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Parameterizing inequality constraints on unique variances in linear structural models

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Abstract

Current computer programs for analyzing linear structural models will apparently handle only two types of constraints: fixed parameters, and equality of parameters. An important constraint not handled is inequality; this is particularly crucial for preventing negative variance estimates. In this paper, a method is described for imposing several kinds of inequality constraints in models, without the necessity for having computer programs which explicitly allow such constraints. The examples discussed include the prevention of Heywood cases, extension to inequalities of parameters to be greater than a specified value, and imposing ordered inequalities.

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Authors and Affiliations

  1. City University of New York, USA

    David Rindskopf

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  1. David Rindskopf
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Additional information

Work on this project was aided by the City University of New York—Professional Staff Congress Research Award Program Grant Number 13631.

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Rindskopf, D. Parameterizing inequality constraints on unique variances in linear structural models. Psychometrika 48, 73–83 (1983). https://doi.org/10.1007/BF02314677

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  • DOI: https://doi.org/10.1007/BF02314677

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