Document Type : Research Manuscript
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Corresponding Author
Abstract
Abstract
In this article, unlike recently developed methods that can only distinguish associations between pairs of variables, Bayesian Multilevel Latent Class (BMLC) model for the multiple imputation of nested categorical data is considered which is flexible enough to automatically deal with complex interactions in the joint distribution of the variables to be estimated. After presenting the model, I run the model on a real data set, that is, mathematics test of TIMSS 2015 which has missing data in itself, and at the end the completed data for both levels (Level 1, students and Level 2, schools) are obtained. To assess the performance of the BMLC model, we compare it with list wise deletion (LD) with the help of R software. Conclusion: Results show that the BMLC model has reliability and is able to cover the Bayesian estimator with lower risk in this research.
Keywords: test of TIMSS, Bayesian analysis, missing data, categorical data, Bayesian Multilevel Latent Class model.
Keywords
- Keywords: test of TIMSS
- Bayesian analysis
- missing data
- categorical data
- Bayesian Multilevel Latent Class model
Main Subjects
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