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Julia Mang; Helmut Küchenhoff; Sabine Meinck – Large-scale Assessments in Education, 2024
Stratification is an important design feature of many studies using complex sampling designs and it is often used in large-scale assessment (LSA) studies, such as the "Programme for International Student Assessment" (PISA), for two main reasons. First, stratification variables that achieve a high between and low within strata variance…
Descriptors: Foreign Countries, Achievement Tests, International Assessment, Secondary School Students
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Miryeong Koo; Jin Eun Yoo – Large-scale Assessments in Education, 2025
Background: Teachers' team innovativeness--the collective capacity of teaching teams to generate, adopt, and implement new ideas and practices--is increasingly recognized for its role in enhancing instructional methods and fostering school innovation. However, empirical research identifying its predictors remains limited, particularly within…
Descriptors: Administrator Surveys, Teacher Surveys, Teamwork, Educational Innovation
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Kaplan, David; Chen, Jianshen; Lyu, Weicong; Yavuz, Sinan – Large-scale Assessments in Education, 2023
The purpose of this paper is to extend and evaluate methods of "Bayesian historical borrowing" applied to longitudinal data with a focus on parameter recovery and predictive performance. Bayesian historical borrowing allows researchers to utilize information from previous data sources and to adjust the extent of borrowing based on the…
Descriptors: Bayesian Statistics, Longitudinal Studies, Children, Surveys
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Mang, Julia; Küchenhoff, Helmut; Meinck, Sabine; Prenzel, Manfred – Large-scale Assessments in Education, 2021
Background: Standard methods for analysing data from large-scale assessments (LSA) cannot merely be adopted if hierarchical (or multilevel) regression modelling should be applied. Currently various approaches exist; they all follow generally a design-based model of estimation using the pseudo maximum likelihood method and adjusted weights for the…
Descriptors: Sampling, Hierarchical Linear Modeling, Simulation, Scaling
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Robitzsch, Alexander; Lüdtke, Oliver – Large-scale Assessments in Education, 2023
One major aim of international large-scale assessments (ILSA) like PISA is to monitor changes in student performance over time. To accomplish this task, a set of common items (i.e., link items) is repeatedly administered in each assessment. Linking methods based on item response theory (IRT) models are used to align the results from the different…
Descriptors: Educational Trends, Trend Analysis, International Assessment, Achievement Tests
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Jin, Ying; Kang, Minsoo – Large-scale Assessments in Education, 2016
Background: The current study compared four differential item functioning (DIF) methods to examine their performances in terms of accounting for dual dependency (i.e., person and item clustering effects) simultaneously by a simulation study, which is not sufficiently studied under the current DIF literature. The four methods compared are logistic…
Descriptors: Comparative Analysis, Test Bias, Simulation, Regression (Statistics)
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Adams, Raymond J.; Lietz, Petra; Berezner, Alla – Large-scale Assessments in Education, 2013
Background: While rotated test booklets have been employed in large-scale assessments to increase the content coverage of the assessments, rotation has not yet been applied to the context questionnaires administered to respondents. Methods: This paper describes the development of a methodology that uses rotated context questionnaires in…
Descriptors: Questionnaires, Item Response Theory, Foreign Countries, Achievement Tests
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Svetina, Dubravka; Rutkowski, Leslie – Large-scale Assessments in Education, 2014
Background: When studying student performance across different countries or cultures, an important aspect for comparisons is that of score comparability. In other words, it is imperative that the latent variable (i.e., construct of interest) is understood and measured equivalently across all participating groups or countries, if our inferences…
Descriptors: Test Items, Item Response Theory, Item Analysis, Regression (Statistics)