British Journal of Mathematical & Statistical Psychology最新文献_第10页

Subtask analysis of process data through a predictive model 通过预测模型对过程数据进行子任务分析

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2022-11-01 DOI: 10.1111/bmsp.12290

Zhi Wang, Xueying Tang, Jingchen Liu, Zhiliang Ying

引用次数: 6

Two efficient selection methods for high-dimensional CD-CAT utilizing max-marginals factor from MAP query and ensemble learning approach 利用MAP查询中的最大边际因子和集成学习方法对高维CD-CAT进行高效选择

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2022-10-26 DOI: 10.1111/bmsp.12288

Fen Luo, Xiaoqing Wang, Yan Cai, Dongbo Tu

{"title":"Two efficient selection methods for high-dimensional CD-CAT utilizing max-marginals factor from MAP query and ensemble learning approach","authors":"Fen Luo, Xiaoqing Wang, Yan Cai, Dongbo Tu","doi":"10.1111/bmsp.12288","DOIUrl":"10.1111/bmsp.12288","url":null,"abstract":"Computerized adaptive testing for cognitive diagnosis (CD-CAT) needs to be efficient and responsive in real time to meet practical applications' requirements. For high-dimensional data, the number of categories to be recognized in a test grows exponentially as the number of attributes increases, which can easily cause system reaction time to be too long such that it adversely affects the examinees and thus seriously impacts the measurement efficiency. More importantly, the long-time CPU operations and memory usage of item selection in CD-CAT due to intensive computation are impractical and cannot wholly meet practice needs. This paper proposed two new efficient selection strategies (HIA and CEL) for high-dimensional CD-CAT to address this issue by incorporating the max-marginals from the maximum a posteriori query and integrating the ensemble learning approach into the previous efficient selection methods, respectively. The performance of the proposed selection method was compared with the conventional selection method using simulated and real item pools. The results showed that the proposed methods could significantly improve the measurement efficiency with about 1/2–1/200 of the conventional methods' computation time while retaining similar measurement accuracy. With increasing number of attributes and size of the item pool, the computation time advantage of the proposed methods becomes more significant.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":"76 2","pages":"283-311"},"PeriodicalIF":2.6,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9254138","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 new goodness-of-fit measure for probit models: Surrogate R2 probit模型的一个新的拟合优度度量：代理R2

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2022-10-17 DOI: 10.1111/bmsp.12289

Dungang Liu, Xiaorui Zhu, Brandon Greenwell, Zewei Lin

{"title":"A new goodness-of-fit measure for probit models: Surrogate R2","authors":"Dungang Liu, Xiaorui Zhu, Brandon Greenwell, Zewei Lin","doi":"10.1111/bmsp.12289","DOIUrl":"https://doi.org/10.1111/bmsp.12289","url":null,"abstract":"Probit models are used extensively for inferential purposes in the social sciences as discrete data are prevalent in a vast body of social studies. Among many accompanying model inference problems, a critical question remains unsettled: how to develop a goodness-of-fit measure that resembles the ordinary least square (OLS) R2 used for linear models. Such a measure has long been sought to achieve ‘comparability’ of different empirical models across multiple samples addressing similar social questions. To this end, we propose a novel R2 measure for probit models using the notion of surrogacy – simulating a continuous variable <math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>S</mi>\u0000 </mrow>\u0000 </semantics></math> as a surrogate of the original discrete response (Liu & Zhang, Journal of the American Statistical Association, 113, 845 and 2018). The proposed R2 is the proportion of the variance of the surrogate response explained by explanatory variables through a linear model, and we call it a surrogate R2. This paper shows both theoretically and numerically that the surrogate R2 approximates the OLS R2 based on the latent continuous variable, preserves the interpretation of explained variation, and maintains monotonicity between nested models. As no other pseudo R2, McKelvey and Zavoina's and McFadden's included, can meet all the three criteria simultaneously, our measure fills this crucial void in probit model inference.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":"76 1","pages":"192-210"},"PeriodicalIF":2.6,"publicationDate":"2022-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bmsp.12289","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50136106","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: 4

Penalization approaches in the conditional maximum likelihood and Rasch modelling context 条件最大似然和Rasch模型情境下的惩罚方法

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2022-09-14 DOI: 10.1111/bmsp.12287

Can Gürer, Clemens Draxler

{"title":"Penalization approaches in the conditional maximum likelihood and Rasch modelling context","authors":"Can Gürer, Clemens Draxler","doi":"10.1111/bmsp.12287","DOIUrl":"10.1111/bmsp.12287","url":null,"abstract":"Recent detection methods for Differential Item Functioning (DIF) include approaches like Rasch Trees, DIFlasso, GPCMlasso and Item Focussed Trees, all of which - in contrast to well established methods - can handle metric covariates inducing DIF. A new estimation method shall address their downsides by mainly aiming at combining three central virtues: the use of conditional likelihood for estimation, the incorporation of linear influence of metric covariates on item difficulty and the possibility to detect different DIF types: certain items showing DIF, certain covariates inducing DIF, or certain covariates inducing DIF in certain items. Each of the approaches mentioned lacks in two of these aspects. We introduce a method for DIF detection, which firstly utilizes the conditional likelihood for estimation combined with group Lasso-penalization for item or variable selection and L1-penalization for interaction selection, secondly incorporates linear effects instead of approximation through step functions, and thirdly provides the possibility to investigate any of the three DIF types. The method is described theoretically, challenges in implementation are discussed. A dataset is analysed for all DIF types and shows comparable results between methods. Simulation studies per DIF type reveal competitive performance of cmlDIFlasso, particularly when selecting interactions in case of large sample sizes and numbers of parameters. Coupled with low computation times, cmlDIFlasso seems a worthwhile option for applied DIF detection.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":"76 1","pages":"154-191"},"PeriodicalIF":2.6,"publicationDate":"2022-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10861048","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: 2

Ordinal state-trait regression for intensive longitudinal data 密集纵向数据的有序状态-特征回归

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2022-09-08 DOI: 10.1111/bmsp.12285

Prince P. Osei, Philip T. Reiss

{"title":"Ordinal state-trait regression for intensive longitudinal data","authors":"Prince P. Osei, Philip T. Reiss","doi":"10.1111/bmsp.12285","DOIUrl":"10.1111/bmsp.12285","url":null,"abstract":"In many psychological studies, in particular those conducted by experience sampling, mental states are measured repeatedly for each participant. Such a design allows for regression models that separate between- from within-person, or trait-like from state-like, components of association between two variables. But these models are typically designed for continuous variables, whereas mental state variables are most often measured on an ordinal scale. In this paper we develop a model for disaggregating between- from within-person effects of one ordinal variable on another. As in standard ordinal regression, our model posits a continuous latent response whose value determines the observed response. We allow the latent response to depend nonlinearly on the trait and state variables, but impose a novel penalty that shrinks the fit towards a linear model on the latent scale. A simulation study shows that this penalization approach is effective at finding a middle ground between an overly restrictive linear model and an overfitted nonlinear model. The proposed method is illustrated with an application to data from the experience sampling study of Baumeister et al. (2020, Personality and Social Psychology Bulletin, 46, 1631).","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":"76 1","pages":"1-19"},"PeriodicalIF":2.6,"publicationDate":"2022-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://bpspsychub.onlinelibrary.wiley.com/doi/epdf/10.1111/bmsp.12285","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9279382","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

Compromised item detection: A Bayesian change-point perspective 折衷项目检测:贝叶斯变更点视角

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2022-09-07 DOI: 10.1111/bmsp.12286

Yang Du, Susu Zhang, Hua-Hua Chang

{"title":"Compromised item detection: A Bayesian change-point perspective","authors":"Yang Du, Susu Zhang, Hua-Hua Chang","doi":"10.1111/bmsp.12286","DOIUrl":"10.1111/bmsp.12286","url":null,"abstract":"Psychometric methods for accurate and timely detection of item compromise have been a long-standing topic. While Bayesian methods can incorporate prior knowledge or expert inputs as additional information for item compromise detection, they have not been employed in item compromise detection itself. The current study proposes a two-phase Bayesian change-point framework for both stationary and real-time detection of changes in each item's compromise status. In Phase I, a stationary Bayesian change-point model for compromise detection is fitted to the observed responses over a specified time-frame. The model produces parameter estimates for the change-point of each item from uncompromised to compromised, as well as structural parameters accounting for the post-change response distribution. Using the post-change model identified in Phase I, the Shiryaev procedure for sequential testing is employed in Phase II for real-time monitoring of item compromise. The proposed methods are evaluated in terms of parameter recovery, detection accuracy, and detection efficiency under various simulation conditions and in a real data example. The proposed method also showed superior detection accuracy and efficiency compared to the cumulative sum procedure.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":"76 1","pages":"131-153"},"PeriodicalIF":2.6,"publicationDate":"2022-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/59/dc/BMSP-76-131.PMC10086862.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9640555","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

The biasing effects of selection and attrition on estimating the mean 选择和损耗对估计平均值的偏置效应

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2022-08-07 DOI: 10.1111/bmsp.12284

Seunghoo Lee, Jorge Mendoza

{"title":"The biasing effects of selection and attrition on estimating the mean","authors":"Seunghoo Lee, Jorge Mendoza","doi":"10.1111/bmsp.12284","DOIUrl":"10.1111/bmsp.12284","url":null,"abstract":"Organizational and validation researchers often work with data that has been subjected to selection on the predictor and attrition on the criterion. These researchers often use the data observed under these conditions to estimate either the predictor or criterion's restricted population means. We show that the restricted means due to direct or indirect selection are a function of the population means plus the selection ratios. Thus, any difference between selected mean groups reflects the population difference plus the selection ratio difference. When there is also attrition on the criterion, the estimation of group differences becomes even more complicated. The effect of selection and attrition induces measurement bias when estimating the restricted population mean of either the predictor or criterion. A sample mean observed under selection and attrition does not estimate either the population mean or the restricted population mean. We propose several procedures under normality that yield unbiased estimates of the mean. The procedures focus on correcting the effects of selection and attrition. Each procedure was evaluated with a Monte Carlo simulation to ascertain its strengths and weaknesses. Given appropriate sample size and conditions, we show that these procedures yield unbiased estimators of the restricted and unrestricted population means for both predictor and criterion. We also show how our findings have implications for replicating selected group differences.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":"76 1","pages":"106-130"},"PeriodicalIF":2.6,"publicationDate":"2022-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9091018","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

CD-polytomous knowledge spaces and corresponding polytomous surmise systems cd -多分知识空间和相应的多分猜测系统

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2022-07-29 DOI: 10.1111/bmsp.12283

Bo Wang, Jinjin Li, Wen Sun

{"title":"CD-polytomous knowledge spaces and corresponding polytomous surmise systems","authors":"Bo Wang, Jinjin Li, Wen Sun","doi":"10.1111/bmsp.12283","DOIUrl":"10.1111/bmsp.12283","url":null,"abstract":"Heller (2021) generalized quasi-ordinal knowledge spaces to polytomous items. Inspired by this paper, we propose CD-polytomous knowledge space and its polytomous surmise system. A Galois connection is established between the collection <math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>K</mi>\u0000 </mrow>\u0000 </semantics></math> of all polytomous knowledge structures and the collection <math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mi>F</mi>\u0000 <mn>1</mn>\u0000 </msub>\u0000 </mrow>\u0000 </semantics></math> of particular polytomous attribute functions. The closed elements of the Galois connection are CD-polytomous knowledge spaces in <math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>K</mi>\u0000 </mrow>\u0000 </semantics></math> and polytomous surmise functions in <math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mi>F</mi>\u0000 <mn>1</mn>\u0000 </msub>\u0000 </mrow>\u0000 </semantics></math>, respectively. With the help of these, this paper provides a characterization of the polytomous knowledge structure corresponding to the polytomous surmise function that is weakly factorial. Based on the finite sets of items and response values, these results generalize the previous approaches for polytomous knowledge spaces.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":"76 1","pages":"87-105"},"PeriodicalIF":2.6,"publicationDate":"2022-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10509096","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: 7

Item selection methods with exposure and time control for computerized classification test 计算机分类测验中具有曝光和时间控制的选题方法

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2022-07-15 DOI: 10.1111/bmsp.12281

Yingshi Huang, He Ren, Ping Chen

引用次数: 0

Modelling multiple problem-solving strategies and strategy shift in cognitive diagnosis for growth 多问题解决策略建模与成长认知诊断中的策略转换

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2022-07-10 DOI: 10.1111/bmsp.12280

Manqian Liao, Hong Jiao

{"title":"Modelling multiple problem-solving strategies and strategy shift in cognitive diagnosis for growth","authors":"Manqian Liao, Hong Jiao","doi":"10.1111/bmsp.12280","DOIUrl":"10.1111/bmsp.12280","url":null,"abstract":"Problem-solving strategies, defined as actions people select intentionally to achieve desired objectives, are distinguished from skills that are implemented unintentionally. In education, strategy-oriented instructions that guide students to form problem-solving strategies are found to be more effective for low-achieving students than the skill-oriented instructions designed for enhancing their skill implementation ability. Although the existing longitudinal cognitive diagnosis models (CDMs) can model the change in students' dynamic skill mastery status over time, they are not designed to model the shift in students' problem-solving strategies. This study proposes a longitudinal CDM that considers both between-person multiple strategies and within-person strategy shift. The model, separating the strategy choice process from the skill implementation process, is intended to provide diagnostic information on strategy choice as well as skill mastery status. A simulation study is conducted to evaluate the parameter recovery of the proposed model and investigate the consequences of ignoring the presence of multiple strategies or strategy shift. Further, an empirical data analysis is conducted to illustrate the use of the proposed model to measure strategy shift, growth in skill implementation ability and skill mastery status.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":"76 1","pages":"20-51"},"PeriodicalIF":2.6,"publicationDate":"2022-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10566849","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