Multi-factor modeling in individual differences research: Some recommendations and suggestions

This paper offers some commentary and recommendations relevant the multi-factor modeling in individual differences research. Several similarities and distinctions between oblique factor modeling, higher-order modeling, Schmid–Leiman transformations, and nested factors modeling are discussed. An empirical illustration of the various multi-factor models is presented, based on 18 items derived from three Neuroticism facets within the NEO PI-R. Researchers are encouraged to always perform a Schmid–Leiman transformation to a higher-order model solution, as well as to consider the possibility that a nested factors model will yield superior model fit, in comparison to a higher-order model, as well as less ambiguous factor solutions.