<p>In self-determination theory (SDT), motivation comprises distinct motivation types organized along a self-determination continuum. Consequently, motivation type measures arguably reflect global (self-determination) and type-specific sources of variance. Until recently, these sources of variance have been difficult to separate. Bifactor modeling methods have recently been introduced as a means of disaggregating the global and type-specific variance. In this research, we demonstrate the added advantages of “augmenting” the bifactor S-1 model for this purpose. In this variation, a direct measure of experienced self-determination is included as an anchor for the global factor and allows for extraction of specific factors reflecting unique variance in all type measures. Using data from employed adults (n = 444) and undergraduate students (n = 656), we found that an augmented model fit the data well and produced a loading pattern consistent with SDT predictions. Importantly, we also found that both the global and specific factors contributed uniquely to prediction of important outcomes (e.g., need satisfaction, commitment, well-being).</p>