结构方程建模：多学科期刊出版所有对结构方程建模感兴趣的学科的学术著作。这些学科包括但不限于心理学、医学、社会学、教育学、政治学、经济学、管理学和商业/市场营销。理论文章涉及新的发展；应用文章涉及创新的结构方程建模应用；教师角提供有关结构方程建模方面的教学模块；书籍和软件评论检查新的建模信息和技术；和广告提醒读者注意新产品。鼓励对期刊发表的文章或评论中涉及的技术或实质性问题发表评论；评论被审查，并邀请原创作品的作者做出回应。

Structural Equation Modeling is a multidisciplinary journal dedicated to academic research in the field of structural equation modeling. The journal welcomes contributions from various disciplines, including but not limited to psychology, medicine, sociology, education, political science, economics, management, and business/marketing. The content includes theoretical articles discussing new developments in the field; applied articles showcasing innovative applications of structural equation modeling; teaching modules in the 'Teacher's Corner' related to structural equation modeling; book and software reviews evaluating new modeling information and techniques; and advertisements alerting readers to new products. We encourage feedback on the technical or substantive issues raised in the articles or comments published in the journal; all feedback will be reviewed, and authors of original works will be invited to respond.

《Structural Equation Modeling》是一本多学科期刊，专注于结构方程建模领域的学术研究。该期刊欢迎来自心理学、医学、社会学、教育学、政治学、经济学、管理学以及商业/市场营销等多个学科的研究成果。期刊内容包括理论文章，探讨该领域的新发展；应用文章，展示结构方程建模的创新应用；教师角，提供关于结构方程建模的教学模块；书籍和软件评论，评估新的建模信息和技术；以及广告，提醒读者关注新产品。我们鼓励读者对期刊发表的文章或评论中涉及的技术或实质性问题进行反馈；所有评论将经过审查，并邀请原创作品的作者进行回应。

Structural Equation Modeling

Abstract The assessment of mediation in dyadic data is an important issue if researchers are to test process models. Using an extended version of the actor–partner interdependence model the estimation and testing of mediation is complex, especially when dyad members are distinguishable (e.g., heterosexual couples). We show how the complexity of the model can be reduced by assuming specific dyadic patterns. Using structural equation modeling, we demonstrate how specific mediating effects and contrasts among effects can be tested by phantom models that permit point and bootstrap interval estimates. We illustrate the assessment of mediation and the strategies to simplify the model using data from heterosexual couples. Keywords: mediationdyadic dataAPIMphantom modelsbootstrapping

Thomas Ledermann

Siegfried Macho

David A. Kenny

Structural Equation Modeling A Multidisciplinary Journal

Structural Equation Modeling: A Multidisciplinary Journal

Assessing Mediation in Dyadic Data Using the Actor-Partner Interdependence Model

评估使用行动者-伙伴互依模型在二元数据中的中介作用

The trait-state-occasion model (TSO) is a popular model within the latent state-trait theory (LST). The TSO allows distinguishing the trait and the state components of the psychological constructs measured in longitudinal data, while also taking into account the carry-over effects between consecutive measurements. In the present study, we extend a multilevel version of the TSO model to allow for the combination of fixed and random situations, namely the mixed-effects TSO (ME-TSO). Hence, the ME-TSO model is a measurement model suitable to analyze intensive longitudinal data that allows studying the psychometric properties of the indicators per individual, the heterogeneity of psychological dynamics, and the person–situation interaction effects. We showcase how to use the model by analyzing the items of positive affect activation of the crowdsourcing study HowNutsAreTheDutch (HoeGekisNL).

Sebastian Castro-Alvarez

Jorge N. Tendeiro

Peter de Jonge

Rob R. Meijer

Laura F. Bringmann

Mixed-Effects Trait-State-Occasion Model: Studying the Psychometric Properties and the Person–Situation Interactions of Psychological Dynamics

Latent class analysis (LCA) refers to techniques for identifying groups in data based on a parametric model. Examples include mixture models, LCA with ordinal indicators, and latent class growth analysis. Despite its popularity, there is limited guidance with respect to decisions that must be made when conducting and reporting LCA. Moreover, there is a lack of user-friendly open-source implementations. Based on contemporary academic discourse, this paper introduces recommendations for LCA which are summarized in the SMART-LCA checklist: Standards for More Accuracy in Reporting of different Types of Latent Class Analysis. The free open-source R-package package tidySEM implements the practices recommended here. It is easy for beginners to adopt thanks to user-friendly wrapper functions, and yet remains relevant for expert users as its models are integrated within the OpenMx structural equation modeling framework and remain fully customizable. The Appendices and tidySEM package vignettes include tutorial examples of common applications of LCA.

C. J. Van Lissa

M. Garnier-Villarreal

D. Anadria

Recommended Practices in Latent Class Analysis Using the Open-Source R-Package tidySEM

使用开源R包tidySEM进行潜在类别分析的推荐实践

This article examines the adequacy of the “rules of thumb” conventional cutoff criteria and several new alternatives for various fit indexes used to evaluate model fit in practice. Using a 2‐index presentation strategy, which includes using the maximum likelihood (ML)‐based standardized root mean squared residual (SRMR) and supplementing it with either Tucker‐Lewis Index (TLI), Bollen's (1989) Fit Index (BL89), Relative Noncentrality Index (RNI), Comparative Fit Index (CFI), Gamma Hat, McDonald's Centrality Index (Mc), or root mean squared error of approximation (RMSEA), various combinations of cutoff values from selected ranges of cutoff criteria for the ML‐based SRMR and a given supplemental fit index were used to calculate rejection rates for various types of true‐population and misspecified models; that is, models with misspecified factor covariance(s) and models with misspecified factor loading(s). The results suggest that, for the ML method, a cutoff value close to .95 for TLI, BL89, CFI, RNI, and G...

Li‐tze Hu

Peter M. Bentler

Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives

协方差结构分析中拟合指数的截断标准：传统标准与新替代方案

Abstract In this investigation, we used a classic latent profile analysis (LPA), a person-centered approach, to identify groups of students who had similar profiles for multiple dimensions of academic self-concept (ASC) and related these LPA groups to a diverse set of correlates. Consistent with a priori predictions, we identified 5 LPA groups representing a combination of profile level (high vs. low overall ASC) and profile shape (math vs. verbal self-concepts) that complemented results based on a traditional variable-centered approach. Whereas LPA groups were substantially and logically related to the set of 10 correlates, much of the predictive power of individual ASC factors was lost in the formation of groups and the inclusion of the correlates into the LPA distorted the nature of the groups. LPA issues examined include distinctions between quantitative (level) and qualitative (shape) differences in LPA profiles, goodness of fit and the determination of the number of LPA groups, appropriateness of correlates as covariates or auxiliary variables, and alternative approaches to present and interpret the results. ACKNOWLEDGMENTS Work on this investigation was conducted, in part, while Professor Herbert W. Marsh was a Visiting Scholar at the Center for Educational Research at the Max Planck Institute for Human Development in Berlin. This research was funded in part by a grant from the United Kingdom Economic and Social Research Council to the first author and support from the Max Planck Institute. The authors would like to thank Bengt Muthén, Gitta Lubke, Dena Pastor, John Uebersax, and Jacqueline Cheng for comments on earlier drafts of this article. Requests for further information about this investigation should be sent to Professor Herbert W. Marsh, Dept. of Education, University of Oxford, 15 Norham Gardens, Oxford OX2 6PY, UK; E-mail: herb.marsh@education.ox.ac.uk. Notes *p < .01. *p < .001 (df = 4).

Herbert W. Marsh

Oliver Lüdtke

Ulrich Trautwein

Alexandre J. S. Morin

Classical Latent Profile Analysis of Academic Self-Concept Dimensions: Synergy of Person- and Variable-Centered Approaches to Theoretical Models of Self-Concept

学术自我概念维度的经典潜在剖面分析：自我概念理论模型中人中心与变量中心方法的协同作用

Many software programs can be used to train psychology graduate students and researchers to perform various types of exploratory and confirmatory factor analyses. However, the mechanics of the model fitting process are generally opaque to the student or researcher when using these programs. We propose that spreadsheets can be a very effective tool for enabling psychology students to obtain a better understanding of what is "under the hood" of the factor analysis methods they are implementing with popular software packages such as SPSS, AMOS, R, or MPlus. In this paper we present an Excel workbook that illustrates exploratory factor analysis via the principal axis factoring method and maximum likelihood estimation. Varimax and oblimin rotation of factor loadings is also demonstrated. We also illustrate several different confirmatory factor analysis models: (i) the unidimensional model, (ii) the multiple uncorrelated factors model, (iii) the multiple correlated factors model, and (iv) the bifactor model. Although we are not advocating Excel spreadsheets as the method of choice for factor analysis, we believe they are excellent tools for teaching the mechanics of factor analysis to psychology graduate students and researchers.

Michael Brusco

Douglas Steinley

Ashley L. Watts

Teaching the Mechanics of Factor Analysis Using Excel Spreadsheets

使用Excel电子表格教授因子分析的力学原理

Penalized structural equation models (PSEM) is a powerful technique that unlocks a variety of new modeling frameworks. PSEM applications have been established previously for standard SEM and ESEM models. In this note we aim to extend these ideas to more general types of models such as finite mixture models, multilevel models as well as models with more general types of outcomes. Maximum likelihood and weighted least squares estimation methods naturally accommodate a penalty term. In Mplus 8.12 the PSEM methodology is implemented for all models that can be estimated with these two estimators. Therefore we can now easily combine the more general models with the features of PSEM such as EFA, Alignment, and parameter invariance. Some additional basic SEM applications are also included.

Tihomir Asparouhov

Bengt Muthén

Methodological Advances with Penalized Structural Equation Models

惩罚结构方程模型的方法论进展

Kjell S. Sluppaug

Mehmet Mehmetoglu

Matthias Mittner

<tt>modsem</tt> : An R Package for Estimating Latent Interactions and Quadratic Effects

<tt>modsem</tt>：用于估计潜在交互作用和二次效应的R包

Steffen Nestler

Alexander Robitzsch

Fitting Single- and Multiple-Indicator STARTS Models as Dynamic Structural Equation Models

拟合单指标和多指标STARTS模型的动态结构方程模型

Jiwon Kim

Tiffany A. Whittaker

Latent Growth Model with Categorical Indicators: Comparison of the Different Estimators

分类指标的潜在增长模型：不同估计方法的比较

Abbreviation	Structural Equation Modeling: A Multidisciplinary J.
Journal Impact	2.48
Quartiles(Global)	SOCIAL SCIENCES, MATHEMATICAL METHODS(Q1)

ISSN	1070-5511, 1532-8007
h-index	125

Publication Information	Publisher: Psychology Press Ltd，Publishing cycle: Quarterly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 1994，Proportion of original research papers: 100.00%，Self Citation Rate:12.00%， Gold OA Rate: 23.18%
Average review cycle	网友分享经验：>12周，或约稿
Average recruitment ratio	网友分享经验：较难

Structural Equation Modeling

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Assessing Mediation in Dyadic Data Using the Actor-Partner Interdependence Model

Mixed-Effects Trait-State-Occasion Model: Studying the Psychometric Properties and the Person–Situation Interactions of Psychological Dynamics

Recommended Practices in Latent Class Analysis Using the Open-Source R-Package tidySEM

Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives

Classical Latent Profile Analysis of Academic Self-Concept Dimensions: Synergy of Person- and Variable-Centered Approaches to Theoretical Models of Self-Concept

Teaching the Mechanics of Factor Analysis Using Excel Spreadsheets

Methodological Advances with Penalized Structural Equation Models

`modsem` : An R Package for Estimating Latent Interactions and Quadratic Effects

Fitting Single- and Multiple-Indicator STARTS Models as Dynamic Structural Equation Models

Latent Growth Model with Categorical Indicators: Comparison of the Different Estimators

Structural Equation Modeling

Journal Citation Format

A journal article with 1 author

A journal article with 2 authors

A journal article with 3 authors

A journal article with 5 or more authors

Books Citation Format

Thesis Citation Format

Web sites Citation Format

Patent Citation Format

Staying up late manually editing references? ivySCI automatically matches journals and helps you generate references with a single click.

Share Submission Experience

Assessing Mediation in Dyadic Data Using the Actor-Partner Interdependence Model

Mixed-Effects Trait-State-Occasion Model: Studying the Psychometric Properties and the Person–Situation Interactions of Psychological Dynamics

Recommended Practices in Latent Class Analysis Using the Open-Source R-Package tidySEM

Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives

Classical Latent Profile Analysis of Academic Self-Concept Dimensions: Synergy of Person- and Variable-Centered Approaches to Theoretical Models of Self-Concept

Teaching the Mechanics of Factor Analysis Using Excel Spreadsheets

Methodological Advances with Penalized Structural Equation Models

modsem : An R Package for Estimating Latent Interactions and Quadratic Effects

Fitting Single- and Multiple-Indicator STARTS Models as Dynamic Structural Equation Models

Latent Growth Model with Categorical Indicators: Comparison of the Different Estimators

`modsem` : An R Package for Estimating Latent Interactions and Quadratic Effects