Bifactor and second-order factor models are two alternative approaches for representing general constructs comprised of several highly related domains. Bifactor and second-order models were compared using a quality of life data set (N = 403). The bifactor model identified three, rather than the hypothesized four, domain specific factors beyond the general factor. The bifactor model fit the data significantly better than the second-order model. The bifactor model allowed for easier interpretation of the relationship between the domain specific factors and external variables, over and above the general factor. Contrary to the literature, sufficient power existed to distinguish between bifactor and corresponding second-order models in one actual and one simulated example, given reasonable sample sizes. Advantages of bifactor models over second-order models are discussed.