Structural Equation Modeling: A Multidisciplinary Journal最新文献

{"title":"Under-Fitting and Over-Fitting: The Performance of Bayesian Model Selection and Fit Indices in SEM","authors":"Sarah Depaoli, Sonja D. Winter, Haiyan Liu","doi":"10.1080/10705511.2023.2280952","DOIUrl":"https://doi.org/10.1080/10705511.2023.2280952","url":null,"abstract":"We extended current knowledge by examining the performance of several Bayesian model fit and comparison indices through a simulation study using the confirmatory factor analysis. Our goal was to de...","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"14 1","pages":""},"PeriodicalIF":6.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138770384","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}

{"title":"On the Performance of Horseshoe Priors for Inducing Sparsity in Structural Equation Models","authors":"Kjorte Harra, David Kaplan","doi":"10.1080/10705511.2023.2280895","DOIUrl":"https://doi.org/10.1080/10705511.2023.2280895","url":null,"abstract":"The present work focuses on the performance of two types of shrinkage priors—the horseshoe prior and the recently developed regularized horseshoe prior—in the context of inducing sparsity in path a...","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"198 1","pages":""},"PeriodicalIF":6.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138770466","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}

{"title":"A Simple Two-Step Procedure for Fitting Fully Unrestricted Exploratory Factor Analytic Solutions with Correlated Residuals","authors":"Pere J. Ferrando, Ana Hernández-Dorado, Urbano Lorenzo-Seva","doi":"10.1080/10705511.2023.2267181","DOIUrl":"https://doi.org/10.1080/10705511.2023.2267181","url":null,"abstract":"A frequent criticism of exploratory factor analysis (EFA) is that it does not allow correlated residuals to be modelled, while they can be routinely specified in the confirmatory (CFA) model. In th...","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"21 1","pages":""},"PeriodicalIF":6.0,"publicationDate":"2023-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138770279","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}

{"title":"<i>Review of Machine Learning for Social and Behavioral Research (Methodology in the Social Sciences)</i> <i>Review of Machine Learning for Social and Behavioral Research (Methodology in the Social Sciences)</i> . By Ross Jacobucci, Kevin J. Grimm, Zhiyong Zhang. New York, NY: The Guilford Press, (2023), 416 pp. $93.00 (Hardback), ISBN: 9781462552931. $62.00 (Paperback), ISBN: 9781462552924. $62.00 (PDF).","authors":"Aszani Aszani, Ruslan Anwar","doi":"10.1080/10705511.2023.2260564","DOIUrl":"https://doi.org/10.1080/10705511.2023.2260564","url":null,"abstract":"Click to increase image sizeClick to decrease image size AcknowledgmentsThe authors express their gratitude to the Indonesian Ministry of Finance’s Indonesia Endowment Fund for Education (LPDP) for providing financial support for the publication of this article and for the authors’ pursuit of postgraduate education.Disclosure StatementThe authors reported no potential conflicts of interest.Additional informationFundingThis study was supported by the Lembaga Pengelola Dana Pendidikan.","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":" 101","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135241723","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}

{"title":"How to Evaluate Causal Dominance Hypotheses in Lagged Effects Models","authors":"Chuenjai Sukpan, Rebecca M. Kuiper","doi":"10.1080/10705511.2023.2265065","DOIUrl":"https://doi.org/10.1080/10705511.2023.2265065","url":null,"abstract":"The (Random Intercept) Cross-Lagged Panel Model ((RI-)CLPM) is increasingly used in psychology and related fields to assess the longitudinal relationship of two or more variables on each other. Res...","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"58 10","pages":""},"PeriodicalIF":6.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92158499","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}

{"title":"Circumplex Models with Multivariate Time Series: An Idiographic Approach","authors":"Dayoung Lee, Guangjian Zhang, Shanhong Luo","doi":"10.1080/10705511.2023.2259105","DOIUrl":"https://doi.org/10.1080/10705511.2023.2259105","url":null,"abstract":"AbstractThe circumplex model posits a circular representation of affect and some personality traits. There is an increasing need to examine the viability of the circumplex model with multivariate time series data collected on the same individuals due to the development of new data collection methods such as smartphone applications and wearable sensors. Estimating the circumplex model with time series data is more complex than with cross-sectional data because scores at nearby time points tend to be correlated. We adapt Browne’s circumplex model to accommodate time series data. We illustrate the proposed method with an empirical data set of daily affect ratings of an individual over 70 days. We conducted a simulation study to explore the statistical properties of the proposed method. The results show that the method provides more satisfactory confidence intervals and test statistics than a method that treats time series data as if they were cross-sectional data.Keywords: Circumplex modelmultivariate time seriestime series Notes1 An idiographic approach is defined to “involve the thorough, intensive study of a single person or case in order to obtain an in-depth understanding of that person or case, as contrasted with a study of the universal aspects of groups of people or cases.” (APA Dictionary of Psychology, n.Citationd.)2 Molenaar (Citation2004) defined ergodic process as “a process in which the structures of intraindividual variation and interindividual variation are (asymptotically) equivalent.”3 Because one variable is chosen as the reference variable, its angle is fixed as 0°. Thus, the model involves only p − 1 angles. Because θj−θi=0 implies a correlation of 1, β0+∑i=1mβi=1. We can compute β0 from other weights.4 We present a sketch of the proof for the adaptation in Appendix B.5 Details of the derivatives were described by Lee and Zhang (Citation2022).6 We present a sketch of the proof for the adaptation in Appendix B.7 We thank David Watson for sharing the data.8 Watson et al. (Citation1999, p. 824) originally designed the 60 items to measure 8 affects, but “disengagement” was not assessed in the within-subject situations. Indicators of high positive affect are enthusiastic, interested, determined, excited, inspired, alert, active, strong, proud, and attentive; indicators of high negative affect are scared, afraid, upset, distressed, jittery, nervous, ashamed, guilty, irritable, and hostile; indicators of low positive affect are sleepy, tired, sluggish, and drowsy; indicators of low negative affect are calm, relaxed, and at ease; indicators of pleasantness are happy, joyful, cheerful, and delighted; indicators of unpleasantness are sad, blue, downhearted, alone, and lonely; and indicators of engagement are surprised, amazed, and astonished.9 The appendix contains R code for the illustration.10 We present common score correlations (Pc) of both models in an online support file (Figures A1 and A2).11 We assume that the time series is weakl","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":" 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135192896","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}

{"title":"Performance of Model Fit and Selection Indices for Bayesian Piecewise Growth Modeling with Missing Data","authors":"Ihnwhi Heo, Fan Jia, Sarah Depaoli","doi":"10.1080/10705511.2023.2264514","DOIUrl":"https://doi.org/10.1080/10705511.2023.2264514","url":null,"abstract":"The Bayesian piecewise growth model (PGM) is a useful class of models for analyzing nonlinear change processes that consist of distinct growth phases. In applications of Bayesian PGMs, it is import...","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"26 2","pages":""},"PeriodicalIF":6.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71436439","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}

{"title":"Does Acquiescence Disagree with Measurement Invariance Testing?","authors":"E. Damiano D’Urso, Jesper Tijmstra, Jeroen K. Vermunt, Kim De Roover","doi":"10.1080/10705511.2023.2260106","DOIUrl":"https://doi.org/10.1080/10705511.2023.2260106","url":null,"abstract":"Measurement invariance (MI) is required for validly comparing latent constructs measured by multiple ordinal self-report items. Non-invariances may occur when disregarding (group differences in) an...","PeriodicalId":21964,"journal":{"name":"Structural Equation Modeling: A Multidisciplinary Journal","volume":"54 6","pages":""},"PeriodicalIF":6.0,"publicationDate":"2023-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72364698","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}

{"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}

{"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}