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Esther Ulitzsch; Janine Buchholz; Hyo Jeong Shin; Jonas Bertling; Oliver Lüdtke – Large-scale Assessments in Education, 2024
Common indicator-based approaches to identifying careless and insufficient effort responding (C/IER) in survey data scan response vectors or timing data for aberrances, such as patterns signaling straight lining, multivariate outliers, or signals that respondents rushed through the administered items. Each of these approaches is susceptible to…
Descriptors: Response Style (Tests), Attention, Achievement Tests, Foreign Countries
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Esther Ulitzsch; Steffi Pohl; Lale Khorramdel; Ulf Kroehne; Matthias von Davier – Journal of Educational and Behavioral Statistics, 2024
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…
Descriptors: Item Response Theory, Response Style (Tests), Questionnaires, Secondary School Students
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Jiyoun Kim; Chia-Wen Chen; Yi-Jhen Wu – Large-scale Assessments in Education, 2024
Learning strategies have been recognized as important predictors of mathematical achievement. In recent studies, it has been found that Asian students use combined learning strategies, primarily including metacognitive strategies, rather than rote memorization. To the best of the authors' knowledge, there is only one prior study including South…
Descriptors: Achievement Tests, Foreign Countries, Learning Strategies, Mathematics Achievement
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Maria Bolsinova; Jesper Tijmstra; Leslie Rutkowski; David Rutkowski – Journal of Educational and Behavioral Statistics, 2024
Profile analysis is one of the main tools for studying whether differential item functioning can be related to specific features of test items. While relevant, profile analysis in its current form has two restrictions that limit its usefulness in practice: It assumes that all test items have equal discrimination parameters, and it does not test…
Descriptors: Test Items, Item Analysis, Generalizability Theory, Achievement Tests
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Sean Joo; Montserrat Valdivia; Dubravka Svetina Valdivia; Leslie Rutkowski – Journal of Educational and Behavioral Statistics, 2024
Evaluating scale comparability in international large-scale assessments depends on measurement invariance (MI). The root mean square deviation (RMSD) is a standard method for establishing MI in several programs, such as the Programme for International Student Assessment and the Programme for the International Assessment of Adult Competencies.…
Descriptors: International Assessment, Monte Carlo Methods, Statistical Studies, Error of Measurement
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Zeynep Uzun; Tuncay Ögretmen – Large-scale Assessments in Education, 2025
This study aimed to evaluate the item model fit by equating the forms of the PISA 2018 mathematics subtest with concurrent common items equating in samples from Türkiye, the UK, and Italy. The answers given in mathematics subtest Forms 2, 8, and 12 were used in this context. Analyzes were performed using the Dichotomous Rasch Model in the WINSTEPS…
Descriptors: Item Response Theory, Test Items, Foreign Countries, Mathematics Tests
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Huang, Hung-Yu – Educational and Psychological Measurement, 2020
In educational assessments and achievement tests, test developers and administrators commonly assume that test-takers attempt all test items with full effort and leave no blank responses with unplanned missing values. However, aberrant response behavior--such as performance decline, dropping out beyond a certain point, and skipping certain items…
Descriptors: Item Response Theory, Response Style (Tests), Test Items, Statistical Analysis
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Kseniia Marcq; Johan Braeken – International Journal of Testing, 2025
The Programme for International Student Assessment (PISA) student questionnaire, despite being designed for low cognitive demand, may induce test burden due to its 306-item length, resulting in increased item nonresponse toward the questionnaire's end. Using the PISA 2018 response data from 80 countries and a cross-classified mixed effects model,…
Descriptors: Achievement Tests, Foreign Countries, Secondary School Students, International Assessment
Cassandra N. Malcom – ProQuest LLC, 2024
Science, Technology, Engineering, and Math (STEM) skills are increasingly required of students to be successful in higher education and the workforce. Therefore, modeling assessment outcomes accurately, often using more types of student data to get a complete picture of student learning, is increasingly relevant. The Program for International…
Descriptors: Student Evaluation, STEM Education, Science Tests, Achievement Tests
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Lundgren, Erik; Eklöf, Hanna – Educational Research and Evaluation, 2020
The present study used process data from a computer-based problem-solving task as indications of behavioural level of test-taking effort, and explored how behavioural item-level effort related to overall test performance and self-reported effort. Variables were extracted from raw process data and clustered. Four distinct clusters were obtained and…
Descriptors: Computer Assisted Testing, Problem Solving, Response Style (Tests), Test Items
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Lu, Jing; Wang, Chun – Journal of Educational Measurement, 2020
Item nonresponses are prevalent in standardized testing. They happen either when students fail to reach the end of a test due to a time limit or quitting, or when students choose to omit some items strategically. Oftentimes, item nonresponses are nonrandom, and hence, the missing data mechanism needs to be properly modeled. In this paper, we…
Descriptors: Item Response Theory, Test Items, Standardized Tests, Responses
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Militsa G. Ivanova; Michalis P. Michaelides – Practical Assessment, Research & Evaluation, 2023
Research on methods for measuring examinee engagement with constructed-response items is limited. The present study used data from the PISA 2018 Reading domain to construct and compare indicators of test-taking effort on constructed-response items: response time, number of actions, the union (combining effortless responses detected by either…
Descriptors: Achievement Tests, Foreign Countries, International Assessment, Secondary School Students
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Carmen Köhler; Lale Khorramdel; Artur Pokropek; Johannes Hartig – Journal of Educational Measurement, 2024
For assessment scales applied to different groups (e.g., students from different states; patients in different countries), multigroup differential item functioning (MG-DIF) needs to be evaluated in order to ensure that respondents with the same trait level but from different groups have equal response probabilities on a particular item. The…
Descriptors: Measures (Individuals), Test Bias, Models, Item Response Theory
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Sachse, Karoline A.; Mahler, Nicole; Pohl, Steffi – Educational and Psychological Measurement, 2019
Mechanisms causing item nonresponses in large-scale assessments are often said to be nonignorable. Parameter estimates can be biased if nonignorable missing data mechanisms are not adequately modeled. In trend analyses, it is plausible for the missing data mechanism and the percentage of missing values to change over time. In this article, we…
Descriptors: International Assessment, Response Style (Tests), Achievement Tests, Foreign Countries
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Qiwei He – International Journal of Assessment Tools in Education, 2023
Collaborative problem solving (CPS) is inherently an interactive, conjoint, dual-strand process that considers how a student reasons about a problem as well as how s/he interacts with others to regulate social processes and exchange information (OECD, 2013). Measuring CPS skills presents a challenge for obtaining consistent, accurate, and reliable…
Descriptors: Cooperative Learning, Problem Solving, Test Items, International Assessment
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