Multivariate Behavioral Research最新文献_第2页

A Systematic Evaluation of Wording Effects Modeling Under the Exploratory Structural Equation Modeling Framework. 探索性结构方程建模框架下的措辞效果建模系统评价。

IF 3.5 3区心理学

Multivariate Behavioral Research Pub Date : 2025-09-08 DOI: 10.1080/00273171.2025.2545362

Luis Eduardo Garrido, Alexander P Christensen, Hudson Golino, Agustín Martínez-Molina, Víctor B Arias, Kiero Guerra-Peña, María Dolores Nieto-Cañaveras, Flávio Azevedo, Francisco J Abad

{"title":"A Systematic Evaluation of Wording Effects Modeling Under the Exploratory Structural Equation Modeling Framework.","authors":"Luis Eduardo Garrido, Alexander P Christensen, Hudson Golino, Agustín Martínez-Molina, Víctor B Arias, Kiero Guerra-Peña, María Dolores Nieto-Cañaveras, Flávio Azevedo, Francisco J Abad","doi":"10.1080/00273171.2025.2545362","DOIUrl":"https://doi.org/10.1080/00273171.2025.2545362","url":null,"abstract":"Wording effects, the systematic method variance arising from the inconsistent responding to positively and negatively worded items of the same construct, are pervasive in the behavioral and health sciences. Although several factor modeling strategies have been proposed to mitigate their adverse effects, there is limited systematic research assessing their performance with exploratory structural equation models (ESEM). The present study evaluated the impact of different types of response bias related to wording effects (random and straight-line carelessness, acquiescence, item difficulty, and mixed) on ESEM models incorporating two popular method modeling strategies, the correlated traits-correlated methods minus one (CTC[M-1]) model and random intercept item factor analysis (RIIFA), as well as the \"do nothing\" approach. Five variables were manipulated using Monte Carlo methods: the type and magnitude of response bias, factor loadings, factor correlations, and sample size. Overall, the results showed that ignoring wording effects leads to poor model fit and serious distortions of the ESEM estimates. The RIIFA approach generally performed best at countering these adverse impacts and recovering unbiased factor structures, whereas the CTC(M-1) models struggled when biases affected both positively and negatively worded items. Our findings also indicated that method factors can sometimes reflect or absorb substantive variance, which may blur their associations with external variables and complicate their interpretation when embedded in broader structural models. A straightforward guide is offered to applied researchers who wish to use ESEM with mixed-worded scales.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-30"},"PeriodicalIF":3.5,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145016636","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

Equilibrium Causal Models: Connecting Dynamical Systems Modeling and Cross-Sectional Data Analysis. 平衡因果模型：连接动力系统建模和横断面数据分析。

IF 3.5 3区心理学

Multivariate Behavioral Research Pub Date : 2025-09-04 DOI: 10.1080/00273171.2025.2522733

O Ryan, F Dablander

{"title":"Equilibrium Causal Models: Connecting Dynamical Systems Modeling and Cross-Sectional Data Analysis.","authors":"O Ryan, F Dablander","doi":"10.1080/00273171.2025.2522733","DOIUrl":"https://doi.org/10.1080/00273171.2025.2522733","url":null,"abstract":"Many psychological phenomena can be understood as arising from systems of causally connected components that evolve over time within an individual. In current empirical practice, researchers frequently study these systems by fitting statistical models to data collected at a single moment in time, that is, cross-sectional data. This raises a central question: Can cross-sectional data analysis ever yield causal insights into systems that evolve over time-and if so, under what conditions? In this paper, we address this question by introducing Equilibrium Causal Models (ECMs) to the psychological literature. ECMs are causal abstractions of an underlying dynamical system that allow for inferences about the long-term effects of interventions, permit cyclic causal relations, and can in principle be estimated from cross-sectional data, as long as information about the resting state of the system is captured by those measurements. We explain the conditions under which ECM estimation is possible, show that they allow researchers to learn about within-person processes from cross-sectional data, and discuss how tools from both the psychological measurement modeling and the causal discovery literature can inform the ways in which researchers collect and analyze their data.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-35"},"PeriodicalIF":3.5,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144994473","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

Unique Contributions of Dynamic Affect Indicators - Beyond Static Variability. 动态影响指标的独特贡献-超越静态变异性。

IF 3.5 3区心理学

Multivariate Behavioral Research Pub Date : 2025-09-02 DOI: 10.1080/00273171.2025.2545367

Kenneth Koslowski, Jana Holtmann

{"title":"Unique Contributions of Dynamic Affect Indicators - Beyond Static Variability.","authors":"Kenneth Koslowski, Jana Holtmann","doi":"10.1080/00273171.2025.2545367","DOIUrl":"https://doi.org/10.1080/00273171.2025.2545367","url":null,"abstract":"Indicators of affect dynamics (IADs) capture temporal dependencies and instability in affective trajectories over time. However, the relevance of IADs for the prediction of time-invariant outcomes (e.g., depressive symptoms) was recently challenged due to results suggesting low predictive utility beyond intraindividual means and variances. We argue that these results may in part be explained by mathematical redundancies between IADs and static variability as well as the chosen modeling strategy. In three extensive simulation studies we investigate the accuracy and power for detecting non-null relations between IADs and an outcome variable in different relevant settings, illustrating the effect of the length of a time series, the presence of missing values or measurement error, as well as of erroneously fixing innovation variances to be equal across persons. We show that, if uncertainty in individual IAD estimates is not accounted for, relations between IADs (i.e., autoregressive effects) and a time-invariant outcome are underestimated even in large samples and propose the use of a latent multilevel one-step approach. In an empirical application we illustrate that the different modeling approaches can lead to different substantive conclusions regarding the role of negative affect inertia in the prediction of depressive symptoms.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-22"},"PeriodicalIF":3.5,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144978248","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

How to Get MAD: Generating Uniformly Sampled Correlation Matrices with a Fixed Mean Absolute Discrepancy. 如何得到MAD：生成具有固定平均绝对差的均匀抽样相关矩阵。

IF 3.5 3区心理学

Multivariate Behavioral Research Pub Date : 2025-08-04 DOI: 10.1080/00273171.2025.2516513

Niels G Waller

{"title":"How to Get MAD: Generating Uniformly Sampled Correlation Matrices with a Fixed Mean Absolute Discrepancy.","authors":"Niels G Waller","doi":"10.1080/00273171.2025.2516513","DOIUrl":"https://doi.org/10.1080/00273171.2025.2516513","url":null,"abstract":"This article describes a simple and fast algorithm for generating uniformly sampled correlation matrices (R) with a fixed mean absolute discrepancy (MAD) relative to a target (population) <math><mrow><msub><mrow><mi>R</mi></mrow><mrow><mtext>pop</mtext></mrow></msub></mrow><mtext>.</mtext></math> The algorithm can be profitably used in many settings including model robustness studies and stress testing of investment portfolios, or in dynamic model-fit analyses to generate R matrices with a known degree of model-approximation error (as operationalized by the MAD). Using results from higher-dimensional geometry, I show that <math><mrow><msub><mrow><mi>R</mi></mrow><mrow><mi>n</mi><mo>×</mo><mi>n</mi></mrow></msub></mrow></math> matrices with a fixed MAD lie in the intersection of two sets that represent: (a) an elliptope and (b) the surface of a cross-polytope. When n = 3, these sets can be visualized as an elliptical tetrahedron and the surface of an octahedron. An online supplement includes <math><mrow><mi>R</mi></mrow></math> code for implementing the algorithm and for reproducing all of the results in the article.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-9"},"PeriodicalIF":3.5,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144776912","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

How to Capture Synchronization in Triads in One Single Measure: Development of the AMPC Measure and an Associated Significance Test. 如何在一个单一的测量中捕捉三位一体的同步：AMPC测量的发展和相关的显著性检验。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2025-07-04 DOI: 10.1080/00273171.2025.2522732

Shiyao Wang, Chiara Carlier, Martine W F T Verhees, Eva Ceulemans

{"title":"How to Capture Synchronization in Triads in One Single Measure: Development of the AMPC Measure and an Associated Significance Test.","authors":"Shiyao Wang, Chiara Carlier, Martine W F T Verhees, Eva Ceulemans","doi":"10.1080/00273171.2025.2522732","DOIUrl":"https://doi.org/10.1080/00273171.2025.2522732","url":null,"abstract":"Interpersonal synchronization is a concept often studied in psychology. Whereas most research focuses on dyads, triadic systems such as family triads warrant increased attention. A crucial challenge in taking a triadic view on synchronization is how to quantify it, since a statistical measure that captures the level of triadic synchronization in one value, while discarding dyadic synchronization only, is lacking so far. The current paper therefore investigated three existing measures that show potential to capture triadic synchronization and proposes two novel ones. We also present a significance test that allows to investigate whether the observed triadic synchronization in a triad is stronger than can be expected by chance, while accounting for potential auto-dependence in the data. By means of a simulation study, we tested (1) how the measures react to different potential synchronization patterns; (2) the Type I error rate and the power of the significance test. The results showed that only one measure, i.e., the newly proposed adapted multiplication of pairwise correlations (AMPC), can effectively capture triadic synchronization, while discarding dyadic synchronization. We then applied the AMPC measure to intensive longitudinal data on attachment-related measures in families, showing that AMPC can detect meaningful triadic synchronization in empirical data.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-19"},"PeriodicalIF":5.3,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144561951","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

Empirical Bayes Priors for MCMC Estimation of the Multivariate Social Relations Model. 多元社会关系模型MCMC估计的经验贝叶斯先验。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2025-07-02 DOI: 10.1080/00273171.2025.2496507

Aditi M Bhangale, Terrence D Jorgensen

{"title":"Empirical Bayes Priors for MCMC Estimation of the Multivariate Social Relations Model.","authors":"Aditi M Bhangale, Terrence D Jorgensen","doi":"10.1080/00273171.2025.2496507","DOIUrl":"https://doi.org/10.1080/00273171.2025.2496507","url":null,"abstract":"The social relations model (SRM) is a linear random-effects model applied to examine dyadic round-robin data within social networks. Such data have a unique multilevel structure in that dyads are cross-classified within individuals who may be nested within different social networks. The SRM decomposes perceptual or behavioral measures into multiple components: case-level random effects (in-coming and out-going effects) and dyad-level residuals (relationship effects), the associations among which are often of substantive interest. Multivariate SRM analyses are increasingly common, requiring more sophisticated estimation algorithms. This article evaluates Markov chain Monte Carlo (MCMC) estimation of multivariate-SRM parameters, compares MCMC to maximum-likelihood estimation, and introduces two methods to reduce bias in MCMC point estimates using empirical-Bayes priors. Four simulation studies are presented, two of which reveal dependency of small-group results on priors by manipulating location and precision hyperparameters, respectively. The third simulation study explores the impact of sampling more small groups on prior sensitivity. The fourth simulation study explores how Bayesian model averaging might compensate for underestimated variance due to empirical-Bayes priors. Finally, recommendations for future research are made and extensions of the SRM are discussed.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"1-24"},"PeriodicalIF":5.3,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144546140","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

Three Approaches to Testing for Statistical Suppression. 统计抑制的三种检验方法。

IF 3.5 3区心理学

Multivariate Behavioral Research Pub Date : 2025-07-01 Epub Date: 2025-05-21 DOI: 10.1080/00273171.2025.2483245

Felix B Muniz, David P MacKinnon

{"title":"Three Approaches to Testing for Statistical Suppression.","authors":"Felix B Muniz, David P MacKinnon","doi":"10.1080/00273171.2025.2483245","DOIUrl":"10.1080/00273171.2025.2483245","url":null,"abstract":"Suppression effects are important for theoretical and applied research because these effects occur when there is an unexpected increase in an effect when it is adjusted for a third variable. This paper investigates three approaches to testing for statistical suppression. The first test was proposed in 1978 and is based on the relationship between the zero-order and semi-partial correlations. The second test comes from a condition that is necessary for suppression proposed in 1997. The third test is an extension of the test for the inconsistent mediated effect. We derive standard errors for the Velicer, and Sharpe and Roberts tests, conduct a statistical simulation study, and apply all three tests to two real data sets and several published correlation matrices. In the simulation study, the test based on inconsistent mediation had the best properties overall. For the data examples, when raw data were available, we constructed bootstrap confidence intervals to assess significance, and for correlations, we compared each test statistic to the normal distribution to assess statistical significance. Each test gave consistent results when applied to the example data sets. Analytical work demonstrated conditions where each test gave conflicting results. The mediation test of suppression based on the sign of the product of the mediated effect and the direct effect had the best overall performance.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"817-839"},"PeriodicalIF":3.5,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12301867/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144112675","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

Model Selection for Mixed-Effects Location-Scale Models with Confidence Interval for LOO or WAIC Difference. 带有 LOO 或 WAIC 差异置信区间的混合效应位置尺度模型的模型选择。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2025-07-01 Epub Date: 2025-02-18 DOI: 10.1080/00273171.2025.2462033

Yue Liu, Fan Fang, Hongyun Liu

{"title":"Model Selection for Mixed-Effects Location-Scale Models with Confidence Interval for LOO or WAIC Difference.","authors":"Yue Liu, Fan Fang, Hongyun Liu","doi":"10.1080/00273171.2025.2462033","DOIUrl":"10.1080/00273171.2025.2462033","url":null,"abstract":"LOO (Leave-One-Out cross-validation) and WAIC (Widely Applicable Information Criterion) are widely used for model selection in Bayesian statistics. Most studies select the model with the smallest value based on point estimates, often without considering the differences in fit indices or the uncertainty of the estimates. To address this gap, we propose a sequential method for comparing models based on confidence intervals for <math><mi>Δ</mi><mtext>LOO</mtext></math> or <math><mi>Δ</mi><mtext>WAIC.</mtext></math> A simulation study was conducted to evaluate this method in selecting mixed-effects location-scale models (MELSMs). Our study revealed that the sequential methods, especially when using a 90% confidence interval, achieved a higher accuracy rate of model selection compared to the point method when the true model was simple, had a large magnitude of random intercept in the scale model, or had a large sample size. Models selected by the sequential methods demonstrated higher power, narrower credible interval width, smaller standard errors for the fixed effect in the location model, and lower bias for the random effect of the intercept in the location model. Differences between LOO and WAIC were significant only when the level-1 sample size was small, with LOO performing better when the true model had homogeneous or severe heterogeneity in residual variances.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"678-694"},"PeriodicalIF":5.3,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442541","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 Growth Curve Modeling with Measurement Error in Time. 具有时间测量误差的贝叶斯生长曲线建模。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2025-07-01 Epub Date: 2025-03-19 DOI: 10.1080/00273171.2025.2473937

Lijin Zhang, Wen Qu, Zhiyong Zhang

引用次数: 0

Multilevel Latent Class Analysis: State-of-the-Art Methodologies and Their Implementation in the R Package multilevLCA. 多层次潜在类分析：最先进的方法及其在R包中的实现。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2025-07-01 Epub Date: 2025-03-25 DOI: 10.1080/00273171.2025.2473935

Johan Lyrvall, Roberto Di Mari, Zsuzsa Bakk, Jennifer Oser, Jouni Kuha

引用次数: 0