处理测量误差的方法,采用多个指标去反映潜在变量,也令估计整个模型因子间关系,较传统回归方法更为准确合理。本文主要用一系列有关学习动机的虚拟例子,指出每个问题的主要分析策略,以展示SEM在教育及心理学可以应用的研究范畴。文内探讨的方法包括：验证性因素、高阶因子、路径及因果分析、多时段（multiwave）设计、单形模型（SimpleModel）、及多组比较等。     

3～5岁儿童表面与结构相似性类比推理的实验研究

采用经典类比推理问题对3~5岁儿童表面相似性与结构相似性类比推理能力进行了实验研究。结果发现:(1)表面相似性与结构相似性对儿童解决类比问题的影响不同。一般来说,前者更有利于3~5岁儿童类比问题的解决;(2)儿童解决表面相似性与结构相似性类比问题的能力随年龄增长而发展,但发展趋势不同。在表面相似性问题方面,4岁以前发展较慢,而4岁以后的发展较快;在结构相似性问题方面,4岁以前发展较快,而4岁以后的发展较慢。4岁是儿童解决类比推理问题能力的转折点;(3)在本实验条件下,工作记忆负荷对儿童解决表面相似性类比问题与结构相似性类比问题的影响不显著;(4)类比映射关系提示能促进儿童解决两类类比问题,但对不同年龄儿童的作用是非线性的。     

Modeling the effects of within-person characteristic and goal-level attributes on personal project pursuit over time

The current study aims to overcome methodological constraints of previous goal pursuit research by exploring how people simultaneously rely upon person-level characteristic adaptations and unique goal-level attributes to pursue their personal projects. Undergraduate participants identified 10 projects they would pursue over an academic quarter and rated project meaningfulness, effort exerted, patience employed, and progress satisfaction at five time points. Multilevel structural equation models revealed the relative influence of person-level and project-level attributes on project appraisals. Person-level adaptations accounted for a large portion of variance in project pursuit appraisals, though significant project-specific trends were found over time as well, including mutual positive cross-lag influences between meaning, patience, and effort, and a negative predictive effect of progress satisfaction on meaning.     

English validation of the short form of the Physical Self-Inventory (PSI-S)

The study examined the psychometric properties of scores on the English version of the short Physical Self-Inventory (PSI-S) among 1368 English-Speaking and 224 French-Speaking adolescents. Participants completed the original PSI-S and a revised version including positively-worded reformulations of the original negatively-worded items. The results supported the a priori factor structure, composite reliability, and test-retest reliability (7–8 months) of scores on the revised PSI-S, and its equivalence with the French version. Compared to confirmatory factor analyses, exploratory structural equation modeling resulted in a superior solution, and more cleanly differentiated factors. PSI-S responses were longitudinally invariant across 7–8 months, and presented no evidence of differential item functioning as a function of age, gender, body mass index (BMI) and physical activity. The results showed latent mean differences as a function of gender, BMI (linear and curvilinear effects), and physical activity, supporting the criterion-related validity of the PSI-S factors.     

简单算术对汉语特定句型的结构启动

结构启动是指个体在产生或理解一个结构时,倾向于重复使用或仿照刚刚加工过的结构。与句法启动不同的是,结构启动除了以句子,也可以以其它抽象结构作为启动项。Scheepers et al. (2011) 首次以具有结构偏向性的抽象算式作为启动项,证实了高附着和低附着的简单算式对英语母语者产出高附着和低附着关系从句具有结构启动效应。在此基础上,本研究引用数学算式作为启动项,以汉语特定句型结构NP1+有+NP2+恨+(AP)为切入点,分别选取汉语成人和儿童受试,检验简单算式对汉语母语者产出或理解特定句型的结构启动效应。本研究发现：1）算式结构对汉语特定句型的产出具有结构启动效应。2）算式结构对汉语歧义句的解读具有结构启动效应。3）儿童和成人在产出和理解的启动实验中具有不同结构启动效应,且儿童的结构启动效应比成人的结构启动效应更强。4）算式结构和语言句式在认知上具有共享的结构表征。     

A penalized likelihood method for multi-group structural equation modelling

In the past two decades, statistical modelling with sparsity has become an active research topic in the fields of statistics and machine learning. Recently, Huang, Chen and Weng (2017, Psychometrika, 82, 329) and Jacobucci, Grimm, and McArdle (2016, Structural Equation Modeling: A Multidisciplinary Journal, 23, 555) both proposed sparse estimation methods for structural equation modelling (SEM). These methods, however, are restricted to performing single-group analysis. The aim of the present work is to establish a penalized likelihood (PL) method for multi-group SEM. Our proposed method decomposes each group model parameter into a common reference component and a group-specific increment component. By penalizing the increment components, the heterogeneity of parameter values across the population can be explored since the null group-specific effects are expected to diminish. We developed an expectation-conditional maximization algorithm to optimize the PL criteria. A numerical experiment and a real data example are presented to demonstrate the potential utility of the proposed method.     

SWLS网络测验中可靠性变量的测量不变性检验

测量不变性在自我报告问卷或量表的心理测量应用中非常重要,是跨组比较的前提条件。测量不变性检验模型包括无任何约束的分组验证性因素分析(M_group)、形态的不变性(M1)、负荷的不变性(M2)、截距的不变性(M3)、严格不变性(M4)、因子方差-协方差的不变性(M5)以及潜均值的不变性(M6)。以生活满意度量表(SWLS)为例,针对1343名大学生(年龄17-25岁,20.01±1.53),进行有急事需要处理(否vs.是),答题时感受(积极情绪vs.消极情绪),噪音水平(无噪音vs.有噪音),答题用时(长vs.短),性别(男vs.女),户口(非农业户口vs.农业户口)等不同组别的生活满意度量表(SWLS)完全因素不变性检验。结果表明:(1)是否有急事需要处理的不变性成立(Δχ²=0.49～10.59,p>0.05);(2)答题时感受不变性部分成立,M5、M6模型不变性不成立(Δχ²₍₁=3.96、20.89,p<0.05);(3)噪音水平不变性部分成立,M3与M4模型不变性检验不成立(Δχ²₍₄₎=14.75,Δχ²₍₅₎=23.91,p<0.05);(4)答题用时不变性不成立(Δχ²=11.01～41.95,均p<0.05);(5)性别的不变性部分成立,M4模型不变性检验不成立(Δχ²₍₅₎=64.40,p<0.05);(6)户口的不变性部分成立,M6模型不变性检验不成立(Δχ²₍₁₎=11.49,p<0.05)。