Open AccessApplication of Principal Component Analysis: Deriving Influencing Factors of Teenagers’ Recycling Practice
Author(s) -
Saslina Kamaruddin,
Alamah Misni,
P.A. Ahmad,
Rostam Yaman
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/385/1/012008
Subject(s) - principal component analysis , uncorrelated , set (abstract data type) , regression analysis , representation (politics) , statistical analysis , data set , statistics , statistical software , psychology , regression , principal (computer security) , factor analysis , mathematics , computer science , econometrics , political science , politics , law , programming language , operating system
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.