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ANALYSIS OF COVARIANCE STRUCTURES AND EXPLORATORY FACTOR ANALYSIS
Author(s) -
Mukherjee Bishwa Nath
Publication year - 1973
Publication title -
british journal of mathematical and statistical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.157
H-Index - 51
eISSN - 2044-8317
pISSN - 0007-1102
DOI - 10.1111/j.2044-8317.1973.tb00511.x
Subject(s) - covariance , exploratory factor analysis , exploratory analysis , axiom , interpretation (philosophy) , factor (programming language) , exploratory data analysis , econometrics , computer science , factor analysis , analysis of covariance , mathematics , statistics , structural equation modeling , data science , programming language , geometry
After discussing the similarities and differences between covariance structural analysis and exploratory factor analysis, the relative superiority of the former is shown both for the generation, as well as for the testing of the hypothesis. Covariance structural analysis provides an objective axiomatic approach for the testing of structural hypotheses because, unlike exploratory factor analysis, it presupposes an explicit prior formulation of the hypothetical partitioning of data into factors. The two techniques are also compared in terms of scientific parsimony and the interpretation of results. It appears that though covariance structural analysis is more promising than exploratory factor analysis in theory building, a systematic joint use of both may lead to a more meaningful interpretation of intercorrelation data than can be reached by factor analysis alone.