Multivariate Behavioral Research最新文献_第10页

Alternative Approaches to Estimate Causal Mediated Effects in the Single-Mediator Model 单中介模型中估算因果中介效应的其他方法

IF 3.8 3区心理学

Multivariate Behavioral Research Pub Date : 2024-02-18 DOI: 10.1080/00273171.2024.2310395

Diana Alvarez-Bartolo, David P. MacKinnon

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Structured Estimation of Heterogeneous Time Series 异质时间序列的结构化估计

IF 3.8 3区心理学

Multivariate Behavioral Research Pub Date : 2024-02-18 DOI: 10.1080/00273171.2023.2283837

Zachary F. Fisher, Younghoon Kim, Vladas Pipiras, Christopher Crawford, Daniel J. Petrie, Michael D. Hunter, Charles F. Geier

引用次数: 0

Assessing Fit in Common Factor Models Using Empirical Moment Functions 使用经验矩函数评估共因子模型的拟合度

IF 3.8 3区心理学

Multivariate Behavioral Research Pub Date : 2024-02-15 DOI: 10.1080/00273171.2024.2310421

Youjin Sung, Yang Liu

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Intensive Longitudinal Adaptive Assessment: Item Selection and Stopping Rules in Highly Multidimensional Computerized Adaptive Tests 强化纵向适应性评估：高度多维计算机化自适应测试中的项目选择和停止规则

IF 3.8 3区心理学

Multivariate Behavioral Research Pub Date : 2024-02-15 DOI: 10.1080/00273171.2024.2310411

Kenneth McClure

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Modeling Intraindividual Variability as Predictors in Longitudinal Research 将个体内部变异性建模为纵向研究中的预测因子

IF 3.8 3区心理学

Multivariate Behavioral Research Pub Date : 2024-02-15 DOI: 10.1080/00273171.2024.2310434

Yuan Fang, Lijuan Wang

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An Extended Taylor Russell Model for Multiple Predictors 多预测因子的扩展泰勒-罗素模型

IF 3.8 3区心理学

Multivariate Behavioral Research Pub Date : 2024-02-15 DOI: 10.1080/00273171.2024.2310427

Ziyu Ren, Niels Waller

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Handling Missing Data in Randomized Controlled Trials with Omitted Moderation Effects 处理具有遗漏调节效应的随机对照试验中的缺失数据

IF 3.8 3区心理学

Multivariate Behavioral Research Pub Date : 2024-02-14 DOI: 10.1080/00273171.2024.2310407

Elizabeth M. Pauley, Manshu Yang

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An Analytical Comparison of Three Modeling Approaches for Longitudinal Mediation Analysis 纵向中介分析三种建模方法的分析比较

IF 3.8 3区心理学

Multivariate Behavioral Research Pub Date : 2024-02-13 DOI: 10.1080/00273171.2024.2310415

Ruoxuan Li, Lijuan Wang

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Go Multivariate: Recommendations on Bayesian Multilevel Hidden Markov Models with Categorical Data. 走向多变量：关于使用分类数据的贝叶斯多层次隐马尔可夫模型的建议。

IF 3.8 3区心理学

Multivariate Behavioral Research Pub Date : 2024-01-01 Epub Date: 2023-05-17 DOI: 10.1080/00273171.2023.2205392

Sebastian Mildiner Moraga, Emmeke Aarts

{"title":"Go Multivariate: Recommendations on Bayesian Multilevel Hidden Markov Models with Categorical Data.","authors":"Sebastian Mildiner Moraga, Emmeke Aarts","doi":"10.1080/00273171.2023.2205392","DOIUrl":"10.1080/00273171.2023.2205392","url":null,"abstract":"The multilevel hidden Markov model (MHMM) is a promising method to investigate intense longitudinal data obtained within the social and behavioral sciences. The MHMM quantifies information on the latent dynamics of behavior over time. In addition, heterogeneity between individuals is accommodated with the inclusion of individual-specific random effects, facilitating the study of individual differences in dynamics. However, the performance of the MHMM has not been sufficiently explored. We performed an extensive simulation to assess the effect of the number of dependent variables (1-8), number of individuals (5-90), and number of observations per individual (100-1600) on the estimation performance of a Bayesian MHMM with categorical data including various levels of state distinctiveness and separation. We found that using multivariate data generally alleviates the sample size needed and improves the stability of the results. Moreover, including variables only consisting of random noise was generally not detrimental to model performance. Regarding the estimation of group-level parameters, the number of individuals and observations largely compensate for each other. However, only the former drives the estimation of between-individual variability. We conclude with guidelines on the sample size necessary based on the level of state distinctiveness and separation and study objectives of the researcher.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"17-45"},"PeriodicalIF":3.8,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9481386","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

Problems of Domain Factors with Small Factor Loadings in Bi-Factor Models. 双因子模型中因子载荷较小的域因子问题。

IF 3.8 3区心理学

Multivariate Behavioral Research Pub Date : 2024-01-01 Epub Date: 2023-09-04 DOI: 10.1080/00273171.2023.2228757

Nils Petras, Thorsten Meiser

{"title":"Problems of Domain Factors with Small Factor Loadings in Bi-Factor Models.","authors":"Nils Petras, Thorsten Meiser","doi":"10.1080/00273171.2023.2228757","DOIUrl":"10.1080/00273171.2023.2228757","url":null,"abstract":"Many measurement designs produce domain factors with small variances and factor loadings. The current study investigates the cause, prevalence, and problematic consequences of such domain factors. We collected a meta-analytic sample of empirical applications, conducted a simulation study on statistical power and estimation precision, and provide a reanalysis of an empirical example. The meta-analysis shows that about a quarter of all standardized domain factor loadings is in the range of <math><mrow><mo>-</mo><mn>.2</mn><mo><</mo><mi>λ</mi><mo><</mo><mn>.2</mn></mrow></math> and about a third of all domains is measured by five or fewer indicators, resulting in small factor variances. The simulation study examines the associated difficulties concerning statistical power, trait recovery, irregular estimates, and estimation precision for a range of such realistic cases. The empirical example illustrates the challenge to develop measures that produce clearly interpretable domain factors. Study planning and interpretation need to take the (expected) sum of squared factor loadings per domain factor into account. This is relevant even if influences of domain factors are desired to be small, and equally applies to different model variants. We propose several strategies for how researchers may better unlock the bifactor model's full potential and clarify its interpretation.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"123-147"},"PeriodicalIF":3.8,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10154752","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