Binary logistic regression modelling with appropriate sample size in determining graduate employability factors for public universities in Malaysia

The performance of variable selection is essential to build an effective logistic regression model. Generally, p-values are used to identify significant variables or factors in the model. However, when dealing with real tracer study data for a country, the size of the data is typically large of whic...

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Main Author:	Tengku Mohamed, Tengku Salbiah
Format:	Thesis
Language:	English
Published:	2020
Subjects:	QA Mathematics
Online Access:	http://eprints.utm.my/id/eprint/101869/1/TengkuSalbiahTengkuMohamedMFS2020.pdf
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id	my-utm-ep.101869
record_format	uketd_dc
spelling	my-utm-ep.1018692023-07-17T02:36:11Z Binary logistic regression modelling with appropriate sample size in determining graduate employability factors for public universities in Malaysia 2020 Tengku Mohamed, Tengku Salbiah QA Mathematics The performance of variable selection is essential to build an effective logistic regression model. Generally, p-values are used to identify significant variables or factors in the model. However, when dealing with real tracer study data for a country, the size of the data is typically large of which causes the p-values to be deflated and affect the variable selection performance. Therefore, it is crucial to have an appropriate sample size and sampling ratio for this purpose. In this study, the appropriate sample size has been proposed based on simulated correlation tests and significant variables in order to improve the accuracy of variable selection. In addition, the sampling ratio in the response variable shows its best when it reflects the population ratio. Based on the proposed samples, the logistic regression model for graduate employability factor is subsequently proposed. It has been found that age, Cumulative Grade Point Average (CGPA), discipline of study, gender, state, and type of universities are the factors that significantly affect graduate employability among public universities in Malaysia. The results show that the proposed model has successfully improved the variable selection, model fitting, and classification accuracy as compared to the full model. Thus, by using a smaller sample size, the proposed model is able to maintain its statistical power in real data scenario by accurately selecting the significant factors. 2020 Thesis http://eprints.utm.my/id/eprint/101869/ http://eprints.utm.my/id/eprint/101869/1/TengkuSalbiahTengkuMohamedMFS2020.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:146177 masters Universiti Teknologi Malaysia Faculty of Science
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Tengku Mohamed, Tengku Salbiah Binary logistic regression modelling with appropriate sample size in determining graduate employability factors for public universities in Malaysia
description	The performance of variable selection is essential to build an effective logistic regression model. Generally, p-values are used to identify significant variables or factors in the model. However, when dealing with real tracer study data for a country, the size of the data is typically large of which causes the p-values to be deflated and affect the variable selection performance. Therefore, it is crucial to have an appropriate sample size and sampling ratio for this purpose. In this study, the appropriate sample size has been proposed based on simulated correlation tests and significant variables in order to improve the accuracy of variable selection. In addition, the sampling ratio in the response variable shows its best when it reflects the population ratio. Based on the proposed samples, the logistic regression model for graduate employability factor is subsequently proposed. It has been found that age, Cumulative Grade Point Average (CGPA), discipline of study, gender, state, and type of universities are the factors that significantly affect graduate employability among public universities in Malaysia. The results show that the proposed model has successfully improved the variable selection, model fitting, and classification accuracy as compared to the full model. Thus, by using a smaller sample size, the proposed model is able to maintain its statistical power in real data scenario by accurately selecting the significant factors.
format	Thesis
qualification_level	Master's degree
author	Tengku Mohamed, Tengku Salbiah
author_facet	Tengku Mohamed, Tengku Salbiah
author_sort	Tengku Mohamed, Tengku Salbiah
title	Binary logistic regression modelling with appropriate sample size in determining graduate employability factors for public universities in Malaysia
title_short	Binary logistic regression modelling with appropriate sample size in determining graduate employability factors for public universities in Malaysia
title_full	Binary logistic regression modelling with appropriate sample size in determining graduate employability factors for public universities in Malaysia
title_fullStr	Binary logistic regression modelling with appropriate sample size in determining graduate employability factors for public universities in Malaysia
title_full_unstemmed	Binary logistic regression modelling with appropriate sample size in determining graduate employability factors for public universities in Malaysia
title_sort	binary logistic regression modelling with appropriate sample size in determining graduate employability factors for public universities in malaysia
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Science
publishDate	2020
url	http://eprints.utm.my/id/eprint/101869/1/TengkuSalbiahTengkuMohamedMFS2020.pdf
_version_	1776100791133667328

Binary logistic regression modelling with appropriate sample size in determining graduate employability factors for public universities in Malaysia

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