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...

Ful tanımlama

Kaydedildi:

Detaylı Bibliyografya
Asıl Yazarlar:	Andrusenko, Y. A., Андрусенко, Ю. А.
Materyal Türü:	Статья
Dil:	English
Baskı/Yayın Bilgisi:	CEUR-WS 2021
Konular:	Principal component analysis Psychometric testing Academic performance Intelligence Missing data imputation Personality Data mining
Online Erişim:	https://dspace.ncfu.ru/handle/20.500.12258/15838
Etiketler:	Etiketle Etiket eklenmemiş, İlk siz ekleyin!

id	ir-20.500.12258-15838
record_format	dspace
spelling	ir-20.500.12258-158382021-04-22T12:14:54Z 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 Andrusenko, Y. A. Андрусенко, Ю. А. Principal component analysis Psychometric testing Academic performance Intelligence Missing data imputation Personality Data mining 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 2021-04-22T12:13:23Z 2021-04-22T12:13:23Z 2021 Статья Timofeeva, A., Avdeenko, T., Razumnikova, O., Andrusenko, Y. 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 // CEUR Workshop Proceedings. - 2021. - Volume 2842. - Pages 119-127 http://hdl.handle.net/20.500.12258/15838 en CEUR Workshop Proceedings application/pdf CEUR-WS
institution	СКФУ
collection	Репозиторий
language	English
topic	Principal component analysis Psychometric testing Academic performance Intelligence Missing data imputation Personality Data mining
spellingShingle	Principal component analysis Psychometric testing Academic performance Intelligence Missing data imputation Personality Data mining Andrusenko, Y. A. Андрусенко, Ю. А. 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
description	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
format	Статья
author	Andrusenko, Y. A. Андрусенко, Ю. А.
author_facet	Andrusenko, Y. A. Андрусенко, Ю. А.
author_sort	Andrusenko, Y. A.
title	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
title_short	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
title_full	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
title_fullStr	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
title_full_unstemmed	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
title_sort	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
publisher	CEUR-WS
publishDate	2021
url	https://dspace.ncfu.ru/handle/20.500.12258/15838
work_keys_str_mv	AT andrusenkoya researchoftheeffectivenessofmethodsformissingdataimputationforassessingtheimpactofintellectualandpersonalcomponentsontheacademicperformanceofstudents AT andrusenkoûa researchoftheeffectivenessofmethodsformissingdataimputationforassessingtheimpactofintellectualandpersonalcomponentsontheacademicperformanceofstudents
_version_	1760600706323054592

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

Benzer Materyaller