A dominant general factor (DGF) is present when a single factor accounts for the majority of reliable variance across a set of measures (Ree, Carretta, & Teachout, 2015). In the presence of a DGF, dimension scores necessarily reflect a blend of both general and specific factors. For some constructs, specific factors contain little unique reliable variance after controlling for the general factor (Reise, 2012), whereas for others, specific factors contribute a more substantial proportion of variance (e.g., Kinicki, McKee-Ryan, Schriesheim, & Carson, 2002). We agree with Ree et al. that the presence of a DGF has implications for interpreting scores. However, we argue that the conflation of general and specific factor variances has the strongest implications for understanding how constructs relate to external variables. When dimension scales contain substantial general and specific factor variance, traditional methods of data analysis will produce ambiguous or even misleading results. In this commentary, we show how several common data analytic methods, when used with data sets containing a DGF, will substantively alter conclusions.

Ree, Carretta, and Teachout (2015) raise the need for further investigation into dominant general factors (DGFs) and their prevalence in measures used for the purposes of employee selection, development, and performance measurement. They imply that a method of choice for estimating the contribution of DGFs is principal components analysis (PCA), and they interpret the variance accounted for by the first component of the PCA solution as indicative of the contribution of a general factor. In this response, we illustrate the hazard of equating the first component of a PCA with a general factor, and we illustrate how this becomes particularly problematic when applying PCA to multifaceted variables. Rather than simply critique this use of PCA, we offer an alternative approach that helps to address and illustrate the problem that we raise.

Being familiar with their earlier work investigating the factor structures of the Armed Services Vocational Aptitude Battery and the Air Force Officer Qualifying Test, we read with interest Ree, Carretta, and Teachout's (2015) proposal to extend the idea of a dominant general factor (DGF) beyond the realm of cognitive abilities to other areas of research and practice in industrial–organizational (I-O) psychology. We found their ideas intriguing and their arguments compelling, but we stumbled on a reference to an article of one of the present authors (Lance, Teachout, & Donnelly, 1992) and Ree et al.’s claim that Lance et al. (1992) had found a DGF that accounted for 59% of the variance in job performance ratings in a military job. They did not. Rather, Lance et al. reported a hierarchical confirmatory factor analysis (CFA) model that explained parsimoniously the correlations among 15 job performance first-order factors in terms of four Job Proficiency and four other Measurement Source second-order factors. We reasoned that Ree et al. must have conducted some secondary analysis on the results presented by Lance et al., and indeed we replicated their claim by finding that the first unrotated principal component accounted for 59% of the variance in correlations among the four Proficiency second-order factors reported in Lance et al.’s Table 6.

In the focal article, Ree, Carretta, and Teachout (2015) address a common error in research methods, in which researchers neglect the shared variance between facets of a multidimensional construct. We agree with the need to attend to the entire factor structure of constructs when using measures, whether in research or application. The objective of this commentary is to elaborate on useful practices when a dominant general factor (DGF), as defined by the focal article, is found to be present and, in particular, to explore cases of DGF results under research paradigms not considered by the focal article.

Ree, Carretta, and Teachout's (2015) arguments for recognizing the importance of general factors are mostly on point, but they neglect two broad issues: (a) an important theoretical problem introduced by the presence of multiple factors (general, group, specific) and (b) the criterion validity of group factors in certain settings.

In their article, Ree, Carretta, and Teachout (2015) argued that a dominant general factor (DGF) is present in most, if not all, psychological measures (e.g., personality, leadership, attitudes, skills). A DGF, according to Ree et al., is identified by two characteristics. First, the DGF accounts for the largest amount of a measure's systematic variance, and second, it influences every subdimension within the construct domain. They indicate that researchers ignore DGFs and pay inappropriate amounts of attention to the specific dimensions (DSs) even though the DGF provides most of the predictive power and the DS adds little predictive power.

Ree, Carretta, and Teachout (2015) outlined a compelling argument for the pervasiveness of dominant general factors (DGFs) in psychological measurement. We agree that DGFs are important and that they are found for various constructs (e.g., cognitive abilities, work withdrawal), especially when an “unrotated principal components” analysis is conducted (Ree et al., p. 8). When studying hierarchical constructs, however, a narrow emphasis on uncovering DGFs would be incomplete at best and detrimental at worst. This commentary largely echoes the arguments made by Wee, Newman, and Joseph (2014), and Schneider and Newman (2015), who provided reasons for considering second-stratum cognitive abilities. We believe these same arguments in favor of second-stratum factors in the ability domain can be applied to hierarchical constructs more generally.