British Journal of Mathematical & Statistical Psychology最新文献_第8页

Assessment of generalised Bayesian structural equation models for continuous and binary data 连续和二元数据的广义贝叶斯结构方程模型的评价

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2023-07-04 DOI: 10.1111/bmsp.12314

Konstantinos Vamvourellis, Konstantinos Kalogeropoulos, Irini Moustaki

{"title":"Assessment of generalised Bayesian structural equation models for continuous and binary data","authors":"Konstantinos Vamvourellis, Konstantinos Kalogeropoulos, Irini Moustaki","doi":"10.1111/bmsp.12314","DOIUrl":"10.1111/bmsp.12314","url":null,"abstract":"The paper proposes a novel model assessment paradigm aiming to address shortcoming of posterior predictive <math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>p</mi>\u0000 </mrow>\u0000 </semantics></math>-values, which provide the default metric of fit for Bayesian structural equation modelling (BSEM). The model framework presented in the paper focuses on the approximate zero approach (Psychological Methods, 17, 2012, 313), which involves formulating certain parameters (such as factor loadings) to be approximately zero through the use of informative priors, instead of explicitly setting them to zero. The introduced model assessment procedure monitors the out-of-sample predictive performance of the fitted model, and together with a list of guidelines we provide, one can investigate whether the hypothesised model is supported by the data. We incorporate scoring rules and cross-validation to supplement existing model assessment metrics for BSEM. The proposed tools can be applied to models for both continuous and binary data. The modelling of categorical and non-normally distributed continuous data is facilitated with the introduction of an item-individual random effect. We study the performance of the proposed methodology via simulation experiments as well as real data on the ‘Big-5’ personality scale and the Fagerstrom test for nicotine dependence.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":"76 3","pages":"559-584"},"PeriodicalIF":2.6,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bmsp.12314","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9748065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Testing indirect effect with a complete or incomplete dichotomous mediator 用完全或不完全二分介质检验间接效应

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2023-06-26 DOI: 10.1111/bmsp.12313

Fan Jia, Wei Wu, Po-Yi Chen

引用次数: 0

Variational Bayes inference for hidden Markov diagnostic classification models 隐马尔可夫诊断分类模型的变异贝叶斯推理。

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2023-05-30 DOI: 10.1111/bmsp.12308

Kazuhiro Yamaguchi, Alfonso J. Martinez

引用次数: 0

A sequential exploratory diagnostic model using a Pólya-gamma data augmentation strategy 使用Pólya-gamma数据增强策略的顺序探索性诊断模型

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2023-05-21 DOI: 10.1111/bmsp.12307

Auburn Jimenez, James Joseph Balamuta, Steven Andrew Culpepper

引用次数: 0

Causality and prediction in structural equation modeling: A commentary by Yutaka Kano on: “Which method delivers greater signal-to-noise ratio: Structural equation modeling or regression analysis with weighted composites?” by Yuan and Fang 结构方程建模中的因果关系和预测:Yutaka Kano对以下问题的评论:“哪种方法提供更大的信噪比:结构方程建模还是加权复合回归分析?”袁、方所著

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2023-05-11 DOI: 10.1111/bmsp.12306

Yutaka Kano

引用次数: 0

A sequential Bayesian changepoint detection procedure for aberrant behaviours in computerized testing 计算机化测试中异常行为的连续贝叶斯变化点检测程序。

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2023-05-10 DOI: 10.1111/bmsp.12305

Jing Lu, Chun Wang, Jiwei Zhang, Xue Wang

{"title":"A sequential Bayesian changepoint detection procedure for aberrant behaviours in computerized testing","authors":"Jing Lu, Chun Wang, Jiwei Zhang, Xue Wang","doi":"10.1111/bmsp.12305","DOIUrl":"10.1111/bmsp.12305","url":null,"abstract":"Changepoints are abrupt variations in a sequence of data in statistical inference. In educational and psychological assessments, it is essential to properly differentiate examinees' aberrant behaviours from solution behaviour to ensure test reliability and validity. In this paper, we propose a sequential Bayesian changepoint detection algorithm to monitor the locations of changepoints for response times in real time and, subsequently, further identify types of aberrant behaviours in conjunction with response patterns. Two simulation studies were conducted to investigate the efficiency and accuracy of the proposed detection procedure in terms of identifying one or multiple changepoints at different locations. In addition to manipulating the number and locations of changepoints, two types of aberrant behaviours were also considered: rapid guessing behaviour and cheating behaviour. Simulation results indicate that ability estimates could be improved after removing responses from aberrant behaviours identified by our approach. Two empirical examples were analysed to illustrate the application of the proposed sequential Bayesian changepoint detection procedure.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":"77 1","pages":"31-54"},"PeriodicalIF":2.6,"publicationDate":"2023-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9813812","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: 1

Premature conclusions about the signal-to-noise ratio in structural equation modeling research: A commentary on Yuan and Fang (2023) 关于结构方程建模研究中信噪比的过早结论——评袁和方(2023)

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2023-04-18 DOI: 10.1111/bmsp.12304

Florian Schuberth, Tamara Schamberger, Mikko Rönkkö, Yide Liu, Jörg Henseler

{"title":"Premature conclusions about the signal-to-noise ratio in structural equation modeling research: A commentary on Yuan and Fang (2023)","authors":"Florian Schuberth, Tamara Schamberger, Mikko Rönkkö, Yide Liu, Jörg Henseler","doi":"10.1111/bmsp.12304","DOIUrl":"10.1111/bmsp.12304","url":null,"abstract":"In a recent article published in this journal, Yuan and Fang (British Journal of Mathematical and Statistical Psychology, 2023) suggest comparing structural equation modeling (SEM), also known as covariance-based SEM (CB-SEM), estimated by normal-distribution-based maximum likelihood (NML), to regression analysis with (weighted) composites estimated by least squares (LS) in terms of their signal-to-noise ratio (SNR). They summarize their findings in the statement that “[c]ontrary to the common belief that CB-SEM is the preferred method for the analysis of observational data, this article shows that regression analysis via weighted composites yields parameter estimates with much smaller standard errors, and thus corresponds to greater values of the [SNR].” In our commentary, we show that Yuan and Fang have made several incorrect assumptions and claims. Consequently, we recommend that empirical researchers not base their methodological choice regarding CB-SEM and regression analysis with composites on the findings of Yuan and Fang as these findings are premature and require further research.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":"76 3","pages":"682-694"},"PeriodicalIF":2.6,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bmsp.12304","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9319019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A dual process item response theory model for polytomous multidimensional forced-choice items 多方位多维强迫选择项目的双过程项目反应理论模型

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2023-03-26 DOI: 10.1111/bmsp.12303

Xuelan Qiu, Jimmy de la Torre

{"title":"A dual process item response theory model for polytomous multidimensional forced-choice items","authors":"Xuelan Qiu, Jimmy de la Torre","doi":"10.1111/bmsp.12303","DOIUrl":"10.1111/bmsp.12303","url":null,"abstract":"The use of multidimensional forced-choice (MFC) items to assess non-cognitive traits such as personality, interests and values in psychological tests has a long history, because MFC items show strengths in preventing response bias. Recently, there has been a surge of interest in developing item response theory (IRT) models for MFC items. However, nearly all of the existing IRT models have been developed for MFC items with binary scores. Real tests use MFC items with more than two categories; such items are more informative than their binary counterparts. This study developed a new IRT model for polytomous MFC items based on the cognitive model of choice, which describes the cognitive processes underlying humans' preferential choice behaviours. The new model is unique in its ability to account for the ipsative nature of polytomous MFC items, to assess individual psychological differentiation in interests, values and emotions, and to compare the differentiation levels of latent traits between individuals. Simulation studies were conducted to examine the parameter recovery of the new model with existing computer programs. The results showed that both statement parameters and person parameters were well recovered when the sample size was sufficient. The more complete the linking of the statements was, the more accurate the parameter estimation was. This paper provides an empirical example of a career interest test using four-category MFC items. Although some aspects of the model (e.g., the nature of the person parameters) require additional validation, our approach appears promising.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":"76 3","pages":"491-512"},"PeriodicalIF":2.6,"publicationDate":"2023-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bmsp.12303","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9229089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian hierarchical response time modelling—A tutorial 贝叶斯分层响应时间建模-教程

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2023-02-22 DOI: 10.1111/bmsp.12302

Christoph Koenig, Benjamin Becker, Esther Ulitzsch

{"title":"Bayesian hierarchical response time modelling—A tutorial","authors":"Christoph Koenig, Benjamin Becker, Esther Ulitzsch","doi":"10.1111/bmsp.12302","DOIUrl":"10.1111/bmsp.12302","url":null,"abstract":"Response time modelling is developing rapidly in the field of psychometrics, and its use is growing in psychology. In most applications, component models for response times are modelled jointly with component models for responses, thereby stabilizing estimation of item response theory model parameters and enabling research on a variety of novel substantive research questions. Bayesian estimation techniques facilitate estimation of response time models. Implementations of these models in standard statistical software, however, are still sparse. In this accessible tutorial, we discuss one of the most common response time models—the lognormal response time model—embedded in the hierarchical framework by van der Linden (2007). We provide detailed guidance on how to specify and estimate this model in a Bayesian hierarchical context. One of the strengths of the presented model is its flexibility, which makes it possible to adapt and extend the model according to researchers' needs and hypotheses on response behaviour. We illustrate this based on three recent model extensions: (a) application to non-cognitive data incorporating the distance-difficulty hypothesis, (b) modelling conditional dependencies between response times and responses, and (c) identifying differences in response behaviour via mixture modelling. This tutorial aims to provide a better understanding of the use and utility of response time models, showcases how these models can easily be adapted and extended, and contributes to a growing need for these models to answer novel substantive research questions in both non-cognitive and cognitive contexts.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":"76 3","pages":"623-645"},"PeriodicalIF":2.6,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bmsp.12302","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10763975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A model-based approach to multivariate principal component regression: Selecting principal components and estimating standard errors for unstandardized regression coefficients 基于模型的多元主成分回归方法:选择主成分和估计非标准化回归系数的标准误差

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2023-02-05 DOI: 10.1111/bmsp.12301

Fei Gu, Mike W.-L. Cheung

引用次数: 0