Psychological methods最新文献_第3页

A tutorial on Bayesian multilevel latent time series models using stan with the mlts R package. 一个使用stan和mlts R包的贝叶斯多水平潜在时间序列模型的教程。

IF 7.5 1区心理学

Psychological methods Pub Date : 2026-09-28 DOI: 10.1037/met0000866

Kenneth Koslowski, Fabian Felix Münch, Tobias Koch, Jana Holtmann

{"title":"A tutorial on Bayesian multilevel latent time series models using stan with the mlts R package.","authors":"Kenneth Koslowski, Fabian Felix Münch, Tobias Koch, Jana Holtmann","doi":"10.1037/met0000866","DOIUrl":"10.1037/met0000866","url":null,"abstract":"This tutorial introduces and illustrates the estimation of Bayesian multilevel latent time series models with the R package mlts. We provide a conceptual overview of dynamic structural equation modeling for intensive longitudinal data, highlighting how it combines time series analysis, multilevel modeling, and latent variable approaches. Step-by-step guidance is given on specifying, estimating, and interpreting two-level vector autoregressive models using mlts. We illustrate modeling extensions provided in mlts, including multiple-indicator measurement models, between-person covariates, latent interaction effects, the handling of censored variables, and multiple-group models. Practical considerations such as data preparation, model diagnostics, and handling of varying measurement intervals are discussed. Assessment of model fit is provided via posterior predictive checks. Using working examples with empirical data, we showcase how mlts enables flexible Bayesian dynamic structural equation modeling analyses while avoiding the complexities of custom Stan programming. This tutorial aims to make advanced dynamic modeling of intensive longitudinal data more accessible to applied researchers across the behavioral sciences. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149075853","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

Model checking for vector autoregressive models. 向量自回归模型的模型检验。

IF 7.5 1区心理学

Psychological methods Pub Date : 2026-09-28 DOI: 10.1037/met0000855

Jonas Haslbeck, Joran Jongerling, Björn Siepe, Sacha Epskamp, Lourens Waldorp

{"title":"Model checking for vector autoregressive models.","authors":"Jonas Haslbeck, Joran Jongerling, Björn Siepe, Sacha Epskamp, Lourens Waldorp","doi":"10.1037/met0000855","DOIUrl":"10.1037/met0000855","url":null,"abstract":"Time series data have become pervasive in psychological science and vector autoregressive (VAR) models are now widely used to study within-person dynamics. However, researchers rarely check systematically whether these models fit their data. This is problematic because model misfit can lead both to incorrect interpretations of model parameters and to missed structure in the data that would be theoretically interesting. This tutorial explains the theory behind model checking, discusses the most common types of VAR model misspecification in psychological time series, and introduces diagnostics for detecting them using plots and simulations. We provide code for extracting predictions and residuals from popular software packages and introduce the new R package VARcheck, which generates diagnostic plots with only a few lines of code. We apply these tools in a realistic setting by performing model checks for a multilevel VAR model estimated from emotion measurements from 179 people over 3 weeks. Finally, we discuss three ways in which model checking can advance psychological time series research: improving measurement, building better statistical models, and supporting theory development. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149075814","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

Identifying and modeling careless and insufficient effort responding in continuous rating scale: A response-time-based mixture model. 连续评定量表中粗心和不够努力响应的识别和建模：一个基于响应时间的混合模型。

IF 7.5 1区心理学

Psychological methods Pub Date : 2026-09-28 DOI: 10.1037/met0000869

Youxiang Jiang, Chongqin Xi, Hongbo Wen

{"title":"Identifying and modeling careless and insufficient effort responding in continuous rating scale: A response-time-based mixture model.","authors":"Youxiang Jiang, Chongqin Xi, Hongbo Wen","doi":"10.1037/met0000869","DOIUrl":"10.1037/met0000869","url":null,"abstract":"With the rise of computerized testing platforms, social science surveys increasingly use continuous rating scales (CRS), such as visual analog scales and sliders, to capture nuanced respondent data. However, careless and insufficient effort responding (C/IER), driven by low respondent motivation, introduces significant response bias, compromising measurement accuracy and the validity of statistical analyses. Traditional methods for detecting C/IER, developed for Likert-type responses, are incompatible with the continuous bounded data from CRS. This study develops the continuous bounded item response model for C/IER (CBIRM-C/IER), which integrates continuous bounded item response theory modeling with response-time-based mixture modeling to identify and model C/IER at the person-by-item level. Through two simulation studies, we evaluated the model's parameter recovery under various conditions, demonstrating consistent accuracy and robust performance with C/IER-contaminated data compared with conventional item response theory models. An empirical application evaluated the CBIRM-C/IER model's fit and parameter estimation against traditional models. By detecting and modeling C/IER at the person-by-item level, CBIRM-C/IER provides more reliable estimates of the trait of interest. In summary, CBIRM-C/IER can help practitioners identify and model C/IER within CRS data to obtain more accurate estimates of the trait of interest. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149075805","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 denominator chooses the estimand: A target-population true-score framework for standardized mean differences. 分母选择估计：标准化平均差异的目标人口真实得分框架。

IF 7.5 1区心理学

Psychological methods Pub Date : 2026-09-24 DOI: 10.1037/met0000875

Daiki Nakamura

{"title":"The denominator chooses the estimand: A target-population true-score framework for standardized mean differences.","authors":"Daiki Nakamura","doi":"10.1037/met0000875","DOIUrl":"10.1037/met0000875","url":null,"abstract":"The standardized mean difference (SMD) is a workhorse effect-size index across psychology, education, and the behavioral sciences, but its denominator embeds estimand-level commitments-about reliability, sample heterogeneity, and the reference scale on which the effect is interpreted-that are rarely made explicit. We treat the choice of SMD denominator as an estimand-level choice (Lundberg et al., 2021) and develop the latent true-score and target-population-anchored geometric SMD (LTG-SMD): an SMD in which the denominator is the geometric mean of group-specific true-score standard deviations in an explicitly chosen target reference population. An algebraic decomposition relates LTG-SMD to Hedges's g through two transparent factors-a reliability factor and a study-to-target factor-that show exactly when conventional SMDs converge with LTG-SMD and when they diverge. We develop plug-in estimation with both analytic delta-method confidence intervals and bias-corrected nonparametric bootstrap intervals, evaluate finite-sample properties across 1,152 simulation conditions, and illustrate the framework in three empirical settings spanning a high-reliability educational trial with a holdout reference, a moderate-reliability brief personality scale with an external reference, and a 23-site Many Labs 2 replication using a cross-site reference. The framework has direct implications for meta-analysis and multisite research: Anchoring effects to a common target reference separates one important source of denominator-driven heterogeneity from substantive heterogeneity, though it does not address all sources of measurement or design heterogeneity. LTG-SMD is offered as a complementary estimand, not a universal replacement for conventional SMDs. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149040396","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

Identifying causes of treatment effect heterogeneity across replication studies: Definitions, identification, and estimation of remaining heterogeneity. 在重复研究中确定治疗效果异质性的原因：定义、鉴定和剩余异质性的估计。

IF 7.5 1区心理学

Psychological methods Pub Date : 2026-09-24 DOI: 10.1037/met0000863

Soojin Park, Steffi Pohl, Peter M Steiner

{"title":"Identifying causes of treatment effect heterogeneity across replication studies: Definitions, identification, and estimation of remaining heterogeneity.","authors":"Soojin Park, Steffi Pohl, Peter M Steiner","doi":"10.1037/met0000863","DOIUrl":"https://doi.org/10.1037/met0000863","url":null,"abstract":"Replication studies are crucial for evaluating treatment efficacy and exploring the conditions that moderate treatment effects. Understanding this treatment effect heterogeneity across studies is essential for building robust theories. However, identifying the causes of this heterogeneity is challenging, as multiple study characteristics often vary simultaneously, even in replication studies in which many aspects are deliberately held constant across studies. Traditional metaregression typically lacks a clear causal framework, obscuring the causal interpretation of moderating effects. Although a more recent causal method provides a way to identify the replication effect after adjusting for unintended differences, it relies on the strong assumption that all effect moderators that cause the treatment effect heterogeneity across studies must be measured and uses a parametric estimation model. We propose an alternative approach by introducing a causal framework that decomposes sources of treatment effect heterogeneity. Our approach uses a directed acyclic graph to identify these sources and to develop strategies to single out their contributions. By focusing on different (causal and noncausal) quantities, the proposed strategies require weaker assumptions than the earlier causal approach. We also propose robust estimation strategies using a variable selection method designed to model these higher order interactions with minimal bias. We illustrate our method using a real-world example of replication studies from social psychology. Through a simulation and case study, we provide useful recommendations for applied researchers on designing future replication studies and identifying moderators that cause effect heterogeneity. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149040347","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

Transitive differential item functioning clustering: A graph-theoretic approach to identifying many-group partial measurement invariance. 传递微分项功能聚类：一种识别多群部分测量不变性的图论方法。

IF 7.5 1区心理学

Psychological methods Pub Date : 2026-09-24 DOI: 10.1037/met0000873

Yale Quan, Chun Wang

{"title":"Transitive differential item functioning clustering: A graph-theoretic approach to identifying many-group partial measurement invariance.","authors":"Yale Quan, Chun Wang","doi":"10.1037/met0000873","DOIUrl":"10.1037/met0000873","url":null,"abstract":"When measuring latent constructs across multiple demographic groups, identifying partial measurement invariance is essential for valid and fair comparisons. Recent advances, such as the neural network approach implemented in InterDIFNet, provide probabilistic differential item functioning (DIF) estimates for all pairwise group comparisons. However, InterDIFNet's output cannot be directly used for parameter estimation or ability scoring. To address this gap, we introduce transitive DIF clustering (TDC), a graph-theoretic algorithm that converts pairwise DIF probabilities into psychometrically coherent, measurement-invariant clusters that can be used to inform item parameter estimation and subsequent theta scoring. TDC represents groups as vertices and pairwise invariance as edges, and then enforces transitivity via a closure operation to identify logically consistent clusters. Extensive simulation studies show that TDC outperforms traditional clustering methods (K-means, hierarchical) and naive approaches (assuming full invariance or noninvariance), yielding more accurate and less biased latent score estimates. An empirical application illustrates TDC's ability to detect complex partial invariance patterns overlooked by conventional methods and its ability to inform initial item screening in operational assessments. By providing a reproducible, theoretically grounded approach to establishing partial measurement invariance across many groups, TDC advances the fair and valid assessment of latent constructs in educational and psychological research. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149040362","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

How to run round-robin social interaction studies: A tutorial for the efficient capture of natural social behavior. 如何进行循环式社会互动研究：有效捕捉自然社会行为的教程。

IF 7.5 1区心理学

Psychological methods Pub Date : 2026-09-21 DOI: 10.1037/met0000868

Erin A Heerey, Samantha M Jones, Jeremias Campos, Amanda Friesen

{"title":"How to run round-robin social interaction studies: A tutorial for the efficient capture of natural social behavior.","authors":"Erin A Heerey, Samantha M Jones, Jeremias Campos, Amanda Friesen","doi":"10.1037/met0000868","DOIUrl":"https://doi.org/10.1037/met0000868","url":null,"abstract":"The study of natural social behavior is a frontier discipline in psychology. Although such work is highly necessary for advancing and refining theories of social behavior, social cognition, evolutionary psychology, relationships, and more, researchers rarely commit to such intensive research designs due to the anticipated effort involved. In particular, to understand social behavior as it naturally unfolds, scientists must be able to collect and handle a high volume of rich social data efficiently, effectively, and reproducibly. Moreover, because social behavior is highly individualistic and context dependent, it is critical to capture variation in people's natural social behavior across multiple interaction partners. Here, we focus on \"round-robin\" research designs. Significant issues can arise in the administration of such designs, disincentivizing the collection of naturalistic round-robin social data. These include issues associated with participant nonattendance, experimenter error, the minimization of demand characteristics, and data management. Here, we provide a tutorial based on a recent study, with customizable resources designed to mitigate the inherent challenges associated with this type of research, including the semiautomation of in-person data collection and the automation of data management. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012797","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

Fitting two-level structural equation models to summary statistics: Leveling up meta-analytic structural equation modeling. 汇总统计的两级结构方程模型拟合：提升元分析结构方程模型的水平。

IF 7.5 1区心理学

Psychological methods Pub Date : 2026-08-31 DOI: 10.1037/met0000864

Suzanne Jak, Mike W-L Cheung

{"title":"Fitting two-level structural equation models to summary statistics: Leveling up meta-analytic structural equation modeling.","authors":"Suzanne Jak, Mike W-L Cheung","doi":"10.1037/met0000864","DOIUrl":"10.1037/met0000864","url":null,"abstract":"Standard two-level structural equation models (SEMs) require access to raw data. In this study, we propose a method for fitting two-level SEMs using meta-analytic structural equation modeling (MASEM) on summary statistics. Although our focus is on the meta-analytic case of individuals nested in studies, the approach could be applied to any two-level structure. An illustration using empirical data showed that fitting a two-level model on individual data versus fitting a two-level model using summary statistics produces essentially identical results. One advantage of the MASEM approach is that it is straightforward to model heterogeneity in the within-cluster covariances. Using simulated data, we demonstrated that the MASEM method performed well in such heterogeneous conditions. In contrast, two-level SEM analyses resulted in inflated Type I errors based on the test statistic and a significant underestimation of the parameters' standard errors. Through an empirical example, we show how two-level SEM through the MASEM method can be employed to evaluate measurement invariance across all studies and how the model can be expanded to incorporate study-level variables that explain some of the variation in parameters across studies. Implications and limitations of the proposed method are discussed, and directions for future research are provided. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148866704","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

Within-person reliability of composite scores or single-item responses with missing intensive longitudinal data. 缺少密集纵向数据的综合得分或单项反应的人内可靠性。

IF 7.5 1区心理学

Psychological methods Pub Date : 2026-08-20 DOI: 10.1037/met0000865

Daniel McNeish

{"title":"Within-person reliability of composite scores or single-item responses with missing intensive longitudinal data.","authors":"Daniel McNeish","doi":"10.1037/met0000865","DOIUrl":"10.1037/met0000865","url":null,"abstract":"Intensive longitudinal data (ILD) are commonly used to study within-person processes, and psychometric methods for computing within-person reliability with ILD have recently appeared in the methodological literature. The dense, frequent nature of data collection in ILD often leads to copious amounts of missing data-recent reviews find that 30%-40% missing data rates are typical. However, given the nascent state of psychometrics for ILD, the intersection of psychometrics and missing ILD has yet to be explored despite the pervasiveness of missing ILD in empirical studies. In addition, given that ILD are frequently used to study sensitive topics such as mental health and substance use, there is a heightened risk of missing not at random (MNAR) data. That is, the reason data are missing is associated with what the value would have been (e.g., depression responses are missing when a person is momentarily too depressed to respond). The goal of this article is to (a) clarify how missing values affect estimates of within-person reliability with ILD and (b) to extend one recently proposed method (the measurement error autoregressive model) with a Diggle-Kenward selection process to evaluate sensitivity to a potential MNAR mechanism. Simulations in the article find that the accuracy of within-person reliability estimates from standard approaches can deteriorate when missing data are present, but the proposed model can better recover population within-person reliability-even with a large amount of MNAR data-under the conditions and missingness mechanisms studied. Limitations and extensions to other methods for computing within-person reliability are also discussed. (PsycInfo Database Record (c) 2026 APA, all rights reserved).","PeriodicalId":20782,"journal":{"name":"Psychological methods","volume":" ","pages":""},"PeriodicalIF":7.5,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148797423","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

Columnwise neural imputation for incomplete ordinal psychometric data. 不完全有序心理测量数据的柱状神经归算。

IF 7.5 1区心理学

Psychological methods Pub Date : 2026-08-13 DOI: 10.1037/met0000867

Longfei Zhang, Minjeong Jeon, Ping Chen

引用次数: 0