Journal of Educational and Behavioral Statistics最新文献_第2页

Erratum to Identifying Informative Predictor Variables With Random Forests 对《用随机森林识别信息预测变量》的勘误

IF 2.4 3区心理学

Journal of Educational and Behavioral Statistics Pub Date : 2023-10-13 DOI: 10.3102/10769986231204871

引用次数: 0

A Multistrategy Cognitive Diagnosis Model Incorporating Item Response Times Based on Strategy Selection Theories 基于策略选择理论的包含项目反应时间的多策略认知诊断模型

3区心理学

Journal of Educational and Behavioral Statistics Pub Date : 2023-10-02 DOI: 10.3102/10769986231200469

Junhuan Wei, Liufen Luo, Yan Cai, Dongbo Tu

引用次数: 0

Utilizing Real-Time Test Data to Solve Attenuation Paradox in Computerized Adaptive Testing to Enhance Optimal Design 利用实时测试数据解决计算机自适应测试中的衰减悖论，提高优化设计

3区心理学

Journal of Educational and Behavioral Statistics Pub Date : 2023-09-25 DOI: 10.3102/10769986231197666

Jyun-Hong Chen, Hsiu-Yi Chao

引用次数: 0

Identifying Informative Predictor Variables With Random Forests 用随机森林识别信息预测变量

IF 2.4 3区心理学

Journal of Educational and Behavioral Statistics Pub Date : 2023-09-05 DOI: 10.3102/10769986231193327

Yannick Rothacher, Carolin Strobl

引用次数: 0

Evaluating Psychometric Differences Between Fast Versus Slow Responses on Rating Scale Items 评估快速反应与慢速反应在评定量表项目上的心理测量差异

IF 2.4 3区心理学

Journal of Educational and Behavioral Statistics Pub Date : 2023-08-30 DOI: 10.3102/10769986231195260

N. Kim, D. Bolt

{"title":"Evaluating Psychometric Differences Between Fast Versus Slow Responses on Rating Scale Items","authors":"N. Kim, D. Bolt","doi":"10.3102/10769986231195260","DOIUrl":"https://doi.org/10.3102/10769986231195260","url":null,"abstract":"Some previous studies suggest that response times (RTs) on rating scale items can be informative about the content trait, but a more recent study suggests they may also be reflective of response styles. The latter result raises questions about the possible consideration of RTs for content trait estimation, as response styles are generally viewed as nuisance dimensions in the measurement of noncognitive constructs. In this article, we extend previous work exploring the simultaneous relevance of content and response style traits on RTs in self-report rating scale measurement by examining psychometric differences related to fast versus slow item responses. Following a parallel methodology applied with cognitive measures, we provide empirical illustrations of how RTs appear to be simultaneously reflective of both content and response style traits. Our results demonstrate that respondents may exhibit different response behaviors for fast versus slow responses and that both the content trait and response styles are relevant to such heterogeneity. These findings suggest that using RTs as a basis for improving the estimation of noncognitive constructs likely requires simultaneously attending to the effects of response styles.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46284745","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

Cross-Classified Item Response Theory Modeling With an Application to Student Evaluation of Teaching 交叉分类项目反应理论模型及其在学生教学评价中的应用

IF 2.4 3区心理学

Journal of Educational and Behavioral Statistics Pub Date : 2023-08-24 DOI: 10.3102/10769986231193351

Sijia Huang, Li Cai

{"title":"Cross-Classified Item Response Theory Modeling With an Application to Student Evaluation of Teaching","authors":"Sijia Huang, Li Cai","doi":"10.3102/10769986231193351","DOIUrl":"https://doi.org/10.3102/10769986231193351","url":null,"abstract":"The cross-classified data structure is ubiquitous in education, psychology, and health outcome sciences. In these areas, assessment instruments that are made up of multiple items are frequently used to measure latent constructs. The presence of both the cross-classified structure and multivariate categorical outcomes leads to the so-called item-level data with cross-classified structure. An example of such data structure is the routinely collected student evaluation of teaching (SET) data. Motivated by the lack of research on multilevel IRT modeling with crossed random effects and the need of an approach that can properly handle SET data, this study proposed a cross-classified IRT model, which takes into account both the cross-classified data structure and properties of multiple items in an assessment instrument. A new variant of the Metropolis–Hastings Robbins–Monro (MH-RM) algorithm was introduced to address the computational complexities in estimating the proposed model. A preliminary simulation study was conducted to evaluate the performance of the algorithm for fitting the proposed model to data. The results indicated that model parameters were well recovered. The proposed model was also applied to SET data collected at a large public university to answer empirical research questions. Limitations and future research directions were discussed.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45352027","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

IRT Models for Learning With Item-Specific Learning Parameters 具有项目特定学习参数的IRT学习模型

IF 2.4 3区心理学

Journal of Educational and Behavioral Statistics Pub Date : 2023-08-20 DOI: 10.3102/10769986231193096

Albert Yu, J. Douglas

{"title":"IRT Models for Learning With Item-Specific Learning Parameters","authors":"Albert Yu, J. Douglas","doi":"10.3102/10769986231193096","DOIUrl":"https://doi.org/10.3102/10769986231193096","url":null,"abstract":"We propose a new item response theory growth model with item-specific learning parameters, or ISLP, and two variations of this model. In the ISLP model, either items or blocks of items have their own learning parameters. This model may be used to improve the efficiency of learning in a formative assessment. We show ways that the ISLP model’s learning parameters can be estimated in simulation using Markov chain Monte Carlo (MCMC), demonstrate a way that the model could be used in the context of adaptive item selection to increase the rate of learning, and estimate the learning parameters in an empirical data analysis using the ISLP. In the simulation studies, the one-parameter logistic model was used as the measurement model to generate random response data with various test lengths and sample sizes. Ability growth was modeled with a few variations of the ISLP model, and it was verified that the parameters were accurately recovered. Secondly, we generated data using the linear logistic test model with known Q-matrix structure for the item difficulties. Using a two-step procedure gave very comparable results for the estimation of the learning parameters even when item difficulties were unknown. The potential benefit of using an adaptive selection method in conjunction with the ISLP model was shown by comparing total improvement in the examinees’ ability parameter to two other methods of item selection that do not utilize this growth model. If the ISLP holds, adaptive item selection consistently led to larger improvements over the other methods. A real data application of the ISLP was given to illustrate its use in a spatial reasoning study designed to promote learning. In this study, interventions were given after each block of ten items to increase ability. Learning parameters were estimated using MCMC.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44586129","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

DINA-BAG: A Bagging Algorithm for DINA Model Parameter Estimation in Small Samples 小样本条件下DINA模型参数估计的Bagging算法

IF 2.4 3区心理学

Journal of Educational and Behavioral Statistics Pub Date : 2023-08-07 DOI: 10.3102/10769986231188442

D. Arthur, Hua-Hua Chang

引用次数: 0

Bayesian Change-Point Analysis Approach to Detecting Aberrant Test-Taking Behavior Using Response Times 利用响应时间检测异常考生行为的贝叶斯变点分析方法

IF 2.4 3区心理学

Journal of Educational and Behavioral Statistics Pub Date : 2023-07-24 DOI: 10.3102/10769986231151961

Hongyue Zhu, Hong Jiao, Wei Gao, Xiangbin Meng

{"title":"Bayesian Change-Point Analysis Approach to Detecting Aberrant Test-Taking Behavior Using Response Times","authors":"Hongyue Zhu, Hong Jiao, Wei Gao, Xiangbin Meng","doi":"10.3102/10769986231151961","DOIUrl":"https://doi.org/10.3102/10769986231151961","url":null,"abstract":"Change-point analysis (CPA) is a method for detecting abrupt changes in parameter(s) underlying a sequence of random variables. It has been applied to detect examinees’ aberrant test-taking behavior by identifying abrupt test performance change. Previous studies utilized maximum likelihood estimations of ability parameters, focusing on detecting one change point for each examinee. This article proposes a Bayesian CPA procedure using response times (RTs) to detect abrupt changes in examinee speed, which may be related to aberrant responding behaviors. The lognormal RT model is used to derive a procedure for detecting aberrant RT patterns. The method takes the numbers and locations of the change points as parameters in the model to detect multiple change points or multiple aberrant behaviors. Given the change points, the corresponding speed of each segment in the test can be estimated, which enables more accurate inferences about aberrant behaviors. Simulation study results indicate that the proposed procedure can effectively detect simulated aberrant behaviors and estimate change points accurately. The method is applied to data from a high-stakes computerized adaptive test, where its applicability is demonstrated.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":"48 1","pages":"490 - 520"},"PeriodicalIF":2.4,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46477500","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

An Improved Inferential Procedure to Evaluate Item Discriminations in a Conditional Maximum Likelihood Framework 条件最大似然框架中评价项目判别的一种改进推理方法

IF 2.4 3区心理学

Journal of Educational and Behavioral Statistics Pub Date : 2023-07-19 DOI: 10.3102/10769986231183335

Clemens Draxler, A. Kurz, Can Gürer, J. Nolte

引用次数: 0