Hostname: page-component-745bb68f8f-v2bm5 Total loading time: 0 Render date: 2025-01-07T11:01:21.570Z Has data issue: false hasContentIssue false
  • English
  • Français

More “Tricks of the Trade”: Reparameterizing LISREL Models using Negative Variances

Published online by Cambridge University Press:  01 January 2025

Donald Philip Green  and
Bradley L. Palmquist
Donald Philip Green*
Affiliation:
Yale University
Bradley L. Palmquist
Affiliation:
University of California, Berkeley
*
Requests for reprints should be sent to Donald Green, 124 Prospect Street, Department of Political Science, Yale University, New Haven, CT 06520-3532.
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper shows how LISREL may be used to estimate simplex models which impose constraints on the variances of endogenous variables. This technique allows us to estimate both the parameters and the standard errors of the correlated measurement error model proposed by Wiley and Wiley (1974).

Keywords

analysis of covariance structuresnonrandom measurement errorreliability
Type
Computational Psychometrics
Information
Psychometrika , Volume 56 , Issue 1 , March 1991 , pp. 137 - 145
Copyright
Copyright © 1991 The Psychometric Society

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

We would like to thank Jim Wiley for his many helpful comments and suggestions on an earlier draft. We are grateful also to an anonymous reviewer for supplying the EQS program presented in Figure 4.

References

Achen, Christopher H. (1983). Toward theories of data: The state of political methodology. In Finifter, Ada (Eds.), Political science: The state of the discipline (pp. 69–93). Washington, DC: American Political Science Association.Google Scholar
Bentler, P. M. (1985). Theory and implementation of EQS: A structural equations program, Los Angeles: BMDP Statistical Software.Google Scholar
Hayduk, Leslie A. (1987). Structural equation modeling with LISREL: Essentials and advances, Baltimore: Johns Hopkins University Press.Google Scholar
Heise, David R. (1969). Separating reliability and stability in test-retest correlation. American Sociological Review, 34, 93–101.CrossRefGoogle Scholar
Jöreskog, Karl, & Sörbom, Dag (1986). LISREL VI: Analysis of linear structural relationships by maximum likelihood, instrumental variables, and least squares methods, Mooresville, IN: Scientific Software.Google Scholar
Kenny, David A., & Judd, Charles M. (1984). Estimating the nonlinear and interactive effects of latent variables. Psychological Bulletin, 96, 201–210.CrossRefGoogle Scholar
Palmquist, Bradley L., & Green, Donald P. (1989, August). Assessing nonrandom measurement error using panel data and single-indicator models. Paper presented at the 1989 Annual Meeting of the American Political Science Association, Atlanta, Georgia.Google Scholar
Rindskopf, David (1983). Parameterizing inequality constraints on unique variances in linear structural equation models. Psychometrika, 49, 73–83.CrossRefGoogle Scholar
Rindskopf, David (1984). Using phantom and imaginary latent variables to parameterize constraints in linear structural models. Psychometrika, 49, 37–47.CrossRefGoogle Scholar
Wiley, David E., & Wiley, James A. (1970). The estimation of measurement error in panel data. American Sociological Review, 35, 112–117.CrossRefGoogle Scholar
Wiley, James A., & Wiley, Mary Glenn (1974). A note on correlated errors in repeated measurements. Sociological Methods and Research, 3, 172–188.CrossRefGoogle Scholar