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Structural Equation Modeling: A Multidisciplinary Journal最新文献

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Utilizing Moderated Non-linear Factor Analysis Models for Integrative Data Analysis: A Tutorial. 利用调节非线性因子分析模型进行综合数据分析：教程。
IF 6 2区 心理学
Structural Equation Modeling: A Multidisciplinary Journal Pub Date : 2023-01-01 Epub Date: 2022-05-23 DOI: 10.1080/10705511.2022.2070753
Joseph M Kush, Katherine E Masyn, Masoumeh Amin-Esmaeili, Ryoko Susukida, Holly C Wilcox, Rashelle J Musci
{"title":"Utilizing Moderated Non-linear Factor Analysis Models for Integrative Data Analysis: A Tutorial.","authors":"Joseph M Kush, Katherine E Masyn, Masoumeh Amin-Esmaeili, Ryoko Susukida, Holly C Wilcox, Rashelle J Musci","doi":"10.1080/10705511.2022.2070753","DOIUrl":"10.1080/10705511.2022.2070753","url":null,"abstract":"<p><p>Integrative data analysis (IDA) is an analytic tool that allows researchers to combine raw data across multiple, independent studies, providing improved measurement of latent constructs as compared to single study analysis or meta-analyses. This is often achieved through implementation of moderated nonlinear factor analysis (MNLFA), an advanced modeling approach that allows for covariate moderation of item and factor parameters. The current paper provides an overview of this modeling technique, highlighting distinct advantages most apt for IDA. We further illustrate the complex modeling building process involved in MNLFA by providing a tutorial using empirical data from five separate prevention trials. The code and data used for analyses are also provided.</p>","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"30 1","pages":"149-164"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9937431/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9363653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Regression-Equivalent Effect Sizes for Latent Growth Modeling and Associated Null Hypothesis Significance Tests. 潜在增长模型的回归等效效应大小及相关的零假设显著性检验。
IF 6 2区 心理学
Structural Equation Modeling: A Multidisciplinary Journal Pub Date : 2023-01-01 Epub Date: 2022-11-28 DOI: 10.1080/10705511.2022.2139702
Alan Feingold
{"title":"Regression-Equivalent Effect Sizes for Latent Growth Modeling and Associated Null Hypothesis Significance Tests.","authors":"Alan Feingold","doi":"10.1080/10705511.2022.2139702","DOIUrl":"10.1080/10705511.2022.2139702","url":null,"abstract":"<p><p>The effect of an independent variable on random slopes in growth modeling with latent variables is conventionally used to examine predictors of change over the course of a study. This tutorial demonstrates that the same effect of a covariate on growth can be obtained by using final status centering for parameterization and regressing the random intercepts (or the intercept factor scores) on both the independent variable and a baseline covariate--the framework used to study change with classical regression analysis. Examples are provided that illustrate the application of an intercept-focused approach to obtain effect sizes--the unstandardized regression coefficient, the standardized regression coefficient, squared semi-partial correlation, and Cohen's <i>f</i><sup>2</sup> --that estimate the same parameters as respective effect sizes from a classical regression analysis. Moreover, statistical power to detect the effect of the predictor on growth was greater when using random intercepts than the conventionally used random slopes.</p>","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"30 4","pages":"672-685"},"PeriodicalIF":6.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10427122/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10095842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Effects of Mixing Weights and Predictor Distributions on Regression Mixture Models. 混合权值和预测因子分布对回归混合模型的影响。
IF 6 2区 心理学
Structural Equation Modeling: A Multidisciplinary Journal Pub Date : 2022-01-01 DOI: 10.1080/10705511.2021.1932508
Phillip Sherlock, Christine DiStefano, Brian Habing
{"title":"Effects of Mixing Weights and Predictor Distributions on Regression Mixture Models.","authors":"Phillip Sherlock,&nbsp;Christine DiStefano,&nbsp;Brian Habing","doi":"10.1080/10705511.2021.1932508","DOIUrl":"https://doi.org/10.1080/10705511.2021.1932508","url":null,"abstract":"ABSTRACT Regression mixture models (RMMs) can be used to specifically test for and model differential effects in heterogeneous populations. Based on the results of the Aim 1 simulation study, enumeration conducted with constrained predictor means appears to be advantageous. Furthermore, researchers should estimate the K and K+1 unconditional models (chosen during initial enumeration), adding the C on X paths, to investigate the potential for model instability as well as the possibility that the models are misspecified because the underlying populations contain predictor variance differences in the subgroups. The Aim 2 simulation study explored the extent to which RMMs are robust to predictor variance differences. Although the coverage rates for the simulation conditions where the predictor variances differed across classes were not the nominal rate, parameter estimates were not biased even in the presence of moderate violations of this assumption.","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"29 1","pages":"70-85"},"PeriodicalIF":6.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/10705511.2021.1932508","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10450481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Fitting Multilevel Vector Autoregressive Models in Stan, JAGS, and Mplus. 在 Stan、JAGS 和 Mplus 中拟合多层次向量自回归模型。
IF 2.5 2区 心理学
Structural Equation Modeling: A Multidisciplinary Journal Pub Date : 2022-01-01 Epub Date: 2021-09-14 DOI: 10.1080/10705511.2021.1911657
Yanling Li, Julie Wood, Linying Ji, Sy-Miin Chow, Zita Oravecz
{"title":"Fitting Multilevel Vector Autoregressive Models in Stan, JAGS, and Mplus.","authors":"Yanling Li, Julie Wood, Linying Ji, Sy-Miin Chow, Zita Oravecz","doi":"10.1080/10705511.2021.1911657","DOIUrl":"10.1080/10705511.2021.1911657","url":null,"abstract":"<p><p>The influx of intensive longitudinal data creates a pressing need for complex modeling tools that help enrich our understanding of how individuals change over time. Multilevel vector autoregressive (mlVAR) models allow for simultaneous evaluations of reciprocal linkages between dynamic processes and individual differences, and have gained increased recognition in recent years. High-dimensional and other complex variations of mlVAR models, though often computationally intractable in the frequentist framework, can be readily handled using Markov chain Monte Carlo techniques in a Bayesian framework. However, researchers in social science fields may be unfamiliar with ways to capitalize on recent developments in Bayesian software programs. In this paper, we provide step-by-step illustrations and comparisons of options to fit Bayesian mlVAR models using Stan, JAGS and Mplus, supplemented with a Monte Carlo simulation study. An empirical example is used to demonstrate the utility of mlVAR models in studying intra- and inter-individual variations in affective dynamics.</p>","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"29 3","pages":"452-475"},"PeriodicalIF":2.5,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9122119/pdf/nihms-1701282.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10807234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Estimating and Testing Random Intercept Multilevel Structural Equation Models with Model Implied Instrumental Variables. 用模型隐含工具变量估计和检验随机截距多水平结构方程模型。
IF 6 2区 心理学
Structural Equation Modeling: A Multidisciplinary Journal Pub Date : 2022-01-01 DOI: 10.1080/10705511.2022.2028261
Michael L Giordano, Kenneth A Bollen, Shaobo Jin
{"title":"Estimating and Testing Random Intercept Multilevel Structural Equation Models with Model Implied Instrumental Variables.","authors":"Michael L Giordano,&nbsp;Kenneth A Bollen,&nbsp;Shaobo Jin","doi":"10.1080/10705511.2022.2028261","DOIUrl":"https://doi.org/10.1080/10705511.2022.2028261","url":null,"abstract":"<p><p>This study develops a new limited information estimator for random intercept Multilevel Structural Equation Models (MSEM). It is based on the Model Implied Instrumental Variable Two-Stage Least Squares (MIIV-2SLS) estimator, which has been shown to be an excellent alternative or supplement to maximum likelihood (ML) in SEMs (Bollen, 1996). We also develop a multilevel overidentification test statistic that applies to equations at the within or between levels. Our Monte Carlo simulation analysis suggests that MIIV-2SLS is more robust than ML to misspecification at within or between levels, performs well given fewer that 100 clusters, and shows that our multilevel overidentification test for equations performs well at both levels of the model.</p>","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"29 4","pages":"584-599"},"PeriodicalIF":6.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10275505/pdf/nihms-1888659.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9762932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Teacher's Corner: An R Shiny App for Sensitivity Analysis for Latent Growth Curve Mediation. 教师园地：用于潜在增长曲线调解敏感性分析的 R Shiny 应用程序。
IF 6 2区 心理学
Structural Equation Modeling: A Multidisciplinary Journal Pub Date : 2022-01-01 Epub Date: 2022-03-25 DOI: 10.1080/10705511.2022.2045203
Eric S Kruger, Davood Tofighi, Yu-Yu Hsiao, David P MacKinnon, M Lee Van Horn, Katie Witkiewitz
{"title":"Teacher's Corner: An R Shiny App for Sensitivity Analysis for Latent Growth Curve Mediation.","authors":"Eric S Kruger, Davood Tofighi, Yu-Yu Hsiao, David P MacKinnon, M Lee Van Horn, Katie Witkiewitz","doi":"10.1080/10705511.2022.2045203","DOIUrl":"10.1080/10705511.2022.2045203","url":null,"abstract":"<p><p>Mechanisms of behavior change are the processes through which interventions are hypothesized to cause changes in outcomes. Latent growth curve mediation models (LGCMM) are recommended for investigating the mechanisms of behavior change because LGCMM models establish temporal precedence of change from the mediator to the outcome variable. The Correlated Augmented Mediation Sensitivity Analyses (CAMSA) App implements sensitivity analysis for LGCMM models to evaluate if a mediating path (mechanism) is robust to potential confounding variables. The CAMSA approach is described and applied to simulated data, and data from a research study exploring a mechanism of change in the treatment of substance use disorder.</p>","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"29 6","pages":"944-952"},"PeriodicalIF":6.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9683348/pdf/nihms-1848235.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9170687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
The Effect of Noninvariance on the Estimation of the Mediated Effect in the Two-Wave Mediation Model. 非不变性对两波中介模型中中介效应估计的影响。
IF 6 2区 心理学
Structural Equation Modeling: A Multidisciplinary Journal Pub Date : 2022-01-01 DOI: 10.1080/10705511.2022.2067164
A R Georgeson, Matthew J Valente, Oscar Gonzalez
{"title":"The Effect of Noninvariance on the Estimation of the Mediated Effect in the Two-Wave Mediation Model.","authors":"A R Georgeson,&nbsp;Matthew J Valente,&nbsp;Oscar Gonzalez","doi":"10.1080/10705511.2022.2067164","DOIUrl":"https://doi.org/10.1080/10705511.2022.2067164","url":null,"abstract":"<p><p>The two-wave mediation model is the most suitable model for examining mediation effects in a randomized intervention and includes measures taken at pretest and posttest. When using self-report measures, the meaning of responses may change for the treatment group over the course of the intervention and result in noninvariance across groups at posttest, a phenomenon referred to as <i>response shift</i>. We investigate how the mediated effect would be impacted by noninvariance when using sum scores (i.e., assuming invariance). In a Monte Carlo simulation study, the magnitude and proportion of items that had noninvariant intercepts, the direction of noninvariance, number of items, effect size of the mediated effect and sample size were varied. Results showed increased Type I and Type II errors due to a biased estimate of the intervention effect on the mediator resulting from noninvariance. Thus, measurement noninvariance could lead to erroneous conclusions about the process underlying the intervention.</p>","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"29 6","pages":"908-919"},"PeriodicalIF":6.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10084817/pdf/nihms-1877187.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9305562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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