[Linear factor analytic models for reliability analysis of composite variables].

S Vautier; J-P Gaudron; S Jmel

doi:10.1016/s0398-7620(04)99080-3

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[Linear factor analytic models for reliability analysis of composite variables].

S Vautier·J-P Gaudron·S Jmel

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Abstract

BACKGROUND: Confirmatory factor analysis allows testing whether a composite variable may be considered as a reliable measure of a psychological attribute which is defined within a population. METHODS: Models for parallel, tau equivalent and congeneric measurements are presented along with their reliability coefficients. RESULTS: When the variables are not tau equivalent, the averaged inter-item correlation should be preferred to coefficient alpha, which is not an estimator of the reliability of the corresponding data. As a rule, interpretation of a coefficient as a reliability coefficient requires that the corresponding structural model of the composite be known. Beyond unidimensionality, simultaneous analysis of several congeneric variables through the use of cross-sectional or longitudinal hierarchical models entails fragmenting the theoretical variables. CONCLUSION: Interpreting a composite variable whose theoretical structure is corroborated by a hierarchical model may raise some difficulties because of its multidimensionality. Reliability formulae which account for this fragmentation are detailed.

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Factor Analysis, Statistical Models, Statistical Reproducibility of Results English Abstract Journal Article

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S Vautier, J-P Gaudron, S Jmel. 2004. [Linear factor analytic models for reliability analysis of composite variables].. https://doi.org/10.1016/s0398-7620(04)99080-3

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