A construction method is given for all factors that satisfy the assumptions of the model for factor analysis, including partially determined factors where certain error variances are zero. Various criteria for the seriousness of indeterminacy are related. It is shown that B. F. Green's (1976) conjecture holds: For a linear factor predictor the mean squared error of prediction is constant over all possible factors. A simple and general geometric interpretation of factor indeterminacy is given on the basis of the distance between multiple factors. It is illustrated that variable elimination can have a large effect on the seriousness of factor indeterminacy. A simulation study reveals that if the mean square error of factor prediction equals .5, then two thirds of the persons are "correctly" selected by the best linear factor predictor. (PsycINFO Database Record (c) 2009 APA, all rights reserved)
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We share insights from our practice-based experimentation with ‘feral’ ways of sensemaking in the context of creative transformational practices. Drawing on three art and design research projects, we discuss how feral ways–open-ended, spontaneous, welcoming indeterminacy – may foster more-than-human co-creation of knowledge and data, and nurture shifts from anthropocentric ‘making sense of’ to relational ‘making sense-with’ other-than-human creatures. Through our cases, we illustrate how experimenting with feralness can foreground issues of power, agency, and control in the currently human-centric discourses around data, technology, and sensemaking in eco-social transformation. Our insights may nurture critical more-than-human perspectives in creative eco-social inquiries.
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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.
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