Multivariate Behavioral Research最新文献_第10页

An Exact Bayesian Model for Meta-Analysis of the Standardized Mean Difference with Its Simultaneous Credible Intervals. 用于标准化均值差及其同时可信区间元分析的精确贝叶斯模型。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2024-09-01 Epub Date: 2024-07-23 DOI: 10.1080/00273171.2024.2358233

Yonggang Lu, Qiujie Zheng, Kevin Henning

{"title":"An Exact Bayesian Model for Meta-Analysis of the Standardized Mean Difference with Its Simultaneous Credible Intervals.","authors":"Yonggang Lu, Qiujie Zheng, Kevin Henning","doi":"10.1080/00273171.2024.2358233","DOIUrl":"10.1080/00273171.2024.2358233","url":null,"abstract":"While Bayesian methodology is increasingly favored in behavioral research for its clear probabilistic inference and model structure, its widespread acceptance as a standard meta-analysis approach remains limited. Although some conventional Bayesian hierarchical models are frequently used for analysis, their performance has not been thoroughly examined. This study evaluates two commonly used Bayesian models for meta-analysis of standardized mean difference and identifies significant issues with these models. In response, we introduce a new Bayesian model equipped with novel features that address existing model concerns and a broader limitation of the current Bayesian meta-analysis. Furthermore, we introduce a simple computational approach to construct simultaneous credible intervals for the summary effect and between-study heterogeneity, based on their joint posterior samples. This fully captures the joint uncertainty in these parameters, a task that is challenging or impractical with frequentist models. Through simulation studies rooted in a joint Bayesian/frequentist paradigm, we compare our model's performance against existing ones under conditions that mirror realistic research scenarios. The results reveal that our new model outperforms others and shows enhanced statistical properties. We also demonstrate the practicality of our models using real-world examples, highlighting how our approach strengthens the robustness of inferences regarding the summary effect.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1058-1076"},"PeriodicalIF":5.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141749748","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

Path and Direction Discovery in Individual Dynamic Factor Models: A Regularized Hybrid Unified Structural Equation Modeling with Latent Variable. 个体动态因素模型中的路径和方向发现：具有潜在变量的正则化混合统一结构方程模型》（A Regularized Hybrid Unified Structural Equation Modeling with Latent Variable.

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2024-09-01 Epub Date: 2024-07-26 DOI: 10.1080/00273171.2024.2354232

Ai Ye, Kenneth A Bollen

{"title":"Path and Direction Discovery in Individual Dynamic Factor Models: A Regularized Hybrid Unified Structural Equation Modeling with Latent Variable.","authors":"Ai Ye, Kenneth A Bollen","doi":"10.1080/00273171.2024.2354232","DOIUrl":"10.1080/00273171.2024.2354232","url":null,"abstract":"There has been an increasing call to model multivariate time series data with measurement error. The combination of latent factors with a vector autoregressive (VAR) model leads to the dynamic factor model (DFM), in which dynamic relations are derived within factor series, among factors and observed time series, or both. However, a few limitations exist in the current DFM representatives and estimation: (1) the dynamic component contains either directed or undirected contemporaneous relations, but not both, (2) selecting the optimal model in exploratory DFM is a challenge, (3) the consequences of structural misspecifications from model selection is barely studied. Our paper serves to advance DFM with a hybrid VAR representations and the utilization of LASSO regularization to select dynamic implied instrumental variable, two-stage least squares (MIIV-2SLS) estimation. Our proposed method highlights the flexibility in modeling the directions of dynamic relations with a robust estimation. We aim to offer researchers guidance on model selection and estimation in person-centered dynamic assessments.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1019-1042"},"PeriodicalIF":5.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141762570","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

Multilevel Semiparametric Latent Variable Modeling in R with "galamm". 利用 "galamm "在 R 中进行多层次半参数潜在变量建模。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2024-09-01 Epub Date: 2024-08-14 DOI: 10.1080/00273171.2024.2385336

Øystein Sørensen

引用次数: 0

Testing Conditional Independence in Psychometric Networks: An Analysis of Three Bayesian Methods. 测试心理测量网络中的条件独立性：对三种贝叶斯方法的分析。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2024-09-01 Epub Date: 2024-05-11 DOI: 10.1080/00273171.2024.2345915

Nikola Sekulovski, Sara Keetelaar, Karoline Huth, Eric-Jan Wagenmakers, Riet van Bork, Don van den Bergh, Maarten Marsman

{"title":"Testing Conditional Independence in Psychometric Networks: An Analysis of Three Bayesian Methods.","authors":"Nikola Sekulovski, Sara Keetelaar, Karoline Huth, Eric-Jan Wagenmakers, Riet van Bork, Don van den Bergh, Maarten Marsman","doi":"10.1080/00273171.2024.2345915","DOIUrl":"10.1080/00273171.2024.2345915","url":null,"abstract":"Network psychometrics uses graphical models to assess the network structure of psychological variables. An important task in their analysis is determining which variables are unrelated in the network, i.e., are independent given the rest of the network variables. This conditional independence structure is a gateway to understanding the causal structure underlying psychological processes. Thus, it is crucial to have an appropriate method for evaluating conditional independence and dependence hypotheses. Bayesian approaches to testing such hypotheses allow researchers to differentiate between absence of evidence and evidence of absence of connections (edges) between pairs of variables in a network. Three Bayesian approaches to assessing conditional independence have been proposed in the network psychometrics literature. We believe that their theoretical foundations are not widely known, and therefore we provide a conceptual review of the proposed methods and highlight their strengths and limitations through a simulation study. We also illustrate the methods using an empirical example with data on Dark Triad Personality. Finally, we provide recommendations on how to choose the optimal method and discuss the current gaps in the literature on this important topic.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"913-933"},"PeriodicalIF":5.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140908878","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

Parametric g-formula for Testing Time-Varying Causal Effects: What It Is, Why It Matters, and How to Implement It in Lavaan. 用于测试时变因果效应的参数 g 公式：它是什么，为什么重要，以及如何在 Lavaan 中实施。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2024-09-01 Epub Date: 2024-07-04 DOI: 10.1080/00273171.2024.2354228

Wen Wei Loh, Dongning Ren, Stephen G West

{"title":"Parametric g-formula for Testing Time-Varying Causal Effects: What It Is, Why It Matters, and How to Implement It in Lavaan.","authors":"Wen Wei Loh, Dongning Ren, Stephen G West","doi":"10.1080/00273171.2024.2354228","DOIUrl":"10.1080/00273171.2024.2354228","url":null,"abstract":"Psychologists leverage longitudinal designs to examine the causal effects of a focal predictor (i.e., treatment or exposure) over time. But causal inference of naturally observed time-varying treatments is complicated by treatment-dependent confounding in which earlier treatments affect confounders of later treatments. In this tutorial article, we introduce psychologists to an established solution to this problem from the causal inference literature: the parametric g-computation formula. We explain why the g-formula is effective at handling treatment-dependent confounding. We demonstrate that the parametric g-formula is conceptually intuitive, easy to implement, and well-suited for psychological research. We first clarify that the parametric g-formula essentially utilizes a series of statistical models to estimate the joint distribution of all post-treatment variables. These statistical models can be readily specified as standard multiple linear regression functions. We leverage this insight to implement the parametric g-formula using lavaan, a widely adopted R package for structural equation modeling. Moreover, we describe how the parametric g-formula may be used to estimate a marginal structural model whose causal parameters parsimoniously encode time-varying treatment effects. We hope this accessible introduction to the parametric g-formula will equip psychologists with an analytic tool to address their causal inquiries using longitudinal data.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"995-1018"},"PeriodicalIF":5.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141499613","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

Beyond Pearson's Correlation: Modern Nonparametric Independence Tests for Psychological Research. 超越皮尔逊相关性：心理学研究中的现代非参数独立性检验》（Modern Nonparametric Independence Tests for Psychological Research）。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2024-09-01 Epub Date: 2024-08-04 DOI: 10.1080/00273171.2024.2347960

Julian D Karch, Andres F Perez-Alonso, Wicher P Bergsma

{"title":"Beyond Pearson's Correlation: Modern Nonparametric Independence Tests for Psychological Research.","authors":"Julian D Karch, Andres F Perez-Alonso, Wicher P Bergsma","doi":"10.1080/00273171.2024.2347960","DOIUrl":"10.1080/00273171.2024.2347960","url":null,"abstract":"When examining whether two continuous variables are associated, tests based on Pearson's, Kendall's, and Spearman's correlation coefficients are typically used. This paper explores modern nonparametric independence tests as an alternative, which, unlike traditional tests, have the ability to potentially detect any type of relationship. In addition to existing modern nonparametric independence tests, we developed and considered two novel variants of existing tests, most notably the Heller-Heller-Gorfine-Pearson (HHG-Pearson) test. We conducted a simulation study to compare traditional independence tests, such as Pearson's correlation, and the modern nonparametric independence tests in situations commonly encountered in psychological research. As expected, no test had the highest power across all relationships. However, the distance correlation and the HHG-Pearson tests were found to have substantially greater power than all traditional tests for many relationships and only slightly less power in the worst case. A similar pattern was found in favor of the HHG-Pearson test compared to the distance correlation test. However, given that distance correlation performed better for linear relationships and is more widely accepted, we suggest considering its use in place or additional to traditional methods when there is no prior knowledge of the relationship type, as is often the case in psychological research.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"957-977"},"PeriodicalIF":5.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141890919","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

Linear Mixed-Effects Models for Dependent Data: Power and Accuracy in Parameter Estimation. 依赖数据的线性混合效应模型：参数估计的功率和准确性。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2024-09-01 Epub Date: 2024-05-23 DOI: 10.1080/00273171.2024.2350236

Yue Liu, Kit-Tai Hau, Hongyun Liu

{"title":"Linear Mixed-Effects Models for Dependent Data: Power and Accuracy in Parameter Estimation.","authors":"Yue Liu, Kit-Tai Hau, Hongyun Liu","doi":"10.1080/00273171.2024.2350236","DOIUrl":"10.1080/00273171.2024.2350236","url":null,"abstract":"Linear mixed-effects models have been increasingly used to analyze dependent data in psychological research. Despite their many advantages over ANOVA, critical issues in their analyses remain. Due to increasing random effects and model complexity, estimation computation is demanding, and convergence becomes challenging. Applied users need help choosing appropriate methods to estimate random effects. The present Monte Carlo simulation study investigated the impacts when the restricted maximum likelihood (REML) and Bayesian estimation models were misspecified in the estimation. We also compared the performance of Akaike information criterion (AIC) and deviance information criterion (DIC) in model selection. Results showed that models neglecting the existing random effects had inflated Type I errors, unacceptable coverage, and inaccurate R-squared measures of fixed and random effects variation. Furthermore, models with redundant random effects had convergence problems, lower statistical power, and inaccurate R-squared measures for Bayesian estimation. The convergence problem is more severe for REML, while reduced power and inaccurate R-squared measures were more severe for Bayesian estimation. Notably, DIC was better than AIC in identifying the true models (especially for models including person random intercept only), improving convergence rates, and providing more accurate effect size estimates, despite AIC having higher power than DIC with 10 items and the most complicated true model.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"978-994"},"PeriodicalIF":5.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141082872","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

Understanding the Consequences of Collinearity for Multilevel Models: The Importance of Disaggregation Across Levels. 了解多层次模型的共线性后果：跨层次分解的重要性。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2024-07-01 Epub Date: 2024-05-09 DOI: 10.1080/00273171.2024.2315549

Haley E Yaremych, Kristopher J Preacher

{"title":"Understanding the Consequences of Collinearity for Multilevel Models: The Importance of Disaggregation Across Levels.","authors":"Haley E Yaremych, Kristopher J Preacher","doi":"10.1080/00273171.2024.2315549","DOIUrl":"10.1080/00273171.2024.2315549","url":null,"abstract":"In multilevel models, disaggregating predictors into level-specific parts (typically accomplished via centering) benefits parameter estimates and their interpretations. However, the importance of level-specificity has been sparsely addressed in multilevel literature concerning collinearity. In this study, we develop novel insights into the interactivity of centering and collinearity in multilevel models. After integrating the broad literatures on centering and collinearity, we review level-specific and conflated correlations in multilevel data. Next, by deriving formal relationships between predictor collinearity and multilevel model estimates, we demonstrate how the consequences of collinearity change across different centering specifications and identify data characteristics that may exacerbate or mitigate those consequences. We show that when all or some level-1 predictors are uncentered, slope estimates can be greatly biased by collinearity. Disaggregation of all predictors eliminates the possibility that fixed effect estimates will be biased due to collinearity alone; however, under some data conditions, collinearity is associated with biased standard errors and random effect (co)variance estimates. Finally, we illustrate the importance of disaggregation for diagnosing collinearity in multilevel data and provide recommendations for the use of level-specific collinearity diagnostics. Overall, the necessity of disaggregation for identifying and managing collinearity's consequences in multilevel models is clarified in novel ways.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"693-715"},"PeriodicalIF":5.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140900141","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 Analysis of Multi-Factorial Experimental Designs Using SEM. 利用 SEM 对多因素实验设计进行贝叶斯分析。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2024-07-01 Epub Date: 2024-07-10 DOI: 10.1080/00273171.2024.2315557

Benedikt Langenberg, Jonathan L Helm, Axel Mayer

{"title":"Bayesian Analysis of Multi-Factorial Experimental Designs Using SEM.","authors":"Benedikt Langenberg, Jonathan L Helm, Axel Mayer","doi":"10.1080/00273171.2024.2315557","DOIUrl":"10.1080/00273171.2024.2315557","url":null,"abstract":"Latent repeated measures ANOVA (L-RM-ANOVA) has recently been proposed as an alternative to traditional repeated measures ANOVA. L-RM-ANOVA builds upon structural equation modeling and enables researchers to investigate interindividual differences in main/interaction effects, examine custom contrasts, incorporate a measurement model, and account for missing data. However, L-RM-ANOVA uses maximum likelihood and thus cannot incorporate prior information and can have poor statistical properties in small samples. We show how L-RM-ANOVA can be used with Bayesian estimation to resolve the aforementioned issues. We demonstrate how to place informative priors on model parameters that constitute main and interaction effects. We further show how to place weakly informative priors on standardized parameters which can be used when no prior information is available. We conclude that Bayesian estimation can lower Type 1 error and bias, and increase power and efficiency when priors are chosen adequately. We demonstrate the approach using a real empirical example and guide the readers through specification of the model. We argue that ANOVA tables and incomplete descriptive statistics are not sufficient information to specify informative priors, and we identify which parameter estimates should be reported in future research; thereby promoting cumulative research.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"716-737"},"PeriodicalIF":5.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141565098","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

Counterfactual Mediation Analysis with a Latent Class Exposure. 潜类暴露的反事实中介分析。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2024-07-01 Epub Date: 2024-05-31 DOI: 10.1080/00273171.2024.2335394

Gemma Hammerton, Jon Heron, Katie Lewis, Kate Tilling, Stijn Vansteelandt

{"title":"Counterfactual Mediation Analysis with a Latent Class Exposure.","authors":"Gemma Hammerton, Jon Heron, Katie Lewis, Kate Tilling, Stijn Vansteelandt","doi":"10.1080/00273171.2024.2335394","DOIUrl":"10.1080/00273171.2024.2335394","url":null,"abstract":"Latent classes are a useful tool in developmental research, however there are challenges associated with embedding them within a counterfactual mediation model. We develop and test a new method \"updated pseudo class draws (uPCD)\" to examine the association between a latent class exposure and distal outcome that could easily be extended to allow the use of any counterfactual mediation method. UPCD extends an existing group of methods (based on pseudo class draws) that assume that the true values of the latent class variable are missing, and need to be multiply imputed using class membership probabilities. We simulate data based on the Avon Longitudinal Study of Parents and Children, examine performance for existing techniques to relate a latent class exposure to a distal outcome (\"one-step,\" \"bias-adjusted three-step,\" \"modal class assignment,\" \"non-inclusive pseudo class draws,\" and \"inclusive pseudo class draws\") and compare bias in parameter estimates and their precision to uPCD when estimating counterfactual mediation effects. We found that uPCD shows minimal bias when estimating counterfactual mediation effects across all levels of entropy. UPCD performs similarly to recommended methods (one-step and bias-adjusted three-step), but provides greater flexibility and scope for incorporating the latent grouping within any commonly-used counterfactual mediation approach.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"818-840"},"PeriodicalIF":5.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11286213/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141184867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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