A bifactor approach to subscore assessment

Bifactor confirmatory factor analysis models and statistical indices computed from them have previously been used to provide evidence for the appropriateness of utilizing a unidimensional interpretation of multidimensional data. However, the ability of bifactor indices to aid in the assessment of subscore strength has not been investigated. A simulation study was conducted to relate bifactor indices to the strength of subscores corresponding to specific factors. The bifactor indices OmegaHS and ECV_SS were found to be strongly predictive of subscore strength conditional upon OmegaS. The number of factors was also found to play a minor role in this relationship. Cutoffs for assessing the appropriateness of interpreting subscores were constructed based on OmegaHS or ECV_SS conditional upon OmegaS and the number of factors. For low subscore reliability (OmegaS = .60), OmegaHS = .25 or ECV_SS = .45 is sufficient that the subscore has a good chance of having added value (VAR > 1.1) above and beyond the total score. For moderate reliability (OmegaS = .80), OmegaHS = .20 or ECV_SS = .30 is sufficient, and the role of OmegaHS or ECVSS diminishes as OmegaS increases further. A subscore having added value does not necessitate its interpretation. Instead, when subscores are desired to be interpreted, high OmegaHS or ECV_SS can be considered as evidence that such an interpretation is statistically appropriate. Finally, we illustrate the use of these cutoffs with an empirical data set. When combined with prior bifactor research, this work extends a framework of using confirmatory bifactor models for dimensionality assessment. (PsycInfo Database Record (c) 2023 APA, all rights reserved).