Publication Date
In 2025 | 0 |
Since 2024 | 1 |
Since 2021 (last 5 years) | 1 |
Since 2016 (last 10 years) | 1 |
Since 2006 (last 20 years) | 2 |
Descriptor
Elementary School Students | 2 |
Structural Equation Models | 2 |
Foreign Countries | 1 |
Grade 4 | 1 |
Grade 5 | 1 |
Grade 6 | 1 |
Hierarchical Linear Modeling | 1 |
Individual Development | 1 |
Intervals | 1 |
Longitudinal Studies | 1 |
Mathematics | 1 |
More ▼ |
Source
Structural Equation Modeling:… | 2 |
Author
Andrea Hasl | 1 |
Charles Driver | 1 |
Dolan, Conor V. | 1 |
Jak, Suzanne | 1 |
Julia Kretschmann | 1 |
Manuel Voelkle | 1 |
Martin Brunner | 1 |
Oort, Frans J. | 1 |
Publication Type
Journal Articles | 2 |
Reports - Research | 2 |
Education Level
Elementary Education | 2 |
Grade 4 | 2 |
Grade 5 | 2 |
Grade 6 | 2 |
Intermediate Grades | 2 |
Middle Schools | 1 |
Secondary Education | 1 |
Audience
Location
Germany | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Andrea Hasl; Manuel Voelkle; Charles Driver; Julia Kretschmann; Martin Brunner – Structural Equation Modeling: A Multidisciplinary Journal, 2024
To examine developmental processes, intervention effects, or both, longitudinal studies often aim to include measurement intervals that are equally spaced for all participants. In reality, however, this goal is hardly ever met. Although different approaches have been proposed to deal with this issue, few studies have investigated the potential…
Descriptors: Foreign Countries, Elementary School Students, Secondary School Students, Student Promotion
Jak, Suzanne; Oort, Frans J.; Dolan, Conor V. – Structural Equation Modeling: A Multidisciplinary Journal, 2013
We present a test for cluster bias, which can be used to detect violations of measurement invariance across clusters in 2-level data. We show how measurement invariance assumptions across clusters imply measurement invariance across levels in a 2-level factor model. Cluster bias is investigated by testing whether the within-level factor loadings…
Descriptors: Statistical Bias, Measurement, Structural Equation Models, Hierarchical Linear Modeling