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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
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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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Sang, Biao; Pan, Tingting; Deng, Xinmei; Zhao, Xu – Educational Psychology, 2018
Numerous studies have suggested that academic stress has negative impact on adolescents' psychological function, few of those studies, however, considered whether and how the impact of stress on adolescents' emotional states is moderated by corresponding regulation. This study aimed to examine the fluctuation of emotional states before and after…
Descriptors: Sampling, Stress Variables, Self Control, Emotional Response
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Tipton, Elizabeth; Yeager, David; Iachan, Ronaldo – Society for Research on Educational Effectiveness, 2016
Questions regarding the generalizability of results from educational experiments have been at the forefront of methods development over the past five years. This work has focused on methods for estimating the effect of an intervention in a well-defined inference population (e.g., Tipton, 2013; O'Muircheartaigh and Hedges, 2014); methods for…
Descriptors: Behavioral Sciences, Behavioral Science Research, Intervention, Educational Experiments
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Yang, Ji Seung; Cai, Li – Grantee Submission, 2014
The main purpose of this study is to improve estimation efficiency in obtaining maximum marginal likelihood estimates of contextual effects in the framework of nonlinear multilevel latent variable model by adopting the Metropolis-Hastings Robbins-Monro algorithm (MH-RM; Cai, 2008, 2010a, 2010b). Results indicate that the MH-RM algorithm can…
Descriptors: Computation, Hierarchical Linear Modeling, Mathematics, Context Effect
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Huang, Francis L. – Practical Assessment, Research & Evaluation, 2014
Clustered data (e.g., students within schools) are often analyzed in educational research where data are naturally nested. As a consequence, multilevel modeling (MLM) has commonly been used to study the contextual or group-level (e.g., school) effects on individual outcomes. The current study investigates the use of an alternative procedure to…
Descriptors: Hierarchical Linear Modeling, Regression (Statistics), Educational Research, Sampling
Strati, Anna D. – ProQuest LLC, 2013
Using data collected through classroom videotaping, student surveys, and the Experience Sampling Method (ESM), the present study explored associations between teacher-provided intellectual challenge, two types of support (instrumental and emotional), and students' momentary academic engagement in high school science classrooms. Results of 3-level…
Descriptors: Teacher Role, Learner Engagement, Teacher Behavior, Correlation
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Johnson, Matthew S.; Jenkins, Frank – ETS Research Report Series, 2005
Large-scale educational assessments such as the National Assessment of Educational Progress (NAEP) sample examinees to whom an exam will be administered. In most situations the sampling design is not a simple random sample and must be accounted for in the estimating model. After reviewing the current operational estimation procedure for NAEP, this…
Descriptors: Bayesian Statistics, Hierarchical Linear Modeling, National Competency Tests, Sampling