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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
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Means, Barbara; Wang, Haiwen; Wei, Xin; Lynch, Sharon; Peters, Vanessa; Young, Viki; Allen, Carrie – Science Education, 2017
Inclusive STEM high schools (ISHSs) (where STEM is science, technology, engineering, and mathematics) admit students on the basis of interest rather than competitive examination. This study examines the central assumption behind these schools--that they provide students from subgroups underrepresented in STEM with experiences that equip them…
Descriptors: STEM Education, High Schools, High School Students, Hierarchical Linear Modeling
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Bauer, Daniel J.; Gottfredson, Nisha C.; Dean, Danielle; Zucker, Robert A. – Psychological Methods, 2013
Researchers commonly collect repeated measures on individuals nested within groups such as students within schools, patients within treatment groups, or siblings within families. Often, it is most appropriate to conceptualize such groups as dynamic entities, potentially undergoing stochastic structural and/or functional changes over time. For…
Descriptors: Hierarchical Linear Modeling, Data Analysis, Science Achievement, High School Students