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Ismail, Yilmaz – International Journal of Educational Administration and Policy Studies, 2016
This study aims to develop a semiotic declarative knowledge model, which is a positive constructive behavior model that systematically facilitates understanding in order to ensure that learners think accurately and ask the right questions about a topic. The data used to develop the experimental model were obtained using four measurement tools…
Descriptors: Science Instruction, Semiotics, Grade 1, Elementary School Science
Thoemmes, Felix J.; West, Stephen G. – Multivariate Behavioral Research, 2011
In this article we propose several modeling choices to extend propensity score analysis to clustered data. We describe different possible model specifications for estimation of the propensity score: single-level model, fixed effects model, and two random effects models. We also consider both conditioning within clusters and conditioning across…
Descriptors: Probability, Scores, Statistical Analysis, Models
Harlow, Danielle B.; Swanson, Lauren H.; Nylund-Gibson, Karen; Truxler, Adam – Science Education, 2011
Understanding what children know is paramount to planning effective science instruction; however, in any classroom, the students hold a variety of ideas. Representing these differences in ways that also acknowledge the common trends among students might facilitate the process of differentiation. To exemplify one such possible process of…
Descriptors: Statistical Analysis, Science Instruction, Student Reaction, Age Differences
Leite, Walter L.; Sandbach, Robert; Jin, Rong; MacInnes, Jann W.; Jackman, M. Grace-Anne – Structural Equation Modeling: A Multidisciplinary Journal, 2012
Because random assignment is not possible in observational studies, estimates of treatment effects might be biased due to selection on observable and unobservable variables. To strengthen causal inference in longitudinal observational studies of multiple treatments, we present 4 latent growth models for propensity score matched groups, and…
Descriptors: Structural Equation Models, Probability, Computation, Observation