Much of cognitive aging research concerns whether age-associated differences in various cognitive performances can be accounted for by general explanatory constructs or whether several specific processes are involved. Structural equation models have been proposed to disentangle general and specific age-associated differences in cognitive performance. This article demonstrates that existing methods that employ stepwise procedures run the risk of biasing results toward general resource accounts. An alternative model representation (i.e., the nested factor model) is proposed that affords simultaneous estimation of general and specific effects and is applied to data from the Berlin Aging Study. Using the nested factor model allowed the authors to detect that specific group factors explained 25% of the age-associated variance in addition to the general factor.