Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics
Open Access
- 1 August 1981
- journal article
- other
- Published by SAGE Publications in Journal of Marketing Research
- Vol. 18 (3) , 382-388
- https://doi.org/10.1177/002224378101800313
Abstract
Several issues relating to goodness of fit in structural equations are examined. The convergence and differentiation criteria, as applied by Bagozzi, are shown not to stand up under mathematical or statistical analysis. The authors argue that the choice of interpretative statistic must be based on the research objective. They demonstrate that when this is done the Fornell-Larcker testing system is internally consistent and that it conforms to the rules of correspondence for relating data to abstract variables.Keywords
This publication has 19 references indexed in Scilit:
- Maximum likelihood in small samples: Estimation in the presence of nuisance parametersBiometrika, 1980
- Interpreting the Likelihood Ratio Statistic in Factor Models When Sample Size is SmallJournal of the American Statistical Association, 1980
- REGRESSION COMPONENT ANALYSISBritish Journal of Mathematical and Statistical Psychology, 1976
- Employing Nominal Variables, Induced Variables, and Block Variables in Path AnalysesSociological Methods & Research, 1972
- The Treatment of Unobservable Variables in Path AnalysisSociological Methodology, 1971
- A general method for analysis of covariance structuresBiometrika, 1970
- On the Estimation of Path Coefficients for Unmeasured Variables from Correlations among Observed VariablesSocial Forces, 1970
- On the Estimation of Path Coefficients for Unmeasured Variables from Correlations Among Observed VariablesSocial Forces, 1970
- Theory, Deduction, and Rules of CorrespondenceAmerican Journal of Sociology, 1969
- Multiple Indicators and the Causal Approach to Measurement ErrorAmerican Journal of Sociology, 1969