Journal of Educational and Behavioral Statistics最新文献_第4页

Bayesian Analysis Methods for Two-Level Diagnosis Classification Models 两级诊断分类模型的贝叶斯分析方法

IF 2.4 3区心理学

Journal of Educational and Behavioral Statistics Pub Date : 2023-05-25 DOI: 10.3102/10769986231173594

K. Yamaguchi

{"title":"Bayesian Analysis Methods for Two-Level Diagnosis Classification Models","authors":"K. Yamaguchi","doi":"10.3102/10769986231173594","DOIUrl":"https://doi.org/10.3102/10769986231173594","url":null,"abstract":"Understanding whether or not different types of students master various attributes can aid future learning remediation. In this study, two-level diagnostic classification models (DCMs) were developed to represent the probabilistic relationship between external latent classes and attribute mastery patterns. Furthermore, variational Bayesian (VB) inference and Gibbs sampling Markov chain Monte Carlo methods were developed for parameter estimation of the two-level DCMs. The results of a parameter recovery simulation study show that both techniques appropriately recovered the true parameters; Gibbs sampling in particular was slightly more accurate than VB, whereas VB performed estimation much faster than Gibbs sampling. The two-level DCMs with the proposed Bayesian estimation methods were further applied to fourth-grade data obtained from the Trends in International Mathematics and Science Study 2007 and indicated that mathematical activities in the classroom could be organized into four latent classes, with each latent class connected to different attribute mastery patterns. This information can be employed in educational intervention to focus on specific latent classes and elucidate attribute patterns.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44040378","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

A Psychometric Framework for Evaluating Fairness in Algorithmic Decision Making: Differential Algorithmic Functioning 评估算法决策公平性的心理测量框架：差分算法函数

IF 2.4 3区心理学

Journal of Educational and Behavioral Statistics Pub Date : 2023-05-10 DOI: 10.3102/10769986231171711

Youmi Suk, K. T. Han

{"title":"A Psychometric Framework for Evaluating Fairness in Algorithmic Decision Making: Differential Algorithmic Functioning","authors":"Youmi Suk, K. T. Han","doi":"10.3102/10769986231171711","DOIUrl":"https://doi.org/10.3102/10769986231171711","url":null,"abstract":"As algorithmic decision making is increasingly deployed in every walk of life, many researchers have raised concerns about fairness-related bias from such algorithms. But there is little research on harnessing psychometric methods to uncover potential discriminatory bias inside decision-making algorithms. The main goal of this article is to propose a new framework for algorithmic fairness based on differential item functioning (DIF), which has been commonly used to measure item fairness in psychometrics. Our fairness notion, which we call differential algorithmic functioning (DAF), is defined based on three pieces of information: a decision variable, a “fair” variable, and a protected variable such as race or gender. Under the DAF framework, an algorithm can exhibit uniform DAF, nonuniform DAF, or neither (i.e., non-DAF). For detecting DAF, we provide modifications of well-established DIF methods: Mantel–Haenszel test, logistic regression, and residual-based DIF. We demonstrate our framework through a real dataset concerning decision-making algorithms for grade retention in K–12 education in the United States.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43676822","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

Model Misspecification and Robustness of Observed-Score Test Equating Using Propensity Scores 使用倾向性得分进行观察得分测试等式的模型不精确性和稳健性

IF 2.4 3区心理学

Journal of Educational and Behavioral Statistics Pub Date : 2023-05-09 DOI: 10.3102/10769986231161575

G. Wallin, M. Wiberg

{"title":"Model Misspecification and Robustness of Observed-Score Test Equating Using Propensity Scores","authors":"G. Wallin, M. Wiberg","doi":"10.3102/10769986231161575","DOIUrl":"https://doi.org/10.3102/10769986231161575","url":null,"abstract":"This study explores the usefulness of covariates on equating test scores from nonequivalent test groups. The covariates are captured by an estimated propensity score, which is used as a proxy for latent ability to balance the test groups. The objective is to assess the sensitivity of the equated scores to various misspecifications in the propensity score model. The study assumes a parametric form of the propensity score and evaluates the effects of various misspecification scenarios on equating error. The results, based on both simulated and real testing data, show that (1) omitting an important covariate leads to biased estimates of the equated scores, (2) misspecifying a nonlinear relationship between the covariates and test scores increases the equating standard error in the tails of the score distributions, and (3) the equating estimators are robust against omitting a second-order term as well as using an incorrect link function in the propensity score estimation model. The findings demonstrate that auxiliary information is beneficial for test score equating in complex settings. However, it also sheds light on the challenge of making fair comparisons between nonequivalent test groups in the absence of common items. The study identifies scenarios, where equating performance is acceptable and problematic, provides practical guidelines, and identifies areas for further investigation.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":"48 1","pages":"603 - 635"},"PeriodicalIF":2.4,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43284852","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

Modeling Item-Level Heterogeneous Treatment Effects With the Explanatory Item Response Model: Leveraging Large-Scale Online Assessments to Pinpoint the Impact of Educational Interventions 用解释性项目反应模型建模项目水平的异质性治疗效果：利用大规模在线评估来确定教育干预的影响

IF 2.4 3区心理学

Journal of Educational and Behavioral Statistics Pub Date : 2023-05-09 DOI: 10.3102/10769986231171710

Josh Gilbert, James S. Kim, Luke W. Miratrix

{"title":"Modeling Item-Level Heterogeneous Treatment Effects With the Explanatory Item Response Model: Leveraging Large-Scale Online Assessments to Pinpoint the Impact of Educational Interventions","authors":"Josh Gilbert, James S. Kim, Luke W. Miratrix","doi":"10.3102/10769986231171710","DOIUrl":"https://doi.org/10.3102/10769986231171710","url":null,"abstract":"Analyses that reveal how treatment effects vary allow researchers, practitioners, and policymakers to better understand the efficacy of educational interventions. In practice, however, standard statistical methods for addressing heterogeneous treatment effects (HTE) fail to address the HTE that may exist within outcome measures. In this study, we present a novel application of the explanatory item response model (EIRM) for assessing what we term “item-level” HTE (IL-HTE), in which a unique treatment effect is estimated for each item in an assessment. Results from data simulation reveal that when IL-HTE is present but ignored in the model, standard errors can be underestimated and false positive rates can increase. We then apply the EIRM to assess the impact of a literacy intervention focused on promoting transfer in reading comprehension on a digital assessment delivered online to approximately 8,000 third-grade students. We demonstrate that allowing for IL-HTE can reveal treatment effects at the item-level masked by a null average treatment effect, and the EIRM can thus provide fine-grained information for researchers and policymakers on the potentially heterogeneous causal effects of educational interventions.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43185120","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

Cognitive Diagnosis Testlet Model for Multiple-Choice Items 多项选择题认知诊断测试模型

IF 2.4 3区心理学

Journal of Educational and Behavioral Statistics Pub Date : 2023-05-09 DOI: 10.3102/10769986231165622

Lei Guo, Wenjie Zhou, Xiao Li

引用次数: 0

A Within-Group Approach to Ensemble Machine Learning Methods for Causal Inference in Multilevel Studies 多层次研究中因果推理集成机器学习方法的组内方法

IF 2.4 3区心理学

Journal of Educational and Behavioral Statistics Pub Date : 2023-04-25 DOI: 10.3102/10769986231162096

Youmi Suk

{"title":"A Within-Group Approach to Ensemble Machine Learning Methods for Causal Inference in Multilevel Studies","authors":"Youmi Suk","doi":"10.3102/10769986231162096","DOIUrl":"https://doi.org/10.3102/10769986231162096","url":null,"abstract":"Machine learning (ML) methods for causal inference have gained popularity due to their flexibility to predict the outcome model and the propensity score. In this article, we provide a within-group approach for ML-based causal inference methods in order to robustly estimate average treatment effects in multilevel studies when there is cluster-level unmeasured confounding. We focus on one particular ML-based causal inference method based on the targeted maximum likelihood estimation (TMLE) with an ensemble learner called SuperLearner. Through our simulation studies, we observe that training TMLE within groups of similar clusters helps remove bias from cluster-level unmeasured confounders. Also, using within-group propensity scores estimated from fixed effects logistic regression increases the robustness of the proposed within-group TMLE method. Even if the propensity scores are partially misspecified, the within-group TMLE still produces robust ATE estimates due to double robustness with flexible modeling, unlike parametric-based inverse propensity weighting methods. We demonstrate our proposed methods and conduct sensitivity analyses against the number of groups and individual-level unmeasured confounding to evaluate the effect of taking an eighth-grade algebra course on math achievement in the Early Childhood Longitudinal Study.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48737730","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

Latent Transition Cognitive Diagnosis Model With Covariates: A Three-Step Approach 具有协变量的潜在过渡认知诊断模型:三步法

IF 2.4 3区心理学

Journal of Educational and Behavioral Statistics Pub Date : 2023-04-25 DOI: 10.3102/10769986231163320

Qianru Liang, Jimmy de la Torre, N. Law

引用次数: 1

Diagnosing Primary Students’ Reading Progression: Is Cognitive Diagnostic Computerized Adaptive Testing the Way Forward? 诊断小学生阅读进展:认知诊断计算机自适应测试是未来的方向吗?

IF 2.4 3区心理学

Journal of Educational and Behavioral Statistics Pub Date : 2023-04-20 DOI: 10.3102/10769986231160668

Yan Li, Chao-Hsien Huang, Jia Liu

{"title":"Diagnosing Primary Students’ Reading Progression: Is Cognitive Diagnostic Computerized Adaptive Testing the Way Forward?","authors":"Yan Li, Chao-Hsien Huang, Jia Liu","doi":"10.3102/10769986231160668","DOIUrl":"https://doi.org/10.3102/10769986231160668","url":null,"abstract":"Cognitive diagnostic computerized adaptive testing (CD-CAT) is a cutting-edge technology in educational measurement that targets at providing feedback on examinees’ strengths and weaknesses while increasing test accuracy and efficiency. To date, most CD-CAT studies have made methodological progress under simulated conditions, but little has applied CD-CAT to real educational assessment. The present study developed a Chinese reading comprehension item bank tapping into six validated reading attributes, with 195 items calibrated using data of 28,485 second to sixth graders and the item-level cognitive diagnostic models (CDMs). The measurement precision and efficiency of the reading CD-CAT system were compared and optimized in terms of crucial CD-CAT settings, including the CDMs for calibration, item selection methods, and termination rules. The study identified seven dominant reading attribute mastery profiles that stably exist across grades. These major clusters of readers and their variety with grade indicated some sort of reading developmental mechanisms that advance and deepen step by step at the primary school level. Results also suggested that compared to traditional linear tests, CD-CAT significantly improved the classification accuracy without imposing much testing burden. These findings may elucidate the multifaceted nature and possible learning paths of reading and raise the question of whether CD-CAT is applicable to other educational domains where there is a need to provide formative and fine-grained feedback but where there is a limited amount of test time.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":"1 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41708221","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

Using Item Scores and Distractors to Detect Item Compromise and Preknowledge 利用项目得分和干扰因素检测项目妥协和预知识

IF 2.4 3区心理学

Journal of Educational and Behavioral Statistics Pub Date : 2023-04-20 DOI: 10.3102/10769986231159923

Kylie Gorney, James A. Wollack, S. Sinharay, Carol Eckerly

引用次数: 1

An Explicit Form With Continuous Attribute Profile of the Partial Mastery DINA Model 部分Mastery DINA模型的一个具有连续属性轮廓的显式形式

IF 2.4 3区心理学

Journal of Educational and Behavioral Statistics Pub Date : 2023-04-10 DOI: 10.3102/10769986231159436

Tian Shu, Guanzhong Luo, Zhaosheng Luo, Xiaofeng Yu, Xiaojun Guo, Yujun Li

{"title":"An Explicit Form With Continuous Attribute Profile of the Partial Mastery DINA Model","authors":"Tian Shu, Guanzhong Luo, Zhaosheng Luo, Xiaofeng Yu, Xiaojun Guo, Yujun Li","doi":"10.3102/10769986231159436","DOIUrl":"https://doi.org/10.3102/10769986231159436","url":null,"abstract":"Cognitive diagnosis models (CDMs) are the statistical framework for cognitive diagnostic assessment in education and psychology. They generally assume that subjects’ latent attributes are dichotomous—mastery or nonmastery, which seems quite deterministic. As an alternative to dichotomous attribute mastery, attention is drawn to the use of a continuous attribute mastery format in recent literature. To obtain subjects’ finer-grained attribute mastery for more precise diagnosis and guidance, an equivalent but more explicit form of the partial-mastery-deterministic inputs, noisy “and” gate (DINA) model (termed continuous attribute profile [CAP]-DINA form) is proposed in this article. Its parameters estimation algorithm based on this form using Bayesian techniques with Markov chain Monte Carlo algorithm is also presented. Two simulation studies are conducted then to explore its parameter recovery and model misspecification, and the results demonstrate that the CAP-DINA form performs robustly with satisfactory efficiency in these two aspects. A real data study of the English test also indicates it has a better model fit than DINA.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":"48 1","pages":"573 - 602"},"PeriodicalIF":2.4,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42823878","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