Multivariate Behavioral Research最新文献_第8页

A Network Study of Family Affect Systems in Daily Life. 日常生活中家庭情感系统的网络研究。

IF 3.8 3区心理学

Multivariate Behavioral Research Pub Date : 2024-03-01 Epub Date: 2024-02-14 DOI: 10.1080/00273171.2023.2283632

Myrthe Veenman, Loes H C Janssen, Lisanne A E M van Houtum, Mirjam C M Wever, Bart Verkuil, Sacha Epskamp, Eiko I Fried, Bernet M Elzinga

{"title":"A Network Study of Family Affect Systems in Daily Life.","authors":"Myrthe Veenman, Loes H C Janssen, Lisanne A E M van Houtum, Mirjam C M Wever, Bart Verkuil, Sacha Epskamp, Eiko I Fried, Bernet M Elzinga","doi":"10.1080/00273171.2023.2283632","DOIUrl":"10.1080/00273171.2023.2283632","url":null,"abstract":"Adolescence is a time period characterized by extremes in affect and increasing prevalence of mental health problems. Prior studies have illustrated how affect states of adolescents are related to interactions with parents. However, it remains unclear how affect states among family triads, that is adolescents and their parents, are related in daily life. This study investigated affect state dynamics (happy, sad, relaxed, and irritated) of 60 family triads, including 60 adolescents (Mage = 15.92, 63.3% females), fathers and mothers (Mage = 49.16). The families participated in the RE-PAIR study, where they reported their affect states in four ecological momentary assessments per day for 14 days. First, we used multilevel vector-autoregressive network models to estimate affect dynamics across all families, and for each family individually. Resulting models elucidated how family affect states were related at the same moment, and over time. We identified relations from parents to adolescents and vice versa, while considering family variation in these relations. Second, we evaluated the statistical performance of the network model via a simulation study, varying the percentage missing data, the number of families, and the number of time points. We conclude with substantive and statistical recommendations for future research on family affect dynamics.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"371-405"},"PeriodicalIF":3.8,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139736686","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 Generalized Bootstrap Procedure of the Standard Error and Confidence Interval Estimation for Inverse Probability of Treatment Weighting. 治疗加权反向概率的标准误差和置信区间估计的通用 Bootstrap 程序。

IF 3.8 3区心理学

Multivariate Behavioral Research Pub Date : 2024-03-01 Epub Date: 2023-09-19 DOI: 10.1080/00273171.2023.2254541

Tenglong Li, Jordan Lawson

{"title":"A Generalized Bootstrap Procedure of the Standard Error and Confidence Interval Estimation for Inverse Probability of Treatment Weighting.","authors":"Tenglong Li, Jordan Lawson","doi":"10.1080/00273171.2023.2254541","DOIUrl":"10.1080/00273171.2023.2254541","url":null,"abstract":"The inverse probability of treatment weighting (IPTW) approach is commonly used in propensity score analysis to infer causal effects in regression models. Due to oversized IPTW weights and errors associated with propensity score estimation, the IPTW approach can underestimate the standard error of causal effect. To remediate this, bootstrap standard errors have been recommended to replace the IPTW standard error, but the ordinary bootstrap (OB) procedure might still result in underestimation of the standard error because of its inefficient resampling scheme and untreated oversized weights. In this paper, we develop a generalized bootstrap (GB) procedure for estimating the standard error and confidence intervals of the IPTW approach. Compared with the OB procedure and other three procedures in comparison, the GB procedure has the highest precision and yields conservative standard error estimates. As a result, the GB procedure produces short confidence intervals with highest coverage rates. We demonstrate the effectiveness of the GB procedure via two simulation studies and a dataset from the National Educational Longitudinal Study-1988 (NELS-88).","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"251-265"},"PeriodicalIF":3.8,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10303023","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 Instrumental Variables to Measure Causation over Time in Cross-Lagged Panel Models. 在跨滞后面板模型中使用工具变量衡量随时间变化的因果关系。

IF 3.8 3区心理学

Multivariate Behavioral Research Pub Date : 2024-03-01 Epub Date: 2024-02-15 DOI: 10.1080/00273171.2023.2283634

Madhurbain Singh, Brad Verhulst, Philip Vinh, Yi Daniel Zhou, Luis F S Castro-de-Araujo, Jouke-Jan Hottenga, René Pool, Eco J C de Geus, Jacqueline M Vink, Dorret I Boomsma, Hermine H M Maes, Conor V Dolan, Michael C Neale

{"title":"Using Instrumental Variables to Measure Causation over Time in Cross-Lagged Panel Models.","authors":"Madhurbain Singh, Brad Verhulst, Philip Vinh, Yi Daniel Zhou, Luis F S Castro-de-Araujo, Jouke-Jan Hottenga, René Pool, Eco J C de Geus, Jacqueline M Vink, Dorret I Boomsma, Hermine H M Maes, Conor V Dolan, Michael C Neale","doi":"10.1080/00273171.2023.2283634","DOIUrl":"10.1080/00273171.2023.2283634","url":null,"abstract":"Cross-lagged panel models (CLPMs) are commonly used to estimate causal influences between two variables with repeated assessments. The lagged effects in a CLPM depend on the time interval between assessments, eventually becoming undetectable at longer intervals. To address this limitation, we incorporate instrumental variables (IVs) into the CLPM with two study waves and two variables. Doing so enables estimation of both the lagged (i.e., \"distal\") effects and the bidirectional cross-sectional (i.e., \"proximal\") effects at each wave. The distal effects reflect Granger-causal influences across time, which decay with increasing time intervals. The proximal effects capture causal influences that accrue over time and can help infer causality when the distal effects become undetectable at longer intervals. Significant proximal effects, with a negligible distal effect, would imply that the time interval is too long to estimate a lagged effect at that time interval using the standard CLPM. Through simulations and an empirical application, we demonstrate the impact of time intervals on causal inference in the CLPM and present modeling strategies to detect causal influences regardless of the time interval in a study. Furthermore, to motivate empirical applications of the proposed model, we highlight the utility and limitations of using genetic variables as IVs in large-scale panel studies.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"342-370"},"PeriodicalIF":3.8,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11014768/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139736688","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

Unreliable Continuous Treatment Indicators in Propensity Score Analysis. 倾向得分分析中不可靠的连续治疗指标。

IF 3.8 3区心理学

Multivariate Behavioral Research Pub Date : 2024-03-01 Epub Date: 2023-07-31 DOI: 10.1080/00273171.2023.2235697

Gail A Fish, Walter L Leite

{"title":"Unreliable Continuous Treatment Indicators in Propensity Score Analysis.","authors":"Gail A Fish, Walter L Leite","doi":"10.1080/00273171.2023.2235697","DOIUrl":"10.1080/00273171.2023.2235697","url":null,"abstract":"Propensity score analyses (PSA) of continuous treatments often operationalize the treatment as a multi-indicator composite, and its composite reliability is unreported. Latent variables or factor scores accounting for this unreliability are seldom used as alternatives to composites. This study examines the effects of the unreliability of indicators of a latent treatment in PSA using the generalized propensity score (GPS). A Monte Carlo simulation study was conducted varying composite reliability, continuous treatment representation, variability of factor loadings, sample size, and number of treatment indicators to assess whether Average Treatment Effect (ATE) estimates differed in their relative bias, Root Mean Squared Error, and coverage rates. Results indicate that low composite reliability leads to underestimation of the ATE of latent continuous treatments, while the number of treatment indicators and variability of factor loadings show little effect on ATE estimates, after controlling for overall composite reliability. The results also show that, in correctly specified GPS models, the effects of low composite reliability can be somewhat ameliorated by using factor scores that were estimated including covariates. An illustrative example is provided using survey data to estimate the effect of teacher adoption of a workbook related to a virtual learning environment in the classroom.","PeriodicalId":53155,"journal":{"name":"Multivariate Behavioral Research","volume":" ","pages":"187-205"},"PeriodicalIF":3.8,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9965491","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

Clustering Analysis of Time Series of Affect in Dyadic Interactions 双向互动中情感时间序列的聚类分析

IF 3.8 3区心理学

Multivariate Behavioral Research Pub Date : 2024-02-26 DOI: 10.1080/00273171.2023.2283633

Samuel D. Aragones, Emilio Ferrer

引用次数: 0

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

引用次数: 0

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

引用次数: 0

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