Psychological methods最新文献_第2页

Supplemental Material for A Two-Stage Approach to Account for Measurement Error When Using Empirical Bayes Estimates of Random Slopes 补充材料两阶段的方法，以说明测量误差时，使用经验贝叶斯估计随机斜率

IF 7 1区心理学

Psychological methods Pub Date : 2026-04-13 DOI: 10.1037/met0000838.supp

引用次数: 0

Supplemental Material for A Time-Varying Interaction Map Approach for Longitudinal Assessments 纵向评估的时变相互作用图方法补充材料

IF 7 1区心理学

Psychological methods Pub Date : 2026-04-13 DOI: 10.1037/met0000825.supp

引用次数: 0

Supplemental Material for How to Apply Bayesian Stochastic Search Variable Selection With Multiply Imputed Data 如何在多重输入数据下应用贝叶斯随机搜索变量选择补充材料

IF 7 1区心理学

Psychological methods Pub Date : 2026-04-13 DOI: 10.1037/met0000837.supp

引用次数: 0

Modeling cyclic patterns using a two-stage hybrid Bayesian approach. 使用两阶段混合贝叶斯方法建模循环模式。

IF 7 1区心理学

Psychological methods Pub Date : 2026-04-09 DOI: 10.1037/met0000815

Han Du,Brian Keller,Lijuan Wang,Robert E Weiss,Lauren Lesko,Martie Haselton

引用次数: 0

Causal mediation analysis with one or multiple mediators: A comparative study. 一个或多个中介的因果中介分析：一项比较研究。

IF 7.8 1区心理学

Psychological methods Pub Date : 2026-04-09 DOI: 10.1037/met0000799

Judith Abécassis, Houssam Zenati, Sami Boumaïza, Julie Josse, Bertrand Thirion

{"title":"Causal mediation analysis with one or multiple mediators: A comparative study.","authors":"Judith Abécassis, Houssam Zenati, Sami Boumaïza, Julie Josse, Bertrand Thirion","doi":"10.1037/met0000799","DOIUrl":"https://doi.org/10.1037/met0000799","url":null,"abstract":"<p><p>Mediation analysis decomposes the causal effect of a treatment on an outcome into an indirect effect, mediated through intermediate variables, and a direct effect, operating through other mechanisms. However, mediation analysis is challenging due to the need to accurately adjust for confounders of the treatment, mediators, and outcomes, which may involve complex nonlinear relationships. Machine learning offers a promising solution by accommodating flexible function forms to account for confounders. It can be integrated into various estimators, resulting in a complex landscape for the practitioner. We evaluate parametric and nonparametric implementations of classical and more recent estimators, providing a thorough assessment of direct and indirect effect estimation in causal mediation analysis for binary, continuous, and multidimensional mediators. Through a comprehensive benchmark using simulated data, we demonstrate that advanced statistical approaches, such as the multiply-robust and double-machine-learning estimators, perform well across most simulated settings and real-world data. As an application example, we analyze hypertension, a factor known to influence cognitive functions, to determine if this effect is mediated by changes in brain morphology, using the U.K. Biobank brain imaging cohort. Our findings indicate that for hypertension, a substantial portion of the effect is mediated by alterations in brain structure. This work provides guidance to the practitioner from the formulation of a valid causal mediation problem, from the verification of identification assumptions to the choice of an appropriate estimator. (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.8,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147646302","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 theory-construction methodology for network theories in psychology. 心理学网络理论的理论建构方法论。

IF 7 1区心理学

Psychological methods Pub Date : 2026-04-06 DOI: 10.1037/met0000829

Adam Finnemann,Lourens Waldorp,Denny Borsboom,Maarten Marsman,Han L J van der Maas

引用次数: 0

You cannot just count: A statistical rethinking of semantic richness in free-generation tasks and semantic norms. 你不能只是计数：对自由生成任务和语义规范中语义丰富性的统计反思。

IF 7 1区心理学

Psychological methods Pub Date : 2026-04-06 DOI: 10.1037/met0000820

Rodrigo Lagos,Sergio E Chaigneau,Enrique Canessa,Felipe Toro-Hernández,Anny Bontempo,Maria T Carthery-Goulart,Felipe A Medina Marín

{"title":"You cannot just count: A statistical rethinking of semantic richness in free-generation tasks and semantic norms.","authors":"Rodrigo Lagos,Sergio E Chaigneau,Enrique Canessa,Felipe Toro-Hernández,Anny Bontempo,Maria T Carthery-Goulart,Felipe A Medina Marín","doi":"10.1037/met0000820","DOIUrl":"https://doi.org/10.1037/met0000820","url":null,"abstract":"Semantic richness refers to the number of distinct features people associate with a concept, a key indicator of how knowledge is represented and accessed in memory. It is typically measured through free-generation tasks-such as the property listing task-where participants list properties of everyday concepts (e.g., a BANANA might be described as \"yellow,\" \"soft,\" or something you \"peel\"). However, most studies simply count the observed features, ignoring sampling variability and leading to biased comparisons across groups. To address this limitation, we adapted the Chao2 estimator-originally developed in ecology-to infer the total number of features associated with a concept, including those not observed in a given sample. We validated this approach for psychological research through extensive Monte Carlo simulations based on empirical data from three languages. Results show that Chao2, and especially its bias-corrected version (Chao2BC), yield more accurate and interpretable estimates than simple counts. This framework reframes semantic richness as a problem of statistical inference, providing a principled basis for comparing conceptual data across languages, populations, and experimental contexts. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"4 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147619524","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

Handling missing values when using neighborhood selection for network analysis. 在使用邻域选择进行网络分析时处理缺失值。

IF 7 1区心理学

Psychological methods Pub Date : 2026-04-02 DOI: 10.1037/met0000828

Kai Jannik Nehler,Martin Schultze

{"title":"Handling missing values when using neighborhood selection for network analysis.","authors":"Kai Jannik Nehler,Martin Schultze","doi":"10.1037/met0000828","DOIUrl":"https://doi.org/10.1037/met0000828","url":null,"abstract":"The handling of missing values in cross-sectional network analysis has been primarily studied in conjunction with regularization techniques. However, nonregularized alternatives, such as neighborhood selection via the Bayesian information criterion (BIC) based on node-wise multiple regression, have been shown to be viable alternatives for psychological networks. Moreover, its localized approach in model selection renders neighborhood selection particularly suitable for situations in which variables show very uneven rates of missing values. In this study, we present two approaches based on multiple imputation (MI), namely stacked and grouped MI, alongside direct and two-step expectation-maximization procedures, to handle missing values. Furthermore, various approaches to calculating sample size, used for computing log-likelihood and BIC, are discussed and evaluated. A simulation study was conducted to assess the performance of these missing data handling methods and sample size definitions. Evaluation criteria included edge recovery, as well as bias in partial correlations and network statistics. The findings indicate that stacked MI performs best overall. The two-step expectation-maximization approach is the fastest and offers adequate performance when the number of observations is very large relative to the proportion of missingness and the size of the network. For sample size calculation, particularly under high levels of missingness, using the local number of observations per node yielded the least bias. The most effective methods for handling missing values in neighborhood selection via BIC are implemented in the new R package mantar. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"195 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147599422","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

Heteroskedasticity-robust inference in Bayesian linear regression via the generalized method of moments. 广义矩法在贝叶斯线性回归中的异方差-鲁棒推理。

IF 7 1区心理学

Psychological methods Pub Date : 2026-04-02 DOI: 10.1037/met0000835

Weicong Lyu

引用次数: 0

Modeling intraindividual variability as a predictor with intensive longitudinal data. 利用密集的纵向数据对个体内部变异性作为预测因子进行建模。

IF 7 1区心理学

Psychological methods Pub Date : 2026-04-02 DOI: 10.1037/met0000818

Lijuan Wang,Xiao Liu

{"title":"Modeling intraindividual variability as a predictor with intensive longitudinal data.","authors":"Lijuan Wang,Xiao Liu","doi":"10.1037/met0000818","DOIUrl":"https://doi.org/10.1037/met0000818","url":null,"abstract":"In many areas of psychology, researchers are interested in studying whether intraindividual variability (IIV) is predictive of behavioral and health outcomes after controlling for the intraindividual mean. To the end, IIV indicators such as the observed intraindividual variance (OIVAR) or the observed intraindividual standard deviation (OISD) are often modeled as a predictor in regular regression analysis. However, OIVAR and OISD have been found to have a low-reliability problem, especially when the number of occasions is small. In this study, we analytically examined statistical features (mean and variance) of OIVAR and OISD. The results revealed that when measurement errors exist, regular regression can yield (a) more accurate results for the coefficient of intraindividual variance (IVAR) but (b) worse results for the coefficient of intraindividual standard deviation (ISD) when the number of occasions increases. Furthermore, we compared the performance of alternative modeling approaches, including the time-parceling, single indicator latent variable, and Bayesian variability modeling approaches, to that of regular regression for modeling IVAR or ISD as a predictor. Simulation results were consistent with the analytical results and further suggested that our proposed (a) time-parceling with bootstrapping and (b) Bayesian variability modeling approaches performed well and better than regression for modeling IVAR as a predictor. When measurement errors exist, only the proposed Bayesian variability modeling approach performed well for modeling ISD as a predictor. Implications of the results and recommendations were discussed. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"64 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147599424","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