Construct validation in secondary data: A guideline for medical data mining

Construct validation is an important step in the formulation of theory and its testing. Any information infers from the construct and its related hypothesis, without the validation, might be misleading the decision. In the literature, the construct validation guidelines for primary data are availabl...

Full description

Bibliographic Details
Main Authors:	Mirza, Rizwan Sajid, Noryanti, Muhammad, Roslinazairimah, Zakaria
Format:	Conference or Workshop Item
Language:	English English
Published:	Universiti Malaysia Pahang 2019
Subjects:	HD28 Management. Industrial Management T Technology (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/26034/ http://umpir.ump.edu.my/id/eprint/26034/1/58.%20Construct%20validation%20in%20secondary%20data%20-%20a%20guideline.pdf http://umpir.ump.edu.my/id/eprint/26034/2/58.1%20Construct%20validation%20in%20secondary%20data%20-%20a%20guideline.pdf

id	ump-26034
recordtype	eprints
spelling	ump-260342019-11-29T07:59:09Z http://umpir.ump.edu.my/id/eprint/26034/ Construct validation in secondary data: A guideline for medical data mining Mirza, Rizwan Sajid Noryanti, Muhammad Roslinazairimah, Zakaria HD28 Management. Industrial Management T Technology (General) Construct validation is an important step in the formulation of theory and its testing. Any information infers from the construct and its related hypothesis, without the validation, might be misleading the decision. In the literature, the construct validation guidelines for primary data are available but the exponential usage of secondary data mining approaches in recent times has created its need for secondary data users. However, it is a less addressed domain, specifically for medical sciences due to underdeveloped theoretical foundations of the field. Usually, an assessment of the validity of the secondary data is not evaluated because primary users already underwent the process especially in the tool development phase. Further, secondary data users are more concerned with data mining that is a data-driven approach rather than based on theoretical knowledge of the field. Therefore, if researchers want to explore the hidden structures of data and try some different combinations of items which were not tested by primary users, then they should validate this newly explored group of items for the creation of knowledge of the particular field. In this paper, a guideline of the construct validation process for secondary data users with its practical issues especially validity and reliability coefficients is discussed. This paper concludes that the construct validation based on secondary data needs special attention of data mining experts and medical researchers while designing the studies to maximize the benefits of the secondary data. Universiti Malaysia Pahang 2019 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/26034/1/58.%20Construct%20validation%20in%20secondary%20data%20-%20a%20guideline.pdf pdf en http://umpir.ump.edu.my/id/eprint/26034/2/58.1%20Construct%20validation%20in%20secondary%20data%20-%20a%20guideline.pdf Mirza, Rizwan Sajid and Noryanti, Muhammad and Roslinazairimah, Zakaria (2019) Construct validation in secondary data: A guideline for medical data mining. In: 2nd International Conference on Applied & Industrial Mathematics and Statistics (ICOAIMS 2019), 23-25 Julai 2019 , Kuantan, Pahang. pp. 1-8.. (Unpublished)
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	HD28 Management. Industrial Management T Technology (General)
spellingShingle	HD28 Management. Industrial Management T Technology (General) Mirza, Rizwan Sajid Noryanti, Muhammad Roslinazairimah, Zakaria Construct validation in secondary data: A guideline for medical data mining
description	Construct validation is an important step in the formulation of theory and its testing. Any information infers from the construct and its related hypothesis, without the validation, might be misleading the decision. In the literature, the construct validation guidelines for primary data are available but the exponential usage of secondary data mining approaches in recent times has created its need for secondary data users. However, it is a less addressed domain, specifically for medical sciences due to underdeveloped theoretical foundations of the field. Usually, an assessment of the validity of the secondary data is not evaluated because primary users already underwent the process especially in the tool development phase. Further, secondary data users are more concerned with data mining that is a data-driven approach rather than based on theoretical knowledge of the field. Therefore, if researchers want to explore the hidden structures of data and try some different combinations of items which were not tested by primary users, then they should validate this newly explored group of items for the creation of knowledge of the particular field. In this paper, a guideline of the construct validation process for secondary data users with its practical issues especially validity and reliability coefficients is discussed. This paper concludes that the construct validation based on secondary data needs special attention of data mining experts and medical researchers while designing the studies to maximize the benefits of the secondary data.
format	Conference or Workshop Item
author	Mirza, Rizwan Sajid Noryanti, Muhammad Roslinazairimah, Zakaria
author_facet	Mirza, Rizwan Sajid Noryanti, Muhammad Roslinazairimah, Zakaria
author_sort	Mirza, Rizwan Sajid
title	Construct validation in secondary data: A guideline for medical data mining
title_short	Construct validation in secondary data: A guideline for medical data mining
title_full	Construct validation in secondary data: A guideline for medical data mining
title_fullStr	Construct validation in secondary data: A guideline for medical data mining
title_full_unstemmed	Construct validation in secondary data: A guideline for medical data mining
title_sort	construct validation in secondary data: a guideline for medical data mining
publisher	Universiti Malaysia Pahang
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/26034/ http://umpir.ump.edu.my/id/eprint/26034/1/58.%20Construct%20validation%20in%20secondary%20data%20-%20a%20guideline.pdf http://umpir.ump.edu.my/id/eprint/26034/2/58.1%20Construct%20validation%20in%20secondary%20data%20-%20a%20guideline.pdf
first_indexed	2023-09-18T22:40:17Z
last_indexed	2023-09-18T22:40:17Z
_version_	1777416869007851520

Construct validation in secondary data: A guideline for medical data mining

Similar Items