Psychological methods最新文献

{"title":"Evaluating local structural-after-measurement and traditional approaches for the estimation of complex nonlinear effects among latent variables.","authors":"Felipe Fontana Vieira,Kjell Solem Slupphaug,Yves Rosseel","doi":"10.1037/met0000851","DOIUrl":"https://doi.org/10.1037/met0000851","url":null,"abstract":"Various methods exist to include interaction or quadratic terms involving latent variables in structural equation models. Most use system-wide estimation, where all parameters are estimated simultaneously and have been investigated in models with few nonlinear effects. Recently, structural-after-measurement approaches have been proposed, where estimation proceeds in two stages: the measurement model first, then the structural model. Rosseel et al. (2025) extended local structural after measurement (LSAM) to handle second-order nonlinear effects among latent variables, although analytical standard errors for this extension had not been derived. In this article, we provide such a two-step standard error formula for LSAM and evaluate its performance, alongside that of LSAM more broadly, in two simulation studies. These varied latent exogenous predictor distributions (normal, right-skewed, and uniform), reliabilities (0.4, 0.6, and 0.8), sample sizes (400 and 1,000), and measurement error and structural disturbance distributions (normal and right-skewed). The first study examined a model with three nonlinear effects. LSAM produced largely unbiased estimates with stable coverage, standard errors, and Type I error rates, although some metrics were less stable under low reliability and with uniform latent exogenous distributions-issues that were mitigated by larger sample sizes. Traditional methods (latent moderated structural equations, quasimaximum likelihood, and unconstrained product indicator) showed results consistent with prior literature. The second study, excluding latent moderated structural equations, tested a more complex model with eight nonlinear effects. LSAM maintained adequate performance despite greater complexity, although similar limitations emerged. Traditional methods yielded more variable results, with generally poorer performance overall. Right-skewed measurement errors affected results more than right-skewed structural disturbances, particularly under low and medium reliability. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"14 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148408585","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}

{"title":"Supplemental Material for Post Hoc Comparisons in Fixed-Effect ANOVA: A Sequential Fusion Approach With Calibrated Error Control","authors":"","doi":"10.1037/met0000859.supp","DOIUrl":"https://doi.org/10.1037/met0000859.supp","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"80 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148394067","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}

{"title":"Supplemental Material for Network Accuracy Across Levels of Analysis Using Stochastic Block Models","authors":"","doi":"10.1037/met0000853.supp","DOIUrl":"https://doi.org/10.1037/met0000853.supp","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"9 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148394009","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}

{"title":"Heterogeneous variance models with Gaussian processes.","authors":"Yvette Baurne,Frédéric Delmar,Jonas Wallin","doi":"10.1037/met0000850","DOIUrl":"https://doi.org/10.1037/met0000850","url":null,"abstract":"Understanding variability is a key focus in many areas of psychological research, with growing interest in modeling individual- and group-level variability. Although multilevel models such as heterogeneous variance models (HVMs) and Mixed-Effects Location-Scale models have been used to capture these dynamics, they typically rely on linear assumptions and restrict temporal changes in variability to a single level. Recent calls for nonlinear approaches in psychology highlight the need for more flexible models that can better account for complex, dynamic processes. This article introduces the use of Gaussian processes (GPs) within the framework of HVMs to address these limitations. By incorporating GPs in HVMs, we allow for the modeling of nonlinear variability across multiple levels, including temporal dynamics at both the individual and group levels. We demonstrate the benefits of this approach in two empirical applications. Our findings show that using GPs provides an improved model fit compared with traditional linear methods and highlight the utility of GPs in variance modeling, offering new possibilities for studying dynamic and emergent processes in psychological and social science research. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"215 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148388782","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}

{"title":"Addressing selective reporting bias in meta-analysis of dependent effect sizes: A tutorial in R.","authors":"Man Chen,James E Pustejovsky","doi":"10.1037/met0000841","DOIUrl":"https://doi.org/10.1037/met0000841","url":null,"abstract":"Selective reporting bias arises when findings from primary research studies are incompletely reported and where the likelihood that a result is reported depends on the magnitude or statistical significance of the effect. When applied to selectively reported data, conventional meta-analytic models can produce systematically biased parameter estimates. Various statistical methods have been proposed for dealing with selective reporting in univariate meta-analysis, where each primary study contributes a single effect size estimate. However, fewer methods have been developed for handling selective reporting and publication bias in meta-analysis with dependent effect sizes, which are a common feature of meta-analyses in psychology. In this tutorial, we provide a guide for how to investigate potential selective reporting in meta-analyses of dependent effects, focusing on diagnosis and correction of selection reporting bias. We review several recently developed methods including a regression-based adjustment technique, a step-function selection model, and a sensitivity analysis approach. We demonstrate the implementation of these methods in the R statistical environment using data from two recent meta-analyses as examples. We discuss the application and interpretation of the methods, highlighting their strengths and limitations. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"79 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148388783","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}

{"title":"Supplemental Material for Adjustment Set Selection for Estimating Optimal Treatment Rules Under Confounding","authors":"","doi":"10.1037/met0000812.supp","DOIUrl":"https://doi.org/10.1037/met0000812.supp","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"203 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148379775","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}

{"title":"Bayesian evaluation for latent variable models: A tutorial on computing information criteria and bayes factors with the r package bleval.","authors":"Xiaohui Luo,Jieyuan Dong,Hongyun Liu,Yang Liu,Edgar C Merkle","doi":"10.1037/met0000852","DOIUrl":"https://doi.org/10.1037/met0000852","url":null,"abstract":"Model evaluation is crucial in psychological methodology, particularly in the context of Bayesian latent variable models. Bayesian evaluation methods require integrating out latent variables to compute marginal likelihoods for information criteria and integrating both latent variables and model parameters to compute fully marginal likelihoods for Bayes factors. These processes can be computationally demanding, as likelihoods in many latent variable models are intractable. Moreover, the role of latent variables in model evaluation has been insufficiently addressed in applied research, likely due to a lack of practical guidance and user-friendly software. This tutorial fills these gaps by (a) offering step-by-step instructions for approximating marginal likelihoods using an efficient numerical method (i.e., adaptive Gauss-Hermite quadrature), (b) developing an R package, bleval, designed specifically for computing information criteria and fully marginal likelihoods in Bayesian latent variable models, and (c) demonstrating the application of this package to various empirical scenarios, including structural equation models, item response theory models, and multilevel models. The empirical examples also investigate practical issues such as the sensitivity of model evaluation results to the number of quadrature nodes, the performance of the numerical quadrature method in high-dimensional latent variable models, and the handling of latent class variables in Bayesian mixture models. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"61 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148342431","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}

{"title":"Supplemental Material for Evaluating Local Structural-After-Measurement and Traditional Approaches for the Estimation of Complex Nonlinear Effects Among Latent Variables","authors":"","doi":"10.1037/met0000851.supp","DOIUrl":"https://doi.org/10.1037/met0000851.supp","url":null,"abstract":"","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"39 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148379774","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}

{"title":"A stochastic block prior for clustering in graphical models.","authors":"Nikola Sekulovski,Giuseppe Arena,Jonas Haslbeck,Karoline Huth,Nial Friel,Maarten Marsman","doi":"10.1037/met0000847","DOIUrl":"https://doi.org/10.1037/met0000847","url":null,"abstract":"Graphical models facilitate the representation of psychological variables as complex systems of interacting variables structured as a network. However, their current statistical estimation procedures overlook the assumption of clustering, which refers to the grouping of subsets of variables that are more densely connected within the network, despite this assumption playing a central role in many psychological theories. We address this gap by proposing the use of the stochastic block model as a prior distribution on the network structure of a graphical model for binary and ordinal data. The stochastic block model assumes that variables belong to latent clusters, where the probability of an edge depends on the cluster membership of the nodes. Embedding this model in a Bayesian graphical modeling framework allows researchers to formally incorporate theoretical expectations about clustering, test hypotheses about the number of clusters, and estimate cluster membership of the nodes from cross-sectional data. We illustrate the performance of the method in a simulation study and apply it to a reanalysis of 30 empirical datasets. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":"187 1","pages":""},"PeriodicalIF":7.0,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148313850","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}

{"title":"Three-level vector autoregressive models.","authors":"Yue Xiao, Hongyun Liu, Zhiyong Zhang","doi":"10.1037/met0000848","DOIUrl":"10.1037/met0000848","url":null,"abstract":"<p><p>The growing use of intensive longitudinal studies has heightened demand for advanced modeling approaches to better understand temporal dynamics of individual change. Although widely used multilevel vector autoregressive models can capture both intraindividual dynamics and interindividual differences, current implementations are restricted to two-level analyses. This can be problematic, as three-level nested structures are common in practice-for instance, individuals may be further clustered within higher level units, or weekday assessments are nested within weeks that are further nested within individuals. Existing two-level frameworks cannot adequately model such hierarchies, potentially leading to incorrect estimation and inferences about intraindividual dynamics. To address this issue, we introduce a three-level vector autoregressive modeling framework and provide corresponding Bayesian estimation algorithms. We evaluate the estimation performance of three key model specifications-unconditional univariate, conditional univariate (with Level 2 and Level 3 covariates), and unconditional bivariate-through a set of simulation studies. By varying sample sizes at all three levels and key population parameters, these simulations offer initial evidence on how sample sizes across levels influence model performance. An empirical example using ratings of children's daily emotional states during their first kindergarten month illustrates the three-level vector autoregressive framework's utility. We conclude by summarizing key findings, offering practical guidance for application, and pointing out limitations and future directions. (PsycInfo Database Record (c) 2026 APA, all rights reserved).</p>","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148302835","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}