Psychological methods最新文献

Continuous-time structural equation model forests. 连续时间结构方程模型森林。

IF 7 1区心理学

Psychological methods Pub Date : 2025-06-05 DOI: 10.1037/met0000766

Pablo F. Cáncer, Manuel Arnold, Eduardo Estrada, Manuel C. Voelkle

引用次数: 0

Improved estimation of autoregressive models through contextual impulses and robust modeling. 基于上下文脉冲和鲁棒建模的自回归模型改进估计。

IF 7 1区心理学

Psychological methods Pub Date : 2025-06-05 DOI: 10.1037/met0000761

Janne K. Adolf, Eva Ceulemans

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Utilizing primary study quality in meta-analyses in psychology: A step-by-step tutorial. 在心理学荟萃分析中利用初级研究质量：一步一步的教程。

IF 7 1区心理学

Psychological methods Pub Date : 2025-06-05 DOI: 10.1037/met0000751

Ronny Scherer, Valentin Emslander

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Supplemental Material for Improved Estimation of Autoregressive Models Through Contextual Impulses and Robust Modeling 通过上下文脉冲和鲁棒建模改进自回归模型估计的补充材料

IF 7 1区心理学

Psychological methods Pub Date : 2025-06-02 DOI: 10.1037/met0000761.supp

引用次数: 0

Estimating causal effects on psychological networks using item response theory. 运用项目反应理论估计心理网络的因果效应。

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-06-02 DOI: 10.1037/met0000764

Joshua B Gilbert, Benjamin W Domingue, James S Kim

{"title":"Estimating causal effects on psychological networks using item response theory.","authors":"Joshua B Gilbert, Benjamin W Domingue, James S Kim","doi":"10.1037/met0000764","DOIUrl":"https://doi.org/10.1037/met0000764","url":null,"abstract":"Network models in which each variable interacts with the others in a complex system have emerged as an important alternative to latent variable models in psychometric research. However, confirmatory methods for group network comparison can be limited by practical constraints, such as the computational intractability of the Ising model in large networks. In this study, we demonstrate how to estimate causal effects on network state and strength when direct network estimation is not feasible by leveraging the mathematical equivalencies between the Ising model and item response theory (IRT) models. We demonstrate through simulation that a two-parameter logistic explanatory IRT model can simultaneously recover causal effects on network state and strength. We first apply the method to a single empirical example of a vocabulary assessment from a content literacy intervention to demonstrate model building and interpretation strategies. We then replicate our approach with 72 empirical data sets from randomized controlled trials with item-level outcome data in education, economics, health, and related fields. Our results show that causal effects on network strength are both common and uncorrelated with effects on network state, suggesting that causal network models can provide new insight into the impact of interventions in the social and behavioral sciences. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144199866","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 Utilizing Primary Study Quality in Meta-Analyses in Psychology: A Step-by-Step Tutorial 在心理学荟萃分析中利用初级研究质量的补充材料：一步一步的教程

IF 7 1区心理学

Psychological methods Pub Date : 2025-06-02 DOI: 10.1037/met0000751.supp

引用次数: 0

Why the use of segmented regression analysis to explore change in relations between variables is problematic: A simulation study. 为什么使用分段回归分析来探索变量之间关系的变化是有问题的：模拟研究。

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-06-01 Epub Date: 2023-03-27 DOI: 10.1037/met0000576

Moritz Breit, Julian Preuß, Vsevolod Scherrer, Franzis Preckel

引用次数: 0

A default Bayes factor for testing null hypotheses about the fixed effects of linear two-level models. 用于检验关于线性两水平模型的固定效应的零假设的默认贝叶斯因子。

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-06-01 Epub Date: 2023-04-27 DOI: 10.1037/met0000573

Nikola Sekulovski, Herbert Hoijtink

{"title":"A default Bayes factor for testing null hypotheses about the fixed effects of linear two-level models.","authors":"Nikola Sekulovski, Herbert Hoijtink","doi":"10.1037/met0000573","DOIUrl":"10.1037/met0000573","url":null,"abstract":"Testing null hypotheses of the form \"β = 0,\" by the use of various Null Hypothesis Significance Tests (rendering a dichotomous reject/not reject decision), is considered standard practice when evaluating the individual parameters of statistical models. Bayes factors for testing these (and other) hypotheses allow users to quantify the evidence in the data that is in favor of a hypothesis. Unfortunately, when testing equality-contained hypotheses, the Bayes factors are sensitive to the specification of prior distributions, which may be hard to specify by applied researchers. The paper proposes a default Bayes factor with clear operating characteristics when used for testing whether the fixed parameters of linear two-level models are equal to zero. This is achieved by generalizing an already existing approach for linear regression. The generalization requires: (a) the sample size for which a new estimator for the effective sample size in two-level models containing random slopes is proposed; (b) the effect size for the fixed effects for which the so-called marginal R² for the fixed effects is used. Implementing the aforementioned requirements in a small simulation study shows that the Bayes factor yields clear operating characteristics regardless of the value for sample size and the estimation method. The paper gives practical examples and access to an easy-to-use wrapper function to calculate Bayes factors for hypotheses with respect to the fixed coefficients of linear two-level models by using the R package bain. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"579-598"},"PeriodicalIF":7.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9356536","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

Simulation-based design optimization for statistical power: Utilizing machine learning. 基于仿真的统计功率设计优化：利用机器学习

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-06-01 Epub Date: 2023-12-14 DOI: 10.1037/met0000611

Felix Zimmer, Rudolf Debelak

{"title":"Simulation-based design optimization for statistical power: Utilizing machine learning.","authors":"Felix Zimmer, Rudolf Debelak","doi":"10.1037/met0000611","DOIUrl":"10.1037/met0000611","url":null,"abstract":"The planning of adequately powered research designs increasingly goes beyond determining a suitable sample size. More challenging scenarios demand simultaneous tuning of multiple design parameter dimensions and can only be addressed using Monte Carlo simulation if no analytical approach is available. In addition, cost considerations, for example, in terms of monetary costs, are a relevant target for optimization. In this context, optimal design parameters can imply a desired level of power at minimum cost or maximum power at a cost threshold. We introduce a surrogate modeling framework based on machine learning predictions to solve these optimization tasks. In a simulation study, we demonstrate the efficiency for a wide range of hypothesis testing scenarios with single- and multidimensional design parameters, including t tests, analysis of variance, item response theory models, multilevel models, and multiple imputations. Our framework provides an algorithmic solution for optimizing study designs when no analytic power analysis is available, handling multiple design dimensions and cost considerations. Our implementation is publicly available in the R package mlpwr. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"513-536"},"PeriodicalIF":7.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138807105","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 receiver operating characteristic area under the curve (or mean ridit) as an effect size. 曲线下的接收者工作特征面积（或平均波幅）作为效应大小。

IF 7.6 1区心理学

Psychological methods Pub Date : 2025-06-01 Epub Date: 2023-07-13 DOI: 10.1037/met0000601

Michael Smithson

{"title":"The receiver operating characteristic area under the curve (or mean ridit) as an effect size.","authors":"Michael Smithson","doi":"10.1037/met0000601","DOIUrl":"10.1037/met0000601","url":null,"abstract":"Several authors have recommended adopting the receiver operator characteristic (ROC) area under the curve (AUC) or mean ridit as an effect size, arguing that it measures an important and interpretable type of effect that conventional effect-size measures do not. It is base-rate insensitive, robust to outliers, and invariant under order-preserving transformations. However, applications have been limited to group comparisons, and usually just two groups, in line with the popular interpretation of the AUC as measuring the probability that a randomly chosen case from one group will score higher on the dependent variable than a randomly chosen case from another group. This tutorial article shows that the AUC can be used as an effect size for both categorical and continuous predictors in a wide variety of general linear models, whose dependent variables may be ordinal, interval, or ratio level. Thus, the AUC is a general effect-size measure. Demonstrations in this article include linear regression, ordinal logistic regression, gamma regression, and beta regression. The online supplemental materials to this tutorial provide a survey of currently available software resources in R for the AUC and ridits, along with the code and access to the data used in the examples. (PsycInfo Database Record (c) 2025 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":"678-686"},"PeriodicalIF":7.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9776732","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