结构方程模型(SEM)的原理及操作

结构方程模型(SEM)是应用线性方程系统表示观测变量与潜在变量之间及潜在变量之间关系的一种统计方法。当前，SEM及相应的LISREL软件已成为心理学等社会学科中广泛应用的一种分析思想和技术。文章简要介绍了SEM的特点、原理及LISREL的操作方法。     

数学核心素养测评之结构方程模型——探析数学学习态度、习惯、动机对数学核心素养形成与发展的影响

<正>结构方程模型（SEM）是基于变量的协方差矩阵，分析变量之间关系的一种统计方法。它融合了因素分析与路径分析两种统计技术，可以帮助我们分析各变量之间的因果关系、中介效应等，并利用图形化模型清晰呈现变量间的关系，为问题的解决提供可参考的框架和方案。数学学习态度、数学学习习惯、数学学习动机能够综合反映学生真实的学习状态和学习效果，影响学生的数学学习体验、数学学习情感及自我价值实现，进而影响学生数学核心素养的形成与发展。     

利用EXCEL进行一元回归分析

回归分析法是研究相关关系的一种数量统计方法。它是通过一定的相关关系方程表达式来研究变量之间的密切程度，从而可从一个变量或几个变量的取值去预测另一个变量取值的一种定量预测方法。这是我们在定量预测时用得最多的一种方法，根据自变量的多少可分为一元回归分析、二顺归分析和多元回归分析。最常用的是一元回归分析。     

利用EXCEL进行一元回归分析

回归分析法是研究相关关系的一种数量统计方法。它是通过一定的相关关系方程表达式来研究变量之间的密切程度,从而可从一个变量或几个变量的取值去预测另一个变量取值的一种定量预测方法。这是我们在定量预测时用得最多的一种方法,根据自变量的多少可分为一元回归分析、二元回归分析和多元回归分析。最常用的是一元回归分析。一元回归分析的基本模型为:Y=a bX其中:X为自变量,Y为因变量,a为常数项,表示回归直线的截距,b为回归系数,表示回归直线的斜率。a、b可用最小二乘法求得:     

结构方程模型在科研机构评估中的应用

该文提出利用含有潜变量和显变量的结构方程模型对科研机构进行评估。该方法一方面能揭示潜变量与其显变量之间的关系，另一方面还可分析出各潜变量与综合评价指数之间的联系。该文以重点实验室的评估为例，采用了反映研究成果、承担经费、人才培养、队伍建设和开放交流的5个变量组，利用结构方程模型建立科研机构综合评估指数，对各实验室进行排序。     

自媒体时代的网络暴力群体极化效应成因研究:结构方程模型的证据分析

通过整合引入三元交互理论、理性行为理论、归因理论探究相关外部变量,最终完成网络暴力行为群体极化效应模型.模型所包含的潜变量有:网络环境、态度观点、主观规范、道德情绪、行为意向和实际行为.利用结构方程模型方法研究各潜变量间的假设关系.在挖掘网络暴力群体极化效应影响因素之间多层复杂相互作用关系的基础上,运用结构方程方法进行模型检验,针对性地提出相应的治理对策.     

基于结构方程的高校学生满意度实证研究——以江苏省八所高校本科生为例

本研究以835名本科生满意度调查数据为依据,基于结构方程模型方法,探索构建了包含6个潜变量、18个标识变量和15种假设关系的高校学生满意度模型。通过因子分析及潜变量的路径关系分析发现:在整个满意度测评体系中,学生的个体发展在学生期望和质量感知中的方差贡献率较大。在模型各变量的相互关系中,高校形象是唯一对其他潜变量都产生效应的变量,学生期望对其他潜变量的影响不大,学生质量感知对学生感知价值、学生满意和学校忠诚有非常重要的影响。关注学生个性发展,注重品牌形象建设,提高软性服务水平,是增强高校在教育市场中的竞争力和实现可持续发展的重要策略。     

建设项目组织文化对组织集成影响的研究

文中运用实证研究的方法探讨组织文化对组织集成的影响关系.使用社会学统计软件SPSS11.50进行探索性因子分析确定项目组织文化的维度,由此建立组织文化结构模型,运用结构分析软件AMOS18.0对潜在变量之间的关系进行路径分析和修正,验证了自变量、中介变量、因变量之间的影响关系.     

基于结构方程的开放大学网上学习满意度研究

如何度量网上学习的满意度水平,已成为开放大学建设过程中亟待解决的问题。本文将结构方程模型方法应用于开放大学满意度研究,构建了满意度模型,利用结构方程分析软件对学生网上学习数据进行了模型验证分析和路径分析,研究潜变量及各变量间的相关性,并依据实证分析得出研究结论,提出改进建议。     

结构方程模型视角下的远程学习者满意度实证研究

学生满意对一个学校的发展至关重要.本文旨在揭示学生期望、感知质量、感知价值、学生满意、学生忠诚五个潜变量之间的结构关系.首先在前人研究的基础上,构建一个反映五个潜变量关系的结构方程模型,然后通过问卷设计、量表开发,对电大学生展开调查得到实际数据,再对量表数据进行缺失值处理,并据此对提出的结构方程模型进行拟合、修正和解释,最后得出潜变量之间关系的几点结论.     

The Best of Both Worlds: Factor Analysis of Dichotomous Data Using Item Response Theory and Structural Equation Modeling

Both structural equation modeling (SEM) and item response theory (IRT) can be used for factor analysis of dichotomous item responses. In this case, the measurement models of both approaches are formally equivalent. They were refined within and across different disciplines, and make complementary contributions to central measurement problems encountered in almost all empirical social science research fields. In this article (a) fundamental formal similiarities between IRT and SEM models are pointed out. It will be demonstrated how both types of models can be used in combination to analyze (b) the dimensional structure and (c) the measurement invariance of survey item responses. All analyses are conducted with Mplus, which allows an integrated application of both approaches in a unified, general latent variable modeling framework. The aim is to promote a diffusion of useful measurement techniques and skills from different disciplines into empirical social research.     

Measuring authoritative teaching

High quality measurements are important to evaluate interventions. The study reports on the development of a measurement to investigate authoritative teaching understood as a two-dimensional construct of warmth and control. Through the application of confirmatory factor analysis (CFA) and structural equation modelling (SEM) the factor structure and measurement invariance is investigated. Generally, results suggest that the two-dimensional model of authoritative teaching has satisfactory psychometric properties for longitudinal measurement invariance, ensuring the measurement of the same concept over time. Different types of missing data in this study are discussed. Also, the relevance of such study for professional development is addressed.     

Multiple-Group Analysis for Structural Equation Modeling With Dependent Samples

Multigroup structural equation modeling (SEM) plays a key role in studying measurement invariance and in group comparison. However, existing methods for multigroup SEM assume that different samples are independent. This article develops a method for multigroup SEM with correlated samples. Parallel to that for independent samples, the focus here is on the cross-group stability of the within-group structure and parameters. In particular, the method does not require the specification of any between-group relationship. Rescaled and adjusted statistics as well as sandwich-type covariance matrices make the developed method work for possibly nonnormal variables with finite 4th-order moments. The method is applied to a longitudinal data set on the development of entrepreneurial teams across 4 phases. Detailed analysis is provided regarding the stability of the effect of psychological compatibility on team performance, as it is mediated by fairness perception and team cohesion.     

A Comparison of Method Effects in Two Confirmatory Factor Models for Structurally Different Methods

It is well known that measurement error in observable variables induces bias in estimates in standard regression analysis and that structural equation models are a typical solution to this problem. Often, multiple indicator equations are subsumed as part of the structural equation model, allowing for consistent estimation of the relevant regression parameters. In many instances, however, embedding the measurement model into structural equation models is not possible because the model would not be identified. To correct for measurement error one has no other recourse than to provide the exact values of the variances of the measurement error terms of the model, although in practice such variances cannot be ascertained exactly, but only estimated from an independent study. The usual approach so far has been to treat the estimated values of error variances as if they were known exact population values in the subsequent structural equation modeling (SEM) analysis. In this article we show that fixing measurement error variance estimates as if they were true values can make the reported standard errors of the structural parameters of the model smaller than they should be. Inferences about the parameters of interest will be incorrect if the estimated nature of the variances is not taken into account. For general SEM, we derive an explicit expression that provides the terms to be added to the standard errors provided by the standard SEM software that treats the estimated variances as exact population values. Interestingly, we find there is a differential impact of the corrections to be added to the standard errors depending on which parameter of the model is estimated. The theoretical results are illustrated with simulations and also with empirical data on a typical SEM model.     

区域可持续发展预警系统研究

从系统观的角度出发，研究了区域可持续发展预警系统及其结构的设计问题，提出将预警活动引入区域可持续发展的活动中来，并从理论上设计了预警系统的结构及其衡量与评价模型，得出一套综合评价和测算的方案（常权、递增权、层次分析和变权），为今后的实际建模奠定了坚实的基础。     

Measurement Bias in Multilevel Data

Measurement bias can be detected using structural equation modeling (SEM), by testing measurement invariance with multigroup factor analysis (Jöreskog, 1971;Meredith, 1993;Sörbom, 1974) MIMIC modeling (Muthén, 1989) or restricted factor analysis (Oort, 1992,1998). In educational research, data often have a nested, multilevel structure, for example when data are collected from children in classrooms. Multilevel structures might complicate measurement bias research. In 2-level data, the potentially “biasing trait” or “violator” can be a Level 1 variable (e.g., pupil sex), or a Level 2 variable (e.g., teacher sex). One can also test measurement invariance with respect to the clustering variable (e.g., classroom). This article provides a stepwise approach for the detection of measurement bias with respect to these 3 types of violators. This approach works from Level 1 upward, so the final model accounts for all bias and substantive findings at both levels. The 5 proposed steps are illustrated with data of teacher–child relationships.     

工程电流源综述

在信号变换及电子测量中，电流源应用广泛，其性能优劣及结构特点对测量结果有直接影响。因此，在电子测量控制工程设计中应正确选用电流源电路结构及参数。     

Comparing Exploratory Structural Equation Modeling and Existing Approaches for Multiple Regression with Latent Variables

Exploratory structural equation modeling (ESEM) is an approach for analysis of latent variables using exploratory factor analysis to evaluate the measurement model. This study compared ESEM with two dominant approaches for multiple regression with latent variables, structural equation modeling (SEM) and manifest regression analysis (MRA). Main findings included: (1) ESEM in general provided the least biased estimation of the regression coefficients; SEM was more biased than MRA given large cross-factor loadings. (2) MRA produced the most precise estimation, followed by ESEM and then SEM. (3) SEM was the least powerful in the significance tests; statistical power was lower for ESEM than MRA with relatively small target-factor loadings, but higher for ESEM than MRA with relatively large target-factor loadings. (4) ESEM showed difficulties in convergence and occasionally created an inflated type I error rate under some conditions. ESEM is recommended when non-ignorable cross-factor loadings exist.     

依托自制仪器对公差实验进行OBE改造的探索

研制开发了模块化径向跳动自动检测仪,并依托模块化径向跳动检测仪,基于OBE理念对齿轮齿圈径向跳动测量实验进行了综合性设计与改造升级,包括实验教学目标设计、自主实验教学模块设计、问题导向的渐进式实验教学方法等。通过学生过程表现分析、实验报告分析和问卷调查分析等对径向跳动测量综合实验成效进行评价与改进,使得传统实验的OBE改造初见成效。