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Chine, Danielle R.; Larwin, Karen H. – International Journal of Research in Education and Science, 2022
Hierarchical linear modeling (HLM) has become an increasingly popular multilevel method of analyzing data among nested datasets, in particular, the effect of specialized academic programming within schools. The purpose of this methodological study is to demonstrate the use of HLM to determine the effectiveness of STEM programming in an Ohio middle…
Descriptors: Middle Schools, STEM Education, Instructional Effectiveness, Program Development
Sulis, Isabella; Toland, Michael D. – Journal of Early Adolescence, 2017
Item response theory (IRT) models are the main psychometric approach for the development, evaluation, and refinement of multi-item instruments and scaling of latent traits, whereas multilevel models are the primary statistical method when considering the dependence between person responses when primary units (e.g., students) are nested within…
Descriptors: Hierarchical Linear Modeling, Item Response Theory, Psychometrics, Evaluation Methods
Gandhi, Allison Gruner; Ogut, Burhan; Stein, Laura; Bzura, Robin; Danielson, Louis – Grantee Submission, 2017
This study reports findings from studies examining potential read-aloud accommodations on standardized reading comprehension assessments for students with decoding difficulties. Three types of accommodations were evaluated: question stems and answer options read aloud; question stems, answer options, and proper nouns read aloud; and full…
Descriptors: Testing Accommodations, Reading Difficulties, Decoding (Reading), Reading Tests
Jak, Suzanne; Oort, Frans J.; Dolan, Conor V. – Structural Equation Modeling: A Multidisciplinary Journal, 2013
We present a test for cluster bias, which can be used to detect violations of measurement invariance across clusters in 2-level data. We show how measurement invariance assumptions across clusters imply measurement invariance across levels in a 2-level factor model. Cluster bias is investigated by testing whether the within-level factor loadings…
Descriptors: Statistical Bias, Measurement, Structural Equation Models, Hierarchical Linear Modeling
Jaciw, Andrew P.; Nguyen, Thanh; Lin, Li; Zacamy, Jenna L.; Kwong, Connie; Lau, Sze-Shun – Grantee Submission, 2020
These appendices accompany the report "Final Report of the i3 Impact Study of Making Sense of SCIENCE, 2016-17 through 2017-18." Science education has experienced a significant transition over the last decade, catalyzed by a re-envisioning of what students should know and be able to do in science. That re-envisioning culminated in the…
Descriptors: Faculty Development, Science Instruction, Science Achievement, Elementary School Teachers
Murphy, Daniel L.; Beretvas, S. Natasha – Applied Measurement in Education, 2015
This study examines the use of cross-classified random effects models (CCrem) and cross-classified multiple membership random effects models (CCMMrem) to model rater bias and estimate teacher effectiveness. Effect estimates are compared using CTT versus item response theory (IRT) scaling methods and three models (i.e., conventional multilevel…
Descriptors: Teacher Effectiveness, Comparative Analysis, Hierarchical Linear Modeling, Test Theory