Publication Date
In 2025 | 1 |
Since 2024 | 2 |
Since 2021 (last 5 years) | 2 |
Since 2016 (last 10 years) | 3 |
Since 2006 (last 20 years) | 3 |
Descriptor
Author
Alexander Robitzsch | 1 |
Jennifer Oser | 1 |
Johan Lyrvall | 1 |
Leckie, George | 1 |
Oliver Lüdtke | 1 |
Roberto Di Mari | 1 |
Zsuzsa Bakk | 1 |
Publication Type
Journal Articles | 3 |
Reports - Descriptive | 3 |
Education Level
Elementary Education | 1 |
Audience
Location
United Kingdom (England) | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Oliver Lüdtke; Alexander Robitzsch – Journal of Experimental Education, 2025
There is a longstanding debate on whether the analysis of covariance (ANCOVA) or the change score approach is more appropriate when analyzing non-experimental longitudinal data. In this article, we use a structural modeling perspective to clarify that the ANCOVA approach is based on the assumption that all relevant covariates are measured (i.e.,…
Descriptors: Statistical Analysis, Longitudinal Studies, Error of Measurement, Hierarchical Linear Modeling
Johan Lyrvall; Zsuzsa Bakk; Jennifer Oser; Roberto Di Mari – Structural Equation Modeling: A Multidisciplinary Journal, 2024
We present a bias-adjusted three-step estimation approach for multilevel latent class models (LC) with covariates. The proposed approach involves (1) fitting a single-level measurement model while ignoring the multilevel structure, (2) assigning units to latent classes, and (3) fitting the multilevel model with the covariates while controlling for…
Descriptors: Hierarchical Linear Modeling, Statistical Bias, Error of Measurement, Simulation
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