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Determination of Selection Gradients Using Multiple Regression versus Structural Equation Models (SEM)
Author(s) -
Pugesek Bruce H.,
Tomer Adrian
Publication year - 1995
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.4710370406
Subject(s) - statistics , latent variable , structural equation modeling , mathematics , variance (accounting) , regression analysis , selection (genetic algorithm) , regression , feature selection , model selection , variables , econometrics , computer science , artificial intelligence , accounting , business
Selection studies involving multiple intercorrelated independent variables have employed multiple regression analysis as a means to estimate and partition natural and sexual selection's direct and indirect effects. These statistical models assume that independent variables are measured without error. Most would conclude that such is not the case in the field studies for which these methods are employed. We demonstrate that the distortion of estimates resulting from error variance is not trivial. When independent variables are intercorrelated, extreme distortions may occur. We propose to use Structural Equation Models (SEM), to estimate error variance and produce highly accurate coefficients for formulation of selection gradients. This method is particularly appropriate when the selection is viewed as happening at the level of the latent variables.