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Umut Atasever; Francis L. Huang; Leslie Rutkowski – Large-scale Assessments in Education, 2025
When analyzing large-scale assessments (LSAs) that use complex sampling designs, it is important to account for probability sampling using weights. However, the use of these weights in multilevel models has been widely debated, particularly regarding their application at different levels of the model. Yet, no consensus has been reached on the best…
Descriptors: Mathematics Tests, International Assessment, Elementary Secondary Education, Foreign Countries
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
Stapleton, Laura M.; Kang, Yoonjeong – Sociological Methods & Research, 2018
This research empirically evaluates data sets from the National Center for Education Statistics (NCES) for design effects of ignoring the sampling design in weighted two-level analyses. Currently, researchers may ignore the sampling design beyond the levels that they model which might result in incorrect inferences regarding hypotheses due to…
Descriptors: Probability, Hierarchical Linear Modeling, Sampling, Inferences
Hedges, Larry V.; Hedberg, Eric C.; Kuyper, Arend M. – Grantee Submission, 2012
Intraclass correlations are used to summarize the variance decomposition in popula- tions with multilevel hierarchical structure. There has recently been considerable interest in estimating intraclass correlations from surveys or designed experiments to provide design parameters for planning future large-scale randomized experiments. The large…
Descriptors: Correlation, Hierarchical Linear Modeling, Computation, Sampling