British Journal of Mathematical & Statistical Psychology最新文献

Jointly modeling responses and omitted items by a competing risk model: A survival analysis approach.

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-01-30 DOI: 10.1111/bmsp.12382

Jinxin Guo, Xin Xu, Guanhua Fang, Zhiliang Ying, Susu Zhang

{"title":"Jointly modeling responses and omitted items by a competing risk model: A survival analysis approach.","authors":"Jinxin Guo, Xin Xu, Guanhua Fang, Zhiliang Ying, Susu Zhang","doi":"10.1111/bmsp.12382","DOIUrl":"https://doi.org/10.1111/bmsp.12382","url":null,"abstract":"Item response theory models are commonly adopted in educational assessment and psychological measurement. Such models need to be modified to accommodate practical situations when statistical sampling assumptions are violated. Omission is a common phenomenon in educational testing. In modern computer-based testing, we have not only examinees' responses but also their response times. This paper utilizes response time and develops a joint model of responses and response times. The new approach is analogous to those developed in survival analysis for dealing with right-censored data. In particular, a key ingredient is the introduction of the omission time (OT), which corresponds to the censoring time in survival analysis. By competing risk formulation, the proposed method provides an alternative narrative to how an item becomes answered versus omitted, depending on the competing relationship of response time and OT, so that the likelihood function can be constructed properly. The maximum likelihood estimator can be computed via the expectation-maximization algorithm. Simulation studies were conducted to evaluate the performance of the proposed method and its robustness against various mis-specifications. The method was applied to a dataset from the PISA 2015 Science Test.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143069619","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

Efficient and accurate variational inference for multilevel threshold autoregressive models in intensive longitudinal data. 纵向数据中多水平阈值自回归模型的高效准确变分推理。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-01-21 DOI: 10.1111/bmsp.12381

Azizur Rahman, Depeng Jiang, Lisa M Lix

{"title":"Efficient and accurate variational inference for multilevel threshold autoregressive models in intensive longitudinal data.","authors":"Azizur Rahman, Depeng Jiang, Lisa M Lix","doi":"10.1111/bmsp.12381","DOIUrl":"https://doi.org/10.1111/bmsp.12381","url":null,"abstract":"Recent technological advancements have enabled the collection of intensive longitudinal data (ILD), consisting of repeated measurements from the same individual. The threshold autoregressive (TAR) model is often used to capture the dynamic outcome process in ILD, with autoregressive parameters varying based on outcome variable levels. For ILD from multiple individuals, multilevel TAR (ML-TAR) models have been proposed, with Bayesian approaches typically used for parameter estimation. However, fitting ML-TAR models can be computationally challenging. This study introduces a mean-field variational Bayes (MFVB) algorithm as an alternative to traditional Bayesian inference. By optimizing to approximate posterior densities, variational Bayes aims to find the best approximation within a defined set of distributions. Simulation results demonstrate that our MFVB algorithm is significantly faster than the standard Markov chain Monte Carlo (MCMC) approach. Moreover, increasing the number of individuals or time points enhances the accuracy of the parameter estimates using MFVB, suggesting that sufficient data are crucial for accurate estimation in complex models like ML-TAR models. When applied to real-world data, the MFVB algorithm was significantly more efficient than MCMC and maintained similar accuracy. Thus, the MFVB algorithm is a faster and more consistent alternative to MCMC for large-scale inference in ILD models.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143016865","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

Data fusion by T3-PCA: A global model for the simultaneous analysis of coupled three-way and two-way real-valued data. 通过 T3-PCA 进行数据融合：同时分析三向和双向实值耦合数据的全局模型。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-01-15 DOI: 10.1111/bmsp.12372

Elisa Frutos-Bernal, Eva Ceulemans, Purificación Galindo-Villardón, Tom F Wilderjans

{"title":"Data fusion by T3-PCA: A global model for the simultaneous analysis of coupled three-way and two-way real-valued data.","authors":"Elisa Frutos-Bernal, Eva Ceulemans, Purificación Galindo-Villardón, Tom F Wilderjans","doi":"10.1111/bmsp.12372","DOIUrl":"https://doi.org/10.1111/bmsp.12372","url":null,"abstract":"In various areas of science, researchers try to gain insight into important processes by jointly analysing different datasets containing information regarding common aspects of these processes. For example, to explain individual differences in personality, researchers collect, for the same set of persons, data regarding behavioural signatures (i.e., the reaction profile of a person across different situations), on the one hand, and traits or dispositions, on the other hand. To uncover the processes underlying such coupled data, to all N-way <math> <semantics><mrow><mi>N</mi></mrow> <annotation>$$ N $$</annotation></semantics> </math> -mode data blocks simultaneously a global model is fitted, in which each data block is represented by an <math> <semantics><mrow><mi>N</mi></mrow> <annotation>$$ N $$</annotation></semantics> </math> -way <math> <semantics><mrow><mi>N</mi></mrow> <annotation>$$ N $$</annotation></semantics> </math> -mode decomposition model (e.g., principal component analysis [PCA], Parafac, Tucker3) and the parameters underlying the common mode are required to be the same for all data blocks this mode belongs to. To estimate the parameters underlying the common mode, a simultaneous strategy is used that pools the information present in all data blocks (i.e., data fusion). In this paper, we propose the T3-PCA model, which represents three- and two-way data with Tucker3 and PCA respectively. This model is less restrictive than the already proposed LMPCA model in which the three-way data block is decomposed according to a Parafac model. To estimate the T3-PCA model parameters, an alternating least-squares algorithm is proposed. The superior performance of the simultaneous T3-PCA strategy over a sequential strategy (i.e., estimating common parameters using information from the three-way data block only) is demonstrated in an extensive simulation study and an application to empirical coupled anxiety data.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142984780","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

Assessment of fit of item response theory models: A critical review of the status quo and some future directions. 项目反应理论模型的拟合性评价：现状及未来发展方向。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-01-06 DOI: 10.1111/bmsp.12378

Sandip Sinharay, Scott Monroe

引用次数: 0

The generalized Hausman test for detecting non-normality in the latent variable distribution of the two-parameter IRT model. 用于检测双参数IRT模型潜变量分布非正态性的广义Hausman检验。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2024-12-26 DOI: 10.1111/bmsp.12379

Lucia Guastadisegni, Silvia Cagnone, Irini Moustaki, Vassilis Vasdekis

{"title":"The generalized Hausman test for detecting non-normality in the latent variable distribution of the two-parameter IRT model.","authors":"Lucia Guastadisegni, Silvia Cagnone, Irini Moustaki, Vassilis Vasdekis","doi":"10.1111/bmsp.12379","DOIUrl":"https://doi.org/10.1111/bmsp.12379","url":null,"abstract":"This paper introduces the generalized Hausman test as a novel method for detecting the non-normality of the latent variable distribution of the unidimensional latent trait model for binary data. The test utilizes the pairwise maximum likelihood estimator for the parameters of the latent trait model, which assumes normality of the latent variable, and the maximum likelihood estimator obtained under a semi-non-parametric framework, allowing for a more flexible distribution of the latent variable. The performance of the generalized Hausman test is evaluated through a simulation study and compared with other test statistics available in the literature for testing latent variable distribution fit and an overall goodness-of-fit test statistic. Additionally, three information criteria are used to select the best-fitted model. The simulation results show that the generalized Hausman test outperforms the other tests under most conditions. However, the results obtained from the information criteria are somewhat contradictory under certain conditions, suggesting a need for further investigation and interpretation. The proposed test statistics are used in three datasets.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142900633","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 sparse latent class model incorporating response times. 包含响应时间的稀疏潜在类模型。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2024-12-25 DOI: 10.1111/bmsp.12380

Siqi He, Steven Andrew Culpepper, Jeffrey A Douglas

{"title":"A sparse latent class model incorporating response times.","authors":"Siqi He, Steven Andrew Culpepper, Jeffrey A Douglas","doi":"10.1111/bmsp.12380","DOIUrl":"https://doi.org/10.1111/bmsp.12380","url":null,"abstract":"Diagnostic models (DM) have been widely used to classify respondents' latent attributes in cognitive and non-cognitive assessments. The integration of response times (RTs) with DM presents additional evidence to understand respondents' problem-solving behaviours. While recent research has explored using sparse latent class models (SLCM) to infer the latent structure of items based on item responses, the incorporation of RT data within these models remains underexplored. This study extends the SLCM framework to include RT, relaxing the conditional independence assumption between RT and latent attributes given individual speed. This adaptation provides a more flexible framework for jointly modelling RT and item responses. While the proposed model holds promise for applications in educational assessment, this study applied the model to the Fisher Temperament Inventory, yielding findings that provide a novel perspective on utilizing DM with RT in personality assessments. Additionally, a Gibbs sampling algorithm is proposed for parameter estimation. Results from Monte Carlo simulations demonstrate the algorithm's accuracy and efficiency.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142900610","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

From missing data to informative GPA predictions: Navigating selection process beliefs with the partial identifiability approach. 从缺失数据到信息性GPA预测：用部分可识别性方法导航选择过程信念。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2024-12-24 DOI: 10.1111/bmsp.12377

Eduardo Alarcón-Bustamante, Jorge González, David Torres Irribarra, Ernesto San Martín

引用次数: 0

A ranking forced choice diagnostic classification model for psychological assessment using forced choice questionnaires. 采用强迫选择问卷进行心理评估的排序强迫选择诊断分类模型。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2024-12-15 DOI: 10.1111/bmsp.12376

Yi-An Zhu, Jingwan Xu, Daxun Wang, Xin Li, Yan Cai, Dongbo Tu

{"title":"A ranking forced choice diagnostic classification model for psychological assessment using forced choice questionnaires.","authors":"Yi-An Zhu, Jingwan Xu, Daxun Wang, Xin Li, Yan Cai, Dongbo Tu","doi":"10.1111/bmsp.12376","DOIUrl":"https://doi.org/10.1111/bmsp.12376","url":null,"abstract":"The diagnostic classification model (DCM) has been widely utilized in non-cognitive tests, offering diagnostic information on latent attributes. However, the model's reliance on single-stimulus (SS) items may lead to response biases (e.g., social desirability), jeopardizing the psychometric properties. As an alternative to SS scales, forced choice questionnaires (FCQ) can effectively control response biases. The combination of FCQs and the DCM not only circumvents response bias but also yields fine-grained diagnostic information on latent attributes. To the best of our knowledge, only one study (Huang, Educ. Psychol. Meas., 83, 2022, 146) has explored this topic and developed a DCM for forced choice (FC) items. However, the existing model has limitations in terms of its modelling assumption, the FC format and the number of attributes measured by statement. To address these limitations, this study proposes a ranking FC-DCM that (1) adopts a generalized assumption, (2) covers all FC formats and (3) eases the limitation on the number of attributes measured by each statement. The simulation study demonstrated that the proposed model exhibited satisfactory person and item parameter recovery under all conditions. This study provided an illustrative example by developing an FC version questionnaire to further explore the applications and advantages of the proposed model in real-world settings.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142830267","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

Frequency-adjusted borders ordinal forest: A novel tree ensemble method for ordinal prediction. 频率调整边界有序森林：一种新的有序预测树集合方法。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2024-12-08 DOI: 10.1111/bmsp.12375

Philip Buczak

{"title":"Frequency-adjusted borders ordinal forest: A novel tree ensemble method for ordinal prediction.","authors":"Philip Buczak","doi":"10.1111/bmsp.12375","DOIUrl":"https://doi.org/10.1111/bmsp.12375","url":null,"abstract":"Ordinal responses commonly occur in psychology, e.g., through school grades or rating scales. Where traditionally parametric statistical models like the proportional odds model have been used, machine learning (ML) methods such as random forest (RF) are increasingly employed for ordinal prediction. With new developments in assessment and new data sources yielding increasing quantities of data in the psychological sciences, such ML approaches promise high predictive performance. As RF does not inherently account for ordinality, several extensions have been proposed. A promising approach lies in assigning optimized numeric scores to the ordinal response categories and using regression RF. However, these optimization procedures are computationally expensive and have been shown to yield only situational benefit. In this work, I propose Frequency-Adjusted Borders Ordinal Forest (fabOF), a novel tree ensemble method for ordinal prediction forgoing extensive optimization while offering improved predictive performance in simulation and an illustrative example of student performance. To aid interpretation, I additionally introduce a permutation variable importance measure for fabOF tailored towards ordinal prediction. When applied to the illustrative example, an interest in higher education, mother's education, and study time are identified as important predictors of student performance. The presented methodology is made available through an accompanying R package.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142796610","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

Investigating dynamics in attentive and inattentive responding together with their contextual correlates using a novel mixture IRT model for intensive longitudinal data. 研究动态的注意和不注意的反应，连同他们的上下文相关使用一个新的混合IRT模型密集的纵向数据。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2024-12-07 DOI: 10.1111/bmsp.12373

Leonie V D E Vogelsmeier, Irina Uglanova, Manuel T Rein, Esther Ulitzsch

{"title":"Investigating dynamics in attentive and inattentive responding together with their contextual correlates using a novel mixture IRT model for intensive longitudinal data.","authors":"Leonie V D E Vogelsmeier, Irina Uglanova, Manuel T Rein, Esther Ulitzsch","doi":"10.1111/bmsp.12373","DOIUrl":"https://doi.org/10.1111/bmsp.12373","url":null,"abstract":"In ecological momentary assessment (EMA), respondents answer brief questionnaires about their current behaviours or experiences several times per day across multiple days. The frequent measurement enables a thorough grasp of the dynamics inherent in psychological constructs, but it also increases respondent burden. To lower this burden, respondents may engage in careless and insufficient effort responding (C/IER), leaving data contaminated with responses that do not reflect what researchers want to measure. We introduce a novel approach to investigating C/IER in EMA data. Our approach combines a confirmatory mixture item response theory model separating C/IER from attentive behaviour with latent Markov factor analysis. This enables gauging the occurrence of C/IER and studying transitions among states of different response behaviours including their contextual correlates. The approach can be implemented using R packages. An empirical application showcases the approach's efficacy in pinpointing C/IER instances and gaining insights into their underlying causes. We showcase that the approach identifies various C/IER response patterns but requires heterogeneous and negatively worded items to detect straightlining. In a simulation investigating robustness against unaccounted for changes in measurement models underlying attentive responses, the approach proved robust against heterogeneity in loading patterns but not against heterogeneity in factor structures. Extensions to accommodate the latter are discussed.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142792957","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