Publication Date
| In 2025 | 0 |
| Since 2024 | 1 |
| Since 2021 (last 5 years) | 1 |
| Since 2016 (last 10 years) | 2 |
| Since 2006 (last 20 years) | 3 |
Descriptor
| Computation | 3 |
| Growth Models | 3 |
| Regression (Statistics) | 3 |
| Academic Achievement | 1 |
| Achievement Tests | 1 |
| Adolescents | 1 |
| Bayesian Statistics | 1 |
| Cohort Analysis | 1 |
| Comparative Analysis | 1 |
| Computer Software | 1 |
| Correlation | 1 |
| More ▼ | |
Publication Type
| Journal Articles | 3 |
| Reports - Research | 2 |
| Reports - Descriptive | 1 |
Education Level
| Early Childhood Education | 1 |
| Elementary Education | 1 |
| Grade 3 | 1 |
| Grade 5 | 1 |
| Intermediate Grades | 1 |
| Middle Schools | 1 |
| Primary Education | 1 |
Audience
Location
Laws, Policies, & Programs
| No Child Left Behind Act 2001 | 1 |
Assessments and Surveys
| Iowa Tests of Basic Skills | 1 |
What Works Clearinghouse Rating
Peer reviewed
Direct linkDaniel Seddig – Structural Equation Modeling: A Multidisciplinary Journal, 2024
The latent growth model (LGM) is a popular tool in the social and behavioral sciences to study development processes of continuous and discrete outcome variables. A special case are frequency measurements of behaviors or events, such as doctor visits per month or crimes committed per year. Probability distributions for such outcomes include the…
Descriptors: Growth Models, Statistical Analysis, Structural Equation Models, Crime
Download full textChoi, Kilchan; Kim, Jinok – Journal of Educational and Behavioral Statistics, 2019
This article proposes a latent variable regression four-level hierarchical model (LVR-HM4) that uses a fully Bayesian approach. Using multisite multiple-cohort longitudinal data, for example, annual assessment scores over grades for students who are nested within cohorts within schools, the LVR-HM4 attempts to simultaneously model two types of…
Descriptors: Regression (Statistics), Hierarchical Linear Modeling, Longitudinal Studies, Cohort Analysis
Monroe, Scott; Cai, Li – Educational Measurement: Issues and Practice, 2015
Student growth percentiles (SGPs, Betebenner, 2009) are used to locate a student's current score in a conditional distribution based on the student's past scores. Currently, following Betebenner (2009), quantile regression (QR) is most often used operationally to estimate the SGPs. Alternatively, multidimensional item response theory (MIRT) may…
Descriptors: Item Response Theory, Reliability, Growth Models, Computation
