Multivariate Behavioral Research最新文献_第5页

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

Using Monte Carlo Simulation to Forecast the Scientific Utility of Psychological App Studies: A Tutorial. 使用蒙特卡罗模拟预测心理应用研究的科学实用性：教程。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2024-07-01 Epub Date: 2024-07-11 DOI: 10.1080/00273171.2024.2335411

Sebastian Kueppers, Richard Rau, Florian Scharf

{"title":"Using Monte Carlo Simulation to Forecast the Scientific Utility of Psychological App Studies: A Tutorial.","authors":"Sebastian Kueppers, Richard Rau, Florian Scharf","doi":"10.1080/00273171.2024.2335411","DOIUrl":"10.1080/00273171.2024.2335411","url":null,"abstract":"Mobile applications offer a wide range of opportunities for psychological data collection, such as increased ecological validity and greater acceptance by participants compared to traditional laboratory studies. However, app-based psychological data also pose data-analytic challenges because of the complexities introduced by missingness and interdependence of observations. Consequently, researchers must weigh the advantages and disadvantages of app-based data collection to decide on the scientific utility of their proposed app study. For instance, some studies might only be worthwhile if they provide adequate statistical power. However, the complexity of app data forestalls the use of simple analytic formulas to estimate properties such as power. In this paper, we demonstrate how Monte Carlo simulations can be used to investigate the impact of app usage behavior on the utility of app-based psychological data. We introduce a set of questions to guide simulation implementation and showcase how we answered them for the simulation in the context of the guessing game app Who Knows (Rau et al., 2023). Finally, we give a brief overview of the simulation results and the conclusions we have drawn from them for real-world data generation. Our results can serve as an example of how to use a simulation approach for planning real-world app-based data collection.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"879-893"},"PeriodicalIF":5.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141581495","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

A Confidence Interval for the Difference Between Standardized Regression Coefficients. 标准化回归系数之差的置信区间。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2024-07-01 Epub Date: 2024-04-01 DOI: 10.1080/00273171.2024.2318784

Samantha F Anderson

{"title":"A Confidence Interval for the Difference Between Standardized Regression Coefficients.","authors":"Samantha F Anderson","doi":"10.1080/00273171.2024.2318784","DOIUrl":"10.1080/00273171.2024.2318784","url":null,"abstract":"Researchers are often interested in comparing predictors, a practice commonly done via informal comparisons of standardized regression slopes. However, formal interval-based approaches offer advantages over informal comparison. Specifically, this article examines a delta-method-based confidence interval for the difference between two standardized regression coefficients, building upon previous work on confidence intervals for single coefficients. Using Monte Carlo simulation studies, the proposed approach is evaluated at finite sample sizes with respect to coverage rate, interval width, Type I error rate, and statistical power under a variety of conditions, and is shown to outperform an alternative approach that uses the standard covariance matrix found in regression textbooks. Additional simulations evaluate current software implementations, small sample performance, and multiple comparison procedures for simultaneously testing multiple differences of interest. Guidance on sample size planning for narrow confidence intervals, an R function to conduct the proposed method, and two empirical demonstrations are provided. The goal is to offer researchers a different tool in their toolbox for when comparisons among standardized coefficients are desired, as a supplement to, rather than a replacement for, other potentially useful analyses.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"758-780"},"PeriodicalIF":5.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140337639","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

Finite Mixtures of Latent Trait Analyzers With Concomitant Variables for Bipartite Networks: An Analysis of COVID-19 Data. 双方网络中带有伴随变量的潜在特质分析器的有限混合物：COVID-19 数据分析

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2024-07-01 Epub Date: 2024-05-24 DOI: 10.1080/00273171.2024.2335391

Dalila Failli, Maria Francesca Marino, Francesca Martella

引用次数: 0

Methodological and Statistical Practices of Using Symptom Networks to Evaluate Mental Health Interventions: A Review and Reflections. 使用症状网络评估心理健康干预措施的方法和统计实践：回顾与思考》。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2024-07-01 Epub Date: 2024-05-11 DOI: 10.1080/00273171.2024.2335401

Lea Schumacher, Julian Burger, Jette Echterhoff, Levente Kriston

{"title":"Methodological and Statistical Practices of Using Symptom Networks to Evaluate Mental Health Interventions: A Review and Reflections.","authors":"Lea Schumacher, Julian Burger, Jette Echterhoff, Levente Kriston","doi":"10.1080/00273171.2024.2335401","DOIUrl":"10.1080/00273171.2024.2335401","url":null,"abstract":"The network approach to psychopathology, which assesses associations between individual symptoms, has recently been applied to evaluate treatments for mental disorders. While various options for conducting network analyses in intervention research exist, an overview and an evaluation of the various approaches are currently missing. Therefore, we conducted a review on network analyses in intervention research. Studies were included if they constructed a symptom network, analyzed data that were collected before, during or after treatment of a mental disorder, and yielded information about the treatment effect. The 56 included studies were reviewed regarding their methodological and analytic strategies. About half of the studies based on data from randomized trials conducted a network intervention analysis, while the other half compared networks between treatment groups. The majority of studies estimated cross-sectional networks, even when repeated measures were available. All but five studies investigated networks on the group level. This review highlights that current methodological practices limit the information that can be gained through network analyses in intervention research. We discuss the strength and limitations of certain methodological and analytic strategies and propose that further work is needed to use the full potential of the network approach in intervention research.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"663-676"},"PeriodicalIF":5.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140908804","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 Multivariate Logistic Regression for Superiority and Inferiority Decision-Making under Observable Treatment Heterogeneity. 在可观察到的治疗异质性条件下，用于优劣决策的贝叶斯多变量逻辑回归。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2024-07-01 Epub Date: 2024-05-11 DOI: 10.1080/00273171.2024.2337340

Xynthia Kavelaars, Joris Mulder, Maurits Kaptein

{"title":"Bayesian Multivariate Logistic Regression for Superiority and Inferiority Decision-Making under Observable Treatment Heterogeneity.","authors":"Xynthia Kavelaars, Joris Mulder, Maurits Kaptein","doi":"10.1080/00273171.2024.2337340","DOIUrl":"10.1080/00273171.2024.2337340","url":null,"abstract":"The effects of treatments may differ between persons with different characteristics. Addressing such treatment heterogeneity is crucial to investigate whether patients with specific characteristics are likely to benefit from a new treatment. The current paper presents a novel Bayesian method for superiority decision-making in the context of randomized controlled trials with multivariate binary responses and heterogeneous treatment effects. The framework is based on three elements: a) Bayesian multivariate logistic regression analysis with a Pólya-Gamma expansion; b) a transformation procedure to transfer obtained regression coefficients to a more intuitive multivariate probability scale (i.e., success probabilities and the differences between them); and c) a compatible decision procedure for treatment comparison with prespecified decision error rates. Procedures for a priori sample size estimation under a non-informative prior distribution are included. A numerical evaluation demonstrated that decisions based on a priori sample size estimation resulted in anticipated error rates among the trial population as well as subpopulations. Further, average and conditional treatment effect parameters could be estimated unbiasedly when the sample was large enough. Illustration with the International Stroke Trial dataset revealed a trend toward heterogeneous effects among stroke patients: Something that would have remained undetected when analyses were limited to average treatment effects.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"859-882"},"PeriodicalIF":5.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11548885/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140908832","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

Information Matrix Test for Item Response Models Using Stochastic Approximation. 使用随机逼近法进行项目反应模型的信息矩阵测试

IF 3.8 3区心理学

Multivariate Behavioral Research Pub Date : 2024-05-01 Epub Date: 2024-02-19 DOI: 10.1080/00273171.2024.2310426

Youngjin Han, Yang Liu, Ji Seung Yang

引用次数: 0

Propensity Score Weighting with Missing Data on Covariates and Clustered Data Structure. 具有协变量缺失数据和聚类数据结构的倾向得分加权。

IF 3.8 3区心理学

Multivariate Behavioral Research Pub Date : 2024-05-01 Epub Date: 2024-02-20 DOI: 10.1080/00273171.2024.2307529

Xiao Liu

{"title":"Propensity Score Weighting with Missing Data on Covariates and Clustered Data Structure.","authors":"Xiao Liu","doi":"10.1080/00273171.2024.2307529","DOIUrl":"10.1080/00273171.2024.2307529","url":null,"abstract":"Propensity score (PS) analyses are increasingly popular in behavioral sciences. Two issues often add complexities to PS analyses, including missing data in observed covariates and clustered data structure. In previous research, methods for conducting PS analyses with considering either issue alone were examined. In practice, the two issues often co-occur; but the performance of methods for PS analyses in the presence of both issues has not been evaluated previously. In this study, we consider PS weighting analysis when data are clustered and observed covariates have missing values. A simulation study is conducted to evaluate the performance of different missing data handling methods (complete-case, single-level imputation, or multilevel imputation) combined with different multilevel PS weighting methods (fixed- or random-effects PS models, inverse-propensity-weighting or the clustered weighting, weighted single-level or multilevel outcome models). The results suggest that the bias in average treatment effect estimation can be reduced, by better accounting for clustering in both the missing data handling stage (such as with the multilevel imputation) and the PS analysis stage (such as with the fixed-effects PS model, clustered weighting, and weighted multilevel outcome model). A real-data example is provided for illustration.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"411-433"},"PeriodicalIF":3.8,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139914051","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 Ability and Reliability Differences Measured with Count Items: The Distributional Regression Test Model and the Count Latent Regression Model. 理解用计数项目测量的能力和可靠性差异：分布回归测试模型和计数潜回归模型。

IF 3.8 3区心理学

Multivariate Behavioral Research Pub Date : 2024-05-01 Epub Date: 2024-02-13 DOI: 10.1080/00273171.2023.2288577

Marie Beisemann, Boris Forthmann, Philipp Doebler

引用次数: 0

Investigating Moderation Effects at the Within-Person Level Using Intensive Longitudinal Data: A Two-Level Dynamic Structural Equation Modelling Approach in Mplus. 利用密集纵向数据调查人内水平的调节效应：Mplus 中的两级动态结构方程建模方法》（A Two-Level Dynamic Structural Equation Modelling Approach in Mplus）。

IF 3.8 3区心理学

Multivariate Behavioral Research Pub Date : 2024-05-01 Epub Date: 2024-02-14 DOI: 10.1080/00273171.2023.2288575

Lydia Gabriela Speyer, Aja Louise Murray, Rogier Kievit

{"title":"Investigating Moderation Effects at the Within-Person Level Using Intensive Longitudinal Data: A Two-Level Dynamic Structural Equation Modelling Approach in Mplus.","authors":"Lydia Gabriela Speyer, Aja Louise Murray, Rogier Kievit","doi":"10.1080/00273171.2023.2288575","DOIUrl":"10.1080/00273171.2023.2288575","url":null,"abstract":"Recent technological advances have provided new opportunities for the collection of intensive longitudinal data. Using methods such as dynamic structural equation modeling, these data can provide new insights into moment-to-moment dynamics of psychological and behavioral processes. In intensive longitudinal data (t > 20), researchers often have theories that imply that factors that change from moment to moment within individuals act as moderators. For instance, a person's level of sleep deprivation may affect how much an external stressor affects mood. Here, we describe how researchers can implement, test, and interpret dynamically changing within-person moderation effects using two-level dynamic structural equation modeling as implemented in the structural equation modeling software Mplus. We illustrate the analysis of within-person moderation effects using an empirical example investigating whether changes in spending time online using social media affect the moment-to-moment effect of loneliness on depressive symptoms, and highlight avenues for future methodological development. We provide annotated Mplus code, enabling researchers to better isolate, estimate, and interpret the complexities of within-person interaction effects.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"620-637"},"PeriodicalIF":3.8,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139736687","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