Publication Date
| In 2025 | 0 |
| Since 2024 | 0 |
| Since 2021 (last 5 years) | 0 |
| Since 2016 (last 10 years) | 0 |
| Since 2006 (last 20 years) | 1 |
Descriptor
| Computation | 1 |
| Elementary School Students | 1 |
| Error of Measurement | 1 |
| Goodness of Fit | 1 |
| Longitudinal Studies | 1 |
| Matched Groups | 1 |
| Mathematics Achievement | 1 |
| Monte Carlo Methods | 1 |
| Observation | 1 |
| Poverty | 1 |
| Probability | 1 |
| More ▼ | |
Source
| Structural Equation Modeling:… | 1 |
Publication Type
| Journal Articles | 1 |
| Reports - Research | 1 |
Education Level
| Elementary Education | 1 |
| Grade 1 | 1 |
| Grade 2 | 1 |
| Grade 3 | 1 |
| Grade 4 | 1 |
| Grade 5 | 1 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
| Early Childhood Longitudinal… | 1 |
What Works Clearinghouse Rating
Peer reviewed
Direct linkLeite, 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
