British Journal of Mathematical & Statistical Psychology最新文献

Optimizing calibration designs with uncertainty in abilities. 在能力不确定的情况下优化校准设计。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-03-10 DOI: 10.1111/bmsp.12387

Jonas Bjermo, Ellinor Fackle-Fornius, Frank Miller

引用次数: 0

Integer programming in psychology: A review and directions for future research.

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-02-26 DOI: 10.1111/bmsp.12386

Michael Brusco, Douglas Steinley, Ashley L Watts

{"title":"Integer programming in psychology: A review and directions for future research.","authors":"Michael Brusco, Douglas Steinley, Ashley L Watts","doi":"10.1111/bmsp.12386","DOIUrl":"https://doi.org/10.1111/bmsp.12386","url":null,"abstract":"Integer programming (IP) is an extension of linear programming (LP) whereby the goal is to determine values for a set of decision variables (some or all of which have integer restrictions) so as to maximize or minimize a linear objective function of the variables subject to a set of linear constraints involving the variables. Although the psychological literature is replete with applications of multivariate statistics, implementations of mathematical modelling methods such as IP are comparatively far fewer. Nevertheless, over the decades, there have been a variety of important applications and the vast majority of these fall within the IP rather than the LP category. In this paper, we offer a brief overview of the history of IP methodology. We subsequently review some domains where IP has been gainfully applied in psychology, such as test assembly, cluster analysis and classification and seriation and unidimensional scaling. An illustrative example of using IP to cluster respondents measured on items pertaining to substance abuse disorder is provided. Finally, we identify areas where IP might be applied in emerging areas of psychology, such as in the domain of network psychometrics.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143517464","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 general dynamic learning model framework for cognitive diagnosis.

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-02-22 DOI: 10.1111/bmsp.12384

Zichu Liu, Shiyu Wang, Houping Xiao, Shumei Zhang, Tao Qiu

引用次数: 0

Correction to "A new Q-matrix validation method based on signal detection theory".

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-02-21 DOI: 10.1111/bmsp.12385

引用次数: 0

Decomposition of WAIC for assessing the information gain with application to educational testing.

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2025-02-21 DOI: 10.1111/bmsp.12383

Fang Liu, Ming-Hui Chen, Xiaojing Wang, Roeland Hancock

{"title":"Decomposition of WAIC for assessing the information gain with application to educational testing.","authors":"Fang Liu, Ming-Hui Chen, Xiaojing Wang, Roeland Hancock","doi":"10.1111/bmsp.12383","DOIUrl":"https://doi.org/10.1111/bmsp.12383","url":null,"abstract":"Nowadays, multidimensional data are often available from educational testing. One natural issue is to identify whether more dimensional data are useful in fitting the item response data. To address this important issue, we develop a new decomposition of Widely Applicable Information Criterion (WAIC) via the posterior predictive ordinate (PPO) under the joint model for the response, response time and two additional educational testing scores. Based on this decomposition, a new model assessment criterion is then proposed, which allows us to determine which of the response time and two additional scores are most useful in fitting the response data and whether other dimensional data are further needed given that one of these dimensional data is already included in the joint model with the response data. In addition, an efficient Monte Carlo method is developed to compute PPO. An extensive simulation study is conducted to examine the empirical performance of the proposed joint model and the model assessment criterion in the psychological setting. The proposed methodology is further applied to an analysis of a real dataset from a computerized educational assessment program.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":" ","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143470029","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

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