Psychological methods最新文献

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

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":"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).","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

The use of large language models for qualitative research: The Deep Computational Text Analyser (DECOTA). 在定性研究中使用大型语言模型：深度计算文本分析器（DECOTA）。

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-04-07 DOI: 10.1037/met0000753

Lois Player, Ryan Hughes, Kaloyan Mitev, Lorraine Whitmarsh, Christina Demski, Nicholas Nash, Trisevgeni Papakonstantinou, Mark Wilson

{"title":"The use of large language models for qualitative research: The Deep Computational Text Analyser (DECOTA).","authors":"Lois Player, Ryan Hughes, Kaloyan Mitev, Lorraine Whitmarsh, Christina Demski, Nicholas Nash, Trisevgeni Papakonstantinou, Mark Wilson","doi":"10.1037/met0000753","DOIUrl":"https://doi.org/10.1037/met0000753","url":null,"abstract":"Machine-assisted approaches for free-text analysis are rising in popularity, owing to a growing need to rapidly analyze large volumes of qualitative data. In both research and policy settings, these approaches have promise in providing timely insights into public perceptions and enabling policymakers to understand their community's needs. However, current approaches still require expert human interpretation-posing a financial and practical barrier for those outside of academia. For the first time, we propose and validate the Deep Computational Text Analyser (DECOTA)-a novel machine learning methodology that automatically analyzes large free-text data sets and outputs concise themes. Building on structural topic modeling approaches, we used two fine-tuned large language models and sentence transformers to automatically derive \"codes\" and their corresponding \"themes\", as in inductive thematic analysis. To fully automate the process, we designed and validated a novel algorithm to choose the optimal number of \"topics\" for the structural topic modeling. DECOTA outputs key codes and themes, their prevalence, and how prevalence varies across covariates such as age and gender. Each code is accompanied by three representative quotes. Four data sets previously analyzed using thematic analysis were triangulated with DECOTA's codes and themes. We found that DECOTA is approximately 378 times faster and 1,920 times cheaper than human coding and consistently yields codes in agreement with or complementary to human coding (averaging 91.6% for codes and 90% for themes). The implications for evidence-based policy development, public engagement with policymaking, and psychometric measure development are discussed. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143803939","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

Zero inflation in intensive longitudinal data: Why is it important and how should we deal with it? 密集纵向数据中的零通胀：为什么它很重要，我们应该如何应对？

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-04-07 DOI: 10.1037/met0000754

Sijing S J Shao, Ziqian Xu, Qimin Liu, Kenneth McClure, Ross Jacobucci, Scott E Maxwell, Zhiyong Zhang

{"title":"Zero inflation in intensive longitudinal data: Why is it important and how should we deal with it?","authors":"Sijing S J Shao, Ziqian Xu, Qimin Liu, Kenneth McClure, Ross Jacobucci, Scott E Maxwell, Zhiyong Zhang","doi":"10.1037/met0000754","DOIUrl":"https://doi.org/10.1037/met0000754","url":null,"abstract":"This study addresses the challenge of analyzing intensive longitudinal data (ILD) with zero-inflated autoregressive processes. ILD, characterized by intensive longitudinal measurements, often exhibit excessive zeros and temporal dependencies. Neglecting zero inflation or mishandling it can lead to biased parameter estimates and inaccurate conclusions. To overcome this issue, we propose a novel zero-inflated process change multilevel autoregressive (ZIP-CAR) model that incorporates zero inflation using a Bayesian framework. We compare the performance of the proposed method with existing methods through a simulation study and demonstrate its advantages in accurately estimating parameters and improving statistical power. Additionally, we apply the ZIP-CAR model to a real intensive longitudinal data set on problematic drinking behaviors, highlighting its effectiveness in capturing autoregressive and cross-lag effects while accounting for zero inflation. The results underscore the importance of addressing zero inflation in ILD analysis and provide practical recommendations for researchers. Our proposed model offers a valuable tool for analyzing ILD with zero-inflated autoregressive processes, facilitating a more comprehensive understanding of dynamic behavioral changes over time. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143804006","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

Intensive longitudinal mediation in Mplus. Mplus 中的密集纵向调解。

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-04-01 Epub Date: 2022-12-22 DOI: 10.1037/met0000536

Daniel McNeish, David P MacKinnon

{"title":"Intensive longitudinal mediation in Mplus.","authors":"Daniel McNeish, David P MacKinnon","doi":"10.1037/met0000536","DOIUrl":"10.1037/met0000536","url":null,"abstract":"Much of the existing longitudinal mediation literature focuses on panel data where relatively few repeated measures are collected over a relatively broad timespan. However, technological advances in data collection (e.g., smartphones, wearables) have led to a proliferation of short duration, densely collected longitudinal data in behavioral research. These intensive longitudinal data differ in structure and focus relative to traditionally collected panel data. As a result, existing methodological resources do not necessarily extend to nuances present in the recent influx of intensive longitudinal data and designs. In this tutorial, we first cover potential limitations of traditional longitudinal mediation models to accommodate unique characteristics of intensive longitudinal data. Then, we discuss how recently developed dynamic structural equation models (DSEMs) may be well-suited for mediation modeling with intensive longitudinal data and can overcome some of the limitations associated with traditional approaches. We describe four increasingly complex intensive longitudinal mediation models: (a) stationary models where the indirect effect is constant over time and people, (b) person-specific models where the indirect effect varies across people, (c) dynamic models where the indirect effect varies across time, and (d) cross-classified models where the indirect effect varies across both time and people. We apply each model to a running example featuring a mobile health intervention designed to improve health behavior of individuals with binge eating disorder. In each example, we provide annotated Mplus code and interpretation of the output to guide empirical researchers through mediation modeling with this increasingly popular type of longitudinal data. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"393-415"},"PeriodicalIF":7.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10419989","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

Cognitive and cultural models in psychological science: A tutorial on modeling free-list data as a dependent variable in Bayesian regression. 心理科学中的认知和文化模型：在贝叶斯回归中将自由表数据建模为因变量的教程。

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-04-01 Epub Date: 2023-03-23 DOI: 10.1037/met0000553

Theiss Bendixen, Benjamin Grant Purzycki

{"title":"Cognitive and cultural models in psychological science: A tutorial on modeling free-list data as a dependent variable in Bayesian regression.","authors":"Theiss Bendixen, Benjamin Grant Purzycki","doi":"10.1037/met0000553","DOIUrl":"10.1037/met0000553","url":null,"abstract":"Assessing relationships between culture and cognition is central to psychological science. To this end, free-listing is a useful methodological instrument. To facilitate its wider use, we here present the free-list method along with some of its many applications and offer a tutorial on how to prepare and statistically model free-list data as a dependent variable in Bayesian regression using openly available data and code. We further demonstrate the real-world utility of the outlined workflow by modeling within-subject agreement between a free-list task and a corollary item response scale on religious beliefs with a cross-culturally diverse sample. Overall, we fail to find a reliable statistical association between these two instruments, an original empirical finding that calls for further inquiry into identifying the cognitive processes that item response scales and free-list tasks tap into. Throughout, we argue that free-listing is an unambiguous measure of cognitive and cultural information and that the free-list method therefore has broad potential across the social sciences aiming to measure and model individual-level and cross-cultural variation in mental representations. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"223-239"},"PeriodicalIF":7.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9367003","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 systematic framework for defining R-squared measures in mediation analysis. 中介分析中定义r平方测度的系统框架。

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-04-01 Epub Date: 2023-05-11 DOI: 10.1037/met0000571

Hongyun Liu, Ke-Hai Yuan, Hui Li

{"title":"A systematic framework for defining R-squared measures in mediation analysis.","authors":"Hongyun Liu, Ke-Hai Yuan, Hui Li","doi":"10.1037/met0000571","DOIUrl":"10.1037/met0000571","url":null,"abstract":"R-squared measures of explained variance are easy to understand, naturally interpretable, and widely used by substantive researchers. In mediation analysis, however, despite recent advances in measures of mediation effect, few effect sizes have good statistical properties. Also, most of these measures are only available for the simplest three-variable mediation model, especially for R²-type measures. By decomposing the mediator into two parts (i.e., the part related to the predictor and the part unrelated to the predictor), this article proposes a systematic framework to develop new effect-size measures of explained variance in mediation analysis. The framework can be easily extended to more complex mediation models and provides more delicate R² measures for empirical researchers. A Monte Carlo simulation study is conducted to examine the statistical properties of the proposed R² effect-size measure. Results show that the new R2 measure performs well in approximating the true value of the explained variance of the mediation effect. The use of the proposed measure is illustrated with empirical examples together with program code for its implementation. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"306-321"},"PeriodicalIF":7.6,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9796970","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