Open AccessVerification of the Unidimensionality of Academic Delay of Gratification Scale in the Indian Context
Author(s) -
Rajib Chakraborty,
Vijay Kumar Chechi
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.e1188.0585c19
Subject(s) - confirmatory factor analysis , psychology , goodness of fit , context (archaeology) , exploratory factor analysis , scale (ratio) , variance (accounting) , construct validity , principal component analysis , statistics , gratification , construct (python library) , sample (material) , mathematics education , social psychology , mathematics , structural equation modeling , developmental psychology , psychometrics , computer science , geography , chemistry , cartography , archaeology , accounting , chromatography , business , programming language
The present study verifies the three models on the dimensionality of the construct academic delay of gratification measured with the academic delay of gratification scale prepared by Bembenutty and Karabenick (1996). Sample of the study comprises of 488 professional courses undergraduate students of Muslim minority community (277 boys and 211 girls) from law, engineering, education and pharmacy faculties of Sultan Ul Uloom Education Society, Banjara Hills, Hyderabad, Telangana, India. Exploratory factor analysis was conducted on the responses of the 10 items provided by the sample using SPSS Statistics Ver.23 to extract the factors of the construct. Confirmatory factor analysis conducted using SPSS Amos Ver.23 provided the goodness of fit measures for each of the models. The unidimensional model produced excellent fit indices. Also, one factor model satisfied Gorsuch (1983) criterion to further verify the unidimensional nature of the construct, where the percentage of variance explained by factor 1 was nearly thrice when compared by the percentage of variance explained by the next subsequent factor 2. Monte Carlo principal component analysis method also revealed single factor for this variable. Implications of the findings are discussed.