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The many reliabilities of psychological dynamics: An overview of statistical approaches to estimate the internal consistency reliability of intensive longitudinal data. 心理动力学的许多可靠性:估计密集纵向数据内部一致性可靠性的统计方法概述。
IF 7 1区 心理学
Psychological methods Pub Date : 2025-10-20 DOI: 10.1037/met0000778
Sebastian Castro-Alvarez,Laura F Bringmann,Jason Back,Siwei Liu
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引用次数: 0
Predictive validity of selection tools: The critical role of applicant-pool composition. 选择工具的预测有效性:申请人组合的关键作用。
IF 7 1区 心理学
Psychological methods Pub Date : 2025-10-16 DOI: 10.1037/met0000795
Meir S. Barneron, Tamar Kennet-Cohen, Dvir Kleper, Tzur M. Karelitz
{"title":"Predictive validity of selection tools: The critical role of applicant-pool composition.","authors":"Meir S. Barneron, Tamar Kennet-Cohen, Dvir Kleper, Tzur M. Karelitz","doi":"10.1037/met0000795","DOIUrl":"https://doi.org/10.1037/met0000795","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"72 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145295069","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
Machine learning for propensity score estimation: A systematic review and reporting guidelines. 倾向评分估计的机器学习:系统审查和报告指南。
IF 7 1区 心理学
Psychological methods Pub Date : 2025-10-16 DOI: 10.1037/met0000789
Walter Leite, Huibin Zhang, Zachary Collier, Kamal Chawla, Lingchen Kong, YongSeok Lee, Jia Quan, Olushola Soyoye
{"title":"Machine learning for propensity score estimation: A systematic review and reporting guidelines.","authors":"Walter Leite, Huibin Zhang, Zachary Collier, Kamal Chawla, Lingchen Kong, YongSeok Lee, Jia Quan, Olushola Soyoye","doi":"10.1037/met0000789","DOIUrl":"https://doi.org/10.1037/met0000789","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"41 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145295075","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
Causal mediation analysis with two mediators: A comprehensive guide to estimating total and natural effects across various multiple mediators setups. 使用两种介质的因果中介分析:评估各种多介质设置的总效应和自然效应的综合指南。
IF 7 1区 心理学
Psychological methods Pub Date : 2025-10-16 DOI: 10.1037/met0000781
Jesse Gervais, Geneviève Lefebvre, Erica E. M. Moodie
{"title":"Causal mediation analysis with two mediators: A comprehensive guide to estimating total and natural effects across various multiple mediators setups.","authors":"Jesse Gervais, Geneviève Lefebvre, Erica E. M. Moodie","doi":"10.1037/met0000781","DOIUrl":"https://doi.org/10.1037/met0000781","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"123 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145295074","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
Supplemental Material for The Many Reliabilities of Psychological Dynamics: An Overview of Statistical Approaches to Estimate the Internal Consistency Reliability of Intensive Longitudinal Data 心理动力学的许多可靠性:估计密集纵向数据内部一致性可靠性的统计方法综述
IF 7 1区 心理学
Psychological methods Pub Date : 2025-10-14 DOI: 10.1037/met0000778.supp
{"title":"Supplemental Material for The Many Reliabilities of Psychological Dynamics: An Overview of Statistical Approaches to Estimate the Internal Consistency Reliability of Intensive Longitudinal Data","authors":"","doi":"10.1037/met0000778.supp","DOIUrl":"https://doi.org/10.1037/met0000778.supp","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"11 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145296318","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
Supplemental Material for Predictive Validity of Selection Tools: The Critical Role of Applicant-Pool Composition 选择工具预测有效性的补充材料:申请人组合的关键作用
IF 7 1区 心理学
Psychological methods Pub Date : 2025-10-06 DOI: 10.1037/met0000795.supp
{"title":"Supplemental Material for Predictive Validity of Selection Tools: The Critical Role of Applicant-Pool Composition","authors":"","doi":"10.1037/met0000795.supp","DOIUrl":"https://doi.org/10.1037/met0000795.supp","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"21 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145241978","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 repeated adjustment of measurement protocols method for developing high-validity text classifiers. 开发高效文本分类器的测量方案重复调整方法。
IF 7.8 1区 心理学
Psychological methods Pub Date : 2025-10-06 DOI: 10.1037/met0000787
Alex Goddard, Alex Gillespie
{"title":"The repeated adjustment of measurement protocols method for developing high-validity text classifiers.","authors":"Alex Goddard, Alex Gillespie","doi":"10.1037/met0000787","DOIUrl":"10.1037/met0000787","url":null,"abstract":"<p><p>The development and evaluation of text classifiers in psychology depends on rigorous manual coding. Yet, the evaluation of manual coding and computational algorithms is usually considered separately. This is problematic because developing high-validity classifiers is a repeated process of identifying, explaining, and addressing conceptual and measurement issues during both the manual coding and classifier development stages. To address this problem, we introduce the Repeated Adjustment of Measurement Protocols (RAMP) method for developing high-validity text classifiers in psychology. The RAMP method has three stages: manual coding, classifier development, and integrative evaluation. These stages integrate the best practices of content analysis (manual coding), data science (classifier development), and psychology (integrative evaluation). Central to this integration is the concept of an inference loop, defined as the process of maximizing validity through repeated adjustments to concepts and constructs, guided by push-back from the empirical data. Inference loops operate both within each stage of the method and across related studies. We illustrate RAMP through a case study, where we manually coded 21,815 sentences for misunderstanding (Krippendorff's α = .79), and developed a rule-based classifier (Matthews correlation coefficient [MCC] = 0.22), a supervised machine learning classifier (Bidirectional Encoder Representations From Transformers; MCC = 0.69) and a large language model classifier (GPT-4o; MCC = 0.47). By integrating manual coding and classifier development stages, we were able to identify and address a concept validity problem with misunderstandings. RAMP advances existing methods by operationalizing validity as an ongoing dynamic process, where concepts and constructs are repeatedly adjusted toward increasingly widespread intersubjective agreement on their utility. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.8,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145233362","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
Supplemental Material for Causal Mediation Analysis With Two Mediators: A Comprehensive Guide to Estimating Total and Natural Effects Across Various Multiple Mediators Setups 补充材料的因果中介分析与两个介质:综合指南,以估计总和自然的影响在各种多介质设置
IF 7 1区 心理学
Psychological methods Pub Date : 2025-10-02 DOI: 10.1037/met0000781.supp
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引用次数: 0
Inferences and effect sizes for direct, indirect, and total effects in continuous-time mediation models. 连续时间中介模型中直接、间接和总效应的推论和效应量。
IF 7.8 1区 心理学
Psychological methods Pub Date : 2025-10-02 DOI: 10.1037/met0000779
Ivan Jacob Agaloos Pesigan, Michael A Russell, Sy-Miin Chow
{"title":"Inferences and effect sizes for direct, indirect, and total effects in continuous-time mediation models.","authors":"Ivan Jacob Agaloos Pesigan, Michael A Russell, Sy-Miin Chow","doi":"10.1037/met0000779","DOIUrl":"10.1037/met0000779","url":null,"abstract":"<p><p>Mediation modeling using longitudinal data is an exciting field that captures the interrelations in dynamic changes, such as mediated changes, over time. Even though discrete-time vector autoregressive approaches are commonly used to estimate indirect effects in longitudinal data, they have known limitations due to the dependency of inferential results on the time intervals between successive occasions and the assumption of regular spacing between measurements. Continuous-time vector autoregressive models have been proposed as an alternative to address these issues. Previous work in the area (e.g., Deboeck & Preacher, 2015; Ryan & Hamaker, 2021) has shown how the direct, indirect, and total effects, for a range of time-interval values, can be calculated using parameters estimated from continuous-time vector autoregressive models for causal inferential purposes. However, both standardized effects size measures and methods for calculating the uncertainty around the direct, indirect, and total effects in continuous-time mediation have yet to be explored. Drawing from the mediation model literature, we present and compare results using the delta, Monte Carlo, and parametric bootstrap methods to calculate SEs and confidence intervals for the direct, indirect, and total effects in continuous-time mediation for inferential purposes. Options to automate these inferential procedures and facilitate interpretations are available in the cTMed R package. (PsycInfo Database Record (c) 2025 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.8,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12494154/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145213089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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
Supplemental Material for The Repeated Adjustment of Measurement Protocols Method for Developing High-Validity Text Classifiers 开发高效文本分类器的测量方案重复调整方法补充材料
IF 7 1区 心理学
Psychological methods Pub Date : 2025-10-02 DOI: 10.1037/met0000787.supp
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引用次数: 0
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