Application of Principal Component Analysis: Deriving Influencing Factors of Teenagers' Recycling Practice

• Main Authors: Ahmad, P. (Author), Hashim N. (Author), Kamaruddin, S.M (Author), Misni, A. (Author), Salleh S.A (Author), Yaman R. (Author), Yaman, R. (Author), Yusuwan N. (Author)
• Format: Article
• Language: English
• Published: Institute of Physics Publishing 2019
• Subjects: Correlated variables; Data representations; Factor analysis; Mathematical transformations; Principal component analysis; Recycling; Recycling programs; Regression analysis; Social influence; Statistical software; Underlying factors
• Online Access: View Fulltext in Publisher; View in Scopus
• ISBN: 17551307 (ISSN)
• DOI: 10.1088/1755-1315/385/1/012008

This study aims to derive the significant factors which influence adolescents' participation in recycling in schools through Principal Component Analysis. This method transforms statistically a set of observations of correlated variables into a set of values of linearly uncorrelated variables for further analysis. It is a classical feature extraction and data representation technique. Using a questionnaire, data were collected from 328 willing participants and Principal Component Analysis (PCA) was applied to group the initial variables into smaller, interpretable underlying factors through the use of statistical software. The factors derived were then analysed using regression analysis. The results indicated that social influence in schools involvement with teachers and other schools in recycling programs and having environmenta knowledge have the greatest influencing factors towards teenagers' involvement in recycling programs in schools. © Published under licence by IOP Publishing Ltd.