Multivariate Behavioral Research最新文献_第3页

Missing Data in Discrete Time State-Space Modeling of Ecological Momentary Assessment Data: A Monte-Carlo Study of Imputation Methods. 生态瞬时评价数据离散时间状态空间建模中的缺失数据：一种蒙特卡罗方法的研究。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2025-07-01 Epub Date: 2025-03-17 DOI: 10.1080/00273171.2025.2469055

Lindley R Slipetz, Ami Falk, Teague R Henry

引用次数: 0

Development of a Method for Handling Doubly-Censored Data in a Latent Growth Curve Modeling Framework. 潜在增长曲线建模框架中处理双截尾数据方法的发展。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2025-07-01 Epub Date: 2025-03-26 DOI: 10.1080/00273171.2025.2478071

Sooyong Lee, Tiffany A Whittaker

{"title":"Development of a Method for Handling Doubly-Censored Data in a Latent Growth Curve Modeling Framework.","authors":"Sooyong Lee, Tiffany A Whittaker","doi":"10.1080/00273171.2025.2478071","DOIUrl":"10.1080/00273171.2025.2478071","url":null,"abstract":"This study addresses the challenge of doubly-censoring effects in longitudinal data structures, particularly within latent growth curve models (LGCMs). Censoring can severely bias estimates and inferences, distorting the relationships between growth factors and covariates. To combat this issue, this study introduces the Generalized Tobit estimator (GBIT), an advancement of the conventional Tobit model, designed to handle mixed censoring effects in longitudinal data. The objectives of this study were threefold: (a) to develop GBIT for doubly-censored data, (b) to evaluate GBIT's performance in LGCMs under mixed censoring, and (c) to examine the impact of such censoring on covariate effects and outcomes within LGCMs. A Monte Carlo simulation was conducted to assess GBIT's effectiveness to handle doubly-censoring effects in the LGCM framework, demonstrating its ability to provide unbiased estimates even in the presence of significant censoring. Also, GBIT was applied for empirical data positing doubly-censoring effects, further supporting the use of GBIT, particularly in situations involving doubly-censored data.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"767-783"},"PeriodicalIF":5.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143712082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Gaussian distributional structural equation models: A framework for modeling latent heteroscedasticity. 高斯分布结构方程模型：潜在异方差建模的框架。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2025-07-01 Epub Date: 2025-04-17 DOI: 10.1080/00273171.2025.2483252

Luna Fazio, Paul-Christian Bürkner

引用次数: 0

Correcting for Differences in Measurement Unreliability in Meta-Analysis of Variances. 方差荟萃分析中测量不信度差异的校正。

IF 3.5 3区心理学

Multivariate Behavioral Research Pub Date : 2025-07-01 Epub Date: 2025-03-14 DOI: 10.1080/00273171.2025.2469789

Katrin Jansen, Steffen Nestler

{"title":"Correcting for Differences in Measurement Unreliability in Meta-Analysis of Variances.","authors":"Katrin Jansen, Steffen Nestler","doi":"10.1080/00273171.2025.2469789","DOIUrl":"10.1080/00273171.2025.2469789","url":null,"abstract":"There is a growing interest of researchers in meta-analytic methods for comparing variances as a means to answer questions on between-group differences in variability. When measurements are fallible, however, the variance of an outcome reflects both the variance of the true scores and the error variance. Consequently, effect sizes based on variances, such as the log variability ratio (lnVR) or the log coefficient of variation ratio (lnCVR), may thus not only reflect between-group differences in the true-score variances but also differences in measurement reliability. In this article, we derive formulas to correct the lnVR and lnCVR and their sampling variances for between-group differences in reliability and evaluate their performance in simulation studies. We find that when the goal is to meta-analyze differences between the true-score variances and reliability differs between groups, our proposed corrections lead to accurate estimates of effect sizes and sampling variances in single studies, accurate estimates of the average effect and the between-study variance in random-effects meta-analysis, and adequate type I error rates for the significance test of the average effect. We discuss how to deal with problems arising from missing or imprecise group-specific reliability estimates in meta-analytic data sets and identify questions for further methodological research.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"711-730"},"PeriodicalIF":3.5,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 Modeling of Longitudinal Multiple-Group IRT Data with Skewed Latent Distributions and Growth Curves. 具有倾斜潜在分布和生长曲线的纵向多组IRT数据的贝叶斯建模。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2025-07-01 Epub Date: 2025-04-10 DOI: 10.1080/00273171.2025.2480437

José Roberto Silva Dos Santos, Caio Lucidius Naberezny Azevedo, Jean-Paul Fox

{"title":"Bayesian Modeling of Longitudinal Multiple-Group IRT Data with Skewed Latent Distributions and Growth Curves.","authors":"José Roberto Silva Dos Santos, Caio Lucidius Naberezny Azevedo, Jean-Paul Fox","doi":"10.1080/00273171.2025.2480437","DOIUrl":"10.1080/00273171.2025.2480437","url":null,"abstract":"In this work, we introduce a multiple-group longitudinal IRT model that accounts for skewed latent trait distributions. Our approach extends the model proposed by Santos et al. in 2022, which introduced a general class of longitudinal IRT models. The latent traits follow a multivariate skew-normal distribution, induced by an antedependence structure with centered skew-normal errors. Additionally, latent mean trajectories are modeled using quadratic curves, while structured covariance matrices capture within-participant dependencies. A three-parameter probit model is employed for dichotomous items. Bayesian parameter estimation and model fit assessment are conducted through a hybrid MCMC algorithm, combining the FFBS sampler with Metropolis-Hastings steps. The model's effectiveness is demonstrated through an application to real data from the Longitudinal Study of the 2005 School Generation in Brazil (GERES project), where it outperforms the normal model by better capturing asymmetry in latent traits. A simulation study further supports its robustness across various test conditions.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"784-816"},"PeriodicalIF":5.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144032936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Autoencoders for Amortized Joint Maximum Likelihood Estimation of Confirmatory Item Factor Models. 验证项因子模型的平摊联合最大似然估计的自编码器。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2025-07-01 Epub Date: 2025-02-12 DOI: 10.1080/00273171.2025.2456598

Dylan Molenaar, Raoul P P P Grasman, Mariana Cúri

引用次数: 0

Disaggregating Associations of Between-Person Differences in Change over Time from Within-Person Associations in Longitudinal Data. 从纵向数据的个人内部关联中分离出随时间变化的人际差异关联。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2025-06-21 DOI: 10.1080/00273171.2025.2519348

Lesa Hoffman

引用次数: 0

How to Estimate Intraclass Correlation Coefficients for Interrater Reliability from Planned Incomplete Data. 如何从计划的不完整数据中估计分类间信度的类内相关系数。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2025-06-16 DOI: 10.1080/00273171.2025.2507745

Debby Ten Hove, Terrence D Jorgensen, L Andries Van der Ark

{"title":"How to Estimate Intraclass Correlation Coefficients for Interrater Reliability from Planned Incomplete Data.","authors":"Debby Ten Hove, Terrence D Jorgensen, L Andries Van der Ark","doi":"10.1080/00273171.2025.2507745","DOIUrl":"https://doi.org/10.1080/00273171.2025.2507745","url":null,"abstract":"The interrater reliability (IRR) of observational data is often estimated by means of intraclass correlation coefficients (ICCs), which are flexible IRR estimators that are based on the variance decomposition of scores obtained by observations. ICCs are typically estimated using mean squares from an ANOVA model, the computation of which is not straightforward for incomplete data. However, many studies in behavioral research use planned missing observational designs, in which the raters partially vary across subjects. Planned missing designs result in incomplete data. Therefore, we simulated planned incomplete data and compared the computational accuracy (bias of point estimates, bias of variability estimates, root mean squared error, and coverage rates) and computational feasibility (convergence rates and estimation time) of three recently proposed estimation methods for ICCs: Markov chain Monte Carlo estimation of Bayesian hierarchical linear models, maximum likelihood estimation of random-effects models, and maximum likelihood estimation of common-factor models. Maximum likelihood estimation of random-effects models with Monte-Carlo confidence intervals is preferred based on all criteria. This article is accompanied by R code, which enables researchers to apply these estimation methods. A demonstration of the R code to a real-data set from an educational context is provided.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-20"},"PeriodicalIF":5.3,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144310809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Estimating IRT Models Under Gaussian Mixture Modeling of Latent Traits: An Application of MSAEM Algorithm. 潜在性状高斯混合建模下的IRT模型估计：MSAEM算法的应用。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2025-06-08 DOI: 10.1080/00273171.2025.2512345

Siyao Cheng, Xiangbin Meng

{"title":"Estimating IRT Models Under Gaussian Mixture Modeling of Latent Traits: An Application of MSAEM Algorithm.","authors":"Siyao Cheng, Xiangbin Meng","doi":"10.1080/00273171.2025.2512345","DOIUrl":"https://doi.org/10.1080/00273171.2025.2512345","url":null,"abstract":"The assumption of a normal distribution for latent traits is a common practice in item response theory (IRT) models. Numerous studies have demonstrated that this assumption is often inadequate, impacting the accuracy of statistical inferences in IRT models. To mitigate this issue, Gaussian mixture modeling (GMM) for latent traits, known as GMM-IRT, has been proposed. Moreover, the GMM-IRT models can also serve as powerful tools for exploring the heterogeneity of latent traits. However, the computation of GMM-IRT model estimation encounters several challenges, impeding its widespread application. The purpose of this paper is to propose a reliable and robust computing method for GMM-IRT model estimation. Specifically, we develop a mixed stochastic approximation EM (MSAEM) algorithm for estimating the three-parameter normal ogive model with GMM for latent traits (GMM-3PNO). Crucially, the GMM-3PNO is augmented to be a complete data model within the exponential family, thereby substantially streamlining the computation of the MSAEM algorithm. Furthermore, the MSAEM algorithm adeptly avoid the label-switching issue, ensuring its convergence. Finally, simulation and empirical studies are conducted to validate the performance of the MSAEM algorithm and demonstrate the superiority of the GMM-IRT models.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-18"},"PeriodicalIF":5.3,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144250780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Rnest: An R Package for the Next Eigenvalue Sufficiency Test for Factor Analysis. 因子分析下一个特征值充分性检验的R包。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2025-06-08 DOI: 10.1080/00273171.2025.2512343

Pier-Olivier Caron

引用次数: 0