British Journal of Mathematical & Statistical Psychology最新文献

A tutorial for understanding SEM using R: Where do all the numbers come from? 用R理解SEM的教程：所有的数字是从哪里来的？

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-07-13 DOI: 10.1111/bmsp.70003

Yves Rosseel, Marc Vidal

引用次数: 0

Effect size comparison for populations with an application in psychology. 人群效应量比较在心理学中的应用。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-06-23 DOI: 10.1111/bmsp.70001

Bhargab Chattopadhyay, Sudeep R Bapat

引用次数: 0

Inferences of associated latent variables by the observable test scores. 由可观察测验分数推断相关潜在变量。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-06-18 DOI: 10.1111/bmsp.70002

Rudy Ligtvoet

引用次数: 0

Testing the validity of instrumental variables in just-identified linear non-Gaussian models. 检验工具变量在刚识别的线性非高斯模型中的有效性。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-06-16 DOI: 10.1111/bmsp.70000

Wolfgang Wiedermann, Dexin Shi

{"title":"Testing the validity of instrumental variables in just-identified linear non-Gaussian models.","authors":"Wolfgang Wiedermann, Dexin Shi","doi":"10.1111/bmsp.70000","DOIUrl":"https://doi.org/10.1111/bmsp.70000","url":null,"abstract":"Instrumental variable (IV) estimation constitutes a powerful quasi-experimental tool to estimate causal effects in observational data. The IV approach, however, rests on two crucial assumptions-the instrument relevance assumption and the exclusion restriction assumption. The latter requirement (stating that the IV is not allowed to be related to the outcome via any path other than the one going through the predictor), cannot be empirically tested in just-identified models (i.e. models with as many IVs as predictors). The present study introduces properties of non-Gaussian IV models which enable one to test whether hidden confounding between an IV and the outcome is present. Detecting exclusion restriction violations due to a direct path between the IV and the outcome, however, is restricted to the over-identified case. Based on these insights, a two-step approach is presented to test IV validity against hidden confounding in just-identified models. The performance of the approach was evaluated using Monte-Carlo simulation experiments. An empirical example from psychological research is given to illustrate the approach in practice. Recommendations for best-practice applications and future research directions are discussed. Although the current study presents important insights for developing diagnostic procedures for IV models, sound universal IV validation in the just-identified case remains a challenging task.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144310869","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

New developments in experience sampling methodology. 经验抽样方法的新发展。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-06-10 DOI: 10.1111/bmsp.12398

Francis Tuerlinckx, Peter Kuppens, Sigert Ariens, Leonie Cloos, Egon Dejonckheere, Ginette Lafit, Koen Niemeijer, Jordan Revol, Evelien Schat, Marieke Schreuder, Niels Vanhasbroeck, Eva Ceulemans

{"title":"New developments in experience sampling methodology.","authors":"Francis Tuerlinckx, Peter Kuppens, Sigert Ariens, Leonie Cloos, Egon Dejonckheere, Ginette Lafit, Koen Niemeijer, Jordan Revol, Evelien Schat, Marieke Schreuder, Niels Vanhasbroeck, Eva Ceulemans","doi":"10.1111/bmsp.12398","DOIUrl":"https://doi.org/10.1111/bmsp.12398","url":null,"abstract":"Experience Sampling Methodology (ESM) has been widely used over the past decades to study feelings, behaviour and thoughts as they occur in daily life. Typically, participants complete several assessments per day via a smartphone for multiple days. The growing adoption of ESM has spurred a number of methodological advancements. In this paper, we provide an overview of recent developments in ESM design, statistical analysis and implementation. In terms of design, we discuss considerations around what to measure-including the reliability and validity of self-report measures as well as mobile sensing-as well as when to measure, where we focus on the pros and cons of burst designs and advances in sample size planning methodology. Regarding statistical analysis, we highlight non-linear models, survival analysis for understanding time-to-event data and real-time monitoring of ESM time series. At the implementation level, we address open science practices and advances in data preprocessing. Although most of the topics discussed in this paper are generic, many of the examples are focused on the study of affect in daily life.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144259430","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

Keeping Elo alive: Evaluating and improving measurement properties of learning systems based on Elo ratings. 保持Elo的活力：评估和改进基于Elo评级的学习系统的测量特性。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-06-06 DOI: 10.1111/bmsp.12395

Maria Bolsinova, Bence Gergely, Matthieu J S Brinkhuis

{"title":"Keeping Elo alive: Evaluating and improving measurement properties of learning systems based on Elo ratings.","authors":"Maria Bolsinova, Bence Gergely, Matthieu J S Brinkhuis","doi":"10.1111/bmsp.12395","DOIUrl":"https://doi.org/10.1111/bmsp.12395","url":null,"abstract":"The Elo Rating System which originates from competitive chess has been widely utilised in large-scale online educational applications where it is used for on-the-fly estimation of ability, item calibration, and adaptivity. In this paper, we aim to critically analyse the shortcomings of the Elo rating system in an educational context, shedding light on its measurement properties and when these may fall short in accurately capturing student abilities and item difficulties. In a simulation study, we look at the asymptotic properties of the Elo rating system. Our results show that the Elo ratings are generally not unbiased and their variances are context-dependent. Furthermore, in scenarios where items are selected adaptively based on the current ratings and the item difficulties are updated alongside the student abilities, the variance of the ratings across items and students artificially increases over time and as a result the ratings do not converge. We propose a solution to this problem which entails using two parallel chains of ratings which remove the dependence of item selection on the current errors in the ratings.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144235988","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

Modelling non-linear psychological processes: Reviewing and evaluating non-parametric approaches and their applicability to intensive longitudinal data. 非线性心理过程建模：回顾和评估非参数方法及其对密集纵向数据的适用性。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-05-30 DOI: 10.1111/bmsp.12397

Jan I Failenschmid, Leonie V D E Vogelsmeier, Joris Mulder, Joran Jongerling

{"title":"Modelling non-linear psychological processes: Reviewing and evaluating non-parametric approaches and their applicability to intensive longitudinal data.","authors":"Jan I Failenschmid, Leonie V D E Vogelsmeier, Joris Mulder, Joran Jongerling","doi":"10.1111/bmsp.12397","DOIUrl":"https://doi.org/10.1111/bmsp.12397","url":null,"abstract":"Psychological concepts are increasingly understood as complex dynamic systems that change over time. To study these complex systems, researchers are increasingly gathering intensive longitudinal data (ILD), revealing non-linear phenomena such as asymptotic growth, mean-level switching, and regulatory oscillations. However, psychological researchers currently lack advanced statistical methods that are flexible enough to capture these non-linear processes accurately, which hinders theory development. While methods such as local polynomial regression, Gaussian processes and generalized additive models (GAMs) exist outside of psychology, they are rarely applied within the field because they have not yet been reviewed accessibly and evaluated within the context of ILD. To address this important gap, this article introduces these three methods for an applied psychological audience. We further conducted a simulation study, which demonstrates that all three methods infer non-linear processes that have been found in ILD more accurately than polynomial regression. Particularly, GAMs closely captured the underlying processes, performing almost as well as the data-generating parametric models. Finally, we illustrate how GAMs can be applied to explore idiographic processes and identify potential phenomena in ILD. This comprehensive analysis empowers psychological researchers to model non-linear processes accurately and select a method that aligns with their data and research goals.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144180327","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

A path signature perspective of process data feature extraction. 过程数据特征提取的路径签名视角。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-05-26 DOI: 10.1111/bmsp.12390

Xueying Tang, Jingchen Liu, Zhiliang Ying

引用次数: 0

A supervised learning approach to estimating IRT models in small samples. 一种估计小样本IRT模型的监督学习方法。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-05-15 DOI: 10.1111/bmsp.12396

Dmitry I Belov, Oliver Lüdtke, Esther Ulitzsch

{"title":"A supervised learning approach to estimating IRT models in small samples.","authors":"Dmitry I Belov, Oliver Lüdtke, Esther Ulitzsch","doi":"10.1111/bmsp.12396","DOIUrl":"https://doi.org/10.1111/bmsp.12396","url":null,"abstract":"Existing estimators of parameters of item response theory (IRT) models exploit the likelihood function. In small samples, however, the IRT likelihood oftentimes contains little informative value, potentially resulting in biased and/or unstable parameter estimates and large standard errors. To facilitate small-sample IRT estimation, we introduce a novel approach that does not rely on the likelihood. Our estimation approach derives features from response data and then maps the features to item parameters using a neural network (NN). We describe and evaluate our approach for the three-parameter logistic model; however, it is applicable to any model with an item characteristic curve. Three types of NNs are developed, supporting the obtainment of both point estimates and confidence intervals for IRT model parameters. The results of a simulation study demonstrate that these NNs perform better than Bayesian estimation using Markov chain Monte Carlo methods in terms of the quality of the point estimates and confidence intervals while also being much faster. These properties facilitate (1) pretesting items in a real-time testing environment, (2) pretesting more items and (3) pretesting items only in a secured environment to eradicate possible compromise of new items in online testing.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144082247","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

A novel nonvisual procedure for screening for nonstationarity in time series as obtained from intensive longitudinal designs. 一个新的非视觉程序筛选非平稳性的时间序列，从密集的纵向设计获得。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-04-25 DOI: 10.1111/bmsp.12394

Steffen Zitzmann, Christoph Lindner, Julian F Lohmann, Martin Hecht

引用次数: 0