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Structural Equation Modeling: A Multidisciplinary Journal最新文献

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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
IF 6 2区 心理学
Structural Equation Modeling: A Multidisciplinary Journal Pub Date : 2023-08-22 DOI: 10.1080/10705511.2023.2233115
Zsuzsa Bakk
{"title":"Latent Class Analysis with Measurement Invariance Testing: Simulation Study to Compare Overall Likelihood Ratio vs Residual Fit Statistics Based Model Selection","authors":"Zsuzsa Bakk","doi":"10.1080/10705511.2023.2233115","DOIUrl":"https://doi.org/10.1080/10705511.2023.2233115","url":null,"abstract":"<p><b>Abstract</b></p><p>A standard assumption of latent class (LC) analysis is conditional independence, that is the items of the LC are independent of the covariates given the LCs. Several approaches have been proposed for identifying violations of this assumption. The recently proposed likelihood ratio approach is compared to residual statistics (bivariate residuals [BVR] and expected parameter change [EPC] statistics) for identifying nonuniform direct effect of covariates on the items of the LC model. The simulation study results show that the likelihood ratio (LR) test correctly identifies direct effects more often than the BVR statistics, showing comparable results to the EPC statistic in many situations- this at the price of having also a higher false positive rate than BVR. A real data example illustrates the use of the three procedures. Overall the combined use of residual statistics and LR testing is recommended for applied research.</p>","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"17 9","pages":""},"PeriodicalIF":6.0,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50165099","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
Finding the Optimal Number of Persons (N) and Time Points (T) for Maximal Power in Dynamic Longitudinal Models Given a Fixed Budget 在给定固定预算的动态纵向模型中寻找最优的最大功率人数(N)和时间点(T)
IF 6 2区 心理学
Structural Equation Modeling: A Multidisciplinary Journal Pub Date : 2023-08-22 DOI: 10.1080/10705511.2023.2230520
Martin Hecht, Julia-Kim Walther, Manuel Arnold, Steffen Zitzmann
{"title":"Finding the Optimal Number of Persons (N) and Time Points (T) for Maximal Power in Dynamic Longitudinal Models Given a Fixed Budget","authors":"Martin Hecht, Julia-Kim Walther, Manuel Arnold, Steffen Zitzmann","doi":"10.1080/10705511.2023.2230520","DOIUrl":"https://doi.org/10.1080/10705511.2023.2230520","url":null,"abstract":"<p><b>Abstract</b></p><p>Planning longitudinal studies can be challenging as various design decisions need to be made. Often, researchers are in search for the optimal design that maximizes statistical power to test certain parameters of the employed model. We provide a user-friendly Shiny app OptDynMo available at https://shiny.psychologie.hu-berlin.de/optdynmo that helps to find the optimal number of persons (<i>N</i>) and the optimal number of time points (<i>T</i>) for which the power of the likelihood ratio test (LRT) for a model parameter is maximal given a fixed budget for conducting the study. The total cost of the study is computed from two components: the cost to include one person in the study and the cost for measuring one person at one time point. Currently supported models are the cross-lagged panel model (CLPM), factor CLPM, random intercepts cross-lagged panel model (RI-CLPM), stable trait autoregressive trait and state model (STARTS), latent curve model with structured residuals (LCM-SR), autoregressive latent trajectory model (ALT), and the latent change score model (LCS).</p>","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"17 4","pages":""},"PeriodicalIF":6.0,"publicationDate":"2023-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50165106","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
Analyzing Multivariate Generalizability Theory Designs within Structural Equation Modeling Frameworks 分析结构方程建模框架下的多元推广理论设计
IF 6 2区 心理学
Structural Equation Modeling: A Multidisciplinary Journal Pub Date : 2023-08-18 DOI: 10.1080/10705511.2023.2222913
Walter P. Vispoel, Hyeryung Lee, Hyeri Hong
{"title":"Analyzing Multivariate Generalizability Theory Designs within Structural Equation Modeling Frameworks","authors":"Walter P. Vispoel, Hyeryung Lee, Hyeri Hong","doi":"10.1080/10705511.2023.2222913","DOIUrl":"https://doi.org/10.1080/10705511.2023.2222913","url":null,"abstract":"","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"80 1","pages":""},"PeriodicalIF":6.0,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80422920","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: 4
Label Switching in Latent Class Analysis: Accuracy of Classification, Parameter Estimates, and Confidence Intervals 潜在类别分析中的标签转换:分类的准确性、参数估计和置信区间
IF 6 2区 心理学
Structural Equation Modeling: A Multidisciplinary Journal Pub Date : 2023-08-14 DOI: 10.1080/10705511.2023.2213842
Meng Qiu, Ke-Hai Yuan
{"title":"Label Switching in Latent Class Analysis: Accuracy of Classification, Parameter Estimates, and Confidence Intervals","authors":"Meng Qiu, Ke-Hai Yuan","doi":"10.1080/10705511.2023.2213842","DOIUrl":"https://doi.org/10.1080/10705511.2023.2213842","url":null,"abstract":"<p><b>Abstract</b></p><p>Latent class analysis (LCA) is a widely used technique for detecting unobserved population heterogeneity in cross-sectional data. Despite its popularity, the performance of LCA is not well understood. In this study, we evaluate the performance of LCA with binary data by examining classification accuracy, parameter estimation accuracy, and coverage rates of confidence intervals (CIs) through Monte Carlo simulation studies. We address the issue of label switching with a distance-based relabeling approach and introduce an index to measure separation among latent classes. Our results show that classification accuracy, parameter estimation accuracy, and CI coverage rates are primarily influenced by class separation and the number of indicators used for LCA. We recommend using a large sample size to mitigate the effects of tiny class sizes. Additionally, the study finds that the parametric bootstrap CIs perform comparably well or better when compared with the CIs based on the standard maximum likelihood method.</p>","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"91 1","pages":""},"PeriodicalIF":6.0,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50165357","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
Review of Handbook of Structural Equation Modeling 结构方程建模手册综述
IF 6 2区 心理学
Structural Equation Modeling: A Multidisciplinary Journal Pub Date : 2023-07-28 DOI: 10.1080/10705511.2023.2235083
Jam Khojasteh
{"title":"Review of Handbook of Structural Equation Modeling","authors":"Jam Khojasteh","doi":"10.1080/10705511.2023.2235083","DOIUrl":"https://doi.org/10.1080/10705511.2023.2235083","url":null,"abstract":"Published in Structural Equation Modeling: A Multidisciplinary Journal (Vol. 30, No. 6, 2023)","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"86 2","pages":""},"PeriodicalIF":6.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50165387","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
Leveraging Observation Timing Variability to Understand Intervention Effects in Panel Studies: An Empirical Illustration and Simulation Study 利用观察时变来理解面板研究中的干预效果:一个实证说明和模拟研究
IF 6 2区 心理学
Structural Equation Modeling: A Multidisciplinary Journal Pub Date : 2023-07-28 DOI: 10.1080/10705511.2023.2224515
Andrea Hasl, Manuel Voelkle, Charles Driver, Julia Kretschmann, Martin Brunner
{"title":"Leveraging Observation Timing Variability to Understand Intervention Effects in Panel Studies: An Empirical Illustration and Simulation Study","authors":"Andrea Hasl, Manuel Voelkle, Charles Driver, Julia Kretschmann, Martin Brunner","doi":"10.1080/10705511.2023.2224515","DOIUrl":"https://doi.org/10.1080/10705511.2023.2224515","url":null,"abstract":"<p><b>Abstract</b></p><p>To examine developmental processes, intervention effects, or both, longitudinal studies often aim to include measurement intervals that are equally spaced for all participants. In reality, however, this goal is hardly ever met. Although different approaches have been proposed to deal with this issue, few studies have investigated the potential benefits of individual variation in time intervals. In the present paper, we examine how continuous time dynamic models can be used to study nonexperimental intervention effects in longitudinal studies where measurement intervals vary between and within participants. We empirically illustrate this method by using panel data (<i>N</i> = 2,877) to study the effect of the transition from primary to secondary school on students’ motivation. Results of a simulation study also show that the precision and recovery of the estimate of the effect improves with individual variation in time intervals.</p>","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"86 3","pages":""},"PeriodicalIF":6.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50165386","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
Dynamic Fit Index Cutoffs for Hierarchical and Second-Order Factor Models 层次和二阶因子模型的动态拟合指标截止值
IF 6 2区 心理学
Structural Equation Modeling: A Multidisciplinary Journal Pub Date : 2023-07-28 DOI: 10.1080/10705511.2023.2225132
Daniel McNeish, Patrick D. Manapat
{"title":"Dynamic Fit Index Cutoffs for Hierarchical and Second-Order Factor Models","authors":"Daniel McNeish, Patrick D. Manapat","doi":"10.1080/10705511.2023.2225132","DOIUrl":"https://doi.org/10.1080/10705511.2023.2225132","url":null,"abstract":"<p><b>Abstract</b></p><p>A recent review found that 11% of published factor models are hierarchical models with second-order factors. However, dedicated recommendations for evaluating hierarchical model fit have yet to emerge. Traditional benchmarks like RMSEA &lt;0.06 or CFI &gt;0.95 are often consulted, but they were never intended to generalize to hierarchical models. Through simulation, we show that traditional benchmarks perform poorly at identifying misspecification in hierarchical models. This corroborates previous studies showing that traditional benchmarks do not maintain optimal sensitivity to misspecification as model characteristics deviate from those used to derive the benchmarks. Instead, we propose a hierarchical extension to the dynamic fit index (DFI) framework, which automates custom simulations to derive cutoffs with optimal sensitivity for specific model characteristics. In simulations to evaluate performance, results showed that the hierarchical DFI extension routinely exceeded 95% classification accuracy and 90% sensitivity to misspecification whereas traditional benchmarks applied to hierarchical models rarely exceeded 50% classification accuracy and 20% sensitivity.</p>","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"87 4","pages":""},"PeriodicalIF":6.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50165383","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
Bayesian Inference of Dynamic Mediation Models for Longitudinal Data 纵向数据动态中介模型的贝叶斯推断
IF 6 2区 心理学
Structural Equation Modeling: A Multidisciplinary Journal Pub Date : 2023-07-28 DOI: 10.1080/10705511.2023.2230519
Saijun Zhao, Zhiyong Zhang, Hong Zhang
{"title":"Bayesian Inference of Dynamic Mediation Models for Longitudinal Data","authors":"Saijun Zhao, Zhiyong Zhang, Hong Zhang","doi":"10.1080/10705511.2023.2230519","DOIUrl":"https://doi.org/10.1080/10705511.2023.2230519","url":null,"abstract":"<p><b>Abstract</b></p><p>Mediation analysis is widely applied in various fields of science, such as psychology, epidemiology, and sociology. In practice, many psychological and behavioral phenomena are dynamic, and the corresponding mediation effects are expected to change over time. However, most existing mediation methods assume a static mediation effect over time, which overlooks the dynamic nature of mediation effect. To address this issue, we propose dynamic mediation models that can capture the dynamic nature of the mediation effect. Specifically, we model the path parameters of mediation models as auto-regressive (AR) processes of time that can vary over time. Additionally, we define the mediation effect under the potential outcome framework, and examine its identification and causal interpretation. Bayesian methods utilizing Gibbs sampling are adopted to estimate unknown parameters in the proposed dynamic mediation models. We further evaluate our proposed models and methods through extensive simulations and illustrate their application through a real data application.</p>","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"87 3","pages":""},"PeriodicalIF":6.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50165384","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 Impact of Ignoring Cross-loadings on the Sensitivity of Fit Measures in Measurement Invariance Testing 测量不变性检验中忽略交叉载荷对拟合测度灵敏度的影响
IF 6 2区 心理学
Structural Equation Modeling: A Multidisciplinary Journal Pub Date : 2023-07-28 DOI: 10.1080/10705511.2023.2223360
Chunhua Cao, Xinya Liang
{"title":"The Impact of Ignoring Cross-loadings on the Sensitivity of Fit Measures in Measurement Invariance Testing","authors":"Chunhua Cao, Xinya Liang","doi":"10.1080/10705511.2023.2223360","DOIUrl":"https://doi.org/10.1080/10705511.2023.2223360","url":null,"abstract":"<p><b>Abstract</b></p><p>Cross-loadings are common in multiple-factor confirmatory factor analysis (CFA) but often ignored in measurement invariance testing. This study examined the impact of ignoring cross-loadings on the sensitivity of fit measures (CFI, RMSEA, SRMR, SRMRu, AIC, BIC, SaBIC, LRT) to measurement noninvariance . The manipulated design factors included the magnitude and percentage of cross-loadings, the magnitude and percentage of noninvariance, location of measurement noninvariance, model size, and sample size. Results suggested that the ignored cross-loadings affected the sensitivity of all fit measures but LRT to metric noninvariance to varying degrees, whereas they did not affect the sensitivity of fit measures to scalar noninvariance except for RMSEA. RMSEA was impacted by the magnitude of cross-loadings in both metric and scalar invariance testing. In the largest model size, CFI failed to detect metric noninvariance when there were no cross-loadings in the population model but detected the metric noninvariance of .30 with ignored cross-loadings.</p>","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"87 2","pages":""},"PeriodicalIF":6.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50165385","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
Revisiting Savalei’s (2011) Research on Remediating Zero-Frequency Cells in Estimating Polychoric Correlations: A Data Distribution Perspective 回顾Savalei(2011)在估计多共时相关性中修复零频率单元的研究:一个数据分布的视角
IF 6 2区 心理学
Structural Equation Modeling: A Multidisciplinary Journal Pub Date : 2023-07-14 DOI: 10.1080/10705511.2023.2220919
Tong-Rong Yang, Li-Jen Weng
{"title":"Revisiting Savalei’s (2011) Research on Remediating Zero-Frequency Cells in Estimating Polychoric Correlations: A Data Distribution Perspective","authors":"Tong-Rong Yang, Li-Jen Weng","doi":"10.1080/10705511.2023.2220919","DOIUrl":"https://doi.org/10.1080/10705511.2023.2220919","url":null,"abstract":"<p><b>Abstract</b></p><p>In Savalei’s (<span>2011<span aria-label=\"reference\" role=\"dialog\"><span aria-label=\"Close reference popup\" role=\"button\" tabindex=\"0\"></span><span></span> <span>Savalei, <span>V.</span></span> (<span>2011</span>). <span>What to do about zero frequency cells when estimating polychoric correlations</span>. <i>Structural Equation Modeling</i>, <i>18</i>, <span>253</span>–<span>273</span>. <span>https://doi.org/10.1080/10705511.2011.557339</span><span><span>[Taylor &amp; Francis Online], [Web of Science ®]</span> <span>, [Google Scholar]</span></span></span></span>) simulation that evaluated the performance of polychoric correlation estimates in small samples, two methods for treating zero-frequency cells, adding 0.5 (ADD) and doing nothing (NONE), were compared. Savalei tentatively suggested using ADD for binary data and NONE for data with three or more categories. Yet, Savalei’s suggestion could be explained by the skewness of the data distribution being severe for binary data and slight for three-category data. To rule out this alternative explanation, we extended Savalei’s design by incorporating the degree of skewness into our simulation. With slightly skewed data, NONE is recommended due to its high-quality estimates. With severely skewed data, only ADD is recommended for binary data when the skewness of two variables is the same-signed and the underlying correlation is expected to be strong. Methods for improving the polychoric correlation estimates with severely skewed data merit further study.</p>","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"24 7","pages":""},"PeriodicalIF":6.0,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50165593","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
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