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Exploratory Factor Analysis as a Construct Validation Tool: (Mis)applications in Applied Linguistics Research
Author(s) -
Karami Hossein
Publication year - 2015
Publication title -
tesol journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.468
H-Index - 14
eISSN - 1949-3533
pISSN - 1056-7941
DOI - 10.1002/tesj.176
Subject(s) - exploratory factor analysis , mainstream , principal component analysis , construct (python library) , psychology , factor (programming language) , computer science , selection (genetic algorithm) , linguistics , statistical analysis , software , exploratory analysis , natural language processing , cognitive psychology , psychometrics , data science , artificial intelligence , statistics , mathematics , political science , clinical psychology , philosophy , programming language , law
Factor analysis has been frequently exploited in applied research to provide evidence about the underlying factors in various measurement instruments. A close inspection of a large number of studies published in leading applied linguistic journals shows that there is a misconception among applied linguists as to the relative merits of exploratory factor analysis and principal components analysis ( PCA ) and the kind of interpretations that can be drawn from each method. In addition, it is argued that the widespread application of orthogonal, rather than oblique, rotations and also the criteria used for factor selection are not in keeping with the findings in psychometrics. It is further argued that the current situation is partly due to the fact that PCA and orthogonal rotation are default options in mainstream statistical packages such as SPSS and the guidebooks on such software do not provide an explanation of the issues discussed in this article.