Covariance Structure Analysis
Updated 2026-08-10
INTRODUCTION
English translation pending.
CORE DEFINITION
Covariance is the degree to which two variables vary together. Structural analysis uses a theoretical model to explain the observed covariance pattern, and model fit measures how closely the implied covariances match the observed ones. Rather than only asking whether variables correlate, it asks whether the correlation pattern matches the assumed causal structure. Covariance itself is the base concept.
SCAFFOLDING EFFECT
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- Model specification: state the path and measurement model implied by your theory. - Fit evaluation: compare the implied covariance matrix with the observed one using fit indices. - Competing models: test rival causal structures against each other rather than accepting the first fit.
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A theoretical latent-variable model generates an implied covariance matrix, which is compared with the sample covariance matrix. Goodness-of-fit statistics then indicate whether the theory holds, so the analysis goes beyond simple correlation and actually tests a causal structure.
MINIMUM ACTION
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Structural_equation_modelingverified
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