Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 0 |
Since 2016 (last 10 years) | 2 |
Since 2006 (last 20 years) | 3 |
Descriptor
Author
Byrnes, James P. | 1 |
Jackman, M. Grace-Anne | 1 |
Jin, Rong | 1 |
Leite, Walter L. | 1 |
MacInnes, Jann W. | 1 |
Miller-Cotto, Dana | 1 |
Sandbach, Robert | 1 |
Shanley, Lina | 1 |
Wang, Aubrey H. | 1 |
Publication Type
Journal Articles | 3 |
Reports - Research | 3 |
Education Level
Grade 1 | 3 |
Elementary Education | 2 |
Early Childhood Education | 1 |
Grade 2 | 1 |
Grade 3 | 1 |
Grade 4 | 1 |
Grade 5 | 1 |
Junior High Schools | 1 |
Kindergarten | 1 |
Middle Schools | 1 |
Primary Education | 1 |
More ▼ |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
Early Childhood Longitudinal… | 3 |
What Works Clearinghouse Rating
Byrnes, James P.; Miller-Cotto, Dana; Wang, Aubrey H. – Journal of Cognition and Development, 2018
As the United States experiences greater income inequality, more and more students experience an early science achievement gap. This study tested several competing theoretical models of early science achievement with a longitudinal sample of 14,624 children who were followed from kindergarten entry to the end of 1st grade. To understand why and…
Descriptors: Cognitive Development, Grade 1, Elementary School Students, Kindergarten
Shanley, Lina – Educational Researcher, 2016
Accurately measuring and modeling academic achievement growth is critical to support educational policy and practice. Using a nationally representative longitudinal data set, this study compared various models of mathematics achievement growth on the basis of both practical utility and optimal statistical fit and explored relationships within and…
Descriptors: Mathematics Achievement, Achievement Gains, Longitudinal Studies, Academic Achievement
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