Understanding Composite-Based Structural Equation Modeling Methods From the Perspective of Regression Component Analysis.

Regression component analysis (RCA) replaces the factors in a factor analysis model with weighted composites of the model's observed variables. The weight matrix may be calculated from the factor model's parameter estimates. Thus, RCA parameter estimates can be obtained using factor model software, but RCA composites have determinate scores, rather than the indeterminate scores of factors.

Parceling Cannot Reduce Factor Indeterminacy in Factor Analysis: A Research Note.

Parceling-using composites of observed variables as indicators for a common factor-strengthens loadings, but reduces the number of indicators. Factor indeterminacy is reduced when there are many observed variables per factor, and when loadings and factor correlations are strong. It is proven that parceling cannot reduce factor indeterminacy.

Factor Indeterminacy as Metrological Uncertainty: Implications for Advancing Psychological Measurement.

Researchers have long been aware of the mathematics of factor indeterminacy. Yet, while occasionally discussed, the phenomenon is mostly ignored. In metrology, the measurement discipline of the physical sciences, uncertainty - distinct from both random error (but encompassing it) and systematic error - is a crucial characteristic of any measurement.