Estimating longitudinal change in latent variable means: a comparison of non-negative matrix factorization and other item non-response methods
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Publication:5887971
DOI10.1080/00949655.2022.2098499OpenAlexW4285496188WikidataQ114101192 ScholiaQ114101192MaRDI QIDQ5887971
Mohammad Jafari Jozani, Unnamed Author, Unnamed Author, Lisa M. Lix, Tolulope T. Sajobi
Publication date: 21 April 2023
Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/00949655.2022.2098499
Uses Software
Cites Work
- A two-tier full-information item factor analysis model with applications
- Measurement invariance, factor analysis and factorial invariance
- Handling missing data in item response theory. Assessing the accuracy of a multiple imputation procedure based on latent class analysis
- Nonnegative matrix factorization for spectral data analysis
- Multiple Imputation After 18+ Years
- Modeling the Drop-Out Mechanism in Repeated-Measures Studies
- Generating missing values for simulation purposes: a multivariate amputation procedure
- A Simulation Study Comparing Multiple Imputation Methods for Incomplete Longitudinal Ordinal Data
- Learning the parts of objects by non-negative matrix factorization
- Multiple imputation of discrete and continuous data by fully conditional specification
- Iterative Multiple Imputation: A Framework to Determine the Number of Imputed Datasets
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
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