Translational Abstract Statistical indices within the bifactor confirmatory factor analytic framework have previously been developed to aid researchers about the acceptability of treating multidimensional data as essentially unidimensional for interpretation purposes. However, despite the utility of bifactor indices, it is unknown if bifactor indices can aid applied researchers wanting to know if subscores can be interpreted with confidence in the presence of a bifactor solution. In this experimental simulation study we evaluated the utility of bifactor indices and provide recommendations around when researchers may consider a subscore as having added value beyond a total score interpretation when such an interpretation is desired. Specifically, cutoffs were devised for a specific factor's bifactor indices OmegaHS and ECVSS, conditioned upon OmegaS and number of specific factors, such that exceeding these cutoffs indicates the subscore has added value over the total score. Second, we illustrate the use of these cutoffs with an empirical data set along with practical interpretations of the bifactor indices. Overall, the current research provides results that aid psychology, education and, more generally, social science researchers in making rigorous decisions about whether to interpret subscores for use in research and in practical settings. 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 ECVSS 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 ECVSS conditional upon OmegaS and the number of factors. For low subscore reliability (OmegaS = .60), OmegaHS = .25 or ECVSS = .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 ECVSS = .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 ECVSS 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.
