
It has been shown by Kaiser that the sum of coefficients alpha of a set of principal components does not change when the components are transformed by an orthogonal rotation. In this paper, Kaiser's result is generalized. First, the invariance property is shown to hold for any set of orthogonal components. Next, a similar invariance property is derived for the reliability of any set of components. Both generalizations are established by considering simultaneously optimal weights for components with maximum alpha and with maximum reliability, respectively. A short-cut formula is offered to evaluate the coefficients alpha for orthogonally rotated principal components from rotation weights and eigenvalues of the correlation matrix. Finally, the greatest lower bound to reliability and a weighted version are discussed.
MAXIMAL-RELIABILITY, reliability, coefficient alpha, factors, greatest lower bound to reliability, TRACE FACTOR-ANALYSIS, rotation, LOWER BOUNDS, Applications of statistics to psychology, components
MAXIMAL-RELIABILITY, reliability, coefficient alpha, factors, greatest lower bound to reliability, TRACE FACTOR-ANALYSIS, rotation, LOWER BOUNDS, Applications of statistics to psychology, components
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