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Sijia Huang; Li Cai – Journal of Educational and Behavioral Statistics, 2024
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…
Descriptors: Classification, Data Collection, Data Analysis, Item Response Theory
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Leventhal, Brian C.; Gregg, Nikole; Ames, Allison J. – Measurement: Interdisciplinary Research and Perspectives, 2022
Response styles introduce construct-irrelevant variance as a result of respondents systematically responding to Likert-type items regardless of content. Methods to account for response styles through data analysis as well as approaches to mitigating the effects of response styles during data collection have been well-documented. Recent approaches…
Descriptors: Response Style (Tests), Item Response Theory, Test Items, Likert Scales
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Sainan Xu; Jing Lu; Jiwei Zhang; Chun Wang; Gongjun Xu – Grantee Submission, 2024
With the growing attention on large-scale educational testing and assessment, the ability to process substantial volumes of response data becomes crucial. Current estimation methods within item response theory (IRT), despite their high precision, often pose considerable computational burdens with large-scale data, leading to reduced computational…
Descriptors: Educational Assessment, Bayesian Statistics, Statistical Inference, Item Response Theory
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Mihyun Son; Minsu Ha – Education and Information Technologies, 2025
Digital literacy is essential for scientific literacy in a digital world. Although the NGSS Practices include many activities that require digital literacy, most studies have examined digital literacy from a generic perspective rather than a curricular context. This study aimed to develop a self-report tool to measure elements of digital literacy…
Descriptors: Test Construction, Measures (Individuals), Digital Literacy, Scientific Literacy
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Zehner, Fabian; Eichmann, Beate; Deribo, Tobias; Harrison, Scott; Bengs, Daniel; Andersen, Nico; Hahnel, Carolin – Journal of Educational Data Mining, 2021
The NAEP EDM Competition required participants to predict efficient test-taking behavior based on log data. This paper describes our top-down approach for engineering features by means of psychometric modeling, aiming at machine learning for the predictive classification task. For feature engineering, we employed, among others, the Log-Normal…
Descriptors: National Competency Tests, Engineering Education, Data Collection, Data Analysis
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Kastberg, David; Murray, Gordon; Ferraro, David; Arieira, Carlos; Roey, Shep; Mamedova, Saida; Liao, Yuqi – National Center for Education Statistics, 2021
The Program for International Student Assessment Young Adult Follow-up Study (PISA YAFS) is a follow-up study with students who participated in PISA 2012 in the United States. The study is designed to measure how performance on PISA 2012 relates to subsequent measures of outcomes and skills of young adults on an online assessment, Education and…
Descriptors: Foreign Countries, Achievement Tests, Secondary School Students, Young Adults
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Guo, Hongwen – ETS Research Report Series, 2017
Data collected from online learning and tutoring systems for individual students showed strong autocorrelation or dependence because of content connection, knowledge-based dependency, or persistence of learning behavior. When the response data show little dependence or negative autocorrelations for individual students, it is suspected that…
Descriptors: Data Collection, Electronic Learning, Intelligent Tutoring Systems, Information Utilization
Martin, Michael O., Ed.; von Davier, Matthias, Ed.; Mullis, Ina V. S., Ed. – International Association for the Evaluation of Educational Achievement, 2020
The chapters in this online volume comprise the TIMSS & PIRLS International Study Center's technical report of the methods and procedures used to develop, implement, and report the results of TIMSS 2019. There were various technical challenges because TIMSS 2019 was the initial phase of the transition to eTIMSS, with approximately half the…
Descriptors: Foreign Countries, Elementary Secondary Education, Achievement Tests, International Assessment
Beheshti, Behzad; Desmarais, Michel C. – International Educational Data Mining Society, 2015
This study investigates the issue of the goodness of fit of different skills assessment models using both synthetic and real data. Synthetic data is generated from the different skills assessment models. The results show wide differences of performances between the skills assessment models over synthetic data sets. The set of relative performances…
Descriptors: Goodness of Fit, Student Evaluation, Skills, Models
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Ivancevic, Vladimir – Journal of Learning Analytics, 2014
Tests targeting the upper limits of student ability could aid students in their learning. This article gives an overview of an approach to the construction of such tests in programming, together with ideas on how to implement and refine them within a learning management system.
Descriptors: Item Banks, Educational Research, Data Collection, Data Analysis
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Ingels, Steven J.; Pratt, Daniel J.; Herget, Deborah R.; Dever, Jill A.; Fritch, Laura Burns; Ottem, Randolph; Rogers, James E.; Kitmitto, Sami; Leinwand, Steve – National Center for Education Statistics, 2013
This manual has been produced to familiarize data users with the design, and the procedures followed for data collection and processing, in the base year and first follow-up of the High School Longitudinal Study of 2009 (HSLS:09), with emphasis on the first follow-up. It also provides the necessary documentation for use of the public-use data…
Descriptors: High School Students, Longitudinal Studies, Annual Reports, Followup Studies
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Ingels, Steven J.; Pratt, Daniel J.; Herget, Deborah R.; Dever, Jill A.; Fritch, Laura Burns; Ottem, Randolph; Rogers, James E.; Kitmitto, Sami; Leinwand, Steve – National Center for Education Statistics, 2013
The manual that accompanies these appendices was produced to familiarize data users with the design, and the procedures followed for data collection and processing, in the base year and first follow-up of the High School Longitudinal Study of 2009 (HSLS:09), with emphasis on the first follow-up. It also provides the necessary documentation for use…
Descriptors: High School Students, Longitudinal Studies, Annual Reports, Followup Studies
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Tourangeau, Karen; Nord, Christine; Lê, Thanh; Wallner-Allen, Kathleen; Vaden-Kiernan, Nancy; Blaker, Lisa; Najarian, Michelle – National Center for Education Statistics, 2018
This manual provides guidance and documentation for users of the longitudinal kindergarten-fourth grade (K-4) public-use data file of the Early Childhood Longitudinal Study, Kindergarten Class of 2010-11 (ECLS-K:2011), which includes the first release of the public version of the third-grade data. This manual mainly provides information specific…
Descriptors: Longitudinal Studies, Children, Surveys, Kindergarten
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Rafferty, Anna N., Ed.; Whitehill, Jacob, Ed.; Romero, Cristobal, Ed.; Cavalli-Sforza, Violetta, Ed. – International Educational Data Mining Society, 2020
The 13th iteration of the International Conference on Educational Data Mining (EDM 2020) was originally arranged to take place in Ifrane, Morocco. Due to the SARS-CoV-2 (coronavirus) epidemic, EDM 2020, as well as most other academic conferences in 2020, had to be changed to a purely online format. To facilitate efficient transmission of…
Descriptors: Educational Improvement, Teaching Methods, Information Retrieval, Data Processing
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Finch, Holmes; Monahan, Patrick – Applied Measurement in Education, 2008
This article introduces a bootstrap generalization to the Modified Parallel Analysis (MPA) method of test dimensionality assessment using factor analysis. This methodology, based on the use of Marginal Maximum Likelihood nonlinear factor analysis, provides for the calculation of a test statistic based on a parametric bootstrap using the MPA…
Descriptors: Monte Carlo Methods, Factor Analysis, Generalization, Methods
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