Premium
REGRESSION COMPONENT ANALYSIS
Author(s) -
Schönemann Peter H.,
Steiger James H.
Publication year - 1976
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.2044-8317.1976.tb00713.x
Subject(s) - component (thermodynamics) , tautology (logic) , mathematics , component analysis , regression analysis , class (philosophy) , falsifiability , regression , latent class model , factor (programming language) , range (aeronautics) , statistics , econometrics , computer science , artificial intelligence , autoepistemic logic , philosophy , physics , materials science , epistemology , multimodal logic , composite material , description logic , thermodynamics , programming language
Regression component decompositions (RCD) are defined as a special class of component decompositions where the pattern contains the regression weights for predicting the observed variables from the latent variables. Compared to factor analysis, RCD has a broader range of applicability, greater ease and simplicity of computation, and a more logical and straightforward theory. The usual distinction between factor analysis as a falsifiable model, and component analysis as a tautology, is shown to be misleading, since a special case of regression component decomposition can be defined which is not only falsifiable, but empirically indistinguishable from the factor model.