Publication Date
In 2025 | 0 |
Since 2024 | 0 |
Since 2021 (last 5 years) | 1 |
Since 2016 (last 10 years) | 3 |
Since 2006 (last 20 years) | 4 |
Descriptor
Publication Type
Reports - Descriptive | 4 |
Journal Articles | 3 |
Education Level
Elementary Education | 3 |
Grade 5 | 2 |
Intermediate Grades | 2 |
Early Childhood Education | 1 |
Middle Schools | 1 |
Primary Education | 1 |
Audience
Location
Italy | 1 |
United Kingdom (England) | 1 |
Laws, Policies, & Programs
Assessments and Surveys
Early Childhood Longitudinal… | 1 |
What Works Clearinghouse Rating
Craig K. Enders – Grantee Submission, 2023
The year 2022 is the 20th anniversary of Joseph Schafer and John Graham's paper titled "Missing data: Our view of the state of the art," currently the most highly cited paper in the history of "Psychological Methods." Much has changed since 2002, as missing data methodologies have continually evolved and improved; the range of…
Descriptors: Data, Research, Theories, Regression (Statistics)
Leckie, George – Journal of Educational and Behavioral Statistics, 2018
The traditional approach to estimating the consistency of school effects across subject areas and the stability of school effects across time is to fit separate value-added multilevel models to each subject or cohort and to correlate the resulting empirical Bayes predictions. We show that this gives biased correlations and these biases cannot be…
Descriptors: Value Added Models, Reliability, Statistical Bias, Computation
Sulis, Isabella; Toland, Michael D. – Journal of Early Adolescence, 2017
Item response theory (IRT) models are the main psychometric approach for the development, evaluation, and refinement of multi-item instruments and scaling of latent traits, whereas multilevel models are the primary statistical method when considering the dependence between person responses when primary units (e.g., students) are nested within…
Descriptors: Hierarchical Linear Modeling, Item Response Theory, Psychometrics, Evaluation Methods
Peugh, James L. – Journal of Early Adolescence, 2014
Applied early adolescent researchers often sample students (Level 1) from within classrooms (Level 2) that are nested within schools (Level 3), resulting in data that requires multilevel modeling analysis to avoid Type 1 errors. Although several articles have been published to assist researchers with analyzing sample data nested at two levels, few…
Descriptors: Early Adolescents, Research, Hierarchical Linear Modeling, Data Analysis