Evaluating measurement models in clinical research: covariance structure analysis of latent variable models of self-conception.

Published

Journal Article

Indirect measures of psychological constructs are vital to clinical research. On occasion, however, the meaning of indirect measures of psychological constructs is obfuscated by statistical procedures that do not account for the complex relations between items and latent variables and among latent variables. Covariance structure analysis (CSA) is a statistical procedure for testing hypotheses about the relations among items that indirectly measure a psychological construct and relations among psychological constructs. This article introduces clinical researchers to the strengths and limitations of CSA as a statistical procedure for conceiving and testing structural hypotheses that are not tested adequately with other statistical procedures. The article is organized around two empirical examples that illustrate the use of CSA for evaluating measurement models with correlated error terms, higher-order factors, and measured and latent variables.

Full Text

Duke Authors

Cited Authors

  • Hoyle, RH

Published Date

  • February 1991

Published In

Volume / Issue

  • 59 / 1

Start / End Page

  • 67 - 76

PubMed ID

  • 2002144

Pubmed Central ID

  • 2002144

Electronic International Standard Serial Number (EISSN)

  • 1939-2117

International Standard Serial Number (ISSN)

  • 0022-006X

Digital Object Identifier (DOI)

  • 10.1037//0022-006x.59.1.67

Language

  • eng