
The subject of factor indeterminacy has a vast history in factor analysis (Guttman, 1955; Lederman, 1938; Wilson, 1928). It has lead to strong differences in opinion (Steiger, 1979). The current paper gives necessary and sufficient conditions for observability of factors in terms of the parameter matrices and a finite number of variables. Five conditions are given which rigorously define indeterminacy. It is shown that (un)observable factors are (in)determinate. Specifically, the indeterminacy proof by Guttman is extended to Heywood cases. The results are illustrated by two examples and implications for indeterminacy are discussed.
IMPROPER SOLUTIONS, COMPONENTS, Heywood cases, Factor analysis and principal components; correspondence analysis, indeterminacy, Indeterminacy, factor score prediction, FACTOR INDETERMINACY, DEFINITION, mean squared error, Wiskunde en Informatica, Applications of statistics to psychology
IMPROPER SOLUTIONS, COMPONENTS, Heywood cases, Factor analysis and principal components; correspondence analysis, indeterminacy, Indeterminacy, factor score prediction, FACTOR INDETERMINACY, DEFINITION, mean squared error, Wiskunde en Informatica, Applications of statistics to psychology
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