British Journal of Mathematical & Statistical Psychology最新文献_第9页

Effect sizes in ANCOVA and difference-in-differences designs ANCOVA和差中差设计的效应量

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2023-01-02 DOI: 10.1111/bmsp.12296

Larry V. Hedges, Elizabeth Tipton, Rrita Zejnullahi, Karina G. Diaz

引用次数: 3

Which method delivers greater signal-to-noise ratio: Structural equation modelling or regression analysis with weighted composites? 哪一种方法的信噪比更大:结构方程模型还是加权复合材料回归分析?

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2022-12-02 DOI: 10.1111/bmsp.12293

Ke-Hai Yuan, Yongfei Fang

{"title":"Which method delivers greater signal-to-noise ratio: Structural equation modelling or regression analysis with weighted composites?","authors":"Ke-Hai Yuan, Yongfei Fang","doi":"10.1111/bmsp.12293","DOIUrl":"10.1111/bmsp.12293","url":null,"abstract":"Observational data typically contain measurement errors. Covariance-based structural equation modelling (CB-SEM) is capable of modelling measurement errors and yields consistent parameter estimates. In contrast, methods of regression analysis using weighted composites as well as a partial least squares approach to SEM facilitate the prediction and diagnosis of individuals/participants. But regression analysis with weighted composites has been known to yield attenuated regression coefficients when predictors contain errors. Contrary 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 signal-to-noise ratio (SNR). In particular, the SNR for the regression coefficient via the least squares (LS) method with equally weighted composites is mathematically greater than that by CB-SEM if the items for each factor are parallel, even when the SEM model is correctly specified and estimated by an efficient method. Analytical, numerical and empirical results also show that LS regression using weighted composites performs as well as or better than the normal maximum likelihood method for CB-SEM under many conditions even when the population distribution is multivariate normal. Results also show that the LS regression coefficients become more efficient when considering the sampling errors in the weights of composites than those that are conditional on weights.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":"76 3","pages":"646-678"},"PeriodicalIF":2.6,"publicationDate":"2022-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41180529","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: 6

Editorial acknowledgement 编辑确认

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2022-11-18 DOI: 10.1111/bmsp.12295

引用次数: 0

Empirical indistinguishability: From the knowledge structure to the skills 经验不可区分:从知识结构到技能

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2022-11-10 DOI: 10.1111/bmsp.12291

Andrea Spoto, Luca Stefanutti

引用次数: 1

A note on the use of rank-ordered logit models for ordered response categories 关于对有序响应类别使用秩有序logit模型的说明

IF 2.6 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2022-11-03 DOI: 10.1111/bmsp.12292

Timothy R. Johnson

引用次数: 0

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