BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY英国数学与统计心理学杂志
BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY(英文缩写 BRIT J MATH STAT PSY),ISSN 0007-1102,eISSN 2044-8317,中文译名:英国数学与统计心理学杂志 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 2.410 | Q1 |
| 2022 | 2.600 | Q1 |
| 2023 | 1.500 | Q2 |
| 2024 | 1.800 | Q1 |
| 2025 | 1.900 | Q1 |
BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY 最新收录文献
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1. Kurtosis-robust estimation of the fixed effects eta-squared: An impossibility theorem for F-based estimators and a practical correction.
PMID:日期:2026-09-30Eta-squared (η) is the most widely reported proportion-of-variance effect size in ANOVA, yet its estimators are biased and non-normality is rarely studied. We prove that, for balanced one-way fixed effects ANOVA, no function of the usual F-statistic-the class containing the estimators in routine use-is exactly unbiased for fixed effects η, though the non-centrality and Cohen's f admit exact unbiased estimation; this clarifies why popular 'unbiased' estimators are only approximate. We derive the leading-order, kurtosis-induced bias of the non-centrality-based estimator and propose a kurtosis-robust correction that uses an L-moment kurtosis estimate to remove this drift. Across Monte Carlo conditions and an out-of-sample heavy-tailed family, it attains the lowest root mean square error among those studied and markedly reduces kurtosis-induced bias drift.
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2. Detecting differential item functioning with unknown subgroups and anchor items in cognitive diagnostic assessment.
PMID:日期:2026-08-30Differential item functioning (DIF) analysis is essential for evaluating measurement invariance in educational and psychological assessments. In cognitive diagnostic assessment, however, most existing methods require prespecified comparison subgroups and anchor items. When subgroup membership and anchor items are unavailable or mis-specified, DIF detection and parameter estimation may be biased. To overcome these limitations, this study puts forward a DIF detection method that incorporates an extended modelling framework and a two-stage estimation algorithm. The proposed modelling framework directly integrates DIF parameters into the measurement model and uses a structural model to characterize subgroup differences in attribute mastery distributions. A two-stage expectation maximization algorithm with an adaptive lasso penalty is developed to identify anchor items, classify respondents into latent subgroups and estimate model parameters. The performance of the proposed method was evaluated through a simulation study and an empirical data analysis. Simulation results indicated generally satisfactory DIF detection and parameter recovery, although subgroup-classification accuracy varied across conditions. When applied to the empirical data, the proposed method identified 10 of the 28 items as exhibiting DIF.
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3. Sensitivity analysis for unmeasured pretreatment confounders in causal mediation analysis with debiased machine learning.
3. 在因果中介分析中，针对未测量的预处理混杂因素进行敏感性分析：基于去偏机器学习的方法PMID:日期:2026-08-28Sensitivity analysis for unmeasured confounders is essential for assessing the robustness of causal mediation conclusions. Most existing methods rely on parametric assumptions, which are ill-suited for machine learning-based estimators that are not tied to specific parametric models. This study develops a sensitivity analysis method for mediation analyses based on the debiased machine learning approach. The proposed method can accommodate categorical (e.g. binary) or continuous mediators and outcomes, quantify robustness to unmeasured pretreatment confounders through contour plots and robustness values defined by -type measures, and allow researchers to avoid parametric assumptions by incorporating data-adaptive machine learning methods. Simulation studies are conducted to evaluate the performance of the proposed method. An empirical example is provided to illustrate the application. We hope this study provides a novel method for quantifying the sensitivity to unmeasured confounding in causal evaluations of mediation effects.
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4. Geometric standardized mean difference and its application to meta-analysis.
PMID:日期:2026-08-17The standardized mean difference (SMD) is a widely used measure of effect size, particularly common in psychology, clinical trials and meta-analysis involving continuous outcomes. Traditionally, under the equal variance assumption, the SMD is defined as the mean difference divided by a common standard deviation. This approach is prevalent in meta-analysis but can be overly restrictive in clinical practice. To accommodate unequal variances, the conventional method averages the two variances arithmetically, which does not allow for an unbiased estimation of the SMD. Inspired by this, we propose a geometric approach to averaging the variances, resulting in a novel measure for standardizing the mean difference with unequal variances. We further propose the Cohen-type and Hedges-type estimators for the new SMD, and derive their statistical properties including the confidence intervals. Simulation results show that the Hedges-type estimator performs optimally across various scenarios, demonstrating lower bias, lower mean squared error and improved coverage probability. A real-world meta-analysis also illustrates that our new SMD and its estimators provide valuable insights to the existing literature and can be highly recommended for practical use.
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5. Calibrating Bayesian inference.
5. 校准贝叶斯推理PMID:日期:2026-07-30Bayesian statistics has gained popularity in psychological research due to its intuitive uncertainty quantification and convenient information-updating rules. In many applications, however, prior distributions are introduced merely as instruments to facilitate computation, rather than as representations of genuine subjective belief. Consequently, relying on standard Bayesian justifications for inferential procedures becomes conceptually ungrounded. In this paper, we recommend evaluating finite-sample performance over repeated sampling of data and parameters as an alternative justification for "pragmatic Bayes." We demonstrate a key vulnerability in the usual posterior-based inference: when analysts' chosen prior distribution mismatches the true parameter-generating process, Bayesian inference can be misleading in the long run. Given that this true process is rarely known in practice, we propose a safer alternative: calibrating Bayesian credible regions to achieve frequentist validity. This latter criterion is stronger and guarantees validity of Bayesian inference regardless of the underlying parameter-generating mechanism. To solve the calibration problem in practice, we propose a novel stochastic approximation algorithm. Monte Carlo experiments are conducted and reported, in which we observe that uncalibrated Bayesian inference can be liberal under certain parameter-generating scenarios, whereas our calibrated solution consistently maintain validity. We also illustrate the proposed calibration procedure using a real-data example involving location-scale regression.
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6. Multidimensional unipolar IRT and applications to the measurement of print exposure.
6. 多维单极项目反应理论及其在印刷品接触测量中的应用PMID:日期:2026-07-22[中文摘要] 项目反应理论（IRT）一直是教育与心理测量领域中重要的建模范式。传统IRT模型是双极性的，通常假定对称的测量链接函数以及在有界于两端的潜在连续体上呈对称的特质分布。然而，许多被测量的构念，如印刷暴露、词汇知识或成瘾行为，被认为具有单极性。尽管以往的单极性应用强调了具有固定下界的测量指标可能具有的意义性，但一个较少被探讨的特征涉及单极性框架如何有可能表征构念水平随构念水平升高而增加的多样化。本文通过考虑一种使用加速失效时间（AFT）框架开发的多维单极性IRT建模范式来说明这一特征。利用作者识别测试-体裁（ART-G）的实证数据，我们展示了多维单极性模型如何有助于表征这种构念多样化。在阅读评估的背景下，我们展示了这种表征如何有可能解释词汇掌握随词汇能力发展而出现的多样化。
[英文摘要] Item response theory (IRT) has been a prominent modelling framework in educational and psychological measurement. Traditional IRT models are bipolar, commonly assuming symmetric measurement link functions and symmetric trait distributions over a latent continuum unbounded at both ends. However, many measured constructs, such as print exposure, vocabulary knowledge, or addictive behaviours, have been argued to be unipolar. While prior unipolar applications have emphasized the potential meaningfulness of measurement metrics having fixed lower bounds, a less-explored feature relates to how the unipolar framework has the potential to represent increased diversification of the construct as the construct level increases. This paper illustrates this feature by considering a form of multidimensional unipolar IRT modelling developed using an Accelerated Failure Time (AFT) framework. Using empirical data from the Author Recognition Test-Genres (ART-G), we show how multidimensional unipolar models can help represent such construct diversification. In the context of reading assessment, we show how such a representation has the potential to explain the diversification of vocabulary mastery as the vocabulary proficiency develops.
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7. On the asymmetry, complexity and predicted data patterns of nontraditional item response theory models.
PMID:日期:2026-07-16Traditional item response theory (IRT) models involve symmetric response probability functions, but a developing area of research has focused on asymmetric alternatives. To date, these studies have focused primarily on introducing new asymmetric models, detailing their mathematical formulations, explaining how to interpret their item parameters and demonstrating their distinctive psychometric properties through simulations and empirical data analysis. We add to this burgeoning research area by exploring model evaluation in the context of asymmetric IRT. We begin by introducing a new asymmetric IRT model based on the Aranda-Ordaz (Biometrika, 68, 1981, 357) link function and comparing it to two established models that parameterize asymmetry in distinct ways. Our comparison of these models then allows us to demonstrate three uncommon methods of IRT model evaluation: (1) characterizing asymmetry in the response probability and pseudo-information functions, (2) quantifying configural complexity through fitting propensity analysis and (3) exploring the pattern signatures of each model. Through this in-depth evaluation, we find that all three models possess subtly unique psychometric properties that will benefit applied research. More generally, the model evaluation methods that we consider in this work can offer unique insights into any IRT model.
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8. Estimation of comparable standardized mean differences in cluster randomized trials with covariate adjustment.
PMID:日期:2026-07-15Standardized mean differences (SMDs) are widely used to quantify treatment effects in cluster-randomized trials. However, covariate adjustment in hierarchical linear models reduces the residual variance components used for standardization, which artificially inflates effect size estimates and undermines comparability across studies. We propose a unified family of estimators that recover the unadjusted variance components by rescaling the covariate-adjusted variance components using pseudo- indices. This rescaling places effect size estimates on a common reference scale, thereby improving comparability across studies and model specifications under standard modeling assumptions. The framework accommodates three covariate adjustment scenarios including level-1, level-2, and simultaneous both-level adjustments. Furthermore, it introduces three estimator types spanning method of moments, maximum likelihood, and a t-statistic reformulation suitable for meta-analysis from published summaries, alongside delta-method variance approximations for each. An empirical example and a simulation study apply the proposed covariate-adjusted SMDs across these scenarios to illustrate their implementation and demonstrate the consequences of omitting the correction.
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9. Revisiting reliability and measurement precision: Towards a unified perspective.
PMID:日期:2026-07-15This article revisits the concepts of reliability and measurement precision across classical test theory (CTT), item response theory (IRT) and cognitive diagnosis models (CDMs), emphasizing their conceptual differences and proposing a unified framework. We show how continuous score estimators in CTT and IRT relate to mastery probabilities in CDMs through integration over proficiency estimator distributions relative to defined thresholds. Building on this link, we introduce reliability indices grounded in the coefficient of determination ( ) as a common measure of association between true scores and estimated true scores, applicable to both continuous proficiencies and discrete classifications. This unified perspective clarifies how reliability can be consistently estimated and reported across frameworks, promoting coherence in psychometric practice and supporting more transparent interpretation of test results. Ultimately, this study aims to clarify reliability concepts, facilitating a unified perspective and aiming to promote consistent reliability reporting across diverse psychometric applications.
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10. New estimation methods for diagnostic classification models.
PMID:日期:2026-07-13This paper introduces a new class of estimators for cognitive diagnosis models (CDMs) based on the Cressie-Read family of -divergences. Focusing on the loglinear CDM (LCDM), for which joint maximum likelihood estimation (JMLE) has been shown to be consistent, we propose a joint minimum divergence estimation (JMDE) framework. We establish the consistency of the proposed estimator and derive its asymptotic distribution. Simulation studies are used to compare the finite-sample performance of JMDE and JMLE under ideal conditions and increasingly challenging settings, including different forms of data contamination-such as random response perturbations and structured model misspecification-and varying levels of model complexity. The results indicate that JMDE provides enhanced robustness and improved stability in finite samples under contaminated scenarios. The approach is empirically illustrated, highlighting its practical behavior in applied settings.