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Structural Modeling with Large Data Sets and Non-Normal Continuous Variables

  • Chapter
Development as Action in Context

Abstract

Although there are a number of different paradigms for the study of developmental trajectories of youth in various social contexts, the multivariate statistical approach that allows a test of structural hypotheses specified by the researcher represents a powerful approach to testing person/situation theories (Bentler, 1980). This approach intrinsically involves data from a large number of subjects, assessed on multiple occasions and on a substantial number of variables. The availability of data from large numbers of subjects allows for the use of newly developed methods of structural modeling that are distribution-free, or that make relatively mild assumptions in comparison with currently popular methods that rely on assumptions of multivariate normality. In many cases, however, the data base available to the researcher outstrips the analytic capability of the statistical methods, at least in practice if not in theory. More specifically, as the number of variables increases, the number of parameters in structural equation models with latent variables can quickly grow to several hundred or more, making it difficult to use statistical estimation involving iterative nonlinear optimization methods. Not only does an iterative process take geometrically longer to converge to an appropriate solution, but the expense of obtaining such a solution also quickly gets out of hand. Some feasible estimators have recently been proposed to solve this problem.

This report was prepared in part with the support of USPHS grants DA01070 and DA00017.

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Author information

Authors and Affiliations

  1. Department of Psychology, University of California, 1282a Franz Hall, Los Angeles, CA, 90024, USA

    P. M. Bentler

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  1. P. M. Bentler
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Editors and Affiliations

  1. Department of Psychology, Technical University of Berlin, Dovestrasse 1-5, D-1000, Berlin 10, West Germany

    Rainer K. Silbereisen  & Klaus Eyferth  & 

  2. Department of Psychology, University of Bonn, An der Schlosskirche 1, D-5300, Bonn, Germany

    Georg Rudinger

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© 1986 Springer-Verlag Berlin Heidelberg

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Bentler, P.M. (1986). Structural Modeling with Large Data Sets and Non-Normal Continuous Variables. In: Silbereisen, R.K., Eyferth, K., Rudinger, G. (eds) Development as Action in Context. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-02475-1_14

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  • DOI: https://doi.org/10.1007/978-3-662-02475-1_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-02477-5

  • Online ISBN: 978-3-662-02475-1

  • eBook Packages: Springer Book Archive

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