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Factor Retention Decisions in Exploratory Factor Analysis Results: A Study Type of Knowledge Management Process at Malaysian University Libraries
Author(s) -
Che Rusuli M. S.,
Rosmaini Tasmin,
Josu Takala,
H. Norazlin
Publication year - 2013
Publication title -
asian social science
Language(s) - English
Resource type - Journals
eISSN - 1911-2025
pISSN - 1911-2017
DOI - 10.5539/ass.v9n15p227
Subject(s) - structural equation modeling , confirmatory factor analysis , exploratory factor analysis , popularity , construct (python library) , computer science , factor (programming language) , flexibility (engineering) , knowledge management , process (computing) , generality , psychology , statistics , mathematics , machine learning , social psychology , psychotherapist , programming language , operating system
Structural equation modeling (SEM) is a versatile statistical modeling tool which uses in the social sciencesresearch. Recently, in Library and Information Science (LIS) environment, structural equation modeling hasgained popularity across many disciplines, due to its generality and flexibility. Its estimation techniques,modeling capabilities and breadth of application are expanding rapidly. This paper reported a structural equationmodeling through an Exploratory Factor Analysis (EFA) result, which involves 300 lead users at six selectedMalaysian university libraries through survey. The decision of how many factors to retain is a critical componentof exploratory factor analysis. Evidence is presented that parallel analysis is one of the most accurate factorretention methods. SPSS 20 was utilized to analyze the factor analysis data. In this regards, the results of EFAcould provide empirical evidence of each hypotheses construct. It is hoped that the EFA results could be used tolevel Confirmatory Factor Analysis (CFA) to perform full Structural Equation Modeling

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