Psychological methods最新文献

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Supplemental Material for When Predictors Sum to a Constant: Trade-Off Effect Analysis Using a Regression Model Based on Isometric Log-Ratio Transformation 当预测因子总和为常数时:使用基于等距对数比变换的回归模型进行权衡效应分析
IF 7 1区 心理学
Psychological methods Pub Date : 2025-05-15 DOI: 10.1037/met0000668.supp
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引用次数: 0
Supplemental Material for Timescale Mismatch in Intensive Longitudinal Data: Current Issues and Possible Solutions Based on Dynamic Structural Equation Models 密集纵向数据中时间尺度失配的补充材料:基于动态结构方程模型的当前问题和可能的解决方案
IF 7 1区 心理学
Psychological methods Pub Date : 2025-05-15 DOI: 10.1037/met0000749.supp
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引用次数: 0
Supplemental Material for Daily Dynamics and Weekly Rhythms: A Tutorial on Seasonal Autoregressive–Moving Average Models Combined With Day-of-the-Week Effects 每日动态和每周节奏的补充材料:季节性自回归移动平均模型与每周效果相结合的教程
IF 7 1区 心理学
Psychological methods Pub Date : 2025-05-15 DOI: 10.1037/met0000756.supp
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引用次数: 0
Supplemental Material for Understanding Measurement Precision From a Regression Perspective 从回归的角度理解测量精度补充材料
IF 7 1区 心理学
Psychological methods Pub Date : 2025-05-15 DOI: 10.1037/met0000763.supp
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引用次数: 0
Episode-contingent experience-sampling designs for accurate estimates of autoregressive dynamics. 情景-偶然经验-抽样设计,用于自回归动力学的准确估计。
IF 7 1区 心理学
Psychological methods Pub Date : 2025-05-12 DOI: 10.1037/met0000758
Jordan Revol,Sigert Ariens,Ginette Lafit,Janne Adolf,Eva Ceulemans
{"title":"Episode-contingent experience-sampling designs for accurate estimates of autoregressive dynamics.","authors":"Jordan Revol,Sigert Ariens,Ginette Lafit,Janne Adolf,Eva Ceulemans","doi":"10.1037/met0000758","DOIUrl":"https://doi.org/10.1037/met0000758","url":null,"abstract":"Affect dynamics are often studied by means of first-order autoregressive (AR) modeling applied to intensive longitudinal data. A key target in these studies is the AR parameter, which is often tied conceptually to regulatory behavior in the affective process. The data are typically gathered using experience sampling methods, which are designed to pick up on fluctuations in affective variables as they evolve over time in naturalistic settings. In this article, we compare classical time-contingent sampling designs to episode-contingent sampling designs, which initiate sampling when an emotional episode has been signaled. We define emotional episodes as periods where an affective process strays relatively far away from its mean. Compared to time-contingent designs, episode-contingent designs leverage on increased affective variability, which can have beneficial implications for the precision of the ordinary least squares AR effect estimator. Using an extensive simulation study, we attempt to delineate which characteristics of an episode-contingent design are important to consider, and how these characteristics are related to estimation benefits. We then turn to an empirical illustration, showing how an episode-contingent design can be implemented in practice. We also show that various patterns we expect based on the theoretical parts of the article are recovered in the data. We conclude that episode-contingent designs can have marked benefits for the precision of the AR effect estimator, and discuss a number of challenges when it comes to implementing episode-contingent designs in practice. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"30 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143992084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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
Episode-contingent experience-sampling designs for accurate estimates of autoregressive dynamics. 情景-偶然经验-抽样设计,用于自回归动力学的准确估计。
IF 7.6 1区 心理学
Psychological methods Pub Date : 2025-05-12 DOI: 10.1037/met0000758
Jordan Revol, Sigert Ariens, Ginette Lafit, Janne Adolf, Eva Ceulemans
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引用次数: 0
Supplemental Material for Episode-Contingent Experience-Sampling Designs for Accurate Estimates of Autoregressive Dynamics 自回归动力学准确估计的情景-偶然经验-抽样设计补充材料
IF 7 1区 心理学
Psychological methods Pub Date : 2025-05-08 DOI: 10.1037/met0000758.supp
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引用次数: 0
Embrace the heterogeneity in exploratory factor analysis but be transparent about what you do-A commentary on Manapat et al. (2023). 接受探索性因素分析中的异质性,但要对你所做的事情保持透明——对Manapat等人(2023)的评论。
IF 7 1区 心理学
Psychological methods Pub Date : 2025-04-28 DOI: 10.1037/met0000759
David Goretzko,Melanie Viola Partsch,Philipp Sterner
{"title":"Embrace the heterogeneity in exploratory factor analysis but be transparent about what you do-A commentary on Manapat et al. (2023).","authors":"David Goretzko,Melanie Viola Partsch,Philipp Sterner","doi":"10.1037/met0000759","DOIUrl":"https://doi.org/10.1037/met0000759","url":null,"abstract":"Manapat et al. (2023) investigated different sources of heterogeneity in exploratory factor analysis in their paper \"Evaluating Avoidable Heterogeneity in Exploratory Factor Analysis Results.\" Their study is an important step toward understanding the volatility of factor analysis results that potentially impair replication attempts in psychology. In this short commentary, we want to address the question which heterogeneity is actually \"avoidable\" and which heterogeneity can also be desirable in an exploratory analysis. Furthermore, we emphasize the need of greater research transparency when performing and reporting exploratory factor analyses and discuss the potential of preregistrations to avoid unwanted or \"avoidable\" heterogeneity. When being transparent about methodological decisions and conceptual assumptions that lead to specific configurations, we believe that it is possible to embrace the heterogeneity in exploratory factor analysis and still develop more robust measurement models. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"10 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143897423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 tutorial on using generative models to advance psychological science: Lessons from the reliability paradox. 使用生成模型推进心理科学的教程:可靠性悖论的启示。
IF 7 1区 心理学
Psychological methods Pub Date : 2025-04-14 DOI: 10.1037/met0000674
Nathaniel Haines,Peter D Kvam,Louis Irving,Colin Tucker Smith,Theodore P Beauchaine,Mark A Pitt,Woo-Young Ahn,Brandon M Turner
{"title":"A tutorial on using generative models to advance psychological science: Lessons from the reliability paradox.","authors":"Nathaniel Haines,Peter D Kvam,Louis Irving,Colin Tucker Smith,Theodore P Beauchaine,Mark A Pitt,Woo-Young Ahn,Brandon M Turner","doi":"10.1037/met0000674","DOIUrl":"https://doi.org/10.1037/met0000674","url":null,"abstract":"Theories of individual differences are foundational to psychological and brain sciences, yet they are traditionally developed and tested using superficial summaries of data (e.g., mean response times) that are disconnected from our otherwise rich conceptual theories of behavior. To resolve this theory-description gap, we review the generative modeling approach, which involves formally specifying how behavior is generated within individuals, and in turn how generative mechanisms vary across individuals. Generative modeling shifts our focus away from estimating descriptive statistical \"effects\" toward estimating psychologically interpretable parameters, while simultaneously enhancing the reliability and validity of our measures. We demonstrate the utility of generative modeling in the context of the \"reliability paradox,\" a phenomenon wherein replicable group effects (e.g., Stroop effect) fail to capture individual differences (e.g., low test-retest reliability). Simulations and empirical data from the Implicit Association Test and Stroop, Flanker, Posner, and delay discounting tasks show that generative models yield (a) more theoretically informative parameters, and (b) higher test-retest reliability estimates relative to traditional approaches, illustrating their potential for enhancing theory development. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"108 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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 multilevel compositional data analysis: Introduction, evaluation, and application. 贝叶斯多层次成分数据分析:介绍、评价与应用。
IF 7.6 1区 心理学
Psychological methods Pub Date : 2025-04-14 DOI: 10.1037/met0000750
Flora Le, Tyman E Stanford, Dorothea Dumuid, Joshua F Wiley
{"title":"Bayesian multilevel compositional data analysis: Introduction, evaluation, and application.","authors":"Flora Le, Tyman E Stanford, Dorothea Dumuid, Joshua F Wiley","doi":"10.1037/met0000750","DOIUrl":"https://doi.org/10.1037/met0000750","url":null,"abstract":"<p><p>Multilevel compositional data are data that are repeatedly measured or clustered within groups, and are nonnegative and sum to a constant value. These data arise in various settings, such as intensive, longitudinal studies using ecological momentary assessments and wearable devices. Examples include 24-hr sleep-wake behaviors, sleep architecture, and macronutrients. This article presents an innovative method for analyzing multilevel compositional data using Bayesian inference. We describe the theoretical details of the data and the models, and outline the steps necessary to implement this method. We introduce the R package multilevelcoda to facilitate the application of this method and illustrate using a real data example. An extensive parameter recovery simulation study verified the robust performance of the method. Across all conditions investigated in the simulation study, the fitted models had minimal convergence issues (convergence rate > 99%) and achieved excellent quality parameter estimates and inference, with an average bias of 0.00 (range = -0.09 to 0.05) and coverage of 0.95 (range = 0.93 to 0.97). We conclude the article with recommendations on the use of the Bayesian multilevel compositional data analysis. We hope to promote wider application of this method to gain novel and robust answers to scientific questions. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144041770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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
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