Structural Equation Modeling: A Multidisciplinary Journal最新文献_第8页

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

引用次数: 0

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

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

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

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

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":"AbstractCausal 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.","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

引用次数: 1

Evaluating the Performance of the LI3P in Latent Profile Analysis Models 评价LI3P在潜在剖面分析模型中的性能

IF 6 2区心理学

Structural Equation Modeling: A Multidisciplinary Journal Pub Date : 2023-08-22 DOI: 10.1080/10705511.2023.2238902

Russell P. Houpt, Kevin J. Grimm, Aaron T. McLaughlin, D. V. Van Tongeren

引用次数: 0

Latent Class Analysis with Measurement Invariance Testing: Simulation Study to Compare Overall Likelihood Ratio vs Residual Fit Statistics Based Model Selection 潜在类分析与测量不变性检验:模拟研究比较整体似然比与残差拟合统计基于模型选择