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Structural Equation Modeling: A Multidisciplinary Journal最新文献

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Review of Handbook of Structural Equation Modeling (2nd ed.) 结构方程建模手册(第二版)
IF 6 2区 心理学
Structural Equation Modeling: A Multidisciplinary Journal Pub Date : 2023-10-12 DOI: 10.1080/10705511.2023.2257890
Jam Khojasteh, Ademola Ajayi
{"title":"Review of Handbook of Structural Equation Modeling (2nd ed.)","authors":"Jam Khojasteh, Ademola Ajayi","doi":"10.1080/10705511.2023.2257890","DOIUrl":"https://doi.org/10.1080/10705511.2023.2257890","url":null,"abstract":"Published in Structural Equation Modeling: A Multidisciplinary Journal (Ahead of Print, 2023)","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"20 23","pages":""},"PeriodicalIF":6.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50164683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
The Sensitivity of Bayesian Fit Indices to Structural Misspecification in Structural Equation Modeling 结构方程建模中贝叶斯拟合指标对结构错配的敏感性
IF 6 2区 心理学
Structural Equation Modeling: A Multidisciplinary Journal Pub Date : 2023-10-12 DOI: 10.1080/10705511.2023.2253497
Chunhua Cao, Benjamin Lugu, Jujia Li
{"title":"The Sensitivity of Bayesian Fit Indices to Structural Misspecification in Structural Equation Modeling","authors":"Chunhua Cao, Benjamin Lugu, Jujia Li","doi":"10.1080/10705511.2023.2253497","DOIUrl":"https://doi.org/10.1080/10705511.2023.2253497","url":null,"abstract":"This study examined the false positive (FP) rates and sensitivity of Bayesian fit indices to structural misspecification in Bayesian structural equation modeling. The impact of measurement quality,...","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"20 22","pages":""},"PeriodicalIF":6.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50164685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Comparing Methods for Factor Score Estimation in Structural Equation Modeling: The Role of Network Analysis 结构方程建模中因子得分估计方法的比较：网络分析的作用
IF 6 2区 心理学
Structural Equation Modeling: A Multidisciplinary Journal Pub Date : 2023-10-12 DOI: 10.1080/10705511.2023.2253496
Jinying Ouyang, Zhehan Jiang, Christine DiStefano, Junhao Pan, Yuting Han, Lingling Xu, Dexin Shi, Fen Cai
{"title":"Comparing Methods for Factor Score Estimation in Structural Equation Modeling: The Role of Network Analysis","authors":"Jinying Ouyang, Zhehan Jiang, Christine DiStefano, Junhao Pan, Yuting Han, Lingling Xu, Dexin Shi, Fen Cai","doi":"10.1080/10705511.2023.2253496","DOIUrl":"https://doi.org/10.1080/10705511.2023.2253496","url":null,"abstract":"Precisely estimating factor scores is challenging, especially when models are mis-specified. Stemming from network analysis, centrality measures offer an alternative approach to estimating the scor...","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"88 21","pages":""},"PeriodicalIF":6.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71435580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Recommended Practices in Latent Class Analysis Using the Open-Source R-Package tidySEM 使用开源r包tidySEM进行潜在类分析的推荐实践
2区 心理学
Structural Equation Modeling: A Multidisciplinary Journal Pub Date : 2023-10-09 DOI: 10.1080/10705511.2023.2250920
C. J. Van Lissa, M. Garnier-Villarreal, D. Anadria
{"title":"Recommended Practices in Latent Class Analysis Using the Open-Source R-Package tidySEM","authors":"C. J. Van Lissa, M. Garnier-Villarreal, D. Anadria","doi":"10.1080/10705511.2023.2250920","DOIUrl":"https://doi.org/10.1080/10705511.2023.2250920","url":null,"abstract":"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.","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135093029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Improving the Statistical Performance of Oblique Bifactor Measurement and Predictive Models: An Augmentation Approach 提高倾斜双因子测量和预测模型的统计性能:一种增强方法
IF 6 2区 心理学
Structural Equation Modeling: A Multidisciplinary Journal Pub Date : 2023-10-09 DOI: 10.1080/10705511.2023.2222229
Bo Zhang, Jing Luo, Susu Zhang, Tianjun Sun, Don C. Zhang
{"title":"Improving the Statistical Performance of Oblique Bifactor Measurement and Predictive Models: An Augmentation Approach","authors":"Bo Zhang, Jing Luo, Susu Zhang, Tianjun Sun, Don C. Zhang","doi":"10.1080/10705511.2023.2222229","DOIUrl":"https://doi.org/10.1080/10705511.2023.2222229","url":null,"abstract":"Oblique bifactor models, where group factors are allowed to correlate with one another, are commonly used. However, the lack of research on the statistical properties of oblique bifactor models ren...","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"20 9","pages":""},"PeriodicalIF":6.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50164718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Comparing MIMIC and MIMIC-interaction to Alignment Methods for Investigating Measurement Invariance concerning a Continuous Violator 研究连续违规者测量不变性的MIMIC和mimi -交互比对方法
IF 6 2区 心理学
Structural Equation Modeling: A Multidisciplinary Journal Pub Date : 2023-09-26 DOI: 10.1080/10705511.2023.2240517
Yuanfang Liu, Mark H. C. Lai, Ben Kelcey
{"title":"Comparing MIMIC and MIMIC-interaction to Alignment Methods for Investigating Measurement Invariance concerning a Continuous Violator","authors":"Yuanfang Liu, Mark H. C. Lai, Ben Kelcey","doi":"10.1080/10705511.2023.2240517","DOIUrl":"https://doi.org/10.1080/10705511.2023.2240517","url":null,"abstract":"Measurement invariance holds when a latent construct is measured in the same way across different levels of background variables (continuous or categorical) while controlling for the true value of ...","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"17 4","pages":""},"PeriodicalIF":6.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50164989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Performance of Estimation Methods in Bifactor Models with Ordered Categorical Data 有序分类数据双因子模型估计方法的性能
IF 6 2区 心理学
Structural Equation Modeling: A Multidisciplinary Journal Pub Date : 2023-09-26 DOI: 10.1080/10705511.2023.2247567
Ismail Cuhadar, Ömür Kaya Kalkan
{"title":"Performance of Estimation Methods in Bifactor Models with Ordered Categorical Data","authors":"Ismail Cuhadar, Ömür Kaya Kalkan","doi":"10.1080/10705511.2023.2247567","DOIUrl":"https://doi.org/10.1080/10705511.2023.2247567","url":null,"abstract":"Simulation studies are needed to investigate how many score categories are sufficient to treat ordered categorical data as continuous, particularly for bifactor models. The current simulation study...","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"17 6","pages":""},"PeriodicalIF":6.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50164987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Comparing Factor Score Approaches to SEM in Multigroup Models with Small Samples 小样本多组模型中SEM的因子评分方法比较
2区 心理学
Structural Equation Modeling: A Multidisciplinary Journal Pub Date : 2023-09-26 DOI: 10.1080/10705511.2023.2243387
Emma Somer, Carl Falk, Milica Miočević
{"title":"Comparing Factor Score Approaches to SEM in Multigroup Models with Small Samples","authors":"Emma Somer, Carl Falk, Milica Miočević","doi":"10.1080/10705511.2023.2243387","DOIUrl":"https://doi.org/10.1080/10705511.2023.2243387","url":null,"abstract":"AbstractFactor Score Regression (FSR) is increasingly employed as an alternative to structural equation modeling (SEM) in small samples. Despite its popularity in psychology, the performance of FSR in multigroup models with small samples remains relatively unknown. The goal of this study was to examine the performance of FSR, namely Croon’s correction and the bias avoiding method, for multigroup models with small samples and compare the methods to SEM. We conducted two simulation studies to evaluate how the sample size, proportion of invariant items, reliability, number of indicators, and measurement model misspecifications affect conclusions about the structural relationships in multigroup models. Additionally, we extended the methods to a multigroup actor-partner interdependence model. Results suggest that Croon’s correction generally outperforms conventional SEM and the bias avoiding method in terms of bias, efficiency, Type I error, and coverage, especially in more complex multigroup models and under difficult estimation conditions.Keywords: Croon’s correctionfactor score regressionmultigroup modelssmall samplesstructural equation modeling Disclosure statementNo potential conflict of interest was reported by the authors.Notes1 https://osf.io/fcujz/.2 When a different identification strategy was used in Study 1, factor reflection was detected less than 1% of the time. Factor reflection was identified by evaluating whether the average value of the loadings for the exogenous and endogenous variable items was of opposite signs. In these cases, the sign of the structural path estimate was flipped, and bias and coverage were recomputed. We provide supplemental files with the results from our factor reflection analysis. The pattern of results was consistent with those presented in the main text.","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134885618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Causal Mediation Analysis for an Ordinal Outcome with Multiple Mediators 多重中介对有序结果的因果中介分析
IF 6 2区 心理学
Structural Equation Modeling: A Multidisciplinary Journal Pub Date : 2023-09-15 DOI: 10.1080/10705511.2022.2148674
Yuejin Zhou, Wenwu Wang, Tao Hu, Tiejun Tong, Zhonghua Liu
{"title":"Causal Mediation Analysis for an Ordinal Outcome with Multiple Mediators","authors":"Yuejin Zhou, Wenwu Wang, Tao Hu, Tiejun Tong, Zhonghua Liu","doi":"10.1080/10705511.2022.2148674","DOIUrl":"https://doi.org/10.1080/10705511.2022.2148674","url":null,"abstract":"<p><b>Abstract</b></p><p>Causal mediation analysis is a popular approach for investigating whether the effect of an exposure on an outcome is through a mediator to better understand the underlying causal mechanism. In recent literature, mediation analysis with multiple mediators has been proposed for continuous and dichotomous outcomes. In contrast, methods for mediation analysis for an ordinal outcome are still underdeveloped. In this paper, we first review mediation analysis methods with a continuous mediator for an ordinal outcome and then develop mediation analysis with a binary mediator for an ordinal outcome. We further consider multiple mediators for an ordinal outcome in the counterfactual framework and provide identification assumptions for identifying the mediation effects. Under the identification assumptions, we propose a regression-based method to estimate the mediation effects through multiple mediators while allowing the presence of exposure-mediator interactions. The closed-form expressions of mediation effects are also obtained for three scenarios: multiple continuous mediators, multiple binary mediators, and multiple mixed mediators. We conduct simulation studies to assess the finite sample performance of our new methods and present the biases, standard errors, and confidence intervals to demonstrate that our proposed estimators perform well in a wide range of practical settings. Finally, we apply our proposed methods to assess the mediation effects of candidate DNA methylation CpG sites in the causal pathway from socioeconomic index to body mass index.</p>","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"13 6","pages":""},"PeriodicalIF":6.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50164780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Deep Learning Generalized Structured Component Analysis: An Interpretable Artificial Neural Network Model with Composite Indexes 深度学习广义结构化成分分析:一种可解释的复合指标人工神经网络模型
IF 6 2区 心理学
Structural Equation Modeling: A Multidisciplinary Journal Pub Date : 2023-08-25 DOI: 10.1080/10705511.2023.2234086
Gyeongcheol Cho, Heungsun Hwang
{"title":"Deep Learning Generalized Structured Component Analysis: An Interpretable Artificial Neural Network Model with Composite Indexes","authors":"Gyeongcheol Cho, Heungsun Hwang","doi":"10.1080/10705511.2023.2234086","DOIUrl":"https://doi.org/10.1080/10705511.2023.2234086","url":null,"abstract":"","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"53 1","pages":""},"PeriodicalIF":6.0,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73856665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
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