British Journal of Mathematical & Statistical Psychology最新文献_第4页

A unified EM framework for estimation and inference of normal ogive item response models 用于估计和推断正态椭圆项目反应模型的统一 EM 框架。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2024-10-10 DOI: 10.1111/bmsp.12356

Xiangbin Meng, Gongjun Xu

{"title":"A unified EM framework for estimation and inference of normal ogive item response models","authors":"Xiangbin Meng, Gongjun Xu","doi":"10.1111/bmsp.12356","DOIUrl":"10.1111/bmsp.12356","url":null,"abstract":"Normal ogive (NO) models have contributed substantially to the advancement of item response theory (IRT) and have become popular educational and psychological measurement models. However, estimating NO models remains computationally challenging. The purpose of this paper is to propose an efficient and reliable computational method for fitting NO models. Specifically, we introduce a novel and unified expectation-maximization (EM) algorithm for estimating NO models, including two-parameter, three-parameter, and four-parameter NO models. A key improvement in our EM algorithm lies in augmenting the NO model to be a complete data model within the exponential family, thereby substantially streamlining the implementation of the EM iteration and avoiding the numerical optimization computation in the M-step. Additionally, we propose a two-step expectation procedure for implementing the E-step, which reduces the dimensionality of the integration and effectively enables numerical integration. Moreover, we develop a computing procedure for estimating the standard errors (SEs) of the estimated parameters. Simulation results demonstrate the superior performance of our algorithm in terms of its recovery accuracy, robustness, and computational efficiency. To further validate our methods, we apply them to real data from the Programme for International Student Assessment (PISA). The results affirm the reliability of the parameter estimates obtained using our method.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":"78 1","pages":"190-224"},"PeriodicalIF":1.5,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142481465","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

Applying support vector machines to a diagnostic classification model for polytomous attributes in small-sample contexts 将支持向量机应用于小样本背景下的多态属性诊断分类模型。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2024-10-01 DOI: 10.1111/bmsp.12359

Xiaoyu Li, Shenghong Dong, Shaoyang Guo, Chanjin Zheng

引用次数: 0

Average treatment effects on binary outcomes with stochastic covariates 随机协变量对二元结果的平均治疗效果。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2024-07-24 DOI: 10.1111/bmsp.12355

Christoph Kiefer, Marcella L. Woud, Simon E. Blackwell, Axel Mayer

{"title":"Average treatment effects on binary outcomes with stochastic covariates","authors":"Christoph Kiefer, Marcella L. Woud, Simon E. Blackwell, Axel Mayer","doi":"10.1111/bmsp.12355","DOIUrl":"10.1111/bmsp.12355","url":null,"abstract":"When evaluating the effect of psychological treatments on a dichotomous outcome variable in a randomized controlled trial (RCT), covariate adjustment using logistic regression models is often applied. In the presence of covariates, average marginal effects (AMEs) are often preferred over odds ratios, as AMEs yield a clearer substantive and causal interpretation. However, standard error computation of AMEs neglects sampling-based uncertainty (i.e., covariate values are assumed to be fixed over repeated sampling), which leads to underestimation of AME standard errors in other generalized linear models (e.g., Poisson regression). In this paper, we present and compare approaches allowing for stochastic (i.e., randomly sampled) covariates in models for binary outcomes. In a simulation study, we investigated the quality of the AME and stochastic-covariate approaches focusing on statistical inference in finite samples. Our results indicate that the fixed-covariate approach provides reliable results only if there is no heterogeneity in interindividual treatment effects (i.e., presence of treatment–covariate interactions), while the stochastic-covariate approaches are preferable in all other simulated conditions. We provide an illustrative example from clinical psychology investigating the effect of a cognitive bias modification training on post-traumatic stress disorder while accounting for patients' anxiety using an RCT.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":"78 1","pages":"141-166"},"PeriodicalIF":1.5,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bmsp.12355","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141753440","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

Are alternative variables in a set differently associated with a target variable? Statistical tests and practical advice for dealing with dependent correlations 一组变量中的其他变量与目标变量的相关性是否不同？处理从属相关性的统计检验和实用建议。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2024-06-24 DOI: 10.1111/bmsp.12354

Miguel A. García-Pérez

{"title":"Are alternative variables in a set differently associated with a target variable? Statistical tests and practical advice for dealing with dependent correlations","authors":"Miguel A. García-Pérez","doi":"10.1111/bmsp.12354","DOIUrl":"10.1111/bmsp.12354","url":null,"abstract":"The analysis of multiple bivariate correlations is often carried out by conducting simple tests to check whether each of them is significantly different from zero. In addition, pairwise differences are often judged by eye or by comparing the p-values of the individual tests of significance despite the existence of statistical tests for differences between correlations. This paper uses simulation methods to assess the accuracy (empirical Type I error rate), power, and robustness of 10 tests designed to check the significance of the difference between two dependent correlations with overlapping variables (i.e., the correlation between X1 and Y and the correlation between X2 and Y). Five of the tests turned out to be inadvisable because their empirical Type I error rates under normality differ greatly from the nominal alpha level of .05 either across the board or within certain sub-ranges of the parameter space. The remaining five tests were acceptable and their merits were similar in terms of all comparison criteria, although none of them was robust across all forms of non-normality explored in the study. Practical recommendations are given for the choice of a statistical test to compare dependent correlations with overlapping variables.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":"78 1","pages":"112-140"},"PeriodicalIF":1.5,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bmsp.12354","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141460882","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

Determining the number of attributes in the GDINA model 确定 GDINA 模型的属性数量。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2024-06-18 DOI: 10.1111/bmsp.12349

Juntao Wang, Jiangtao Duan

引用次数: 0

Nonparametric CD-CAT for multiple-choice items: Item selection method and Q-optimality 多选题的非参数 CD-CAT：项目选择方法和 Q-最优性。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2024-05-25 DOI: 10.1111/bmsp.12350

Yu Wang, Chia-Yi Chiu, Hans Friedrich Köhn

{"title":"Nonparametric CD-CAT for multiple-choice items: Item selection method and Q-optimality","authors":"Yu Wang, Chia-Yi Chiu, Hans Friedrich Köhn","doi":"10.1111/bmsp.12350","DOIUrl":"10.1111/bmsp.12350","url":null,"abstract":"Computerized adaptive testing for cognitive diagnosis (CD-CAT) achieves remarkable estimation efficiency and accuracy by adaptively selecting and then administering items tailored to each examinee. The process of item selection stands as a pivotal component of a CD-CAT algorithm, with various methods having been developed for binary responses. However, multiple-choice (MC) items, an important item type that allows for the extraction of richer diagnostic information from incorrect answers, have been underemphasized. Currently, the Jensen–Shannon divergence (JSD) index introduced by Yigit et al. (Applied Psychological Measurement, 2019, 43, 388) is the only item selection method exclusively designed for MC items. However, the JSD index requires a large sample to calibrate item parameters, which may be infeasible when there is only a small or no calibration sample. To bridge this gap, the study first proposes a nonparametric item selection method for MC items (MC-NPS) by implementing novel discrimination power that measures an item's ability to effectively distinguish among different attribute profiles. A Q-optimal procedure for MC items is also developed to improve the classification during the initial phase of a CD-CAT algorithm. The effectiveness and efficiency of the two proposed algorithms were confirmed by simulation studies.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":"78 1","pages":"61-83"},"PeriodicalIF":1.5,"publicationDate":"2024-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bmsp.12350","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141096972","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

Incorporating calibration errors in oral reading fluency scoring 将校准误差纳入口语阅读流利度评分。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2024-05-10 DOI: 10.1111/bmsp.12348

Xin Qiao, Akihito Kamata, Cornelis Potgieter

引用次数: 0

Pairwise stochastic approximation for confirmatory factor analysis of categorical data 用于分类数据确证因子分析的成对随机近似法

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2024-04-27 DOI: 10.1111/bmsp.12347

Giuseppe Alfonzetti, Ruggero Bellio, Yunxiao Chen, Irini Moustaki

{"title":"Pairwise stochastic approximation for confirmatory factor analysis of categorical data","authors":"Giuseppe Alfonzetti, Ruggero Bellio, Yunxiao Chen, Irini Moustaki","doi":"10.1111/bmsp.12347","DOIUrl":"10.1111/bmsp.12347","url":null,"abstract":"Pairwise likelihood is a limited-information method widely used to estimate latent variable models, including factor analysis of categorical data. It can often avoid evaluating high-dimensional integrals and, thus, is computationally more efficient than relying on the full likelihood. Despite its computational advantage, the pairwise likelihood approach can still be demanding for large-scale problems that involve many observed variables. We tackle this challenge by employing an approximation of the pairwise likelihood estimator, which is derived from an optimization procedure relying on stochastic gradients. The stochastic gradients are constructed by subsampling the pairwise log-likelihood contributions, for which the subsampling scheme controls the per-iteration computational complexity. The stochastic estimator is shown to be asymptotically equivalent to the pairwise likelihood one. However, finite-sample performance can be improved by compounding the sampling variability of the data with the uncertainty introduced by the subsampling scheme. We demonstrate the performance of the proposed method using simulation studies and two real data applications.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":"78 1","pages":"22-43"},"PeriodicalIF":1.5,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140811401","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

Advances in meta-analysis: A unifying modelling framework with measurement error correction 荟萃分析的进展：测量误差校正的统一建模框架

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2024-04-26 DOI: 10.1111/bmsp.12345

Betsy Jane Becker, Qian Zhang

{"title":"Advances in meta-analysis: A unifying modelling framework with measurement error correction","authors":"Betsy Jane Becker, Qian Zhang","doi":"10.1111/bmsp.12345","DOIUrl":"10.1111/bmsp.12345","url":null,"abstract":"In psychological studies, multivariate outcomes measured on the same individuals are often encountered. Effects originating from these outcomes are consequently dependent. Multivariate meta-analysis examines the relationships of multivariate outcomes by estimating the mean effects and their variance–covariance matrices from series of primary studies. In this paper we discuss a unified modelling framework for multivariate meta-analysis that also incorporates measurement error corrections. We focus on two types of effect sizes, standardized mean differences (d) and correlations (r), that are common in psychological studies. Using generalized least squares estimation, we outline estimated mean vectors and variance–covariance matrices for d and r that are corrected for measurement error. Given the burgeoning research involving multivariate outcomes, and the largely overlooked ramifications of measurement error, we advocate addressing measurement error while conducting multivariate meta-analysis to enhance the replicability of psychological research.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":"77 3","pages":"395-428"},"PeriodicalIF":1.5,"publicationDate":"2024-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/bmsp.12345","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140798185","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

Combining regularization and logistic regression model to validate the Q-matrix for cognitive diagnosis model 结合正则化和逻辑回归模型，验证认知诊断模型的 Q 矩阵。

IF 1.5 3区心理学

British Journal of Mathematical & Statistical Psychology Pub Date : 2024-04-22 DOI: 10.1111/bmsp.12346

Xiaojian Sun, Tongxin Zhang, Chang Nie, Naiqing Song, Tao Xin

{"title":"Combining regularization and logistic regression model to validate the Q-matrix for cognitive diagnosis model","authors":"Xiaojian Sun, Tongxin Zhang, Chang Nie, Naiqing Song, Tao Xin","doi":"10.1111/bmsp.12346","DOIUrl":"10.1111/bmsp.12346","url":null,"abstract":"Q-matrix is an important component of most cognitive diagnosis models (CDMs); however, it mainly relies on subject matter experts' judgements in empirical studies, which introduces the possibility of misspecified q-entries. To address this, statistical Q-matrix validation methods have been proposed to aid experts' judgement. A few of these methods, including the multiple logistic regression-based (MLR-B) method and the Hull method, can be applied to general CDMs, but they are either time-consuming or lack accuracy under certain conditions. In this study, we combine the L1 regularization and MLR model to validate the Q-matrix. Specifically, an L1 penalty term is imposed on the log-likelihood of the MLR model to select the necessary attributes for each item. A simulation study with various factors was conducted to examine the performance of the new method against the two existing methods. The results show that the regularized MLR-B method (a) produces the highest Q-matrix recovery rate (QRR) and true positive rate (TPR) for most conditions, especially with a small sample size; (b) yields a slightly higher true negative rate (TNR) than either the MLR-B or the Hull method for most conditions; and (c) requires less computation time than the MLR-B method and similar computation time as the Hull method. A real data set is analysed for illustration purposes.","PeriodicalId":55322,"journal":{"name":"British Journal of Mathematical & Statistical Psychology","volume":"78 1","pages":"1-21"},"PeriodicalIF":1.5,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140677462","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