Testing Multivariable Covariance Structure and Means Hypotheses via Structural Equation Modeling

A didactic collection of multivariate covariance and mean structure hypotheses is presented, which can be tested using structural equation modeling. The hypotheses reflect specific structures of the manifest covariance matrix or means, which are often of interest in social, behavioral, or educational research or represent assumptions of widely applied multivariate analysis methods. This large-sample method (a) is generally applicable with normal and nonnormal data (particularly with very large samples in the latter case), (b) can be considered complementing corresponding likelihood ratio tests in the nonnormality case with very large samples, and (c) is straightforwardly implemented in widely circulated structural modeling programs such as LISREL, EQS, AMOS, RAMONA, and SEPATH. The approach is illustrated using data from a two-group cognitive intervention study.     

A Systematic Evaluation and Comparison Between Exploratory Structural Equation Modeling and Bayesian Structural Equation Modeling

In this study, we contrast two competing approaches, not previously compared, that balance the rigor of CFA/SEM with the flexibility to fit realistically complex data. Exploratory SEM (ESEM) is claimed to provide an optimal compromise between EFA and CFA/SEM. Alternatively, a family of three Bayesian SEMs (BSEMs) replace fixed-zero estimates with informative, small-variance priors for different subsets of parameters: cross-loadings (CL), residual covariances (RC), or CLs and RCs (CLRC). In Study 1, using three simulation studies, results showed that (1) BSEM-CL performed more closely to ESEM; (2) BSEM-CLRC did not provide more accurate model estimation compared with BSEM-CL; (3) BSEM-RC provided unstable estimation; and (4) different specifications of targeted values in ESEM and informative priors in BSEM have significant impacts on model estimation. The real data analysis (Study 2) showed that the differences in estimation between different models were largely consistent with those in Study1 but somewhat smaller.     

基于结构方程模型的保险意识测度

文章采用福州市居民保险意识调查的样本数据,用因子分析法探究了影响居民保险意识的主要因素,用结构方程模型研究了各因素内部及相互间的关系,建立了居民保险意识指数的三级测量指标体系,测度出了居民保险意识的单项指数、影响因子指数和总指数。研究发现：居民保险意识由风险意识、保险认知、保险替代和保险情感4个因子构成,保险认知对保险意识的综合影响最大,保险情感次之,保险替代的影响稍弱,风险意识的总效应最小,表明普及保险知识和重塑行业诚信形象对民众保险意识的提高至关重要;以＂养儿防老＂为代表的传统保障模式的衰退正在成为民众保险意识提高的重要推动力量;民众风险意识不强,侥幸心理过重,将在长期内阻滞其保险意识的提高。     

The Role of Structural Equation Modeling in Scale Development

Across a variety of disciplines and areas of inquiry, reliable and valid measures are a cornerstone of quality research. This is the case because to have confidence in the findings of our studies, we must first have confidence in the quality of our measures. This article briefly reviews the literature on scale development and provides an empirical demonstration of the scale development process. The example considered is the development and validation of a condom influence strategy questionnaire-short form (CISQ-S), a scale to measure ways individuals persuade their partners to use condoms. A special focus is put on the unique contribution that structural equation modeling techniques, particularly confirmatory factor analysis, bring to scale development. Latent variable modeling and its applications to scale development are also considered. Suggestions and implications for scale developers are discussed.     

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。     

结构方程建模分析影响大学排名因素

对影响大学排名的师资、学科、经费、硬件因素进行分析,分别构建测量方程,这四因素对大学排名的影响构建结构方程,运用结构方程建模（SEM）,得到了影响大学排名因素的全模型,内容效度较高,由此模型得出结论：大学排名受师资力量影响最大。     

A Note on Structural Equation Modeling Estimates of Reliability

Reliability can be estimated using structural equation modeling (SEM). Two potential problems with this approach are that estimates may be unstable with small sample sizes and biased with misspecified models. A Monte Carlo study was conducted to investigate the quality of SEM estimates of reliability by themselves and relative to coefficient alpha. The SEM approach showed minimal bias when the model was correctly specified if items were relatively well defined by their underlying factor(s). They tended to demonstrate somewhat greater bias when the model was misspecified, particularly underspecified. Overall, SEM estimates were more stable than anticipated. Researchers are more likely to obtain accurate estimates of reliability using SEM by conducting large-sample studies with well-constructed scales and critically assessing model fit.     

C2STEM: a System for Synergistic Learning of Physics and Computational Thinking

Journal of Science Education and Technology - Synergistic learning combining computational thinking (CT) and STEM has proven to be an effective method for advancing learning and understanding in a...     

The Impact of Computational Experiment and Formative Assessment in Inquiry-Based Teaching and Learning Approach in STEM Education

In this study, an instructional design model, based on the computational experiment approach, was employed in order to explore the effects of the formative assessment strategies and scientific abilities rubrics on students’ engagement in the development of inquiry-based pedagogical scenario. In the following study, rubrics were used during the model development, based on prompts provided to students during the development of their models. Our results indicate that modelling is a process that needs sequencing and instructional support, in the form of rubrics, focused on the scientific abilities needed for the inquiry process. In this research, eighty (80) prospective primary school teachers participated, and the results of the research indicate that the development of inquiry-based scenario is strongly affected by the scientific abilities rubrics.     

结构方程模型在英语阅读语料库中的研究

该文从建立基础型英语阅读语料库(English Reading Corpus,ERC),然后采用结构方程模型(Structural EquationModeling,SEM)及语言统计学方法,从英语阅读语料库的语篇复杂度、学习者个体的信息获取水平及情感因素三方面进行了建模及相关关系的探索性研究,在数据统计和分析的基础上,找到了满足置信度及可拟合的数学模型,以期能对英语阅读教学和学习有所启示。在通过对SEM的ERC建模之后的数据进行全面、准确的统计分析,能够为提高英语阅读教学质量提供有价值的统计数据和分析资料。     

Comparisons of Structural Equation Modeling and Hierarchical Linear Modeling Approaches to Couples' Data

This article compares 2 statistical approaches for the analysis of data obtained from married couples. The article summarizes a current multilevel (or hierarchical) model that has demonstrated considerable utility in marital research; it also extends this formulation in several respects. This model is then respecified into a more familiar structural equation modeling (SEM) formulation, highlighting the similarities and the differences in the 2 approaches. Cross-sectional data on 348 American married couples is used to examine the influence of age, duration of marriage, and number of children on marital satisfaction. Results of the 2 sets of analyses yielded nearly identical findings. The strengths and possible extensions of the SEM approach are discussed.     

Comparison of Frequentist and Bayesian Regularization in Structural Equation Modeling

Research in regularization, as applied to structural equation modeling (SEM), remains in its infancy. Specifically, very little work has compared regularization approaches across both frequentist and Bayesian estimation. The purpose of this study was to address just that, demonstrating both similarity and distinction across estimation frameworks, while specifically highlighting more recent developments in Bayesian regularization. This is accomplished through the use of two empirical examples that demonstrate both ridge and lasso approaches across both frequentist and Bayesian estimation, along with detail regarding software implementation. We conclude with a discussion of future research, advocating for increased evaluation and synthesis across both Bayesian and frequentist frameworks.     

Applications of Multilevel Structural Equation Modeling to Cross-Cultural Research

Multilevel structural equation modeling (MSEM) has been proposed as an extension to structural equation modeling for analyzing data with nested structure. We have begun to see a few applications in cross-cultural research in which MSEM fits well as the statistical model. However, given that cross-cultural studies can only afford collecting data from a relatively small number of countries, the appropriateness of MSEM has been questioned. Using the data from the International Social Survey Program (1997; N = 15,244 from 27 countries), we first showed how Muth?n's MSEM procedure could be applied to a real data set on cross-cultural research. Given a small country-level sample size (27 countries) we then demonstrated that results on the individual level were quite stable even when using small individual-level sample sizes, whereas the group-level parameter estimates and their standard errors were affected unsystematically by varying individual-level sample sizes. Use of the findings for cross-cultural research and other areas with limited numbers of groups are discussed.     

Modeling of Nonrecursive Structural Equation Models With Categorical Indicators

Nonrecursive structural equation models generally take the form of feedback loops, involving 2 latent variables that are connected by 2 unidirectional paths, 1 starting with each variable and terminating in the other variable. Nonrecursive models belong to a larger class of path models that require the use of instrumental variables (IVs) to achieve model identification. Prior research has focused on SEM parameter estimation with IVs when indicators were continuous and normally distributed. Much less is known about how estimators function in the presence of categorical indicators, which are commonly used in the social sciences, such as with cognitive and affective instruments. In this study, there was specific interest in comparing the 2-stage least squares (2SLS) estimator and its categorical variant to other recommended estimators. This study compares the performance of several estimation approaches for fitting structural equation models with categorical indicator variables when IVs are necessary to obtain proper model estimates. Across conditions, 1 extension of the nonlinear 2SLS (N2SLS) approach, the nonlinear 3-stage least squares (N3SLS), which accounts for correlated errors among regressors within each model (as does the N2SLS), as well as correlations of errors across models, which N2SLS does not, appears to work the best among methods compared.     

Investigating Supervisory Relationships and Therapeutic Alliances Using Structural Equation Modeling

The authors used structural equation modeling to examine the contribution of supervisees' supervisory relationship levels to therapeutic alliance (TA) scores with their clients in practicum. Results showed that supervisory relationship scores positively contributed to the TA. Client and counselor ratings of the TA also differed.     

Structural Equation Modeling Diagnostics Using R Package semdiag and EQS

Yuan and Hayashi (2010) Yuan, K.-H. and Hayashi, K. 2010. Fitting data to model: Structural equation modeling diagnosis using two scatter plots. Psychological Methods, 15: 335–351. [Crossref], [Web of Science ®] , [Google Scholar] introduced 2 scatter plots for model and data diagnostics in structural equation modeling (SEM). However, the generation of the plots requires in-depth understanding of their underlying technical details. This article develops and introduces an R package semdiag for easily drawing the 2 plots. With a model specified in EQS syntax, one only needs to supply as few as 2 parameters to generate the 2 plots using the semdiag package. Two examples are provided to illustrate the use of the package. Multiple figures are used to explain the elements of data and model diagnostics. Advice on selecting proper estimation methods following the diagnostics is also given.     

Introduction to Structural Equation Modeling: Issues and Practical Considerations

Structural equation modeling (SEM) is a versatile statistical modeling tool. Its estimation techniques, modeling capacities, and breadth of applications are expanding rapidly. This module introduces some common terminologies. General steps of SEM are discussed along with important considerations in each step. Simple examples are provided to illustrate some of the ideas for beginners. In addition, several popular specialized SEM software programs are briefly discussed with regard to their features and availability. The intent of this module is to focus on foundational issues to inform readers of the potentials as well as the limitations of SEM. Interested readers are encouraged to consult additional references for advanced model types and more application examples.