Psychological methods最新文献_第2页

A tool to simulate and visualize dyadic interaction dynamics. 一种模拟和可视化二元交互动力学的工具。

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-06-01 Epub Date: 2023-05-25 DOI: 10.1037/met0000575

Sophie W Berkhout, Noémi K Schuurman, Ellen L Hamaker

{"title":"A tool to simulate and visualize dyadic interaction dynamics.","authors":"Sophie W Berkhout, Noémi K Schuurman, Ellen L Hamaker","doi":"10.1037/met0000575","DOIUrl":"10.1037/met0000575","url":null,"abstract":"ynamic models are becoming increasingly popular to study the dynamic processes of dyadic interactions. In this article, we present a Dyadic Interaction Dynamics (DID) Shiny app which provides simulations and visualizations of data from several models that have been proposed for the analysis of dyadic data. We propose data generation as a tool to inspire and guide theory development and elaborate on how to connect substantive ideas to specific features of these models. We begin by discussing the basics of dynamic models with dyadic interactions. Then we present several models and illustrate model-implied behavior through generated data, accompanied by the DID Shiny app which allows researchers to generate and visualize their own data. Specifically, we consider: (a) the first-order vector autoregressive (VAR(1)) model; (b) the latent VAR(1) model; (c) the time-varying VAR(1) model; (d) the threshold VAR(1) model; (e) the hidden Markov model; and (f) the Markov-switching VAR(1) model. Finally, we demonstrate these models using empirical examples. We aim to give researchers more insight into what dynamic modeling approach fits their research question and data best. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"599-621"},"PeriodicalIF":7.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9876145","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

Let sleeping dogs lie? How to deal with the night gap problem in experience sampling method data. 杞人忧天？如何处理经验采样法数据中的夜隙问题。

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-05-22 DOI: 10.1037/met0000762

Sophie W Berkhout, Noémi K Schuurman, Ellen L Hamaker

{"title":"Let sleeping dogs lie? How to deal with the night gap problem in experience sampling method data.","authors":"Sophie W Berkhout, Noémi K Schuurman, Ellen L Hamaker","doi":"10.1037/met0000762","DOIUrl":"https://doi.org/10.1037/met0000762","url":null,"abstract":"Night gaps are inherent to data obtained with the experience sampling method (ESM). When such data are used to study lagged relations between variables-such as autoregression within the same variable, and cross-lagged regressions between different variables-the actual role of night gaps is typically not investigated. However, there are various methods to handle them in analyses. Common solutions involve (a) ignoring the night gap by considering the night interval as a regular interval; (b) removing the night gap by not regressing the first measurement of the day on the last measurement of the previous day; or (c) treating the night gap as a missing data problem. The goal of this article is to make explicit the theoretical implications of these three methods within the context of the first-order autoregressive model. Additionally, we propose an alternative modeling approach that allows us to study the implications of the night gap in more detail. Moreover, given that the current methods are special cases of the proposed alternative, we can test which method best describes the process of interest. Through an empirical N = 1 example with various ESM variables, we demonstrate that the best-fitting method differs per variable. This implies that some processes may exhibit different dynamics during the night than during the daytime, providing a stepping stone to understanding and modeling night gaps in ESM. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144128429","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

Reliability for multilevel data: A correlation approach. 多水平数据的可靠性：一种相关方法。

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-05-22 DOI: 10.1037/met0000738

Tzu-Yao Lin, Francis Tuerlinckx, Sophie Vanbelle

{"title":"Reliability for multilevel data: A correlation approach.","authors":"Tzu-Yao Lin, Francis Tuerlinckx, Sophie Vanbelle","doi":"10.1037/met0000738","DOIUrl":"https://doi.org/10.1037/met0000738","url":null,"abstract":"Studying the reliability of a measurement instrument is essential. Despite the recognition of the importance of reliability in psychology and medicine and the various reliability coefficients that have been proposed, research on reliability for nested or multilevel data, ubiquitously in observational studies, remains limited. Two recent articles (Schönbrodt et al., 2022; ten Hove et al., 2022) address how to quantify reliability in multilevel settings based on generalizability theory. Specifically, ten Hove et al. (2022) defined between-cluster and within-cluster interrater intraclass correlation coefficients for multilevel designs where persons or raters are nested within clusters. Schönbrodt et al. (2022) also defined reliability coefficients at between-cluster and within-cluster (i.e., between-person) levels for designs where persons nested in couples are assessed numerous times daily over a number of days. Nevertheless, when applied to a common design, both approaches give inconsistent results regarding their definition of cluster-level reliability. In this article, we propose an alternative approach to defining reliability coefficients for multilevel data that are based on calculating the expected correlation between repeated measurements. We will compare our approach with that of Schönbrodt et al. (2022) and ten Hove et al. (2022) and explain the differences between the three approaches in a number of common nested data structures: (a) raters crossed with both persons and clusters, but persons are nested within clusters, (b) raters nested within both persons and clusters, and (c) persons nested in clusters and crossed with raters and days. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144128431","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

Timescale mismatch in intensive longitudinal data: Current issues and possible solutions based on dynamic structural equation models. 密集纵向数据的时间尺度不匹配：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 : 2025-05-19 DOI: 10.1037/met0000749

Xiaohui Luo,Yueqin Hu,Hongyun Liu

{"title":"Timescale mismatch in intensive longitudinal data: Current issues and possible solutions based on dynamic structural equation models.","authors":"Xiaohui Luo,Yueqin Hu,Hongyun Liu","doi":"10.1037/met0000749","DOIUrl":"https://doi.org/10.1037/met0000749","url":null,"abstract":"Intensive longitudinal data have been increasingly used to examine dynamic bidirectional relations between variables. However, the problem of timescale mismatch between variables faced by applied researchers remains understudied. Under the dynamic structural equation modeling framework, previous studies used the partial-path model and the average-score model, respectively, to explore the dynamic interaction processes and overall reciprocal effects between variables with mismatched timescales. The present study aimed to evaluate the performance of the existing modeling approaches and the effectiveness of the improved approaches (i.e., the full-path model, the factor model, and the adjusted factor model). Study 1 showed that the full-path model, which considered the cross-lagged effects of all time points of variables with denser timescales, better reflected dynamic interaction processes and time-specific effects between variables than the partial-path model. Study 2-1 found that the estimates of autoregressive and cross-lagged effects between timescale mismatched variables were biased in the average-score model, but accurate in the factor model. Study 2-2 further suggested that when there were regression effects between different time points of variables with denser timescales, the adjusted factor model obtained less bias than the factor model, yet the difference is negligible when the regression effects are small. Study 3 used empirical data with timescale mismatched variables to illustrate the differences of all modeling approaches. This study identified the important problem of timescale mismatch in intensive longitudinal data and its possible solutions, providing methodological guidance and valuable insights for data collection and analysis of variables with mismatched timescales. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"10 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144087853","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

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：季节性自回归移动平均模型与周的影响相结合的教程。

IF 7 1区心理学

Psychological methods Pub Date : 2025-05-19 DOI: 10.1037/met0000756

Mohammadhossein Manuel Haqiqatkhah,Ellen L Hamaker

{"title":"Daily dynamics and weekly rhythms: A tutorial on seasonal autoregressive-moving average models combined with day-of-the-week effects.","authors":"Mohammadhossein Manuel Haqiqatkhah,Ellen L Hamaker","doi":"10.1037/met0000756","DOIUrl":"https://doi.org/10.1037/met0000756","url":null,"abstract":"Daily diary data of emotional experiences are typically modeled with a first-order autoregressive model to account for possible day-to-day dynamics. However, our emotional experiences are likely influenced by the weekly rhythm of our activities, which may be reflected by (a) day-of-the-week effects (DOWEs), where different weekdays are characterized by different means; and (b) week-to-week dynamics, where weekday-specific activities and experiences have a delayed effect on the emotions that we experience on the same weekday a week later. While DOWEs have been studied occasionally, week-to-week dynamics have been largely ignored in psychological research. We present a set of complementary visualization techniques for detecting weekly rhythms and day-to-day dynamics in time series data. Subsequently, we introduce the family of seasonal autoregressive-moving average models from the econometrics literature, extend them with DOWEs models, and show how their components appear in visualizations. We then provide a tutorial on fitting these models in R, discuss model fit and model selection, and apply them to a daily diary dataset of 56-101 daily measures from 98 individuals. The results suggest that most individuals in the sample may be characterized by patterns and dynamics that the current practices in psychological research cannot capture adequately, and we discuss their implications for current psychological research practices. Reflecting critically on the limitations of our approach, we regard our findings as an initial step to encourage researchers to move beyond the ubiquitous paradigm of lag-1 autoregressive modeling and consider other types of dynamics at different timescales, and put forth ways forward. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"32 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144087848","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

Understanding measurement precision from a regression perspective. 从回归的角度理解测量精度。

IF 7 1区心理学

Psychological methods Pub Date : 2025-05-19 DOI: 10.1037/met0000763

Yang Liu,Jolynn Pek,Alberto Maydeu-Olivares

{"title":"Understanding measurement precision from a regression perspective.","authors":"Yang Liu,Jolynn Pek,Alberto Maydeu-Olivares","doi":"10.1037/met0000763","DOIUrl":"https://doi.org/10.1037/met0000763","url":null,"abstract":"We adopt and expand McDonald's (2011) regression framework for measurement precision, integrating two key perspectives: (a) reliability of observed scores and (b) optimal prediction of latent scores. Reliability arises from a measurement decomposition of an observed score into its true score and measurement error. In contrast, proportional reduction in mean squared error (PRMSE) arises from a prediction decomposition of a latent score into its optimal predictor (the observed expected a posteriori [EAP] score) and prediction error. Reliability is the coefficient of determination obtained by two isomorphic regressions: regressing the observed score on its true score or on all the latent variables. Similarly, PRMSE is the coefficient of determination obtained from two isomorphic regressions: regressing the latent score on its observed EAP score or all the manifest variables. A key implication of this regression framework is that both reliability and PRMSE can be estimated using a Monte Carlo (MC) method, which is particularly useful when no analytic formula is available or when the analytic calculation is involved. We illustrate these concepts with a factor analysis model and a two-parameter logistic model, in which we compute reliability coefficients for different observed scores and PRMSE for different latent scores. Additionally, we provide a numerical example demonstrating how the MC method can be used to estimate reliability and PRMSE within a two-dimensional item response tree model. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"44 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144087847","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 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

{"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":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144005101","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

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

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