Abstract
Analysis of covariance structures is the common term for a number of techniques for analyzing multivariate data in order to detect and assess latent (unobserved) sources of variation and covariation in the observed measurements. The techniques of covariance structure analysis are general and flexible in that they can handle many types of covariance structures useful especially in the behavioral and social sciences. Although these techniques can be used for exploratory analysis, they have been most successfully applied to confirmatory analysis where the type of covariance structure is specified in advance. A covariance structure of a specified kind may arise because of a specified substantive theory or hypothesis, a given classificatory design for the measures, known experimental conditions, or because of results from previous studies based on extensive data. Sometimes the observed variables are ordered through time, as in longitudinal studies, or according to linear or circular patterns, as in Guttman’s (1954) simplex and circumplex models, or according to a given causal scheme, as in path analysis.
The contents of this chapter were first presented in lectures held at the Eighth Nordic Conference on Mathematical Statistics, held in Mariehamn, Finland, in May 1980. The chapter in its present form is reproduced, with modifications, from the Scandinavian Journal of Statistics, 1981, 8, 65–92.
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Jöreskog, K.G. (1988). Analysis of Covariance Structures. In: Nesselroade, J.R., Cattell, R.B. (eds) Handbook of Multivariate Experimental Psychology. Perspectives on Individual Differences. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0893-5_5
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DOI: https://doi.org/10.1007/978-1-4613-0893-5_5
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