British Journal of Mathematical & Statistical Psychology最新文献_第3页

Unravelling the small sample bias in AR(1) models: The pros and cons of available bias correction methods. 揭示AR(1)模型中的小样本偏差：可用偏差校正方法的优缺点

IF 1.8 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2026-03-04 DOI: 10.1111/bmsp.70038

Zhiwei Dou, Sigert Ariens, Eva Ceulemans, Ginette Lafit

{"title":"Unravelling the small sample bias in AR(1) models: The pros and cons of available bias correction methods.","authors":"Zhiwei Dou, Sigert Ariens, Eva Ceulemans, Ginette Lafit","doi":"10.1111/bmsp.70038","DOIUrl":"https://doi.org/10.1111/bmsp.70038","url":null,"abstract":"The first-order autoregressive [AR(1)] model is widely used to investigate psychological dynamics. This study focusses on the estimation and inference of the autoregressive (AR) effect in AR(1) models under a limited sample size-a common scenario in psychological research. State-of-the-art estimators of the autoregressive effect are known to be biased when sample sizes are small. We analytically demonstrate the causes and consequences of this small sample bias on the estimation of the AR effect, its variance and the AR(1) model's intercept, particularly when using OLS. In addition, we reviewed various bias correction methods proposed in the time-series literature. A simulation study compares the OLS estimator with these correction methods in terms of estimation accuracy and inference. The main result indicates that the small sample bias of the OLS estimator of the autoregressive effect is a consequence of limited information and correcting for this bias without more information always induces a bias-variance trade-off. Nevertheless, correction methods discussed in this research may offer improved statistical power under moderate sample sizes when the primary research goal is hypothesis testing.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147357621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Shedding some light on the relationship between measurement error and statistical power in multilevel models applied to intensive longitudinal designs. 揭示了应用于密集纵向设计的多水平模型中测量误差与统计功率的关系。

IF 1.8 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2026-02-25 DOI: 10.1111/bmsp.70040

Ginette Lafit, Sigert Ariens, Richard Artner

{"title":"Shedding some light on the relationship between measurement error and statistical power in multilevel models applied to intensive longitudinal designs.","authors":"Ginette Lafit, Sigert Ariens, Richard Artner","doi":"10.1111/bmsp.70040","DOIUrl":"https://doi.org/10.1111/bmsp.70040","url":null,"abstract":"We examine multilevel models applied to intensive longitudinal (IL) designs. Many measurements in IL research are influenced by measurement error, which can compromise the consistency of estimates obtained through maximum likelihood estimation (MLE). While previous research has addressed the impact of measurement error in broader multilevel contexts, its effects on statistical power-particularly concerning cross-level interaction effects-have not been thoroughly explored. This study aims to clarify the relationship between measurement error and statistical power by deriving analytical formulas for the asymptotic bias and precision matrix of the MLE of fixed effects in multilevel models that account for additive measurement errors and autoregressive [AR(1)] within-person errors. Furthermore, we analyse how different sources of measurement error affect the standard errors of fixed effects estimates and the overall statistical power, specifically when both time-varying and time-invariant predictors, as well as the response variable, are measured with error. Our findings show that measurement error in predictors leads to downward bias in the MLE of fixed effects, whereas MLE estimates of fixed effects remain unbiased when there is measurement error in the response variable. Finally, we explore how these errors influence statistical power for cross-level interaction effects between time-varying and time-invariant predictors.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147312733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

ReMoDe - Recursive modality detection in distributions of ordinal data. 在有序数据的分布中进行递归模态检测。

IF 1.8 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2026-02-19 DOI: 10.1111/bmsp.70037

Madlen Hoffstadt, Lourens Waldorp, Javier Garcia-Bernardo, Han van der Maas

引用次数: 0

Extending reliability to intensive longitudinal data with the Kalman filter. 利用卡尔曼滤波将可靠性扩展到密集的纵向数据。

IF 1.8 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2026-02-18 DOI: 10.1111/bmsp.70039

Michael D Hunter

{"title":"Extending reliability to intensive longitudinal data with the Kalman filter.","authors":"Michael D Hunter","doi":"10.1111/bmsp.70039","DOIUrl":"https://doi.org/10.1111/bmsp.70039","url":null,"abstract":"Reliability is central to how researchers approach measurement in standard, group-based analyses of single-time-point data, yet this critical aspect is often overlooked in the analysis of repeated observations. Since its inception, reliability has been a between-person concept, but we redevelop this notion for within-person designs by proposing a new coefficient <math> <semantics><mrow><mi>κ</mi></mrow> <annotation>$$ kappa $$</annotation></semantics> </math> of reliability for single-subject designs. This coefficient shares the same general definition of reliability as former coefficients-the ratio of the true score variance to the total variance-but applies to time-dependent within-person variability rather than independent between-person variability. Coefficient <math> <semantics><mrow><mi>κ</mi></mrow> <annotation>$$ kappa $$</annotation></semantics> </math> begins with a latent variable time series model called a state space model, and is then extended to a state space model for multiple subjects with continuous or discrete variation across people. Using analytic methods, we derive coefficient <math> <semantics><mrow><mi>κ</mi></mrow> <annotation>$$ kappa $$</annotation></semantics> </math> and prove its relations to other coefficients of reliability.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146222108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

An efficient MCMC-INLA algorithm for Bayesian inference of logistic graded response models. logistic梯度响应模型贝叶斯推理的高效MCMC-INLA算法。

IF 1.8 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2026-02-09 DOI: 10.1111/bmsp.70033

Yu Zhou, Yincai Tang, Siliang Zhang

{"title":"An efficient MCMC-INLA algorithm for Bayesian inference of logistic graded response models.","authors":"Yu Zhou, Yincai Tang, Siliang Zhang","doi":"10.1111/bmsp.70033","DOIUrl":"https://doi.org/10.1111/bmsp.70033","url":null,"abstract":"This paper proposes a Bayesian MCMC-INLA algorithm specifically designed for both unidimensional and multidimensional logistic graded response models (LGRMs). The algorithm incorporates a computationally efficient data augmentation approach by introducing Pólya-Gamma variables and latent variables, thereby addressing the limitations of traditional Bayesian MCMC methods in handling item response theory (IRT) models with logistic link functions. By integrating the advanced and efficient integrated nested Laplace approximation (INLA) framework, the MCMC-INLA algorithm achieves both high computational efficiency and estimation accuracy. The paper provides detailed derivations of the posterior and conditional distributions for IRT models, outlines the incorporation of Pólya-Gamma and latent variables within the Gibbs sampling procedure, and presents the implementation of the MCMC-INLA algorithm for both unidimensional and multidimensional cases. The performance of the proposed algorithm is evaluated through extensive simulation studies and an empirical application to the IPIP-NEO dataset. Potential extensions of the MCMC-INLA framework to other IRT models are also discussed.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146151406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Variational Bayesian inference for sparse item response theory models. 稀疏项目反应理论模型的变分贝叶斯推理。

IF 1.8 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2026-02-04 DOI: 10.1111/bmsp.70032

Yemao Xia, Yu Xue, Depeng Jiang

引用次数: 0

Latent Poisson count models for action count data from technology-enhanced assessments. 来自技术增强评估的行动计数数据的潜在泊松计数模型。

IF 1.8 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2026-02-03 DOI: 10.1111/bmsp.70036

Gregory Arbet, Hyeon-Ah Kang

引用次数: 0

Revisiting reliability with human and machine learning raters under scoring design and rater configuration in the many-facet Rasch model. 在多面Rasch模型的评分设计和评分配置下，重新审视人类和机器学习评分器的可靠性。

IF 1.8 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2026-01-31 DOI: 10.1111/bmsp.70034

Xingyao Xiao, Richard J Patz, Mark R Wilson

{"title":"Revisiting reliability with human and machine learning raters under scoring design and rater configuration in the many-facet Rasch model.","authors":"Xingyao Xiao, Richard J Patz, Mark R Wilson","doi":"10.1111/bmsp.70034","DOIUrl":"https://doi.org/10.1111/bmsp.70034","url":null,"abstract":"Constructed-response (CR) items are widely used to assess higher order skills but require human scoring, which introduces variability and is costly at scale. Machine learning (ML)-based scoring offers a scalable alternative, yet its psychometric consequences in rater-mediated models remain underexplored. This study examines how scoring design, rater bias, ML inconsistency and model specification affect the reliability of ability estimation in polytomous CR assessments. Using Monte Carlo simulation, we manipulated human and ML rater bias, ML inconsistency and scoring density (complete, overlapping, isolated). Five estimation models were compared, including the Partial Credit Model (PCM) with fixed thresholds and the Many-Facet Partial Credit Model (MFPCM) with and without free calibration. Results showed that systematic bias, not random inconsistency, was the main source of error. Hybrid human-ML scoring improved estimation when raters were unbiased or exhibited opposing biases, but error compounded when biases aligned. Across designs, PCM with fixed thresholds consistently outperformed more complex alternatives, while anchoring CR items to selected-response metrics stabilized MFPCM estimation. The real data application replicated these patterns. Findings show that scoring design and bias structure, rather than model complexity, drive the benefits of hybrid scoring and that anchoring offers a practical strategy for stabilizing estimation.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146094849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Bayesian inference for dynamic Q matrices and attribute trajectories in hidden Markov diagnostic classification models. 隐马尔可夫诊断分类模型中动态Q矩阵和属性轨迹的贝叶斯推理。

IF 1.8 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2026-01-20 DOI: 10.1111/bmsp.70028

Chen-Wei Liu

引用次数: 0

Enhancing generalizability theory with mixed-effects models for heteroscedasticity in psychological measurement: A theoretical introduction with an application from EEG data. 用混合效应模型增强心理测量中异方差的泛化理论：理论介绍及脑电数据的应用。

IF 1.8 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2026-01-18 DOI: 10.1111/bmsp.70026

Philippe Rast, Peter E Clayson

{"title":"Enhancing generalizability theory with mixed-effects models for heteroscedasticity in psychological measurement: A theoretical introduction with an application from EEG data.","authors":"Philippe Rast, Peter E Clayson","doi":"10.1111/bmsp.70026","DOIUrl":"10.1111/bmsp.70026","url":null,"abstract":"Generalizability theory (G-theory) defines a statistical framework for assessing measurement reliability by decomposing observed variance into meaningful components attributable to persons, facets, and error. Classic G-theory assumes homoscedastic residual variances across measurement conditions, an assumption that is often violated in psychological and behavioural data. The main focus of this work is to extend G-theory using a mixed-effects location-scale model (MELSM) that allows residual error variance to vary systematically across conditions and persons. By modeling heteroscedasticity, we can extend the computation of condition-specific generalizability ( <math> <semantics> <mrow> <msub><mrow><mi>G</mi></mrow> <mrow><mi>t</mi></mrow> </msub> </mrow> <annotation>$$ {G}_t $$</annotation></semantics> </math> ) and dependability ( <math> <semantics> <mrow> <msub><mrow><mi>D</mi></mrow> <mrow><mi>t</mi></mrow> </msub> </mrow> <annotation>$$ {D}_t $$</annotation></semantics> </math> ) coefficients to reflect local reliability under varying degrees of measurement precision. As an illustration, we apply the model to empirical data from an EEG experiment and show that failing to account for variance heterogeneity can mask meaningful differences in measurement quality. A simulation-based decision study further demonstrates how targeted increases in measurement density can improve reliability for low-precision conditions or participants. The proposed framework retains the interpretative character of classical G-theory while enhancing its flexibility. We argue that it supports finer-grained insights on conditions that influence reliability and better-informed design decisions in psychological measurements. We discuss implications for individualized reliability assessment, adaptive measurement strategies, and future extensions to multi-facet designs.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2026-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145999604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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