Journal of Educational and Behavioral Statistics最新文献_第3页

A Multidimensional Partially Compensatory Response Time Model on Basis of the Log-Normal Distribution 基于对数正态分布的多维部分补偿响应时间模型

IF 2.4 3区心理学

Journal of Educational and Behavioral Statistics Pub Date : 2023-07-11 DOI: 10.3102/10769986231184153

Jochen Ranger, C. König, B. Domingue, Jörg-Tobias Kuhn, Andreas Frey

{"title":"A Multidimensional Partially Compensatory Response Time Model on Basis of the Log-Normal Distribution","authors":"Jochen Ranger, C. König, B. Domingue, Jörg-Tobias Kuhn, Andreas Frey","doi":"10.3102/10769986231184153","DOIUrl":"https://doi.org/10.3102/10769986231184153","url":null,"abstract":"In the existing multidimensional extensions of the log-normal response time (LNRT) model, the log response times are decomposed into a linear combination of several latent traits. These models are fully compensatory as low levels on traits can be counterbalanced by high levels on other traits. We propose an alternative multidimensional extension of the LNRT model by assuming that the response times can be decomposed into two response time components. Each response time component is generated by a one-dimensional LNRT model with a different latent trait. As the response time components—but not the traits—are related additively, the model is partially compensatory. In a simulation study, we investigate the recovery of the model’s parameters. We also investigate whether the fully and the partially compensatory LNRT model can be distinguished empirically. Findings suggest that parameter recovery is good and that the two models can be distinctly identified under certain conditions. The utility of the model in practice is demonstrated with an empirical application. In the empirical application, the partially compensatory model fits better than the fully compensatory model.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45247254","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

Alternatives to Weighted Item Fit Statistics for Establishing Measurement Invariance in Many Groups 在多组中建立测量不变性的加权项目拟合统计的替代方法

IF 2.4 3区心理学

Journal of Educational and Behavioral Statistics Pub Date : 2023-07-04 DOI: 10.3102/10769986231183326

Sean Joo, Montserrat Valdivia, Dubravka Svetina Valdivia, Leslie Rutkowski

引用次数: 1

Extending an Identified Four-Parameter IRT Model: The Confirmatory Set-4PNO Model 扩展已识别的四参数IRT模型：验证性集合4PNO模型

IF 2.4 3区心理学

Journal of Educational and Behavioral Statistics Pub Date : 2023-07-03 DOI: 10.3102/10769986231181587

Justin L. Kern

{"title":"Extending an Identified Four-Parameter IRT Model: The Confirmatory Set-4PNO Model","authors":"Justin L. Kern","doi":"10.3102/10769986231181587","DOIUrl":"https://doi.org/10.3102/10769986231181587","url":null,"abstract":"Given the frequent presence of slipping and guessing in item responses, models for the inclusion of their effects are highly important. Unfortunately, the most common model for their inclusion, the four-parameter item response theory model, potentially has severe deficiencies related to its possible unidentifiability. With this issue in mind, the dyad four-parameter normal ogive (Dyad-4PNO) model was developed. This model allows for slipping and guessing effects by including binary augmented variables—each indicated by two items whose probabilities are determined by slipping and guessing parameters—which are subsequently related to a continuous latent trait through a two-parameter model. Furthermore, the Dyad-4PNO assumes uncertainty as to which items are paired on each augmented variable. In this way, the model is inherently exploratory. In the current article, the new model, called the Set-4PNO model, is an extension of the Dyad-4PNO in two ways. First, the new model allows for more than two items per augmented variable. Second, these item sets are assumed to be fixed, that is, the model is confirmatory. This article discusses this extension and introduces a Gibbs sampling algorithm to estimate the model. A Monte Carlo simulation study shows the efficacy of the algorithm at estimating the model parameters. A real data example shows that this extension may be viable in practice, with the data fitting a more general Set-4PNO model (i.e., more than two items per augmented variable) better than the Dyad-4PNO, 2PNO, 3PNO, and 4PNO models.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45508174","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 General Mixture Model for Cognitive Diagnosis 认知诊断的一般混合模型

IF 2.4 3区心理学

Journal of Educational and Behavioral Statistics Pub Date : 2023-06-14 DOI: 10.3102/10769986231176012

Joemari Olea, Kevin Carl P. Santos

引用次数: 0

Bayesian Exploratory Factor Analysis via Gibbs Sampling 基于吉布斯抽样的贝叶斯探索因子分析

IF 2.4 3区心理学

Journal of Educational and Behavioral Statistics Pub Date : 2023-06-13 DOI: 10.3102/10769986231176023

Adrian Quintero, E. Lesaffre, G. Verbeke

引用次数: 0

Bayesian Estimation of Attribute Hierarchy for Cognitive Diagnosis Models 认知诊断模型属性层次的贝叶斯估计

IF 2.4 3区心理学

Journal of Educational and Behavioral Statistics Pub Date : 2023-06-13 DOI: 10.3102/10769986231174918

Yinghan Chen, Shiyu Wang

引用次数: 1

Deep Learning Imputation for Asymmetric and Incomplete Likert-Type Items 非对称和不完全Likert型项目的深度学习推断

IF 2.4 3区心理学

Journal of Educational and Behavioral Statistics Pub Date : 2023-06-13 DOI: 10.3102/10769986231176014

Zachary K. Collier, Minji Kong, Olushola Soyoye, Kamal Chawla, Ann M. Aviles, Y. Payne

引用次数: 0

Generalizing Beyond the Test: Permutation-Based Profile Analysis for Explaining DIF Using Item Features 超越测试的泛化：基于排列的概要分析用于使用项目特征解释DIF

IF 2.4 3区心理学

Journal of Educational and Behavioral Statistics Pub Date : 2023-06-12 DOI: 10.3102/10769986231174927

M. Bolsinova, J. Tijmstra, Leslie Rutkowski, david. rutkowski

引用次数: 0

Using Response Times for Joint Modeling of Careless Responding and Attentive Response Styles 用反应时间联合建模粗心反应和注意反应风格

IF 2.4 3区心理学

Journal of Educational and Behavioral Statistics Pub Date : 2023-05-28 DOI: 10.3102/10769986231173607

Esther Ulitzsch, S. Pohl, Lale Khorramdel, Ulf Kroehne, Matthias von Davier

{"title":"Using Response Times for Joint Modeling of Careless Responding and Attentive Response Styles","authors":"Esther Ulitzsch, S. Pohl, Lale Khorramdel, Ulf Kroehne, Matthias von Davier","doi":"10.3102/10769986231173607","DOIUrl":"https://doi.org/10.3102/10769986231173607","url":null,"abstract":"Questionnaires are by far the most common tool for measuring noncognitive constructs in psychology and educational sciences. Response bias may pose an additional source of variation between respondents that threatens validity of conclusions drawn from questionnaire data. We present a mixture modeling approach that leverages response time data from computer-administered questionnaires for the joint identification and modeling of two commonly encountered response bias that, so far, have only been modeled separately—careless and insufficient effort responding and response styles (RS) in attentive answering. Using empirical data from the Programme for International Student Assessment 2015 background questionnaire and the case of extreme RS as an example, we illustrate how the proposed approach supports gaining a more nuanced understanding of response behavior as well as how neglecting either type of response bias may impact conclusions on respondents’ content trait levels as well as on their displayed response behavior. We further contrast the proposed approach against a more heuristic two-step procedure that first eliminates presumed careless respondents from the data and subsequently applies model-based approaches accommodating RS. To investigate the trustworthiness of results obtained in the empirical application, we conduct a parameter recovery study.","PeriodicalId":48001,"journal":{"name":"Journal of Educational and Behavioral Statistics","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49014848","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

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