Bandwidth and Fidelity on the NEO-Five Factor Inventory: Replicability and Reliability of Saucier's (1998) Item Cluster Subcomponents

Abstract

Many users of the NEO-Five Factor Inventory (NEO-FFI; Costa & McCrae, 1992) are unaware that Saucier (1998) developed item cluster subcomponents for each broad domain of the instrument similar to the facets of the Revised NEO Personality Inventory (Costa & McCrae, 1992). In this study, I examined the following: the replicability of the subcomponents in young adult university and middle-aged community samples; whether item keying accounted for additional covariance among items; subcomponent correlations with a measure of socially desirable responding; subcomponent reliabilities; and subcomponent discriminant validity with respect to age-relevant criterion items expected to reflect varying associations with broad and narrow traits. Confirmatory factor analyses revealed that all subcomponents were recoverable across samples and that the addition of method factors representing positive and negative item keying improved model fit. The subcomponents correlated no more with a measure of socially desirable responding than their parent domains and showed good average reliability. Correlations with criterion items suggested that subcomponents may prove useful in specifying which elements of NEO-FFI domains are more or less related to variables of interest. I discuss their use for enhancing the precision of findings obtained with NEO-FFI domain scores.

ACKNOWLEDGMENTS

Preparation of this work was supported by National Institute of Mental Health Public Health Service Grant T32MH073452 to Jeffrey Lyness, University of Rochester Medical Center, Department of Psychiatry. I thank Lewis Goldberg and an anonymous reviewer for helpful comments on an earlier draft of the article. Additional thanks to Paul R. Duberstein who provided valuable input during the writing process, to Bert Hayslip Jr. who supervised the research on which the article is based, and to Gerard Saucier who offered impressions on the subcomponents that helped orient the article early on.

Notes

¹I would like to thank an anonymous reviewer for noting this important distinction.

² Jang, McCrae, Angleitner, Riemann, and Livesley (1998) also suggest that genetic bases underlie phenotypic variation at the level of specific, lower order traits as well as higher order trait factors.

³ Although Hu and Bentler (1999) provided more stringent guidelines for evaluating model fit (i.e., CFI > .95, RMSEA < .05), Marsh and colleagues' (2004) recent simulation results suggest that these cut points for model rejection may be less reliable than previously thought.

⁴Note that in baseline models of clusters of three items, the unconstrained model itself is just identified and always fits the data perfectly. In such cases, the constraints imposed by essential tau-equivalence overidentify the model and permit it to be tested. In these cases, the essential tau-equivalence of the item cluster was evaluated merely by the significance of the Satorra-Bentler chi square for this testable model. However, this approach may be less optimal than comparing the fit of an essentially tau-equivalent model nested within one in which loadings are permitted to vary.

⁵For instance, Watson, Clark, and Harkness (1994) characterized multistratum trait taxonomies as statistical variance-covariance hierarchies in which covariance among lower order dimensions constitute the variance of higher order factors.

⁶Note that even though internal consistency estimates may be high in multidimensional scales, the interpretation of true score variance is less clear because of factorial complexity (Cortina, 1993).