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Personality and Social Psychology Review

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First published online May 20, 2009

Higher Order Factors of Personality: Do They Exist?

Michael C. Ashton, Kibeom Lee, Lewis R. Goldberg, and Reinout E. de VriesView all authors and affiliations

https://doi.org/10.1177/1088868309338467

Abstract

Scales that measure the Big Five personality factors are often substantially intercorrelated. These correlations are sometimes interpreted as implying the existence of two higher order factors of personality. The authors show that correlations between measures of broad personality factors do not necessarily imply the existence of higher order factors and might instead be due to variables that represent same-signed blends of orthogonal factors. Therefore, the hypotheses of higher order factors and blended variables can only be tested with data on lower level personality variables that define the personality factors. The authors compared the higher order factor model and the blended variable model in three participant samples using the Big Five Aspect Scales, and found better fit for the latter model. In other analyses using the HEXACO Personality Inventory, they identified mutually uncorrelated markers of six personality factors. The authors conclude that correlations between personality factor scales can be explained without postulating any higher order dimensions of personality.

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1. More recently, other researchers (Musek, 2007; Rushton, Bons, & Hur, 2008) have proposed a general factor of personality that accounts for positive correlations among all of the Big Five factors.

2. The Big Five factors themselves have often been found in principal components analyses of personality variable sets constructed as markers of those factors (e.g., McCrae, Zonderman, Costa, Bond, & Paunonen, 1996). However, the Big Five are also recovered from those variable sets when exploratory common factor analysis is applied.

3. Here is one simple way to test the hypothesis that same-signed blends of the Big Five personality factors are more important in person description than opposite-signed blends: Participants would be given the descriptions of personality traits representing same-signed and opposite-signed blends of the Big Five factors (e.g., from the AB5C system; see Hofstee, de Raad, & Goldberg, 1992) and would be asked to rate the importance of assessing individuals' levels of each trait in various contexts (e.g., as potential employee, babysitter, roommate, romantic partner, etc.). We hypothesize that ratings would be higher for the same-signed blends than for the opposite-signed blends, particularly in the regions of the personality space that correspond to alpha and beta.

4. The very high correlations between alpha and beta—ranging from .64 in the Oregon sample to .96 in the Ontario sample—reflect the fact that the correlations of alpha-related Big Five factors with beta-related Big Five factors were generally almost as large as the correlations between alpha-related factors and between beta-related factors. Because the weak distinction between alpha and beta suggested the possibility of a general factor, we also repeated the analyses of Figure 5 with a single higher order factor instead of alpha and beta. The fit of the general factor model in the three samples (SRMR = .148, .137, and .152, and RMSEA = .210, .202, and .210, for the Oregon, Ontario, and Alberta samples, respectively) was similar to that of the alpha/beta model and, thus, much poorer than that of the blended variable model. The general factor model, like the alpha/beta model, does not explain the finding of several near-zero correlations among BFAS variables.

Because the strong link between alpha and beta contributed strongly to the fit of the higher order factor model, we did not restrict the blended variable model to exclude secondary loadings of an alpha-related variable on a beta-related factor and vice versa. For the sake of completeness, we did compare a higher order factor model based on uncorrelated alpha and beta factors with a blended variable model in which the four secondary loadings did not cross between alpha-related and beta-related factors. The blended variable model again showed better fit than did the higher order factor model in all three samples. (Details of this analysis are available from the authors upon request.)

5. Of course, the variables must be scored independently. Their intercorrelations will be artificially depressed if, instead, the scores have been standardized within subjects or if one or more scales contrasts one trait with other traits (whether through an ipsative item format, through heterogeneous item content, or through “doublebarreled” items). In addition, if the scales are not at least roughly balanced for the direction of keying of their items, then intercorrelations between scales having positively keyed items and scales having negatively keyed items will also be artificially depressed.

6. We thank an anonymous reviewer for suggesting this analysis.

7. The latter sample subsumes the sample of Alberta university students used in analyses of the Big Five Aspect Scales.

8. The only case in which neither of these problems occurred was that of the general factor model in the Dutch sample. However, this general factor was loaded by high Agreeableness and high Honesty-Humility along with low Openness to Experience and low Extraversion. The opposing signs of these loadings are inconsistent with the definition of the general factor of personality as described elsewhere (e.g., Musek, 2007).

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9. By extension, the hypothesis of a general factor of personality is rendered even more implausible by the existence of six mutually uncorrelated variables representing the six HEXACO factors.

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10. Contemporary objections to the g factor are not based on any claim that there exist mutually uncorrelated markers of the major aspects of mental ability, but rather on the use of the common factor model itself, and in particular the problem of factor indeterminacy (see Schönemann, 1997).

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11. It is, of course, possible to construct a personality variable set in such a way as to produce an apparent higher order factor, simply by ensuring that each marker variable of the lower order factors is actually a same-signed blend of each of those factors. (In fact, such a variable set will tend to occur by default if the researcher includes only those variables that are highly desirable or undesirable.) But again, the fact that one can identify sets of mutually uncorrelated marker variables suggests that such a higher order factor is spurious. (Note that some previous studies—Church and Burke (1994) and McCrae et al. (1996)—have demonstrated that the addition of many secondary loadings allows models of orthogonal personality factors to produce levels of fit approximating those of models of oblique personality factors; however, those studies did not explicitly identify mutually orthogonal marker variables.)

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12. In contrast to the situation for the personality domain as a whole, there are no sets of several mutually uncorrelated variables within the subdomain of any one broad personality factor. As one example, consider the five sets of 15 facet-level scales (i.e., 9 IPIP-AB5C and 6 NEO-PI-R) analyzed by DeYoung, Quilty, and Peterson (2007); each of these variable sets consists of facet-level scales that are primarily associated with the same Big Five factor. In the Oregon participant sample, none of those five variable sets contained even one set of 3 facet-level scales whose intercorrelations were all below .15.

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13. It is presumed that rater biases can be identified for any personality variable, regardless of its social desirability. But, when an inventory consists mainly of variables that represent certain same-signed blends of factors—blends that tend to represent highly desirable traits—then rater biases associated with those sets of correlated traits will become large sources of variance across the scales of the inventory. (See Bäckström, Björklund, and Larsson, 2009, for a measure of the Big Five in which variance due to rater biases associated with socially desirable traits has been minimized.)

References

Anusic, I., Schimmack, U., Pinkus, R., & Lockwood, P. (2009). The nature and structure of correlations among Big Five ratings: The halo-alpha-beta model. Unpublished manuscript.

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