Publication Date
| In 2026 | 0 |
| Since 2025 | 0 |
| Since 2022 (last 5 years) | 0 |
| Since 2017 (last 10 years) | 0 |
| Since 2007 (last 20 years) | 1 |
Descriptor
| Academic Achievement | 1 |
| Computation | 1 |
| Data Analysis | 1 |
| Dropouts | 1 |
| Grade 10 | 1 |
| Grade 12 | 1 |
| Grade 8 | 1 |
| Hierarchical Linear Modeling | 1 |
| Longitudinal Studies | 1 |
| Maximum Likelihood Statistics | 1 |
| Minority Group Students | 1 |
| More ▼ | |
Source
| Journal of Educational and… | 1 |
Publication Type
| Journal Articles | 1 |
| Reports - Research | 1 |
Education Level
| Grade 10 | 1 |
| Grade 12 | 1 |
| Grade 8 | 1 |
| High Schools | 1 |
| Junior High Schools | 1 |
| Middle Schools | 1 |
| Secondary Education | 1 |
Audience
Location
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Peer reviewed
Direct linkFeldman, Betsy J.; Rabe-Hesketh, Sophia – Journal of Educational and Behavioral Statistics, 2012
In longitudinal education studies, assuming that dropout and missing data occur completely at random is often unrealistic. When the probability of dropout depends on covariates and observed responses (called "missing at random" [MAR]), or on values of responses that are missing (called "informative" or "not missing at random" [NMAR]),…
Descriptors: Dropouts, Academic Achievement, Longitudinal Studies, Computation
