Psychological methods最新文献

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

{"title":"The many reliabilities of psychological dynamics: An overview of statistical approaches to estimate the internal consistency reliability of intensive longitudinal data.","authors":"Sebastian Castro-Alvarez,Laura F Bringmann,Jason Back,Siwei Liu","doi":"10.1037/met0000778","DOIUrl":"https://doi.org/10.1037/met0000778","url":null,"abstract":"Reliability is a key concept in psychology that has been broadly studied since the introduction of Cronbach's α, which is a measure of internal consistency. Despite its importance, reliability has been relatively understudied when dealing with intensive longitudinal data. Although intensive longitudinal measurements are often considered more ecologically valid and less prone to recall bias than survey data collected using traditional methods, there is no warranty that they are more reliable. Hence, empirical researchers need tools to study and report the reliability of the scales used in intensive longitudinal research. In recent years, psychologists have proposed different approaches to estimate the reliability of scales and items used when studying psychological dynamics. However, it is unclear how these approaches compare to one another, making it difficult to determine what options researchers have given a particular data set and specific research questions. Specifically, these approaches estimate reliability indices based on different statistical models, such as linear multilevel analysis, vector autoregressive models, and dynamic factor models. Furthermore, while some methods involve estimating one reliability index for the scores that applies to the whole sample, others estimate person-specific reliability indices. This wide variety of approaches can provoke some confusion. In this article, we aim to bridge this gap by reviewing and highlighting the similarities and differences of different methods used to estimate the reliability of intensive longitudinal data. We also showcase their application with empirical data. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"352 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145319245","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

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

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

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

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

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

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":"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).","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

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":"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).","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

引用次数: 0