Multivariate Behavioral Research最新文献_第6页

Counterfactual Mediation Analysis with a Latent Class Exposure. 潜类暴露的反事实中介分析。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2024-07-01 Epub Date: 2024-05-31 DOI: 10.1080/00273171.2024.2335394

Gemma Hammerton, Jon Heron, Katie Lewis, Kate Tilling, Stijn Vansteelandt

{"title":"Counterfactual Mediation Analysis with a Latent Class Exposure.","authors":"Gemma Hammerton, Jon Heron, Katie Lewis, Kate Tilling, Stijn Vansteelandt","doi":"10.1080/00273171.2024.2335394","DOIUrl":"10.1080/00273171.2024.2335394","url":null,"abstract":"Latent classes are a useful tool in developmental research, however there are challenges associated with embedding them within a counterfactual mediation model. We develop and test a new method \"updated pseudo class draws (uPCD)\" to examine the association between a latent class exposure and distal outcome that could easily be extended to allow the use of any counterfactual mediation method. UPCD extends an existing group of methods (based on pseudo class draws) that assume that the true values of the latent class variable are missing, and need to be multiply imputed using class membership probabilities. We simulate data based on the Avon Longitudinal Study of Parents and Children, examine performance for existing techniques to relate a latent class exposure to a distal outcome (\"one-step,\" \"bias-adjusted three-step,\" \"modal class assignment,\" \"non-inclusive pseudo class draws,\" and \"inclusive pseudo class draws\") and compare bias in parameter estimates and their precision to uPCD when estimating counterfactual mediation effects. We found that uPCD shows minimal bias when estimating counterfactual mediation effects across all levels of entropy. UPCD performs similarly to recommended methods (one-step and bias-adjusted three-step), but provides greater flexibility and scope for incorporating the latent grouping within any commonly-used counterfactual mediation approach.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"818-840"},"PeriodicalIF":5.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11286213/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141184867","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

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

Understanding the Consequences of Collinearity for Multilevel Models: The Importance of Disaggregation Across Levels. 了解多层次模型的共线性后果：跨层次分解的重要性。

IF 5.3 3区心理学

Multivariate Behavioral Research Pub Date : 2024-07-01 Epub Date: 2024-05-09 DOI: 10.1080/00273171.2024.2315549

Haley E Yaremych, Kristopher J Preacher

{"title":"Understanding the Consequences of Collinearity for Multilevel Models: The Importance of Disaggregation Across Levels.","authors":"Haley E Yaremych, Kristopher J Preacher","doi":"10.1080/00273171.2024.2315549","DOIUrl":"10.1080/00273171.2024.2315549","url":null,"abstract":"In multilevel models, disaggregating predictors into level-specific parts (typically accomplished via centering) benefits parameter estimates and their interpretations. However, the importance of level-specificity has been sparsely addressed in multilevel literature concerning collinearity. In this study, we develop novel insights into the interactivity of centering and collinearity in multilevel models. After integrating the broad literatures on centering and collinearity, we review level-specific and conflated correlations in multilevel data. Next, by deriving formal relationships between predictor collinearity and multilevel model estimates, we demonstrate how the consequences of collinearity change across different centering specifications and identify data characteristics that may exacerbate or mitigate those consequences. We show that when all or some level-1 predictors are uncentered, slope estimates can be greatly biased by collinearity. Disaggregation of all predictors eliminates the possibility that fixed effect estimates will be biased due to collinearity alone; however, under some data conditions, collinearity is associated with biased standard errors and random effect (co)variance estimates. Finally, we illustrate the importance of disaggregation for diagnosing collinearity in multilevel data and provide recommendations for the use of level-specific collinearity diagnostics. Overall, the necessity of disaggregation for identifying and managing collinearity's consequences in multilevel models is clarified in novel ways.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"693-715"},"PeriodicalIF":5.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140900141","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