Is there a G in HEXACO? Testing for a general factor in personality self-reports under different conditions of responding

Personality and Individual Differences 156 (2020) 109750

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Is there a G in HEXACO? Testing for a general factor in personality selfreports under different conditions of responding☆

T

Michael C. Ashtona, , Xiaoyang Xiaa, Kibeom Leeb ⁎

a b

Department of Psychology, Brock University, St. Catharines, ON L2S 3A1, Canada University of Calgary, Canada

ABSTRACT

We test for the existence of a general factor of personality self-reports in HEXACO Personality Inventory—Revised (HEXACO-PI-R) facet scales, using two samples of persons who responded under normal conditions and two samples of persons who responded under conditions in which at least some participants would perceive some incentive to describe themselves favorably. Using bi-factor (or nested factor) modeling, we found that a truly general factor was not obtained in the “normal conditions” samples; instead, the putative general factor was a broad variant of Extraversion, leaving a smaller residual Extraversion factor. Moreover, the fit of the general factor model in these samples was poorer than that of a more parsimonious “blended variable” model that incorporated secondary loadings instead of a general factor. In the samples whose participants had some incentive to “fake good”, a truly general factor was obtained, and the fit of the general factor model was at least as good as that of the blended factor model. These results suggest that a general factor of personality self-reports does not typically emerge from HEXACO-PI-R facet scores obtained under normal conditions of responding and therefore that such a factor obtained under other conditions is not a general factor of personality.

1. Introduction In recent decades, some researchers have suggested that correlations between self-report scales measuring the “Big Five” personality factors imply the existence of “higher-order” personality factors. One proposal involves two such factors—called either “alpha” and “beta” (Digman, 1997) or “stability” and “plasticity” (DeYoung, 2006)—and another involves a single “general” factor (e.g., Musek, 2007). If there did exist one or two very broad dimensions of personality, then persons’ scores on measures of those dimensions would be expected to predict many important outcome variables, much as the g factor of general mental ability has been shown to do. And if the very broad factor(s) accounted for a large amount of variance in personality variables, then researchers might even want to shift their focus away from Big Five or HEXACO factors, much as mental ability researchers have focused mainly on g rather than on the several narrower (though still rather broad) mental ability factors that are commonly identified. However, previous work has shown that the correlations between Big Five (or HEXACO) scales are not attributable to higher-order personality factors. Instead, those correlations are attributable to the inclusion, within scales assessing a broad Big Five or HEXACO factor, of some narrower traits (“facets”) having secondary loadings on other Big Five or HEXACO factors (see Ashton, Lee, Goldberg & de Vries, 2009). That is, the correlations between the various facets that define Big Five

(or HEXACO) factors are better summarized by a “blended variable” model—in which some of those facets have secondary loadings on orthogonal factors—than by models involving one or two higher-order factors. The results of Ashton et al. (2009) are strong evidence against the existence of higher-order factors of personality. But a very broad factor of personality—such as the proposed general factor or the proposed alpha/stability and beta/plasticity factors—does not necessarily have to be a higher-order factor. That is, the very broad factor(s) do not need to be defined by the Big Five or HEXACO factors; the former factor(s) could instead be defined by narrower, facet-level traits that also define the latter factors, with the latter being orthogonal to the former. A “bifactor” or nested factor model (e.g., Gustafsson, 2002; Harman, 1976, pp. 120–132; Holzinger & Swineford, 1939) of this kind is widely used as an alternative to higher-order factor models in representing the structure of mental ability variables. It may be useful to discuss briefly the differences between higherorder factor models and bi-factor (or nested factor) models. One feature of higher-order factor models is that the correlations of variables defining the same lower-order factor with a variable defining another lower-order factor are presumed to be proportional to the former variables’ loadings on their lower order factor. Consider a case where factors A, B, and C (each univocally defined by observed variables A1, A2, etc., B1, B2, etc., C1, C2, etc.) all define a single higher-order factor

The authors thank Jeromy Anglim for helpful comments on an earlier version of this manuscript and for making available the data of the applicant and incumbent samples. ⁎ Corresponding author. E-mail address: [email protected] (M.C. Ashton). ☆

https://doi.org/10.1016/j.paid.2019.109750 Received 29 October 2019; Accepted 26 November 2019 0191-8869/ © 2019 Elsevier Ltd. All rights reserved.

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Fig. 1. Higher-order factor (a), bi-factor (b), and blended variable (c) models.

(Fig. 1a), with these factors perfectly reproducing the correlations between the observed variables. If variable A1 loads twice as strongly as variable A2 does on lower-order factor A, then variable A1 would correlate twice as strongly as variable A2 does with the variables that define lower-order factor B (B1, B2, etc.) or lower-order factor C (C1, C2, etc.), and so on. This proportionality is not required in a bi-factor or nested factor model (Fig. 1b). In such a model, the narrower factors (analogous to the lower-order factors of a higher-order factor model) do not themselves load on a broader factor (analogous to a higher-order factor of a higherorder factor model). Instead, the very broad factor(s)—often a single general factor—are independent of the narrow factors, and each observed variable loads directly on a broader factor as well as on a narrower factor. In this bi-factor or nested factor model, a variable that defines a given narrower factor might show relatively high correlations with the variables that define different narrower factors without showing an especially high loading on its own narrower factor (e.g., Yung, Thissen & McLeod, 1999). In this way, a variable might have a rather high loading on a broad factor (which accounts for variance shared between variables that define different narrower factors) while having only a low loading on a narrow factor (which accounts for variance shared between its defining variables beyond the variance accounted for in those variables by the broad factor). As noted above, previous research has shown that the correlations between HEXACO-PI-R facets are better summarized by a blended variable model (Fig. 1c) than by models having one or two higher-order factors. However, if the loadings of personality facet scales on any very broad factors are not proportional to their loadings on lower-order factors, then it is possible that bi-factor models could outperform blended variable models even though higher-order factor models cannot. In the present research, therefore, we used a bi-factor modeling approach to investigate whether a general factor could be obtained from self-report scores on the facet scales of the HEXACO Personality Inventory—Revised (HEXACO-PI-R).1

We examined this issue in several datasets, which differed widely in the correlations between the HEXACO-PI-R factor-level scales and could thus be expected to differ widely in the size of any general factor that might be obtained. In particular, we examined datasets from two samples in which persons would likely be motivated to describe themselves accurately, and we also examined datasets from two samples in which persons would likely be motivated to describe themselves favorably. We expected that in the latter but not the former samples, individual differences in the extent of “faking good” would affect scores across nearly all facets and would therefore inflate correlations across those variables. We therefore anticipated that the latter samples would yield a general factor that is truly general, in the sense of showing appreciable loadings across nearly all facets from all HEXACO factors, whereas the former samples would not. Moreover, we expected that only in the latter samples would a general factor model outperform a blended variable model. We emphasize, though, that such a general factor would not be a general factor of personality, but rather a general factor of personality self-reports, and one induced by the circumstances in which the self-reports were provided. (If a true general factor could be found even in the former samples, then further analysis would be required in order to determine whether it represented a general factor of personality or merely a general factor of personality self-reports.)2 In several previous studies, researchers have extracted a general factor in bi-factor models based on item-level self-report HEXACO-PI-R data (e.g., Anglim, Morse, de Vries, MacCann & Marty, 2017; Biderman, McAbee, Chen & Hendy, 2018, 2019), with the factor being interpreted in terms of evaluation or socially desirable responding in self-reports. We think that this item-level approach has some important benefits, particularly in that it allows for examination of differences between items of the same facet-level scale. In the present case, however, we instead use facet-level self-report data. Our main reason for relying on the facet scale scores is that the facets are of some theoretical interest given that they assess specified constructs; moreover, the facets of any given factor are intended to span the defining content of that factor with some near completeness. In contrast, items are generated

1 We also considered examining a bi-factor model with two broad factors—alpha/ stability and beta/plasticity—but in our datasets the low correlations between Extraversion and Openness facets made it obvious that such a model would be unsuccessful.

2 Analogously, one could examine the conditions in which a general factor of personality observer reports would be found, but in the present investigation we examine only self-reports.

2

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simply as varied exemplars of their intended facet-level traits, with no particular conceptual significance.3 An additional reason for relying on facet scale scores is that a model based on the item responses involves the additional complication of requiring an acquiescence or elevation factor, one that is not needed for facet-level analyses given that the HEXACO-PI-R facet scales are roughly balanced in terms of their items’ direction of keying.4,5 Before testing the bi-factor model involving a general factor as well as the six HEXACO factors, we planned simply to inspect (a) the correlations between factor-level scales as well as (b) the correlations between the facet-level scales belonging to different factors (as compared with the correlations between facet-level scales belonging to the same factor). If the factor-scale intercorrelations are large, this would at least suggest the possibility of a large general factor, but if some of those intercorrelations are approximately zero (or even in the opposite-toexpected direction), then a general factor of any appreciable size would be unlikely. But if the factor-scale correlations are consistent in direction and size with the existence of a general factor, some further indication of the plausibility of such a general factor could then be gained by examining the facet-scale intercorrelations across factors: If those correlations are overwhelmingly of the same sign, then a general factor becomes more plausible; but if many of those correlations are zero or of sign opposite to the others, then a blended variable model becomes more plausible. When examining results of a bi-factor general-factor model, we planned to consider the extent to which the “general” factor is in fact general. Although we do not suggest any precise guideline, the factor can be interpreted as general to the extent that it shows appreciable loadings for all or nearly all facet scales. If instead this factor shows near-zero loadings for many facets, and especially if some of the narrower (in this case, HEXACO) factors have no facets with appreciable loadings on the “general” factor, then it is not properly interpretable as general. Beyond the issue of how general a putative general factor really is, another issue to consider in interpreting a bi-factor general factor model is that of how well it fits the data in comparison with a model that does not incorporate any general factor. Although the correlations between facets that define different HEXACO factors might be attributable to a general factor, an alternative possibility is that those correlations are attributable to secondary loadings for various facets on certain factors. As noted above, “blended variable” models incorporating secondary loadings for facet-level variables have previously been shown to better account for facet-scale intercorrelations than have higher-order factor models, and it is of interest to find out whether a blended variable model can also outperform a bi-factor general factor model. In the present report, we investigate the question of whether or not a general factor can be extracted from several samples of participants who completed self-reports on the HEXACO-PI-R. We begin by examining self-report scores in two samples in which participants would likely have been motivated to describe themselves accurately, with no incentive to portray themselves favorably. One of these samples consisted of Canadian university students who independently completed self-report personality questionnaires, along with other assessments, as part of research studies conducted in supervised group sessions; those

students received, at their choosing, either cash payment or course credit for their participation. Another sample consisted of persons around the world who completed a brief (typically under 15 min) selfreport personality assessment in exchange for results and feedback about their personality trait profile. We then continue by examining self-report scores in two samples in which at least some participants would have perceived an incentive to portray themselves favorably. In one of these samples, this incentive was likely very strong, as the participants were completing the personality measures as part of a job application process. In the other of these samples, the incentive was likely considerably weaker, as the participants were not applying for a job; instead, they were currently employed workers who agreed to participate in exchange for a chance to win one of several large travel vouchers. We suspect that although these participants of this “incumbent” sample were not applying for a job, some subset of them might nevertheless have been motivated to portray themselves rather favorably, “just in case” their responses would end up being seen by a prospective future employer or by their current employer. 2. Method 2.1. Materials The participants of the student and online samples completed the self-report form of the 100-item version of the HEXACO-PI-R. The participants of the applicant and incumbent samples completed the selfreport form of the 200-item HEXACO-PI-R, but here we score the inventory for the subset of 100 items that are included in the 100-item version, so that results will be comparable with those of the online and student samples. 3. Participants and procedure 3.1. Student sample A total of 3388 students at two Canadian universities independently completed self-report personality questionnaires in paper-and-pencil format, along with other assessments, as part of research studies; those students received, at their choosing, either cash payment or course credit for their participation. Conditions of participation were comfortable, with supervised group sessions kept reasonably brief and with varied questionnaire content. This sample subsumes various smaller samples accumulated over a period of many years, each of which has been used in examining personality-related research questions described in previous articles. The mean age in this sample was 21 years (SD = 4); 67% of those who indicated their sex were female. 3.2. Online sample This sample consists of persons who visited the hexaco.org website between October 19, 2014 and October 18, 2018. Participants completed the personality self-reports, along with a series of demographic and related questions, in exchange for results and feedback about their personality trait profile. Participants who omitted any items, who failed any of three “attention check” items, or whose responses indicated some gross inconsistency were excluded from the dataset. (See descriptions of the attention check items and responses inconsistency index in Lee and Ashton (2018); fewer than 1% of respondents were excluded on the basis of the latter criterion, which was set conservatively.) The typical duration of participation was under 15 min. Of the final sample of 381,655 participants, 42% were female; mean age was 32 years (SD = 13).

3

Note also the parallel with the use of bi-factor models in mental ability research, where such models are generally applied to mental ability subtests rather than to mental ability items. 4 The problem of acquiescence or elevation is that participants vary in their overall level of endorsement of item statements independent of content; the problem is not that participants on average are inclined to endorse item statements above some neutral value 5 As we note below, the data of Anglim et al. are examined in the present study. In the Discussion section, we comment on the item-level general factors obtained by Biderman et al. 3

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3.3. Applicant sample

factor: in the student sample, Emotionality correlated positively with Conscientiousness, and in both samples, Honesty-Humility correlated positively with Emotionality but negatively with Extraversion. The implausibility of a general factor in these samples is further shown by the pattern of correlations between facet-level scales (see Supplementary Table 1). Even for the pairs of factor-level scales that showed correlations consistent with a general factor—that is, negative values for correlations involving Emotionality and positive values otherwise—the heterogeneity of the facet-level correlations seems more likely to be due to a circumplexical arrangement of variables than to a general factor.6 For example, several pairs of factors show some facet correlations well above zero and others well below zero. As would be expected on the basis of these correlations, attempts to make a bi-factor model involving a general factor defined by all 24 facets, alongside six HEXACO factors each defined by the appropriate four facets, were unsuccessful.7 In both the student sample and the online sample (see Table 2), the originally specified bi-factor model produced five factors interpretable as the HEXACO dimensions other than Extraversion, plus a similar set of two other factors: One of these was a narrow Extraversion factor that was dominated by the Sociability facet, with low loadings for the other facets. The other was the putative “general” factor, which actually resembled a broad Extraversion factor, being defined mainly by the four Extraversion facets, especially Liveliness and Social Self-esteem; this factor was also defined by a few nonExtraversion facets such as Diligence and low Anxiety, but most nonExtraversion facets loaded nearly zero. (In both of those samples, the loading of Sociability on the narrow Extraversion factor actually exceeded 1, so we then constrained the uniqueness term for Sociability to zero; however, this “Extraversion” factor continued to be dominated by Sociability, and the “general” factor continued to be dominated by the Extraversion facets.) The above results indicate that there was no truly general factor of personality self-reports in either of these samples. Instead, the obtained “general” factor was at best a broad version of Extraversion, with many facets showing near-zero loadings; those near-zero loadings undermine any interpretation of the latter as a real general factor. (This putative “general” factor accounted for about 9% and 11% of the total variance in the student and online samples, respectively. Variance due to Extraversion facets represented 77% and 64% of that “general” factor's variance in those same samples.) We next compared the fit of the above “general” factor models—in which, as noted above, the general factor actually emerged as a broad version of one HEXACO factor—against the fit of a model in which several facets were allowed to cross-load on a second or third factor. (These cross-loadings were assigned by examining the exploratory factor solutions reported in Ashton and Lee (2018, Table 2) for subsets of the current samples and finding the secondary loadings with averaged absolute values of 0.20 or above; all of the 11 selected loadings from those exploratory analyses had absolute values of 0.18 or above in both samples, a result which suggests that the selected loadings have not benefited substantially from capitalization on chance.) In both samples, this “blended variable” model (see Table 3) showed slightly better fit than did the “general factor” model even though the former model was more parsimonious—having only 11 secondary loadings, as opposed to the 24 general-factor loadings—and even though the latter did not produce a truly general factor.8,9

This sample from Anglim et al. (2017); see also Anglim, Lievens, Everton, Grant & Marty, 2018) consists of 1613 Australian job applicants. The mean age was 42 years (SD = 10); 54% were female. As described by Anglim et al., this sample was selected from a somewhat larger original sample of 2207 job applicants who were matched on age and sex with the persons of their other, non-job-applicant sample, described below. The item-level data for the applicant and incumbent samples of Anglim et al. (2017) have been made available by those researchers at https://osf.io/9e3a9/. 3.4. Incumbent sample This sample from Anglim et al. (2017, 2018) consists of 1613 Australian persons recruited by a consultancy firm to participate in research on personality measurements. Participants of this sample were invited by email to participate in confidential research and were offered a chance to win one of several travel vouchers (value AUD 3000). Note that, unlike the participants of the job applicant sample, above, the participants of this sample had no explicit incentive to provide socially desirable self-reports, but as noted above, we nevertheless suspect that some of these participants would have been inclined to respond in a desirable way, if they believed that their responses might be used for some purpose such as personnel evaluation or selection. The mean age was 42 years (SD = 10); 54% were female. As described by Anglim et al., this sample was selected from a somewhat larger original sample of 1969 persons who were matched on age and sex with the job applicants of their other sample, described above. 4. Results 4.1. Student and online samples Table 1 shows correlations between self-report scores on the six factor-level scales of the 100-item HEXACO-PI-R, within the Canadian student sample and the online sample. As seen in the tables, the scale intercorrelations tended to be weak, with a mean correlation (after reversing the signs of correlations involving Emotionality, which tended to correlate negatively with other scales) of 0.07 for the student sample and 0.11 for the online sample. In some cases, the direction of association was opposite to that expected on the basis of a general Table 1 Means, standard deviations, and intercorrelations of the HEXACO-PI-R scales in student and online samples. Mean

SD

1

2

3

4

5

3.24 3.42 3.45 2.98 3.46 3.32

0.60 0.61 0.59 0.58 0.57 0.61

.13 −.09 .30 .14 .14

−.09 −.14 .12 −.05

.13 .16 .05

.05 .08

.03

Student sample (N = 3388) 1. 2. 3. 4. 5. 6.

Honesty-Humility Emotionality Extraversion Agreeableness Conscientiousness Openness to Experience

6 For illustrations of circumplexically arranged variables, see Hofstee et al. (1992) and Saucier (1992). 7 We used IBM SPSS AMOS version 26 for all CFA models of this study. 8 Student blended vs. general: CFI = .808 vs. .779, RMSEA = .069 vs. .076; online blended vs. general: CFI = .794 vs. .773, RMSEA = .079 vs. .085. Degrees of freedom were 241 for the blended variable model and 228 for the general factor model. 9 We also compared the general factor and blended variable models after modifying both models to incorporate a correlation between the latent Honesty-

Online sample (N = 381,655) 1. 2. 3. 4. 5. 6.

Honesty-Humility Emotionality Extraversion Agreeableness Conscientiousness Openness to Experience

3.31 3.10 3.20 2.93 3.52 3.70

0.71 0.65 0.69 0.65 0.59 0.58

.14 −.04 .37 .14 .12

−.17 −.11 −.09 −.05

.19 .23 .11

.07 .11

.09

4

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Table 2 Factor loadings from Bi-factor model: student and online samples. Student Sample

Sincerity Fairness Greed Avoidance Modesty Fearfulness Anxiety Dependence Sentimentality Social Self-Esteem Social Boldness Sociability Liveliness Forgivingness Gentleness Flexibility Patience Organization Diligence Perfectionism Prudence Aesthetic Appreciation Inquisitiveness Creativity Unconventionality

“G”

H

.02 .02 −.04 −.08 −.20 −.38 .04 .07 .73 .52 .49 .76 .25 .12 .07 .19 .15 .32 .01 .05 −.06 .00 .16 .06

.50 .56 .63 .57

Online Sample E

.55 .47 .71 .72

X

−.02 .20 .88 .10

A

.54 .64 .58 .70

C

.54 .67 .72 .54

O

“G”

H .58 .59 .67 .70

.77 .49 .60 .60

.04 .21 −.02 −.06 −.29 −.51 −.07 .01 .79 .56 .45 .78 .29 .11 .20 .25 .27 .47 .01 .22 .06 .13 .16 .06

E

.49 .47 .68 .70

X

−.01 .30 .89 .17

A

.57 .71 .63 .60

C

.56 .55 .63 .55

O

.68 .49 .63 .61

Note. G = putative general factor; H = Honesty-Humility; E = Emotionality; X = eXtraversion; A = Agreeableness; C = Conscientiousness; O = Openness to Experience.

4.2. Applicant and incumbent samples

(N = 5,983, 56% women, mean age 41.7 years (SD = 9.2)). In relation to the distribution of scores in this Australian working-age adult subsample (see the third panel of Table 4), the incumbent sample averaged about 3/5 SD unit higher in Extraversion, nearly 1/2 SD unit higher in Agreeableness, and about 1/3 SD unit higher in Conscientiousness. Such differences might be attributable to selection (and perhaps other influences) rather than faking, if for example the incumbents occupy their jobs in part because of their desirable personality characteristics. However, the hypothesis that some modest amount of faking has occurred is supported by the fact that the average correlations between the scales is higher in the incumbent sample than in this subset of the online sample (in which the mean correlation, again after reversing the signs of correlations involving Emotionality, was 0.13). This result is consistent with the existence of some degree of faking because individual differences in faking would inflate correlations between scales, whereas selection on the characteristics measured by these scales would tend to deflate those correlations (e.g., Sackett, Lievens, Berry & Landers, 2007; see also Murray, Johnson, McGue & Iacono, 2014). The applicant sample would be expected to show more faking than would the incumbent sample. This expectation is supported by the mean scores for the applicant sample on the various HEXACO-PI-R scales, as these means were considerably higher than those of the incumbent sample for Extraversion, Agreeableness, Conscientiousness, and Honesty-Humility. However, the applicant sample did not show a higher average correlation between the HEXACO-PI-R scales than did the incumbent sample. Although the applicant sample would be expected to show inflated scale intercorrelations due to individual differences in the extent of faking good, the incumbent sample would be expected to contain some mixture of persons who faked good and persons who perceived no incentive to do so, and the contrast between those groups would also inflate the correlations between the scales. As would be expected on the basis of these correlations, a plausible general factor of personality self-reports could be extracted from these samples. In both the applicant sample and the incumbent sample (see Table 5), the general factor showed non-trivial loadings for nearly all HEXACO facets, with facets from all six factors represented. Even though the highest general-factor loadings were for Extraversion facets,

We now consider data from samples in which the emergence of a general factor is much more likely to occur. Table 4 shows correlations between self-report scores on the six HEXACO-PI-R scales for a sample of job applicants and for a sample of job incumbents, using the dataset of Anglim et al. (2017)). (Recall that Anglim et al. administered the 200-item HEXACO-PI-R but that here we score the inventory for the subset of items included in the 100-item version, so that results will be comparable with those of the online and student samples.) As seen in Table 4, even though a few correlations were approximately zero, in both samples several of the correlations were fairly large, with several exceeding 0.30 and with an average (after reversing the signs of correlations involving Emotionality) of 0.175 in the applicant sample and 0.185 in the incumbent sample. Also, the correlations between facets belonging to different factor scales were mainly (though not entirely) of consistent direction—for example, in the applicant sample, all but one of the correlations involving two facets of Extraversion, Conscientiousness, and Agreeableness were positive (see Supplementary Table 2). Although the incumbent sample consisted of already employed persons who participated as part of a research study rather than a job application process, the descriptive statistics and intercorrelations of the HEXACO-PI-R self-report scales are consistent with the hypothesis of some degree of faking by some subset of the respondents in this sample. We compared the incumbent sample with the subset of our online sample that indicated Australian nationality, an age between 30 and 65 years inclusive, and completion of postsecondary education (footnote continued) Humility and Agreeableness factors, given the tendency for all facets of these two factors to be positively (though in many cases weakly) intercorrelated; in the case of the blended variable model, this correlation replaced the two secondary loadings involving a facet of one of these factors on the other factor. This modification produced small improvements in fit to both models, but the blended variable model continued to show better fit than did the general factor model, in both samples. 5

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Table 3 Factor loadings from blended variable model: student and online samples. Student Sample H Sincerity Fairness Greed Avoidance Modesty Fearfulness Anxiety Dependence Sentimentality Social Self-Esteem Social Boldness Sociability Liveliness Forgivingness Gentleness Flexibility Patience Organization Diligence Perfectionism Prudence Aesthetic Appreciation Inquisitiveness Creativity Unconventionality

E

.48 .55 .63 .55

Online Sample X

A

C

O

H .57 .57 .67 .66

.29 .54 .45 .72 .71

.22

.15

−.26 −.36

−.23

.67 .61 .59 .73

.26 .13

.29

.16

E

.15

.55 .70 .69 .57

.28

.48 .50 .71 .65

−.33 −.45 .69 .69 .63 .76

.34 .75 .49 .60 .61

A

C

O

.30

.34

.20 .55 .65 .59 .72

X

.22 −.21 .22 .20 .60 .70 .67 .65

.62 .63 .55 .65

.68 .51 .64 .62

Note. H = Honesty-Humility; E = Emotionality; X = eXtraversion; A = Agreeableness; C = Conscientiousness; O = Openness to Experience. Primary loadings are given in bold type. .

those facets did not dominate the general factor to the extent observed in the student and online samples. (This “general” factor accounted for about 14% of the variance in each of the applicant and incumbent

samples, and Extraversion facets represented 40% and 36% of its variance in the applicant and incumbent samples.) The Extraversion factor itself was defined more strongly by the Sociability facet than by the other facets, but for the incumbent sample the differences in loadings were much less pronounced than for the student and online samples; for the applicant sample, the loadings of the other three facets were quite small. (Note that in the applicant and incumbent samples it was not necessary to fix the unique variance of Sociability to zero, as the loading of that facet on Extraversion did not exceed 1, unlike the situation in the student and online samples.) The other HEXACO factors were defined well by most or all of their respective facet scales, a result which suggests that those scales retained considerable construct validity in spite of any faking variance (see also Anglim et al., 2018). We next compared the fit of the above general factor models against the fit of two blended variable models. In the first blended variable model (see Table 6), we specified the same secondary loadings as in the online and student samples, to examine the performance of a blended variable model derived from samples of respondents who had no incentive to portray themselves favorably. In both the applicant and incumbent samples, the general factor model showed better fit than did this first “blended variable” model, a result opposite to that obtained for the online and student samples.10 In the second blended variable model (see Table 7), we specified secondary loadings on the basis of exploratory factor analyses loadings within the applicant and incumbent samples themselves, following a procedure analogous to that described above for the student and online samples. The fit of this blended variable model closely approached that of the general factor model in the applicant sample and approximated that of the general factor model in the incumbent sample.11

Table 4 Means, standard deviations, and intercorrelations of the HEXACO-PI-R scales in applicant and incumbent samples and in online subsample of Australian working-age adults. Mean

SD

1

2

3

4

5

3.91 2.99 3.99 3.63 4.09 3.62

0.40 0.43 0.37 0.39 0.35 0.46

.01 .02 .29 .13 .07

−.20 −.22 −.03 −.07

.36 .37 .27

.29 .19

.12

3.55 3.07 3.69 3.20 3.78 3.60

0.49 0.51 0.51 0.50 0.45 0.54

−.05 .12 .30 .21 .16

−.21 −.25 −.11 −.09

.33 .28 .26

.15 .15

.11

3.49 3.13 3.31 2.92 3.60 3.71

0.67 0.61 0.63 0.62 0.53 0.59

.11 .02 .41 .17 .10

Applicant sample (N = 1613) 1. 2. 3. 4. 5. 6.

Honesty-Humility Emotionality Extraversion Agreeableness Conscientiousness Openness to Experience

Incumbent sample (N = 1613) 1. 2. 3. 4. 5. 6.

Honesty-Humility Emotionality Extraversion Agreeableness Conscientiousness Openness to Experience

Australian working-age adult online subsample (N = 5,983) 1. 2. 3. 4. 5. 6.

Honesty-Humility Emotionality Extraversion Agreeableness Conscientiousness Openness to Experience

10

−.19 −.14 −.06 −.13

.25 .17 .13

.07 .14

Applicant blended vs. general: CFI = .768 vs. .813, RMSEA = .075 vs. .069; incumbent blended vs. general: CFI = .780 vs. .818, RMSEA = .076 vs. .071. Degrees of freedom were 241 for this first blended variable model and 229 for the general factor model. 11 Fit indices for this alternative blended variable model: applicant

.02

6

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Table 5 Factor loadings from Bi-factor model: applicant and incumbent samples. Applicant Sample

Sincerity Fairness Greed Avoidance Modesty Fearfulness Anxiety Dependence Sentimentality Social Self-Esteem Social Boldness Sociability Liveliness Forgivingness Gentleness Flexibility Patience Organization Diligence Perfectionism Prudence Aesthetic Appreciation Inquisitiveness Creativity Unconventionality

“G”

H

.10 .38 .07 .11 −.27 −.47 −.18 −.03 .67 .50 .31 .72 .40 .22 .31 .52 .37 .59 .10 .45 .15 .28 .28 .15

.51 .30 .70 .64

Incumbent Sample E

X

.43 .40 .56 .66

A

.19 .29 .71 .21

C

.39 .55 .51 .43

.62 .24 .65 .35

O

“G”

H .41 .32 .62 .60

.70 .58 .55 .58

.23 .46 .15 .09 −.30 −.56 −.22 −.11 .65 .47 .18 .70 .43 .23 .35 .47 .25 .50 −.04 .51 .16 .29 .24 .16

E

X

.46 .34 .54 .66

A

.29 .46 .72 .39

C

.43 .67 .47 .46

.56 .43 .62 .41

O

.68 .49 .61 .71

Note. G = putative general factor; H = Honesty-Humility; E = Emotionality; X = eXtraversion; A = Agreeableness; C = Conscientiousness; O = Openness to Experience.

5. Discussion

might be due to a desire to obtain accurate feedback about one's personality (as in the case of our online sample) or even due to a willingness to comply with directions in a research study in which they are participating freely and with compensation (as in the case of our student sample). Another feature of these normal conditions, as we will describe below, is that participants do not feel overburdened by the task of providing the self-reports.

The results of this study have shown that a general factor of personality self-reports is not typically recovered from HEXACO-PI-R facet scale scores in samples of participants who are responding under what we might call normal conditions. In these normal conditions, participants are motivated to describe themselves accurately. This motivation

Table 6 Factor loadings from blended variable model: applicant and incumbent samples (with secondary loadings specified on the basis of exploratory factor loadings in student and online samples). Applicant Sample H Sincerity Fairness Greed Avoidance Modesty Fearfulness Anxiety Dependence Sentimentality Social Self-Esteem Social Boldness Sociability Liveliness Forgivingness Gentleness Flexibility Patience Organization Diligence Perfectionism Prudence Aesthetic Appreciation Inquisitiveness Creativity Unconventionality

.51 .32 .72 .62

.17

E

X

A

C

O

.45 .42 .61 .61

−.29 −.33 .60 .65 .54 .63

.45

H .45 .38 .65 .56

.36

.36 .11

Incumbent Sample

.10 −.32

.15

.29 .31 .55 .54 .57 .68

E

.70 .45 .52 .60

.47 .40 .66 .60

−.29 −.37 .62 .71 .57 .72

.34 .70 .63 .61 .60

A

C

O

.43

.41 .09

X

.13 −.34 .25 .24 .59 .62 .60 .67

.58 .57 .41 .67

.70 .55 .65 .73

Note. H = Honesty-Humility; E = Emotionality; X = eXtraversion; A = Agreeableness; C = Conscientiousness; O = Openness to Experience. Primary loadings are given in bold type. 7

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Table 7 Factor loadings from blended variable model: applicant and incumbent samples (with secondary loadings specified on the basis of exploratory factor loadings in those samples). Applicant Sample H Sincerity Fairness Greed Avoidance Modesty Fearfulness Anxiety Dependence Sentimentality Social Self-Esteem Social Boldness Sociability Liveliness Forgivingness Gentleness Flexibility Patience Organization Diligence Perfectionism Prudence Aesthetic Appreciation Inquisitiveness Creativity Unconventionality

.52 .36 .71 .63

E

Incumbent Sample X .22

.48 .46 .63 .60 .34

−.29 .63 .60 .55 .67 .18

−.24 .47 .20

A

−.25

C

O

.27 −.26

−.29 .24 .28 .51 .56 .59 .65

H .46 .42 .63 .58

.15

E

X .22

.48 .45 .64 .62 .39

−.38 .66 .65 .57 .76 .22

−.22

.72 .41 .56 .58

.36 .67 .64 .62 .62

.17

A

−.31

C

O

.34 −.25

−.29 .21 .19 .55 .65 .60 .64

.60 .54 .47 .62

.21

.67 .56 .64 .74

Note. H = Honesty-Humility; E = Emotionality; X = eXtraversion; A = Agreeableness; C = Conscientiousness; O = Openness to Experience. Primary loadings are given in bold type.

Ziegler & Buehner, 2009).13 We anticipate that a general factor of personality self-reports might also be induced by some situations other than those in which participants perceive an incentive to describe themselves favorably. One such situation, we believe, would occur when participants feel overburdened by the task of providing self-reports, typically because of the large number of questions to be answered or (in the case of participants whose comprehension levels are lower) the cognitive demand of responding to the items. When participants feel overburdened by task of responding to the self-report items, we expect that they will become less attentive to the specific item content and will focus instead on the desirability of the item, with participants implicitly adopting tendencies to respond in more or in less desirable ways.14 The effects of such overburdening might be greater or lesser depending on various other circumstances, such as the presence or absence of any fellow participants or of any supervision while providing self-reports. We think it is possible that this overburdening was implicated, at least to some extent, in the results for the applicant and incumbent samples examined here. We remind readers that the online and student samples of the present report were collected by two of the authors (see, e.g., Lee & Ashton, 2018). However, the results obtained from these samples are likely to be typical of those found in other samples whose participants provided self-reports under the normal conditions described above. A recent cross-national investigation of the factor structure of the HEXACO-PI-R, based on a total sample of about 30,000 participants from 16 countries, showed quite low correlations between factor scales (Thielmann et al., 2019), even lower than those reported here for the student sample. (Note that the international sample of 30,000

Our results have also shown that a general factor of personality selfreports can at least sometimes be recovered from samples of participants who have some incentive to describe themselves favorably. (Moreover, in these samples, a general factor model was even able to outperform a blended variable model—albeit a more parsimonious blended variable model—at least when the latter was derived from samples of participants responding under normal conditions.) The job applicant sample of the present study is an obvious case in which participants would clearly have an incentive to describe themselves favorably, although it also seems likely that some participants of the incumbent sample also perceived such an incentive.12 In these samples, differences between participants in the extent of their “faking good” are apparently enough to produce a general factor of appreciable size in their self-reports. Another situation in which participants would be likely to describe themselves favorably is one in which they are directly asked to do so, as in an “instructed faking” condition of a research study. Under such instructions, the overall level of faking good is very high, and variation between participants is also quite wide, with the result that correlations between self-report personality scales assessing diverse traits are heavily inflated (MacCann, Pearce & Jiang, 2017;

(footnote continued) CFI = .795, RMSEA = .071; incumbent CFI = .813, RMSEA = .070. Degrees of freedom were 238 for this second blended variable model. 12 As noted earlier in this article, the differences in mean scores between the applicant and incumbent samples suggest considerably more widespread faking in the former than in the latter. This inference is also consistent with another finding by Anglim et al. (2018), which involved correlations of the HEXACO-PIR self-reports with self-reports on scales assessing similar constructs, as obtained from a subset of the original participants who completed the latter measures typically more than one year later. Anglim et al. found that convergent correlations were considerably higher for the 260 applicants (mean r = .68) than for the 347 incumbents (mean r = .54), a result which suggests that the HEXACO-PI-R scores of the applicants had been more influenced by faking than had those of the incumbents.

13 We expect that conditions in which respondents have an incentive to “fake bad”, or are directly instructed to do so, would also yield a general factor of personality self-reports. 14 Under these conditions of overburdening, the time taken to respond to items might or might not change: on the one hand, the induced style of (un) desirable responding could speed up the process, but on the other hand, the overburdening might lead to longer and more frequent bouts of inattention.

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participants from Thielmann et al. included nearly all participants of the student sample reported here as its Canadian subsample, but when the Canadian subsample is removed from the international sample, the scale intercorrelations are virtually unchanged.) The levels of fit for the various models examined in this study were rather poor as judged by conventional guidelines, but we remind readers that this is universally the case for parsimonious CFA models of the structure of omnibus personality inventories (e.g., Hopwood & Donnellan, 2010). This situation reflects the fact that personality characteristics do not show a particularly simple structure; instead they are distributed widely throughout the space of the major personality dimensions (e.g., Hofstee, de Raad & Goldberg, 1992; Saucier, 1992).15 Even when personality inventories are constructed in such a way that various “facet” level scales are assigned—as a conceptual and scoring convenience—to single factors, many of those facets will show theoretically meaningful secondary loadings (e.g., McCrae, Zonderman, Costa, Bond & Paunonen, 1996). This result is due to the non-simplestructured arrangement of characteristics throughout the personality factor space; in fact, to construct an inventory consisting exclusively of facets having univocal loadings on one's preferred set of factor axes would require one to discard many (and perhaps most) potential facetlevel traits, particularly those involving interpersonal or affective tendencies. Because of the poor fit inevitably produced by parsimonious CFA models of the structure of omnibus personality inventories, exploratory SEM (ESEM) analyses have recently been widely adopted. For example, Biderman et al. (2019) conducted ESEM analyses of self-reports on HEXACO items and obtained a general factor (interpreted as a dimension of evaluation in self-reports) along with an acquiescence factor, in addition to six content-based HEXACO factors. These ESEM analyses have some advantages over CFA methods, both for producing better fit to the covariation between variables and also for examining a variable's projections on a set of axes that span a given space. In such analyses, however, the rotational positions of factor axes are not uniquely determined, and a putative general factor will be larger or smaller (and broader or narrower in its defining content) depending on the target loadings of the items on the various factors. It can therefore be difficult to know how large and how general a putative general factor actually is. Thus, for the purpose of estimating a general factor, it can be preferable to use CFA methods in which many loadings are restricted to zero, despite the advantages of ESEM methods noted above. It may be of interest to compare the various results obtained here with those from the classic bi-factor model of mental ability test scores in schoolchildren by Holzinger and Swineford (1939). In their study (see also Gustafsson, 2002), mental ability tests intended to load on a reasoning factor instead loaded strongly on a general factor, with no reasoning factor emerging at all. But that general factor was truly general, being defined substantially by mental ability tests that also defined the other “group” factors (verbal, speed, visualization, memory), and with less than half of the general factor variance (43%) accounted for by the five reasoning tests (the remainder being accounted for by the 19 remaining tests). In the online and student samples of the present report, the four Extraversion facet scales loaded mainly on the putative “general” factor, but those scales dominated that factor, with most other facets not loading substantially on it; also, the residual Extraversion factor was dominated by the Sociability facet, with modest or even zero loadings for the other three facets of Extraversion. In the applicant and incumbent samples of the present report, the four Extraversion facet scales also loaded mainly on the general factor, but that factor really was general, being defined substantially by most other facet scales, and the residual Extraversion factor was

plausibly interpretable as Extraversion (albeit with much stronger loadings for Sociability than for the other three Extraversion facets). It is entirely possible that a general factor of personality self-reports would be obtained from facet scale scores of other personality inventories, even when the participants are responding under the normal conditions described above. In fact, if the facet scales of a given inventory are heavily saturated with self-report response styles—such as (un)desirability but also elevation or acquiescence—then such a factor is likely to emerge. For example, we think that the facet scales of the Personality Inventory for DSM-5 (PID-5) would be likely to produce a large general factor of self-reports (see, e.g., Ashton, de Vries & Lee, 2017). However, the present results show that one can construct an omnibus personality inventory whose facet scales do not produce a general factor of personality self-reports when participants are responding under normal conditions. A general factor of personality selfreports is not an inevitable result of self-report assessment of the major dimensions of personality.16 Any debate about the meaning of a general factor of personality selfreports is rendered moot by the non-emergence of such a factor from large samples of normal persons who are responding under normal conditions. With no general factor of personality self-reports under these conditions, there cannot exist any general factor of personality. A general factor of personality self-reports might well emerge—as it did in the present study—when participants have some reason to fake good (or when they are overburdened by their task), but that dimension is not a general factor of personality. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.paid.2019.109750. References Anglim, J., Lievens, F., Everton, L., Grant, S. L., & Marty, A. (2018). HEXACO personality predicts counterproductive work behavior and organizational citizenship behavior in low-stakes and job applicant contexts. Journal of Research in Personality, 77, 11–20. Anglim, J., Morse, G., de Vries, R. E., MacCann, C., & Marty, A. (2017). Comparing job applicants to non-applicants using an item-level bifactor model on the HEXACO personality inventory. European Journal of Personality, 31, 669–684. Ashton, M. C., de Vries, R. E., & Lee, K. (2017). Trait variance and response style variance in the scales of the personality inventory for DSM–5 (PID–5). Journal of Personality Assessment, 99, 192–203. Ashton, M. C., & Lee, K. (2010). Trait and source factors in HEXACO‐PI‐R self‐ and observer reports. European Journal of Personality, 24, 278–289. Ashton, M. C., Lee, K., Goldberg, L. R., & de Vries, R. E. (2009). Higher order factors of personality: Do they exist? Personality and Social Psychology Review, 13, 79–91. Biderman, M. D., McAbee, S. T., Chen, M. Z., & Hendy, N. T. (2018). Assessing the evaluative content of personality questionnaires using bifactor models. Journal of Personality Assessment, 100, 375–388. Biderman, M. D., McAbee, S. T., Hendy, N. T., & Chen, Z. J. (2019). Validity of evaluative factors from Big Five and HEXACO questionnaires. Journal of Research in Personality, 80, 84–96. DeYoung, C. G. (2006). Higher-order factors of the Big Five in a multi-informant sample. Journal of Personality and Social Psychology, 91, 1138–1151. Digman, J. M. (1997). Higher-order factors of the Big Five. Journal of Personality and Social Psychology, 73, 1246–1256. Gustafsson, J.-. E. (2002). Measurement from a hierarchical point of view. In H. I. Braun, D. N. Jackson, & D. E. Wiley (Eds.). The role of constructs in psychological and educational measurement (pp. 73–95). NJErlbaum: Mahwah. Harman, H. H. (1976). Modern factor analysis (3rd 3d). Chicago: University of Chicago

16

In joint analyses of self- and observer reports of personality, one can obtain dimensions representing the overall social desirability of self- and of observer reports, respectively (e.g., Ashton & Lee, 2010). The present results suggest that, for an inventory such as the HEXACO-PI-R administered under normal conditions, these overall desirability dimensions would not represent true general factors. The results of Ashton and Lee (2010) are consistent with this interpretation, insofar as the CFA-derived self-report source factor (see Table 3 of that article) was less “general” in terms of its defining variables than was the self-report source factor derived in exploratory analyses using targeted rotations.

15 Moreover, beyond the variance of those major dimensions, many small sets of personality variables are each likely to show nontrivial residual correlations, implying the existence of many additional but much smaller dimensions.

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M.C. Ashton, et al. Press. Hofstee, W. K. B., de Raad, B., & Goldberg, L. R. (1992). Integration of the Big Five and circumplex approaches to trait structure. Journal of Personality and Social Psychology, 63, 146–163. Holzinger, K. J., & Swineford, F. (1939). A study in factor analysis: The stability of a bi-factor solution. Supplementary educational monographs, 48. Chicago: Department of Education, University of Chicago. Hopwood, C. J., & Donnellan, M. B. (2010). How should the internal structure of personality inventories be evaluated. Personality and Social Psychology Review, 14, 332–346. Lee, K., & Ashton, M. C. (2018). Psychometric properties of the HEXACO-100. Assessment, 25, 543–556. MacCann, C., Pearce, N., & Jiang, Y. (2017). The general factor of personality is stronger and more strongly correlated with cognitive ability under instructed faking. Journal of Individual Differences, 38, 46–54. McCrae, R. R., Zonderman, A. B., Costa, P. T., Bond, M. H., Jr., & Paunonen, S. V. (1996). Evaluating replicability of factors in the revised NEO personality inventory: Confirmatory factor analysis versus Procrustes rotation. Journal of Personality and Social Psychology, 70, 552–566.

Murray, A. L., Johnson, W., McGue, M., & Iacono, W. G. (2014). How are conscientiousness and cognitive ability related to one another? A re-examination of the intelligence compensation hypothesis. Personality and Individual Differences, 70, 17–22. Musek, J. (2007). A general factor of personality: Evidence for the Big One in the fivefactor model. Journal of Research in Personality, 41, 1213–1233. Sackett, P. R., Lievens, F., Berry, C. M., & Landers, R. N. (2007). A cautionary note on the effects of range restriction on predictor intercorrelations. Journal of Applied Psychology, 92, 538–544. Saucier, G. (1992). Benchmarks: Integrating affective and interpersonal circles with the Big-Five personality factors. Journal of Personality and Social Psychology, 62, 1025–1035. Thielmann, I., Akrami, N., Babarović, T., Belloch, A., Bergh, R., Chirumbolo, A. et al. (2019). The HEXACO-100 across 16 languages: A large-scale test of measurement invariance. Journal of Personality Assessment. 2019. Yung, Y. F., Thissen, D., & McLeod, L. D. (1999). On the relationship between the higherorder factor model and the hierarchical factor model. Psychometrika, 64, 113–128. Ziegler, M., & Buehner, M. (2009). Modeling socially desirable responding and its effects. Educational and Psychological Measurement, 69, 548–565.

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