Breadth of data dispersion and number of variables as sources of measurement effects on factor variances in confirmatory factor analysis.

Schweizer, Karl; Troche, Stefan; Wang, Tengfei (2023). Breadth of data dispersion and number of variables as sources of measurement effects on factor variances in confirmatory factor analysis. Psychological test and assessment modeling, 65(2), pp. 294-311. Pabst Science Publishers

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The present study investigates how simple measurement effects influence factor variances in CFA. The considered measurement effects refer to a) deviations from the expected dispersion of data and b) the number of manifest variables (e.g., items of a scale) loading on a factor while the underlying data structure is kept constant. In this investigation, the factor variance is conceptualized as the scaled variance parameter of the model-implied covariance matrix of the Maximum Likelihood approach. The results of model analyses and a simulation study revealed that the modification of the breadth of data dispersion and the number of manifest variables systematically influenced scaled factor variances despite the constancy of the latent structure. Furthermore, the results revealed that the effects on the factor variance could be eliminated by either estimating factor variances using standardized data or by standardizing estimated factor loadings before their conversion into factor variances.

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