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Structural Equation Models

Author

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  • DAVID RINDSKOPF

    (City University of New York)

Abstract

Heywood cases represent the most common form of a series of related problems in confirmatory factor analysis and structural equation modeling. Other problems include factor loadings and factor correlations outside the usual range, large variances of parameter estimates, and high correlations between parameter estimates. The concept of empirical underidentification is used here to show how these problems can arise, and under what conditions they can be controlled. The discussion is centered around examples showing how small factor loadings, factor correlations near zero, and factor correlations near one can lead to empirical underidentification.

Suggested Citation

  • David Rindskopf, 1984. "Structural Equation Models," Sociological Methods & Research, , vol. 13(1), pages 109-119, August.
    • Handle: RePEc:sae:somere:v:13:y:1984:i:1:p:109-119
    DOI: 10.1177/0049124184013001004
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    References listed on IDEAS

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    1. David Rindskopf, 1983. "Parameterizing inequality constraints on unique variances in linear structural models," Psychometrika, Springer;The Psychometric Society, vol. 48(1), pages 73-83, March.
    2. Otto Driel, 1978. "On various causes of improper solutions in maximum likelihood factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 43(2), pages 225-243, June.
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    Cited by:

    1. Klaus, Bettina, 2008. "The coordinate-wise core for multiple-type housing markets is second-best incentive compatible," Journal of Mathematical Economics, Elsevier, vol. 44(9-10), pages 919-924, September.

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