Research of the effectiveness of methods for missing data imputation for assessing the impact of intellectual and personal components on the academic performance of students
The article explores the problem of missing data to determine the contribution of different components of intelligence and personality factors to student performance. The specificity of the available data is that missed data cannot be considered random. This leads to the instability of the results o...
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| Autori principali: | , |
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| Natura: | Статья |
| Lingua: | English |
| Pubblicazione: |
CEUR-WS
2021
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| Soggetti: | |
| Accesso online: | https://dspace.ncfu.ru/handle/20.500.12258/15838 |
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| Riassunto: | The article explores the problem of missing data to determine the contribution of different components of intelligence and personality factors to student performance. The specificity of the available data is that missed data cannot be considered random. This leads to the instability of the results of filling in the gaps using multiple imputation. Therefore, the task of comparing their performance and choosing the best method for a particular set of data arises. It is suggested to use average dispersion of regression parameter estimates and average statistics reflecting the significance of regression parameters as indicators of method effectiveness. Two methods of multiple imputation for source data the missForest and Amelia were investigated, as well as after selecting the principal components and applying a special procedure of forming limited sets of principal components. The missForest method using the principal components shows the best results and allows identifying informative personal and intellectual predictors of students' academic performance: the level of general, emotional and social intelligence and indicators of introversion and social conformality |
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