Factors Underlying Religious Orientation Scale - a Methodological Approach -

Authors : Ebrahim KHODADADY, Ehsan GOLPARVAR

Pages : 215-235

Doi:10.12730/13091719.2011.22.38

View : 46 | Download : 10

Publication Date : 2012-04-11

Article Type : Research Paper

Abstract :This study translated the 21-item Religious Orientation Scale insert ignore into journalissuearticles values(ROS); into Persian and explored its factorial validity in Iran by administering it to 329 undergraduate university students and employing three methods of factor extraction, i.e., Maximum Likelihood insert ignore into journalissuearticles values(ML);, Principal Axis Factoring insert ignore into journalissuearticles values(PAF); and Principal Component Analysis insert ignore into journalissuearticles values(PCA);. Among the three methods, ML seems to be favored in the literature recently because it forms the basis of the structural equation modeling insert ignore into journalissuearticles values(SEM); upon which studies such as Brew-czynski and MacDonald’s insert ignore into journalissuearticles values(2006); are developed. The ML, PAF and PCA all extracted four latent variables insert ignore into journalissuearticles values(LVs); when they were applied to the partici-pants’ responses and the LVs were rotated via Varimax with Kaiser Nor-malization. When the highest loading of a cross loading item was kept and its loadings on other LVs were removed, it was found that the three meth-ods had the same items loading on factors three and four. The one-way ANOVA analysis of the mean of loadings and post hoc tests, however, showed that the PCA differed significantly from the ML and PAF. It was al-so found that the first factor extracted by the PAF is the same as the sec-ond factor of the ML and vice versa. Based on the items loading on the first two factors it is suggested that the PAF be adopted as the best method of factor extraction in both exploratory and confirmatory studies.
Keywords : Latent variables, maximum likelihood, principal axis factoring, principal component analysis, SEM, Latent variables, maximum likelihood, principal axis factoring, principal component analysis, SEM

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