British Journal of Mathematical & Statistical Psychology最新文献

An extension of the basic local independence model to multiple observed classifications. 将基本局部独立模型扩展到多个观察分类。

IF 1.8 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-09-21 DOI: 10.1111/bmsp.70008

Pasquale Anselmi, Debora de Chiusole, Egidio Robusto, Alice Bacherini, Giulia Balboni, Andrea Brancaccio, Ottavia M Epifania, Noemi Mazzoni, Luca Stefanutti

{"title":"An extension of the basic local independence model to multiple observed classifications.","authors":"Pasquale Anselmi, Debora de Chiusole, Egidio Robusto, Alice Bacherini, Giulia Balboni, Andrea Brancaccio, Ottavia M Epifania, Noemi Mazzoni, Luca Stefanutti","doi":"10.1111/bmsp.70008","DOIUrl":"https://doi.org/10.1111/bmsp.70008","url":null,"abstract":"The basic local independence model (BLIM) is appropriate in situations where populations do not differ in the probabilities of the knowledge states and the probabilities of careless errors and lucky guesses of the items. In some situations, this is not the case. This work introduces the multiple observed classification local independence model (MOCLIM), which extends the BLIM by allowing the above probabilities to vary across populations. In the MOCLIM, each individual is characterized by proficiency, careless and guessing classes, which are observed and determine the probabilities of knowledge states, careless errors and lucky guesses of a population. Given a particular class type (proficiency, careless, or guessing), the probabilities are the same for populations with the same class but may vary between populations with different classes. Algorithms for maximum likelihood estimation of the MOCLIM parameters are provided. The results of a simulation study suggest that the true parameter values are well recovered by the estimation algorithm and that the true model can be uncovered by comparing the goodness-of-fit of alternative models. The results of an empirical application to data from Raven-like matrices suggest that the MOCLIM effectively discriminates between situations where group differences are expected and those where they are not.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145115131","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 Bayes factor framework for unified parameter estimation and hypothesis testing. 统一参数估计和假设检验的贝叶斯因子框架。

IF 1.8 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-09-18 DOI: 10.1111/bmsp.70011

Samuel Pawel

{"title":"A Bayes factor framework for unified parameter estimation and hypothesis testing.","authors":"Samuel Pawel","doi":"10.1111/bmsp.70011","DOIUrl":"https://doi.org/10.1111/bmsp.70011","url":null,"abstract":"The Bayes factor, the data-based updating factor of the prior to posterior odds of two hypotheses, is a natural measure of statistical evidence for one hypothesis over the other. We show how Bayes factors can also be used for parameter estimation. The key idea is to consider the Bayes factor as a function of the parameter value under the null hypothesis. This 'support curve' is inverted to obtain point estimates ('maximum evidence estimates') and interval estimates ('support intervals'), similar to how p-value functions are inverted to obtain point estimates and confidence intervals. This provides data analysts with a unified inference framework as Bayes factors (for any tested parameter value), support intervals (at any level), and point estimates can be easily read off from a plot of the support curve. This approach shares similarities but is also distinct from conventional Bayesian and frequentist approaches: It uses the Bayesian evidence calculus, but without synthesizing data and prior, and it defines statistical evidence in terms of (integrated) likelihood ratios, but also includes a natural way for dealing with nuisance parameters. Applications to meta-analysis, replication studies and logistic regression illustrate how our framework is of practical value for making quantitative inferences.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145082536","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

Detecting Critical Change in Dynamics Through Outlier Detection with Time-Varying Parameters. 通过时变参数的离群值检测检测动力学的关键变化。

IF 1.8 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-09-14 DOI: 10.1111/bmsp.70010

Meng Chen, Michael D Hunter, Sy-Miin Chow

引用次数: 0

Residual permutation tests for feature importance in machine learning. 机器学习中特征重要性的残差排列测试。

IF 1.8 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-08-30 DOI: 10.1111/bmsp.70009

Po-Hsien Huang

{"title":"Residual permutation tests for feature importance in machine learning.","authors":"Po-Hsien Huang","doi":"10.1111/bmsp.70009","DOIUrl":"https://doi.org/10.1111/bmsp.70009","url":null,"abstract":"Psychological research has traditionally relied on linear models to test scientific hypotheses. However, the emergence of machine learning (ML) algorithms has opened new opportunities for exploring variable relationships beyond linear constraints. To interpret the outcomes of these 'black-box' algorithms, various tools for assessing feature importance have been developed. However, most of these tools are descriptive and do not facilitate statistical inference. To address this gap, our study introduces two versions of residual permutation tests (RPTs), designed to assess the significance of a target feature in predicting the label. The first variant, RPT on Y (RPT-Y), permutes the residuals of the label conditioned on features other than the target. The second variant, RPT on X (RPT-X), permutes the residuals of the target feature conditioned on the other features. Through a comprehensive simulation study, we show that RPT-X maintains empirical Type I error rates under the nominal level across a wide range of ML algorithms and demonstrates appropriate statistical power in both regression and classification contexts. These findings suggest the utility of RPT-X for hypothesis testing in ML applications.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144979556","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

Joint analysis of dispersed count-time data using a bivariate latent factor model. 使用双变量潜在因子模型对分散计数时间数据进行联合分析。

IF 1.8 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-08-28 DOI: 10.1111/bmsp.70005

Cornelis J Potgieter, Akihito Kamata, Yusuf Kara, Xin Qiao

{"title":"Joint analysis of dispersed count-time data using a bivariate latent factor model.","authors":"Cornelis J Potgieter, Akihito Kamata, Yusuf Kara, Xin Qiao","doi":"10.1111/bmsp.70005","DOIUrl":"https://doi.org/10.1111/bmsp.70005","url":null,"abstract":"In this study, we explore parameter estimation for a joint count-time data model with a two-factor latent trait structure, representing accuracy and speed. Each count-time variable pair corresponds to a specific item on a measurement instrument, where each item consists of a fixed number of tasks. The count variable represents the number of successfully completed tasks and is modeled using a Beta-binomial distribution to account for potential over-dispersion. The time variable, representing the duration needed to complete the tasks, is modeled using a normal distribution on a logarithmic scale. To characterize the model structure, we derive marginal moments that inform a set of method-of-moments (MOM) estimators, which serve as initial values for maximum likelihood estimation (MLE) via the Monte Carlo Expectation-Maximization (MCEM) algorithm. Standard errors are estimated using both the observed information matrix and bootstrap resampling, with simulation results indicating superior performance of the bootstrap, especially near boundary values of the dispersion parameter. A comprehensive simulation study investigates estimator accuracy and computational efficiency. To demonstrate the methodology, we analyze oral reading fluency (ORF) data, showing substantial variation in item-level dispersion and providing evidence for the improved model fit of the Beta-binomial specification, assessed using standardized root mean square residuals (SRMSR).","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144979632","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

Reduced rank regression for mixed predictor and response variables. 混合预测变量和反应变量的降低秩回归。

IF 1.8 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-08-28 DOI: 10.1111/bmsp.70004

Mark de Rooij, Lorenza Cotugno, Roberta Siciliano

引用次数: 0

A tutorial on Bayesian model averaging for exponential random graph models. 一个关于指数随机图模型的贝叶斯模型平均的教程。

IF 1.8 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-08-18 DOI: 10.1111/bmsp.70007

Ihnwhi Heo, Jan-Willem Simons, Haiyan Liu

{"title":"A tutorial on Bayesian model averaging for exponential random graph models.","authors":"Ihnwhi Heo, Jan-Willem Simons, Haiyan Liu","doi":"10.1111/bmsp.70007","DOIUrl":"https://doi.org/10.1111/bmsp.70007","url":null,"abstract":"The use of exponential random graph models (ERGMs) is becoming prevalent in psychology due to their ability to explain and predict the formation of edges between vertices in a network. Valid inference with ERGMs requires correctly specifying endogenous and exogenous effects as network statistics, guided by theory, to represent the network-generating process while ensuring key effects shaping network topology are not omitted. However, specifying a comprehensive model is challenging, particularly when relying on a single model. Despite this, most applied research continues to use a single ERGM, raising two concerns: Selecting misspecified models compromises valid statistical inference, and single-model inference ignores uncertainty in model selection. One approach to addressing these issues is Bayesian model averaging (BMA), which evaluates multiple candidate models, accounts for uncertainty in parameter estimation and model selection, and is more robust to model misspecification than single-model inference. This tutorial provides a guide to implementing BMA for ERGMs. We illustrate its application using data from a college friendship network, with a supplementary example based on the Florentine marriage network; both focus on averaging exogenous covariate effects. We demonstrate how BMA incorporates theoretical considerations and addresses modelling challenges in ERGMs, with annotated R code provided for replication and extension.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144876847","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

IRT-based response style models and related methodology: Review and commentary. 基于irt的回应风格模型及相关方法：回顾与评论。

IF 1.8 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-08-17 DOI: 10.1111/bmsp.70006

Daniel M Bolt, Lionel Meng

引用次数: 0

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。