Multivariate Behavioral Research最新文献_第10页

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

引用次数: 0

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

引用次数: 0

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

引用次数: 0

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

Individual Mobility across Clusters: The Impact of Ignoring Cross-Classified Data Structures in Discrete-Time Survival Analysis. 跨群组的个体流动性：在离散时间生存分析中忽略跨分类数据结构的影响》（The Impact of Ignoring Cross-Classified Data Structures in Discrete-Time Survival Analysis）。

IF 3.8 3区心理学

Multivariate Behavioral Research Pub Date : 2024-01-01 Epub Date: 2023-09-04 DOI: 10.1080/00273171.2023.2230481

Christopher J Cappelli, Audrey J Leroux, Katherine E Masyn

{"title":"Individual Mobility across Clusters: The Impact of Ignoring Cross-Classified Data Structures in Discrete-Time Survival Analysis.","authors":"Christopher J Cappelli, Audrey J Leroux, Katherine E Masyn","doi":"10.1080/00273171.2023.2230481","DOIUrl":"10.1080/00273171.2023.2230481","url":null,"abstract":"A multilevel-discrete time survival model may be appropriate for purely hierarchical data, but when data are non-purely hierarchical due to individual mobility across clusters, a cross-classified discrete time survival model may be necessary. The purpose of this research was to investigate the performance of a cross-classified discrete-time survival model and assess the impact of ignoring a cross-classified data structure on the model parameters of a conventional discrete-time survival model and a multilevel discrete-time survival model. A Monte Carlo simulation was used to examine the performance of three discrete-time survival models when individuals are mobile across clusters. Simulation factors included the value of the between-clusters variance, number of clusters, within-cluster sample size, Weibull scale parameter, and mobility rate. The results suggest that substantial relative parameter bias, unacceptable coverage of the 95% confidence intervals, and severely biased standard errors are possible for all model parameters when a discrete-time survival model is used that ignores the cross-classified data structure. The findings presented in this study are useful for methodologists and practitioners in educational research, public health, and other social sciences where discrete-time survival analysis is a common methodological technique for analyzing event-history data.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"171-186"},"PeriodicalIF":3.8,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10154753","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

Modeling Heterogeneity in Temporal Dynamics: Extending Latent State-Trait Autoregressive and Cross-lagged Panel Models to Mixture Distribution Models. 时间动态中的异质性建模：将潜在状态-特质自回归模型和交叉滞后面板模型扩展到混合分布模型。

IF 3.8 3区心理学

Multivariate Behavioral Research Pub Date : 2024-01-01 Epub Date: 2023-05-02 DOI: 10.1080/00273171.2023.2201824

Jana Holtmann, Michael Eid, Philip S Santangelo, Tobias D Kockler, Ulrich W Ebner-Priemer

{"title":"Modeling Heterogeneity in Temporal Dynamics: Extending Latent State-Trait Autoregressive and Cross-lagged Panel Models to Mixture Distribution Models.","authors":"Jana Holtmann, Michael Eid, Philip S Santangelo, Tobias D Kockler, Ulrich W Ebner-Priemer","doi":"10.1080/00273171.2023.2201824","DOIUrl":"10.1080/00273171.2023.2201824","url":null,"abstract":"Longitudinal models suited for the analysis of panel data, such as cross-lagged panel or autoregressive latent-state trait models, assume population homogeneity with respect to the temporal dynamics of the variables under investigation. This assumption is likely to be too restrictive in a myriad of research areas. We propose an extension of autoregressive and cross-lagged latent state-trait models to mixture distribution models. The models allow researchers to model unobserved person heterogeneity and qualitative differences in longitudinal dynamics based on comparatively few observations per person, while taking into account temporal dependencies between observations as well as measurement error in the variables. The models are extended to include categorical covariates, to investigate the distribution of encountered latent classes across observed groups. The potential of the models is illustrated with an application to self-esteem and affect data in patients with borderline personality disorder, an anxiety disorder, and healthy control participants. Requirements for the models' applicability are investigated in an extensive simulation study and recommendations for model applications are derived.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"148-170"},"PeriodicalIF":3.8,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9768967","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

Permutation Tests for Assessing Potential Non-Linear Associations between Treatment Use and Multivariate Clinical Outcomes. 用于评估治疗使用与多变量临床结果之间潜在非线性关联的置换检验。

IF 3.8 3区心理学

Multivariate Behavioral Research Pub Date : 2024-01-01 Epub Date: 2023-06-28 DOI: 10.1080/00273171.2023.2217662

Boyu Ren, Stuart R Lipsitz, Garrett M Fitzmaurice, Roger D Weiss

{"title":"Permutation Tests for Assessing Potential Non-Linear Associations between Treatment Use and Multivariate Clinical Outcomes.","authors":"Boyu Ren, Stuart R Lipsitz, Garrett M Fitzmaurice, Roger D Weiss","doi":"10.1080/00273171.2023.2217662","DOIUrl":"10.1080/00273171.2023.2217662","url":null,"abstract":"In many psychometric applications, the relationship between the mean of an outcome and a quantitative covariate is too complex to be described by simple parametric functions; instead, flexible nonlinear relationships can be incorporated using penalized splines. Penalized splines can be conveniently represented as a linear mixed effects model (LMM), where the coefficients of the spline basis functions are random effects. The LMM representation of penalized splines makes the extension to multivariate outcomes relatively straightforward. In the LMM, no effect of the quantitative covariate on the outcome corresponds to the null hypothesis that a fixed effect and a variance component are both zero. Under the null, the usual asymptotic chi-square distribution of the likelihood ratio test for the variance component does not hold. Therefore, we propose three permutation tests for the likelihood ratio test statistic: one based on permuting the quantitative covariate, the other two based on permuting residuals. We compare via simulation the Type I error rate and power of the three permutation tests obtained from joint models for multiple outcomes, as well as a commonly used parametric test. The tests are illustrated using data from a stimulant use disorder psychosocial clinical trial.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"110-122"},"PeriodicalIF":3.8,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10753035/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9791428","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