A Comparison of Bifactor and Second-Order Models of Quality of Life

TLDR

Bifactor and second-order factor models are two alternative approaches for representing general constructs comprised of several highly related domains. The study discusses the advantages of bifactor models over second-order models. The authors compared bifactor and second-order models using a quality of life dataset of 403 participants. The bifactor model fit the data significantly better, identified only three domain‑specific factors instead of four, facilitated clearer interpretation of domain–external variable relationships, and demonstrated that adequate power can distinguish it from second-order models with realistic sample sizes.

Abstract

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.