![]() For example, the variance of a latent variable is used for evaluating development across time and for gaining insight about differences between groups. Even though it is commonly ignored, the variance of a latent variable has been recognized as a useful source of information for some specific areas, in particular, longitudinal research and invariance analyses ( McArdle and Cattell, 1994 Schmitt and Kuljanin, 2008 McArdle, 2009). Such dependency does not endorse the variance of the latent variable as a reliable source of information.ĭespite the dependency on indicator selection, factor variance can be an important piece of information for evaluation. It is well-known that modifying scaling by replacing one indicator by another one changes the value of the variance among other consequences (e.g., Gonzalez and Griffin, 2001 Steiger, 2002). A reason for ignoring the variance as a source of information is its dependency on the indicator selected for scaling in order to achieve model identification. While also a parameter of the model, under factor analysis, the variance of the latent variable is largely ignored as a source of information for evaluation. In evaluating the results of factor analysis, the focus is usually on the factor loadings as related to the magnitude and the direction of the relationship to the latent variable. It enables the adaptation of scaling methods to the requirements of the field of application. The impact of the number of manifest variables on the scaled variance parameter can be modified and the range of possible values. ![]() Furthermore, it is shown that available scaling methods are in line with this constancy framework and that the criterion number included in some scaling methods enables modifications. It provides the basis for a scaling method that enables the comparison of the contribution of different latent variables of the same confirmatory factor model to observed scores, as for example, the contributions of trait and method latent variables. A constancy framework, based upon the underlying factor analysis formula that enables scaling by modifying components through scalar multiplication, is described a proof is included to demonstrate the constancy property of the framework. This paper investigates how the major outcome of a confirmatory factor investigation is preserved when scaling the variance of a latent variable by the various scaling methods. 3Department of Educational Studies, University of South Carolina, Columbia, SC, United States. ![]() 2Department of Psychology, University of Bern, Bern, Switzerland.1Institute of Psychology, Goethe University Frankfurt, Frankfurt, Germany. ![]()
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