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Parallel analysis- accuracy in factor retention

Citation

Jaikaran-Doe, S, Parallel analysis- accuracy in factor retention, Proceedings of the IFIP TC3 Working Conference 'A New Culture of Learning: Computing and next Generations', 01-03 July 2015, Vilnius University, Lithuania, pp. 418-421. (2015) [Refereed Conference Paper]


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Official URL: http://www.ifip2015.mii.vu.lt/proceedings#.V8z-faK...

Abstract

Parallel analysis (Horn, 1965) is the most accurate method to confirm the number of factors/ components to retain in instruments such as surveys, however, the method is infrequently used. This paper describes the process of utilising parallel analysis with Monte Carlo simulation techniques (Watkins, 2000) as the final process to correctly establish factors after the following is completed: Principal Component Analysis (PCA); Varimax with Kaiser Normalization; an examination of the eigenvalues greater than 1; and Catellís screeplot . A simple survey instrument which investigates teachersí confidence to use ICT devices for their teaching and learning demonstrates how parallel analysis was implemented to generate eigenvalues from randomly generated correlation matrices. These were compared with the eigenvalues extracted from the researcherís dataset. The number of factors retained was the number of eigenvalues larger than the corresponding generated random eigenvalues.

Item Details

Item Type:Refereed Conference Paper
Keywords:Parallel Analysis, principal component analysis, factor retention, Monte Carlo, eigenvalues
Research Division:Mathematical Sciences
Research Group:Statistics
Research Field:Statistical Theory
Objective Division:Education and Training
Objective Group:Teaching and Instruction
Objective Field:Teacher and Instructor Development
Author:Jaikaran-Doe, S (Dr Seeta Jaikaran-Doe)
ID Code:110949
Year Published:2015
Deposited By:Office of the School of Engineering
Deposited On:2016-08-23
Last Modified:2017-10-05
Downloads:0

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