Multivariate Behavioral Research最新文献_第2页

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

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

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

Killing Two Birds with One Stone: Accounting for Unfolding Item Response Process and Response Styles Using Unfolding Item Response Tree Models. 一石二鸟：使用展开式项目反应树模型考虑展开式项目反应过程和反应风格。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2024-08-31 DOI: 10.1080/00273171.2024.2394607

Zhaojun Li, Lingyue Li, Bo Zhang, Mengyang Cao, Louis Tay

{"title":"Killing Two Birds with One Stone: Accounting for Unfolding Item Response Process and Response Styles Using Unfolding Item Response Tree Models.","authors":"Zhaojun Li, Lingyue Li, Bo Zhang, Mengyang Cao, Louis Tay","doi":"10.1080/00273171.2024.2394607","DOIUrl":"https://doi.org/10.1080/00273171.2024.2394607","url":null,"abstract":"Two research streams on responses to Likert-type items have been developing in parallel: (a) unfolding models and (b) individual response styles (RSs). To accurately understand Likert-type item responding, it is vital to parse unfolding responses from RSs. Therefore, we propose the Unfolding Item Response Tree (UIRTree) model. First, we conducted a Monte Carlo simulation study to examine the performance of the UIRTree model compared to three other models - Samejima's Graded Response Model, Generalized Graded Unfolding Model, and Dominance Item Response Tree model, for Likert-type responses. Results showed that when data followed an unfolding response process and contained RSs, AIC was able to select the UIRTree model, while BIC was biased toward the DIRTree model in many conditions. In addition, model parameters in the UIRTree model could be accurately recovered under realistic conditions, and mis-specifying item response process or wrongly ignoring RSs was detrimental to the estimation of key parameters. Then, we used datasets from empirical studies to show that the UIRTree model could fit personality datasets well and produced more reasonable parameter estimates compared to competing models. A strong presence of RS(s) was also revealed by the UIRTree model. Finally, we provided examples with R code for UIRTree model estimation to facilitate the modeling of responses to Likert-type items in future studies.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-23"},"PeriodicalIF":5.3,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142114707","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

From Behavioral Genetics to Idiographic Science: Methodological Developments and Applications Inspired by the Work of Peter C. M. Molenaar. 从行为遗传学到图像学：从行为遗传学到成语科学：受彼得-莫伦纳尔（Peter C. M. Molenaar）著作启发的方法论发展与应用》。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2024-08-30 DOI: 10.1080/00273171.2024.2394054

Sy-Miin Chow, Ellen L Hamaker, Nilam Ram

引用次数: 0

Equivalence Testing Based Fit Index: Standardized Root Mean Squared Residual. 基于等效检验的拟合指数：标准化均方根残差。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2024-08-17 DOI: 10.1080/00273171.2024.2386686

Nataly Beribisky, Robert A Cribbie

引用次数: 0