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Running head: RELATIVE PERSONALITY JUDGMENTS
A Cognitive Model of Reference Group Effects on Personality Judgment
This is the pre-publication version of the following paper, the full version of which can be downloaded at:
http://onlinelibrary.wiley.com/doi/10.1111/j.1467-6494.2012.00763.x/abstract
Reference: Wood, A. M., Brown, G. D. A., Maltby, J., & Watkinson, P. (2012). How are personality
judgments made? A cognitive model of reference group effects, personality scale responses, and behavioral
reactions. Journal of Personality, 80, 1275-1311.
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Abstract
This paper provides a cognitive model of reference group effects on personality judgment. Five
experimental studies show that the same person is evaluated differently depending on how their
behavior (a) ranks within a reference group, and (b) falls within the overall range of behavior
shown by other reference group members. Rank and range position strongly predict how the
individual will be judged. The results were invariant across stimulus type and response options
(seven point Likert scale, 990 point allocation task, or dichotomous choice), and persisted even
when participants were specifically told not to compare the target person to the reference group.
Simulated occupational scenarios suggested that these effects would lead participants to give
different sized bonuses and employ different people as a function of context. The results suggest
that personality judgments are made using the same cognitive mechanisms as are used to judge
psychophysical stimuli.
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A Cognitive Model of Reference Group Effects on Personality Judgment
Understanding how people make personality judgments is essential to personality
psychology, both to understand the cognitive processes underlying important social phenomena
and to enable accurate use and interpretation of the self-report Likert scales that underpin many
of the findings of the field. Since Hyman (1942), it has been understood that personality
judgments are affected by the social context within which a person is being judged. Reference
group (or “social comparison”) perspectives see personality judgments as arising from a
comparison of an individual with a reference group of other people (e.g., Festinger, 1954; Heine,
Buchtel, & Norenzayan, 2008; Heine, Lehman, Peng, & Greenholtz, 2002). Thus a person who
starts conversation and feels comfortable around people 50% of the time may be viewed as quite
extroverted by their colleagues if they are a librarian, but quite introverted if they are an
entertainer (as they will respectively be relatively high and low on extroversion within the
relevant reference group). Thus two people with identical personalities may be viewed very
differently on the same personality dimension if they are being compared to different reference
groups.
Although the existence of the reference group effect is well known (e.g., Festinger, 1954;
Heine et al., 2008; Heine et al., 2002; Peng, Nisbett, & Wong, 1997), there is little understanding
of how these relative judgments are made. Here we present a quantitative model of how people
compare a target’s personality to a reference group, where the model makes use of the same
principles as are used to judge such psychophysical stimuli as tone, weight, or size. Linking these
processes to psychophysical judgments is in line with aims for a more “unified psychology”
(Sternberg & Grigorenko, 2001), where universal psychological processes are identified and
integrated between the basic level of cognitive science and more social and applied areas of
psychology.
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The expectation above that reference group theory should be able to predict an
individual’s rating of a target personality is subject to the qualification “all other factors held
constant”, as personality judgments would normally be influenced by many other processes
including halo effects (Thorndike, 1920), similarity of the target to self (Markus, Smith, &
Moreland, 1985), and aspects of survey design (Tourangeau & Rasinski, 1988), to mention just a
few important influences on personality judgment. However, in principle, if these factors were
held constant (for example through experimental design), a clean test of the reference group
predictions should be possible.
Reference Group Theory in Personality Research
The study of reference group effects on personality judgments has a long history of
research and a sizable empirical base (e.g., Festinger, 1954; Heine et al., 2008; Heine et al.,
2002; Peng et al., 1997). However it would be to misread reference group theory to suggest that
personality judgments are normally inaccurate, when in reality people are actually surprisingly
good at judging each other’s personalities (Funder, 1995). The validity of personality judgments
are supported, for example, by the high stability of personality ratings over time and agreement
between self- and peer ratings (McCrae & Costa, 1987; McCrae & Costa, 1990). Such findings
would, however, be expected from reference group theory, which would predict that personality
judgments are valid when two people share the same reference group. For example, correlations
between self-ratings and the peer-ratings from a friend would be expected to converge, as both
people would likely share the same reference group. Similarly, personality ratings should remain
the same over time, as the person’s reference group would not generally change much after a
certain age (indeed, personality is much less variable after age 30, Srivastava, John, Gosling, &
Potter, 2003). Research designs associating multiple self-ratings (e.g., associating a given
personality trait with happiness) should be equally unaffected, as the person would likely use the
same comparison group when making both ratings, providing an accurate indication of the
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association between the two constructs. In trying to understand a person’s personality, knowing
the percentage of time a target behaves extrovertedly is not very useful information when
presented in isolation. Rather one needs to know how this person compares to the social context
of other people. If a person moves, for example, from an individualist to a collectivistic culture,
simply classifying almost everyone as highly collectivistic would not help decision making, as it
would allow little discrimination between individuals. It would be far more adaptive to form a
new context of personality, and judge the least collectivistic person as individualistic, even if in
different cultural contexts they would not be seen as such. Using reference groups to make
personality judgments is normally an adaptive and informative process, which can provide
accurate information for decisions based within the same social context.
Problems, however, emerge when comparing the personality ratings of people who use
different reference groups. Heine et al. (2002) asked participants to complete measures of
individualism vs. collectivism who were either (a) Canadian students with experience living in
Japan, or (b) Japanese students with experience of living in Canada. Three versions of the
questionnaire were produced, with items either asking participants specifically to (a) compare
themselves to North Americans, (d) compare themselves to Japanese people, or (c) use no
cultural referent and simply complete the questionnaire as per normal instructions. Contrary to
the opinion of a panel of cultural experts, there were weak and largely non-significant
differences between Canadian and Japanese people on collectivism when participants used the
normal questionnaire instructions or rated themselves relative to people from their own culture.
However, there were large differences when participants rated themselves relative to people in
the other culture. Thus people appear able to switch reference group easily and provide different
personality self-ratings depending on which reference group they are using. This research is
consistent with findings that increased contact with other cultures leads to greater agreement
about cultural differences (Triandis & Vassilio, 1967). Such effects may also explain why
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cultural differences in personality normally appear when using behavioral indices but not always
when comparing mean levels of self-reported personality across different cultures (Heine et al.,
2008; Peng et al., 1997).
Cognitive Basis of Relative Judgments
Central, although often implicit, in accounts of reference group effects is a cognitive
process whereby a rater compares a target personality to a reference group. How this process
occurs is not known. Previous accounts of reference group theory have largely not addressed this
point, and where it has been addressed the explanation has been some form of Helson’s (1947)
adaptation level theory (e.g., Wilson & Gilbert, 2008). This approach to relative judgment
suggests the target is compared to the mean of the reference group, so that the rating of an
individual’s extroversion would depend, for example, on how frequently the individual behaved
extrovertedly compared to the average behavior of the reference group. Such an approach is
intuitive but contrasts with independently motivated expectations derived from well-established
models of judgment as described below. Specifically it would not make use of the three
additional and readily available pieces of information at the rater’s disposal; (a) how the
individual’s behavior compares to the least extroverted person in the group, (b) how the behavior
compares to the most extroverted person, (c) how the individual ranks within the reference
group.
The first two pieces of information can be combined into the range principle; where the
target’s behavior falls on the overall range of the reference group’s behavior. Formally,
Range position = (Si – Smin) / (Smax – Smin)
(1)
where Smin and Smax are respectively the lowest and highest values in the reference group, and Si
is the target’s behavior. The range principle is supported by social psychology research into
anchoring, where people are pay attention to the highest or lowest value in a context (Stewart,
2009; Tversky & Kahneman, 1974).
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The third piece of information, the individual’s rank standing in the reference group, is
likely to be particularly essential to judgment. Both humans and other animals are very sensitive
to rank position (e.g., Allen & Badcock, 2003; Gilbert, 2006; Grant et al., 1998; Yeh, Fricke, &
Edwards, 1996). Whilst people are rather bad at estimating the actual size of stimuli they are
good at judging the size of one stimuli relative to another (e.g., Stewart, Brown, & Chater, 2005),
and encoding and manipulating frequencies (e.g., Gigerenzer & Hoffrage, 1995). The recent
decision by sampling (Stewart, Chater, & Brown, 2006) account of relative judgment is based
solely on rank and specifies the lower-level cognitive processes whereby such judgments are
made. Formally, judgment depends on the relative ranked position of the stimulus in the set:
Rank position = (ri – 1)/(N-1)
(2)
where ri is the ranked position of the stimulus in the context, and N is the number of stimuli in
the set.
Range Frequency Theory (Parducci, 1965, 1995; Wood, Brown, & Maltby, in press)
suggests that relative judgments are based on a weighted average of the range and rank principle:
Judgment of stimuli = wRange position + (1-w) Range position
(3)
where w is an empirically derived weighting constant. RFT originally developed in
psychophysics, where it was shown to provide highly accurate prediction of how people would
judge such stimuli as weights (Parducci, 1963), length (Parducci & Marshall, 1961), size
(Parducci, Calfee, Marshall, & Davidson, 1960; Parducci & Perrett, 1971; Parducci & Wedell,
1986), and sweetness (Riskey, Parducci, & Beauchamp, 1979). Somewhat later, RFT has also
been shown to explain how a wide variety of more social judgments are made, including (a)
fairness of wages and taxation (Mellers, 1982, 1986), (b) satisfaction with wages (Brown,
Gardner, Oswald, & Qian, 2008), (c) other people’s psychopathology (Wedell, Parducci, &
Lane, 1990), likability (Wedell, 1994), and attractiveness (Wedell, Parducci, & Geiselman,
1987), (d) personal happiness (Smith, Diener, & Wedell, 1989; Wedell & Parducci, 1988), (e)
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body image satisfaction (Wedell, Santoyo, & Pettibone, 2005), (f) satisfaction with performance
(Mellers & Birnbaum, 1983), (g) price perception (Niedrich, Sharma, & Wedell, 2001; Niedrich,
Weathers, Hill, & Bell, 2009) and (g) gratitude following aid (Wood et al., in press).
These processes have not, however, previously been used to account for reference group
effects on personality judgment. We suggest that people will compare a target personality to the
reference group using the rank and range principles, combined into a single judgment as
predicted by RFT. Further, we suggest that when a rater’s reference group and the target’s
position in the reference group are known, RFT can be used to predict what personality
judgments the rater will make.
Overview of Studies
Five studies were conducted, each of which involved three procedures: (a) providing
participants with a reference group of people varying in behaviors associated with a personality
trait (e.g., with varying likelihood of starting conversations), (b) experimentally manipulating the
placement of a target person’s behavior within the group, and (c) testing whether the target’s
rank and range position within the group affected how they were rated on a personality trait (e.g.,
extroversion). Studies 1 and 2 performed these three procedures specifically to test the rank
principle. Study 1 focused on extroversion as an initial test of the hypothesis and Study 2
expanded the results to the remaining Big Five traits of agreeableness, conscientiousness,
openness, and neuroticism (Costa & McCrae, 1995; Goldberg, 1992). Study 3 used a more
precise methodology, allowing mathematical modeling of the results to test how well personality
judgments can be predicted from the target’s rank and range position within the reference group.
This modeling was also used to test whether both the rank and the range principle are necessary
to predict personality judgments. Study 4 experimentally tests the range principle, examining
whether the results persist when studying behavioral intentions, and when telling people
specifically not to make relative judgments (through asking participants to rate each person
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singly and objectively without regard to the reference group). Study 4 also explored how the
results may influence behavior in a workplace. Study 5 showed that the results generalize to a
dichotomous decision – deciding on whom to employ from a group of potential candidates.
Taken together the studies were designed to show that rank and range principles explain
reference group effects on person perception and that the results are invariant across (a) stimulus
type, (b) response option (7 point anchored scale, 990 point scale, or dichotomous choice), and
(c) task instruction. The studies provide 50 replications of the basic finding, demonstrating the
reliability of the model across various different personality traits and situations.
Study 1
Method
Participants
Participants were a convenience sample of 53 adults (30 female) aged between 18 and 78
years (M = 33.75, SD = 15.59) who volunteered to take part in the study, without payment. Most
participants described themselves as White (90.6%). Participants were randomly allocated to one
of two groups (25 to 28 per group).
Design and Procedure
All participants read descriptions of nine different people. Each description focused on
how likely the person was to exhibit characteristics associated with extroversion. Four
characteristics were chosen: (1) start conversations around people, (2) feel comfortable around
people, (3) have little to say (indicative of introversion), and (4) be quite around strangers
(indicative of introversion). The characteristics were taken from the extroversion sub-scale of the
International Personality Item Pool - Five Factor Model (IPIP-FFM scale) (Goldberg, 1999;
Goldberg et al., 2006) which was developed based on the Goldberg’s (1992) Big Five Markers.
These characteristics appear to reliably and validity assess extroversion, with the IPIP-FFM
extroversion scale showing high internal consistency (= .87), 3-week test-retest reliability of r
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= .89 (Donnellan, Oswald, Baird, & Lucas, 2006), and convergence with several behavioral
indices of extroversion, including interpersonal behavior (r = .45) and leadership (r = .48)
(Oswald, Schmit, Kim, Ramsay, & Gillespie, 2004). Selecting the characteristics from an
established and psychometrically valid inventory increases confidence that the characteristics
genuinely represent extroversion.
The four characteristics were put together into a coherent sentence: “In general, [name]
will [probability word] start conversations and feel comfortable around people. There is
[probability word] that she will have little to say or be quiet around strangers”. The same
sentence was presented for each of the nine people, with the exception of each person having a
different name (counterbalanced between the conditions to avoid any name association effects)
and probability word that indicated how likely they were to behave extrovertedly. Descriptions
of people were presented on a single A4 sheet and participants were asked to read these
carefully, spending as long as they wished doing so. After they had finished, they separately
rated each of the nine people on the extroversion sub-scale of the Mini International Personality
Item Pool (Mini-IPIP) scale (Donnellan et al., 2006), which provides four items assessing
extroversion which are rated on a 1 (“very inaccurate”) to 5 (“very accurate”) scale. This scale
was developed as a shorter version of the IPIP-FFM from which the extroversion characteristics
used in the descriptions were drawn (we ensured there was no direct overlap between the IPIPFFM descriptions used in this study and those used to developed the Mini-IPIP scale). The use of
these two scales was designed to ensure that both the descriptions and the questions were
assessing the same construct; empirically this is the case with the IPIP-FFM and Mini-IPIP
correlating at r = .97 (Donnellan et al., 2006). The Mini-IPIP extroversion scale has good
properties, with 3-week test-retest reliabilities of r = .87 and high convergence with other
common extroversion measures (e.g., r = .81 with the Big Five Inventory [John & Srivastava,
1999]) (Donnellan et al., 2006). The Mini-IPIP extroversion scale was coded according to
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normal instructions so that each participant had provided a single extroversion rating for each of
the nine people (9 scores in total).
Experimental Manipulation
The probability words used to describe each of the nine people differed between the two
groups and constituted the experimental manipulation. Previous work has established the
percentages that people naturally with certain probability words. Specifically, “no chance” is
associated with 0%, small chance with 14.43%, even chance with 50%, very likely with 81.53%,
and definitely with 100% (Stewart et al., 2006). On this basis, we were able to create two groups
of people that varied in how extroverted members were; technically the groups respectively had
unimodal or bimodal distributions of extroverted behavior. These groups were designed to
manipulate the rank position of certain “target” persons who appeared in both groups. This
procedure follows the standard RFT paradigm (e.g., Brown et al., 2008; Wood et al., in press).
Table 1 shows the probability words associated with each of 9 people seen by both the
unimodal and bimodal group. Where available, Table 1 also shows the probabilities that people
naturally associate with these words (taken from Stewart et al., 2006). As the descriptions
included both positively and negative worded descriptions of extroversion, each probability word
was associated with an opposite word (e.g., no chance [0%] was associated with definitely
[100%]) which was used for the negatively coded description; these are also indicted in Table 1.
INSERT TABLE 1 HERE
Note that both groups saw people whose likelihood of behaving in an extroverted manner
ranged from “no chance” (0%) to “definitely” (100%) and the groups saw the same three target
people in addition to these endpoints (1 “small chance”, 14.43%; 2 “even chance”, 50%; and 3
“very likely”, 81.53%; all in bold in Table 1). If people’s ratings of extroversion depended
simply on the absolute, objective behavior of the individual, then there should be no difference in
the ratings given to these target people between the two groups. However, if ratings of
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extroversion are rank-dependent, the target people should be rated as more or less extroverted
depending on group. Specifically, the person who had a small chance (14.43%) of behaving in an
extroverted way should be rated as less extroverted in the unimodal group (where rank = 2, i.e.,
the person is the second least extroverted person) than in the bimodal group (where rank = 4, i.e.,
it is the fourth least extroverted person). The person who has an even chance (50%) of behaving
extrovertedly should be judged as equally extroverted in each group, as rank = 5 in both cases.
However, the person who is very likely (81.53%) to behave in an extroverted way should be
judged to be more extroverted in the unimodal group (rank = 8) than in the bimodal group (rank
= 6). Note that this implies a cross over interaction; the unimodal group should rate the first
target person as higher on extroversion, both groups should rate the second person equally, and
the unimodal group should rate the third person as lower on extroversion. Given that the only
differences between the people involve the rank positions of their behavior, comparison of these
conditions tests whether judgments of extroversion are influenced by rank standing within the
reference group. As each of the target people are equal distance from the mean of the group
(50%) this is specifically a test of rank; the dominant adaptation to the mean account of reference
group theory would again predict no differences between the groups (thus any significant results
cannot be explained by this rival hypothesis).
Results and Brief Discussion
The results showed the expected interaction between target person and group. Target
person 1, who had a small chance of behaving extrovertedly, was rated more as less extroverted
in the unimodal condition (where rank = 2; M = 1.92, SD = 0.49) than in the bimodal condition
(where rank = 4; M = 2.25, SD = 0.38). Target person 2, who had an even chance of behaving
extrovertedly (and ranked 5th in both groups), was rated as approximately equal on extroversion
in both groups (unimodal M = 2.86, SD = 0.45; bimodal M = 2.90, SD = 0.34). In contrast, target
person 3, who was very likely to behave extrovertedly, was rated as more extroverted in the
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unimodal condition (where rank = 8; M = 4.10, SD = 0.57) than in the bimodal condition (where
rank = 6; M = 3.57, SD = 0.66).
The interaction is graphed in Figure 1 (with 95% confidence intervals). Confidence
intervals for a group that do not bound the mean of the other group indicate statistically
significant differences; thus as expected the extroversion ratings of the first and third common
people differed, whereas the ratings of the second common person did not. The interaction was
confirmed with 2 (between: group) X 3 (within: common person) mixed model ANOVA. As
expected, there was a main effect of target person, with people who behaved more frequently
extrovertedly rated higher on extroversion (F [2,102] = 178.81, p < .001). There was also the
expected interaction between group and comparison point, which was highly significant (F
[2,102] = 11.16, p < .001), suggesting that the effect of increasing levels of extroverted behavior
on ratings of extroversion partially depends on the rank order of the behavior within the
reference group.
INSERT FIGURE 1 HERE
The results showed that people’s ratings of a target person’s personality depend on how
that person ranks within a reference group.
Study 2
Introduction
Study 1 provided the first test of whether people’s assessment of a target person’s
personality was influenced by how the target’s behavior ranked within a reference group.
However, the study focused on only one particular personality trait – extroversion. Study 2
aimed to test whether these results would apply to personality more generally, using the same
paradigm to show that rank position equally effects ratings of the remaining “Big Five” traits of
agreeableness, openness, conscientiousness, and neuroticism. Together with extroversion, these
traits represent most of personality at a high level of abstraction (Goldberg, 1993; McCrae &
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Costa, 1987) and can act as a broad map of personality psychology (Watson, Clark, & Harkness,
1994). Expanding the results to these traits would increase confidence that the results are
generalizable to personality generally, rather than being specific to some aspect of extroversion.
Method
Participants
Participants were 157 (135 female) undergraduate students aged between 18 and 47 (85%
< 21). Participants most frequently described themselves as White (84.7%), of mixed race
(6.4%), or Pakistani (3.8%).
Design and Procedure
The design broadly followed Study 1 although incorporating more personality traits.
Participants essentially completed the Study 1 task five times, once for each of the Big Five
traits. First, participants read descriptions of nine people who varied in how likely they would be
to “start conversations or feel comfortable around people”. Descriptions of the nine people were
presented on a single A4 page, and participants could read them for as long as they wished. After
reading the descriptions, participants then rated to what extent they agreed that each person was
“extroverted and enthusiastic” on a 1 (“definitely agree”) to 7 to (”definitely disagree”) scale.
Second, after completing this task, participant read descriptions of nine further people (with
different names than in the first task to reduce any cross-task contamination). These people
varied on how frequently they be “considerate and kind to almost everyone, or be helpful and
unselfish with others”. After reading these descriptions participants rated how “sympathetic and
warm” their people were on the same 1 to 7 scale. Third, with the same procedure, participants
read and rated people who may “do a thorough job or persevere until the task is finished” on the
extent to which they were “dependable and self-disciplined”. Fourth, participants read about
people who may “worry about things or get stressed out easily” who they rated on the extent to
which they were “anxious and easily upset”. Fifth, participants read about people who may
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“value artistic, aesthetic experiences or be curious about many things” who they rated on the
extent to which they were “complex and open to new experiences”.
Each of the five tasks was presented entirely separately, and participants were required to
finish one task before starting on the next. In each of the tasks the participants first read about
nine people, presented on one page of A4, and then rated these individuals on a second A4 page.
The order in which the five tasks were presented was fully counterbalanced so as to reduce order
effects. The wording of the behaviors associated with each person in each of the tasks based on
the Big Five Inventory (John & Srivastava, 1999) (with the exception of extroversion, which
used the same descriptions as Study 1 for consistency). This is one of the most commonly used
Big Five scales and has very good psychometric properties (alpha and three-month test-retest
reliability ranging from .79 to .90, unreliability corrected convergence with the corresponding
scales of other common Big Five measures of between r = .83 and r = .99, John & Srivastava,
1999). The questions that each participant rated were the positively worded items from the Ten
Item Personality Inventory (Gosling, Rentfrow, & Swann, 2003). This scale also has good
psychometric properties, converging between self and observer ratings and other widely used
Big Five measures (regardless of whether assessed by self, observer, or peer reports), and
showing a mean test-retest reliability of r = .72. The TIPI is fast becoming a measure of choice
when burden is a particular issue, and the scale has been used in over 150 studies over the last six
years (ISI Social Science Citation Index, 2009). The rationale for departing slightly from the
items chosen in Study 1 was to reduce participant burden through providing shorter descriptions
and fewer rating scales, due to concern about fatigue given the task now needing to be completed
five times. Additionally, it was desirable to see that the results were not somehow specific to the
particular behaviors used in the IPIP-FFM that was used in Study 1.
Experimental Manipulations
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Previous research using the RFT paradigm has used one of two paired experimental
conditions. First, some research uses the unimodal and bimodal distributions explained in Study
1 (e.g., Brown et al., 2008; Wood et al., in press) to manipulate rank position of target people.
These conditions have the advantage of ensuring the target people in each condition are equal
distance from the mean of the reference group. However, they have the disadvantage of using
distributions that are not common in the real world. In contrast, other previous research has
alternatively used positive and negatively skewed distributions (e.g., Mellers, 1986; Parducci &
Wedell, 1986). In the current research, we used both types of distributional pairs to ensure
generalizability and to provide a replication of each of the results. Participants were randomly
assigned to the (a) positive skew, (b) negative skew, (c) unimodal, or (d) bimodal groups (named
after the shape of the distribution of the behavior in the reference group).
Skew Comparisons. Table 1 shows the probability words associated with both the two
skew conditions. For both groups, the person with the least and most likelihood of exhibiting the
behavior (respectively, “no chance”, 0%, and “definitely”, 100%) was the same. In addition to
these end points, the two groups saw two target people in common, who respectively had a
“small chance” (14.43%) or were “very likely” (81.53%) of exhibiting the behavior. In both
cases, these individual would be expected to be rated higher on the personality trait in the
positive skew group as these target people ranked higher in the reference group (respectively
ranking 5th versus 2nd and 8th versus 5th).
Modality Comparison. The probability terms used for the unimodal and bimodal groups
were exactly the same as used in Study 1 (see the top of Table 1) with the minor exception that in
the bimodal condition “very certain” was changed to “pretty certain” and “almost certain” was
changed to “very certain” (these changes were based on participant feedback that the ordering of
these terms was less ambiguous; given that the modification did not change the rank of the target
people it should not affect the results). The expectations for these conditions are as in Study 1.
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Results
Comparison of Skew
For the first test of rank, the mean personality ratings given of the two target people (who
had a “small chance” or were “very likely” of exhibiting the behavior) were compared between
the positive and negative skew groups. Both common points had a higher rank in the positive
skew group than in the negative skew group (respectively ranking 5th versus 2nd and 8th versus
5th) and thus were expected to attract higher personality ratings in this condition. Table 2 shows
this appears to be the case; both target people attracted a higher mean personality rating in the
positive skew condition for each of the personality traits. This finding was shown to be
statistically significant with a 2 (between: group) X 5 (within: personality trait) X 3 (within:
target person) mixed model ANOVA, which showed a main effect of group (F [1,79] = 38.44, p
< .001) and no interactions between group X personality trait (F [4,316] = 0.55, p =.70) or group
X personality trait X target person (F [4,316] = 2.18 p = .07). The ANOVA confirmed that
people’s personalities were rated more highly in the positive skew group (where their rank was
higher) than in the negative skew group (where their rank was lower), and this effect generalized
across each of the Big Five.
INSERT TABLE 2 HERE
Comparison of Modality
For the replication test of rank, the unimodal and bimodal groups were examined. The
mean levels of personality for the three target people are presented in Table 3. For each of the
Big Five, this table shows the expected differences between the groups. The first target person
(having a “small chance” of exhibiting the trait) appears to be rated lower on the trait in the
unimodal group (where rank = 2), than in the bimodal group (where rank = 4). The second target
person (where rank = 5 in both groups) was rated equally in each group. The third target person
(being “very likely” to exhibiting the trait) appears to be rated higher on the trait in the unimodal
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group (rank = 8) than in the bimodal group (rank = 6). Thus the personality rating of a person
again depended on how they ranked amongst the comparison group. The interaction is graphed
in Figure 2 with 95% CI. As in Study 1, the confidence intervals shows that the ratings of the
first and third peopled differed in the expected directions, whereas there was no difference in the
ratings of the second target person.
The significance of the interaction was tested with a 2 (between: group) X 5 (within:
personality trait) X 3 (within: target person) mixed model ANOVA. As expected, there was a
main effect of target person, with people exhibiting more of the trait being rated more highly on
the trait (F [2,148] = 485.27, p < .001). There was also the expected interaction between group
and comparison point, which was highly significant (F [2,148] = 26.40, p < .001), suggesting
that the rating of a given person’s personality depends on the rank order that person's behavior in
the context of the behaviors of other people in the reference group. There was no main effect of
group, personality trait, target person X personality trait, or common point X trait X group,
suggesting that again the results are equally applicable across each of the Big Five.
INSERT TABLE 3 AND FIGURE 2 HERE
Brief Discussion
Study 2 replicated Study 1, additionally showing that not only judgments of extroversion
are predicted by the rank principle, but that the principle applies in equal degree to
agreeableness, conscientiousness, openness, and neuroticism. Further, Study 2 provided a
replication of all of the results presented, showing the same principle applies for different shapes
of distributions in a theoretically consistent manner.
Study 3
Introduction
Studies 1 and 2 provided evidence for the rank principle. Further, it seemed that people’s
judgments of target personalities could be predicted quite well from the context alone, when
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other factors were held experimentally constant. This can be inferred from the small standard
deviations around the ratings of the target personalities and from the small confidence intervals
in Figures 1 and 2 (relative to the sample size). However, this is quite indirect evidence of the
starting hypothesis that personality judgments can be strongly predicted from context alone
(when other factors were held experimentally constant) given knowledge about (a) the reference
group, (b) the target’s placement within the group, and (c) the judgment processes used. Further,
whilst the using of probability words to alter the placement of the target personalities has the
benefit of presenting the stimuli using natural language – as people may commonly describe
others – it has the disadvantage of providing noisy data. The numeric probabilities that people
naturally associate with probability words show moderate standard deviations, suggesting that
people interpreted the meaning of the words somewhat differently (Stewart et al., 2006). This
increased error would count against the hypothesis so would not be expected to affect the basic
finding, but would reduce the size of the effects. Finally, and importantly, Studies 1 and 2 have
only tested the rank and not the range principle.
Study 3 addresses these limitations. First, percentage information is presented on the
frequency with which people undertake different behaviors associated with specific personality
traits, reducing the error associated with using probability words. Second, we explicitly
mathematically model the results, comparing people’s actual responses with the responses that
would have been expected if the rank and range principle were. This allows precise
quantification of the variance in personality judgments accounted for by these principles, directly
testing the starting hypothesis. The model fitting is made possible due to the increased precision
associated with moving from probability words to percentages. Third, the modeling allowed
testing of the range principle; a rank only model was directly compared to a full RFT model
including the range parameter.
Method
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Participants were 119 people (76 female) aged between 18 and 61 (M = 22.76, SD =
6.19), predominantly undergraduate students, who mostly described themselves as White
(85.7%) or Chinese (3.4%). Participants were randomly allocated to one of four groups (27 to 33
per group).
Design and Procedure
The design broadly followed Study 2, with the same descriptions of the nine people, same
rating scales, and same procedure. The only differences between the studies were that Study 3
gave information on 11 rather than 9 people, and that these people were described in terms of the
percentage of time they undertook the various behaviours (instead of probability words
indicating likelihood). Participants were randomly allocated to one of four groups as in Study 2
(positive skew, negative skew, unimodal, bimodal groups). The percentages associated with the
behavior of the nine people are listed in Table 4.
INSERT TABLE 4 HERE
Experimental Manipulations
Skew Comparisons. All participants saw people who performed the behaviors between
9% and 63% of the time. In addition to these end points, there was one person who was the same
in both groups, who performed the behaviors 36% of the time. This person would be expected to
be rated more highly on the personality traits in the positive skew group (where rank = 9) than in
the negative skew group (where rank = 3).
Modality Comparisons. All participants again saw people who performed the behaviors
between 9% and 63% of the time. In addition to these end points, there were three target people
in common between the two groups (who respectively performed the behaviors 23%, 36%, and
49% of the time). The first target person (23%) would be expected to be rated lower on the
personality traits in the unimodal group (where rank = 2) than the bimodal group (where rank =
5). The second target person (36%) would be expected to be rated identically in each group (as
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rank = 6 in both cases). The third target person (49%) would be expected to be rated higher in
the unimodal group (where rank = 10) than in the bimodal group (where rank = 7). Note that this
implies a crossover interaction, mirroring the expectations and results of the modality
comparisons in Study 1 and 2.
Results
Comparison of Skew
Statistical Testing. The mean personality ratings for the target person (who performed
the behaviors 36% of the time) were compared between the positive and negative skew groups.
As expected, Table 5 shows that the target person attracted higher personality ratings in the
positive skew group (where rank = 9) than in the negative skew group (where rank = 3). The
significance of this effect was tested with a 2 (between: group) X 5 (within: personality trait)
mixed model ANOVA. As expected there was the main effect of group, which was highly
significant (F [1,55] = 42.15, p < .001). There was no main effect of personality trait, suggesting
that overall no trait was rated more highly at the common point (F [1,55] = 2.07, p = .16), and
there was no personality X group interaction (F [1,55] = .001, p = .98), suggesting that the
positive skew group rated the common point higher across each of the Big Five traits.
INSERT TABLE 5 HERE
Data Modeling. In order to examine effects of rank and range using all the data points,
we fit RFT (equations 1-3 in the introduction) to the data. First, the lower half of Figure 3 shows
the fit of RFT to the mean data (line represents model; symbols represent data).. As can be seen,
there is substantial convergence between model and data. The fit was good for each of the Big
Five, with R2 = .99 for each trait. The relative weighting between rank and the range principle
(where 0 = completely rank and 1 = completely range) was extroversion = .60, openness = .49,
neuroticism = .59, conscientiousness = .55, agreeableness = .61. The w estimates of around .5
mirror those from previous work in RFT (e.g., Smith et al., 1989).
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INSERT FIGURE 3 HERE
Next, to enable nested model comparison, we fit RFT to the data for each participant
individually. Maximum likelihood parameters (those that would maximize the probability of the
data given the model) were estimated using a simplex algorithm. In order to calculate exact
probabilities, we assumed that each discrete response from each participant reflected an
underlying response tendency that was normally distributed on an underlying internal scale with
standard deviation s, and mean as predicted by either RFT, a range-only model (w = 1), or a
rank-only (w = 0) model. The free parameters s (and w for RFT; cf. Equation 3) were estimated,
as were scale endpoints.
We examined whether the full RFT fit better than either a range-only model (w = 1) or a
rank-only model (w = 0), using a simple likelihood ratio test as the range-only and rank-only
models are nested within RFT. Between zero and four participants were excluded from each
condition due to exhibiting a response range < 3 or a Kendal coefficient between stimuli and
responses < .50. The resulting chi-squared are shown in Table 6; in each case the RFT model fit
significantly better than either the rank or range only models (p < .001), suggesting that both
principles are needed to explain reference group effects on personality judgments.
INSERT TABLE 6 HERE
Comparison of Modality
Statistical Testing. The mean ratings of personality at the three common points are
presented in Table 7. For each of the Big Five, this table shows the expected differences between
the groups. Target person 1 (exhibiting the behavior 23% of the time) appears to be rated lower
on the trait in the unimodal group (where rank = 2), than in the bimodal group (where rank = 5).
Target person 2 (exhibiting the behavior 36% of the time) was rated identically in both
conditions (in both cases rank = 6). Target person 3 (exhibiting the behavior 49% of the time)
appears to be rated higher on the trait in the unimodal group (rank = 10) than in the bimodal
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group (rank = 7). Thus the personality rating of a person again depended on how they ranked
amongst the comparison group. This interaction was tested with a 2 (between: group) X 5
(within: personality trait) X 3 (within: target person) mixed model ANOVA. As expected, there
was a main effect of target person, with people exhibiting more of the trait being rated more
highly on the trait (F [2,480] = 417.06, p < .001). There was also the expected interaction
between group and target person, which was highly significant (F [2,480] = 70.05, p < .001),
suggesting that the rating of a given person depends on how their behavior ranks in the reference
group. There was no main effect of group, personality trait, common point X personality trait, or
common point X trait X group, suggesting that again the results are equally applicable across
each of the Big Five.
Data Modeling. Data modeling was performed as for the skew comparisons. The top half
of Figure 3 illustrates the results. The prediction of the personality ratings from the target’s range
and rank position within the reference group was again near perfect for each of the Big Five,
with R2 = .99 for each trait. The relative weighting between rank and the range principle (where
0 = completely rank and 1 = completely range) was extroversion = .39, openness = .46,
neuroticism = .39, conscientiousness = .42, agreeableness = .40, again showing the importance of
the both principles. Table 6 also shows the results of comparing an RFT model to a range or rank
only model. In each case the RFT model substantially improved prediction (p < .001) again
suggesting that both principles are needed to understand reference group effects on personality
judgments.
Brief Discussion
Study 3 demonstrated that the rank and range principle strongly predicted the personality
ratings that the person would receive (R2 = .99). Further, modeling showed that both the rank and
range principle was needed to predict responses. This study was consistent with the original
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hypothesis that, when other factors were held constant, personality ratings could be strongly
predicted simply from position within the reference group using rank and range principles.
Study 4
Introduction
Study 4 (a) tested whether the same effects occurred when people were specifically told
not to compare target personalities to the reference group, (b) expanded the results to behavioral
intentions, and (c) expanded the results to the applied domain of how people may be treated in
the workplace, (d) showed the results are not specific to the rating scale used, and (e) provided
an experimental test of the range principle.
First, from the results of Studies 1 to 3 it is not clear that people were genuinely engaging
in the task, and giving their real opinions, or whether they were simply ranking the stimuli. To
address this, in Study 3 participants were specifically told not to compare the people to each
other, but to judge them specifically on their objective behavior. If rank and range effects still
occurred, this would suggest there are automatic processes operating outside of conscious
control, which would strengthen confidence in the results representing how these judgments are
actually made in non-experimental contexts.
Second, to further emphasize that the task should be based on objective performance, we
created a task where participants imagined they worked for the human resources department of a
firm who allocated bonuses based on how frequently certain behaviors were performed at work.
Intuitively, it should be easy for participants to allocate bonuses based on objective behavior, and
thus this represents a conservative test of the automaticity of the rank and range hypotheses. This
procedure also has the benefit of extending the results of the first two studies to actual behavioral
intention, and would also have applied significance in showing that how people are viewed and
treated at work may depend on how their behavior ranks and falls on the range of the reference
group of their colleagues. Personality research has been influential in occupational settings, for
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example through predicting job performance (Barrick & Mount, 1991), and as such are used in
employment selection decisions (Anderson & Shackleton, 1993). It seemed of applied
importance to show how reference group effects influence occupational decisions personality
judgments.
Third, in allowing people to allocate bonuses from £10 to £1,000 we effectively allowed
ratings on a 990-point scale. This is important as previous work in RFT provides the suggestion
that rank effects may be influenced by the rating scale used, with stronger rank effects observed
for scales with a smaller number of response options (Parducci & Perrett, 1971; Parducci &
Wedell, 1986; Wedell & Parducci, 1988). By providing an effectively unlimited scale, we aimed
to show the results were not confounded by response scale.
Forth, we experimentally tested the range hypothesis. Study 3 supported the importance
of range principle through data modeling, which showed the results were better predicted when a
range parameter was included. Study 4 demonstrates the causal importance of the range principle
through experimental manipulation.
Method
Participants were 152 undergraduate students (94 female) with ages ranging from 18 to
44 years (94% under 23). Ethnicity was predominantly White (74.3%).
Design and Procedure
Participants completed the bonus allocation task five times, once for each of the Big Five
personality traits, broadly as in Studies 2 and 3. The order in which the tasks were presented was
counterbalanced across participants. For each of the tasks, participants were given the
instructions:
“Imagine that you work in the human resources department of a major company. For a given
position, [description of the personality trait] is particularly important, and the company has
collected accurate and objective data on how [trait name] employees have been over the last
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year. At the end of the year, the company wants to reward people with a bonus of between £10
and £1000, depending on how [trait name] they have been. The table below shows information
on 11 employees. Your task is to decide how much to give each employee, based on the % of
time they have been [trait name]. You should rate each employee individually, based on how
objectively [trait name] they have been, rather than how they compare to other employees. Please
allocate between £10 and £1000 to each employee based on how objectively [trait name] they
have been.”
The descriptions of the behaviors presented in each counterbalanced task were (1)
dependability and self-discipline, (2) sympathy and emotional warmness, (3) creative and open
to new experiences, (4) extroverted and enthusiastic, (5) calm and emotionally stable.
Descriptions were based on the TIPI (Gosling et al., 2003) as described in Study 2. Participants
were randomly allocated to one of four groups (unimodal, bimodal, negative skew, and high
mean positive skew). Depending on condition, participants saw workers who varied in the
percentage of time they exhibited the desirable behaviors; the percentages that each group saw
are presented in Table 4. After participants had completed all tasks they were asked the debrief
question: “For the tasks you just completed, did you understand that you were meant to assign a
bonus to each person based on their own individual objective performance, rather than how they
compared to the other employees that you also rated?”
Experimental Manipulations
Modality comparisons (test of rank). The test of rank broadly followed the methodology
of the earlier studies. Both groups saw people who performed the behaviors between 10.7% and
75% of the time. In addition to these end points, there were three target people in common
between the two groups (who respectively performed the behaviors 27.4%, 42.8%, and 58.3% of
the time). The first target person (27.4%) would be expected to be given a lower bonus in the
unimodal group (where rank = 2) than the bimodal group (where rank = 5). The second target
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person (42.8%) would be expected to be given an identical bonus in each group (as rank = 6 in
both cases). The third target person (58.3%) would be expected to be given a higher bonus in the
unimodal group (where rank = 10) than in the bimodal group (where rank = 7). Note that this
implies a crossover interaction mirroring the results of the modality comparisons in Study 1 to 3.
Skew Comparisons (test of range). The skew comparisons used in Study 4 differed
substantially in design and purpose from the previous skew comparison in Studies 2 and 3. The
two distributions (see Table 1) tested whether the overall bonus given to the group of people
depended on the skew of behavior of the composing individuals, as predicted by the range
principle. The design and implementation of this test followed previous studies in the RFT
paradigm that experimentally test range (e.g., Smith et al., 1989; Wedell & Parducci, 1988;
Wood et al., in press). The positively skewed high mean distribution was designed to have the
same overall mean as the negatively skewed distribution; in both groups people exhibited the
desirable behavior 55% of the time. Both distributions also had a range of 64 (the negative skew
group beginning at a lower number is necessary for the mean to be equal as the values cluster at
the higher end of the range). For each of the Big Five (within group) tasks the bonuses given to
the 11 employees were summed to provide the total value of the bonuses allocated. As the high
mean positive and negative skew groups both exhibited the desirable traits for the same amount
of time on average, if people were judging each employee individually, without comparison to
the reference group, then there should be equal bonuses allocated to each group. However,
according to the range principle, greater bonuses should be allocated to the negative skew group,
as the majority of the employee’s behavior clustered at the highest end of the range (e.g., Smith
et al., 1989; Wedell & Parducci, 1988; Wood et al., in press). Note that this is exclusively a test
of range which is unaffected by the rank principle. Any individual point within the two skewed
distributions would be determined by a compromise between range and rank (as in Studies 2 and
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3), however when the bonuses given to the 11 people are summed any effects of rank are
averaged out and can no longer effect the results (Wood et al., in press).
Results
In the debrief, all of the participants indicated that they had understood that they were
meant to be assigning bonuses to each person based solely on individual performance, rather
than how the people compared with each other.
Results of the Test of the Rank Principle
For both of the conditions, and each of the Big Five, allocated bonuses ranged from £10
for the lowest behavior (exhibiting the trait 10.7% of the time) to £1000 for the highest behavior
(exhibiting the trait 75% of the time). However, in general participants did not allocate the
minimum or maximum bonuses; for the lowest behavior (10.7%) average bonuses ranged from
£69.42 (SD = 53.31) to £81.31 (SD = 52.89) across the conditions and the Big Five, whilst for
the highest behaviors (75%), average bonuses ranged from £686.84 (SD = 219.08) to £745.47
(SD = 175.48).
The mean levels of bonuses give to the three target people unimodal and bimodal groups
are presented in Table 8. For each of the Big Five, this table shows the expected differences
between the groups. Target person 1 (exhibiting the personality characteristic 27.4% of the time)
appears to be given a lower bonus in the unimodal group (where rank = 2), than in the bimodal
group (where rank = 5). Target person 2 (exhibiting the characteristic 42.8% of the time) was
rated similarly in both groups (rank = 6 in both cases). Target person 3 (exhibiting the
characteristic 58.3% of the time) appears to be given a higher bonus in unimodal group (rank =
10), than in the bimodal group (rank = 7). Thus the amount of bonuses assigned to people on the
basis of their "objective" personality expression is influenced by how that person ranks against
other people. This interaction was tested with a 2 (between: group) X 5 (within: personality trait)
X 3 (within: common point) mixed model ANOVA. As expected, there was a main effect of
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comparison point, with people exhibiting more of the trait being given a higher bonus (F [2,148]
= 404.39, p < .001). There was also the expected interaction between group and comparison
point, which was highly significant (F [2,148] = 14.11, p < .001), suggesting that the bonuses
assigned to a given person partially depends on the rank order that person's personality in the
context of the personalities of other workers. There was no main effect of group, personality
trait, common point X personality trait, or common point X trait X group, suggesting that the
results are equally applicable across each of the Big Five. Thus the amount of bonuses assigned
to people on the basis of their "objective" personality expression is influenced by how that
person ranks amongst other people in the reference group.
INSERT TABLE 8 HERE
Results of the Test of the Range Principle
We tested whether the amount of bonus given to the total group of people depended on
the skew of the people's personalities, as predicted by the range principle. Total expenditure on
bonuses was calculated for both the positive and negative skew conditions, by totalling the
bonuses given to each of the 11 employees. As can be seen from Table 9, substantially more
money was to the negative skew group (where most people clustered at the top end of the range)
compared to the positive skew group (where most people clustered at the bottom end of the
range), even though on average both groups exhibited the desirable personality trait the same
amount of time (55%). The differences were substantial; between £521.10 and £1,168.89 (11%
to 26%). These differences was tested with 2 (between: group) X 5 (within: personality trait)
mixed model ANOVA, showed a main effect of group (F [1,73] = 5.92, p = .02), and a main
effect of trait (F [1,73] = 12.97, p < .001), with some traits attracting more bonuses than others,
but no group X trait interaction (F [1,73] = .04, p < .84). Thus the skew of people's personalities
within a group will determine how much bonuses that group will be given overall, and the
findings are equally as applicable for each of the Big Five.
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INSERT TABLE 9 HERE
Brief Discussion
Study 4 showed that even when trying to rate people based on their objective
characteristics people were still affected by the rank and range position of the target in the
reference group as predicted by RFT. Specifically, rank and range position influenced the size of
bonuses people assigned to employees for exhibiting desirable behavior, suggesting these
processes may be important in occupational settings. The range principle was supported
experimentally, and the results using 990 response options were substantively similar to earlier
studies with seven response options, suggesting invariance across output options.
Study 5
Introduction
Study 5 tested whether the RFT would predict how people would behave in another
simulated occupational task, where participants decide on which potential employee to hire
(from a pool of other candidates). This expands on the earlier studies through showing that the
results still occur when people make a single decision about an individual target, rather than rate
every person in the reference group.
Method
Participants
Participants were 118 people (101 female) with a median age of 19.5, of predominantly
White ethnicity (83%), who participated voluntarily or in return for course credit.
Design and Procedure
Participants were randomly assigned to a positive skew or negative skew group, and told
“Imagine that you work in the human resources department of a major company. You have been
given a task of hiring new employees for five positions.” Each participant completed the
candidate selection task five times, once for each of the Big Five personality traits, as in Studies
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2 – 4. The order in which the tasks were presented was counterbalanced across participants. For
each of the tasks, participants first saw information on 13 different candidates, who varied in
percentage of time they exhibited the desirable personality trait (described respectively in the
five tasks as “extroversion”, “openness”, “emotional stability”, “conscientious”, and
“agreeableness”). Participants were told “Although special technical skills are not required,
certain personal traits will be more desirable for each post” and asked to choose the most
appropriate candidate in each task. The salaries that each of the 13 candidates would require
were also provided, and were linearly related to the percentage of time the candidates engaged in
the desirable behavior, so that more desirable candidates were more expensive to hire. Better
candidates may be expected to want a higher starting salary, as they would be in greater demand
from other employers. Participants were told “you must choose from those available who
represents the best value. In each case, the budget is tight, and rather than just spend anything
you want on salaries, you must also keep costs to a minimum, so there will be a trade-off
between choosing the person with the most appropriate personal traits, and keeping costs down”.
This was to ensure that participants didn’t all simply choose the most desirable candidate. The
relationship between salaries and personality was determined by:
Salaries = (P * 100)*107+10000
(4)
where P = the percentage of time exhibiting the trait. This ensured that no candidate represented
better value for money as each additional 1 % increment in personality had the same value.
After they had read about the 13 candidates participants turned to a separate sheet where
they were told:
Some of the candidates are not available for [condition specific name of job] for reasons
other than their [trait name] (e.g., they have already accepted another job, or lack an essential
qualification). Assuming that the remaining candidates are equal in every other way apart from
their [trait name], which of the candidates would you hire? Remember that although there would
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be a small benefit in choosing a more [trait name] employee, budget is tight, so you can't spend
anything you want, but must keep costs down to a minimum. Please place a tick in the relevant
box. Select 1 candidate only (you cannot choose a candidate who is not available).
In both groups participants could choose between the same seven candidates. The
respective percentages of time they exhibited the desirably trait and the wages they would need
were: (1) 13.9%, £11,486.1 (2) 27.8%, £12,972.2, (3) 41.7%, £14,458.3, (4) 55.6%, £15,944.4,
(5) 69.4%, £17,430.6, (6) 83.3%, £18,916.7, and (7) 97.2%, £20,402.8. However, the two groups
saw different initial candidate pools, designed so that each of the seven available candidates
ranked higher amongst the initial candidate pool in the positively skewed group. In addition to
the seven available candidates the initial pool of the positive skewed group contained; (8) 16.7%,
£11,783.3, (9) 19.4%, £12,080.6, (10) 22.2%, £12,377.8, (11) 25.0%, £12,675.0, (12) 33.3%,
£13,566.7, (13) 38.9%, £14,161.1. The negative skewed group additionally contained (8) 75.0%,
£18,025.0, (9) 77.8%, £18,322.2, (10) 86.1%, £19,213.9, (11) 88.9%, £19,511.1, (12) 91.7%,
£19,808.3, (13) 94.4%, and £20,105.6. Thus of the seven selectable candidates, the lowest
(13.9%) and highest (97.2%) candidate ranked the same amongst both groups, however each of
the five remaining candidates ranked higher in the positive skew group (respectively ranking 6
vs. 2, 9 vs. 3, 10 vs. 4, 11 vs. 5, and 12 vs. 8). The expectation of RFT is that the positive skew
group will choose relatively cheaper candidates who exhibited the desirable traits a lower
percentage of the time than the chosen candidates in the in the negative skew group. This is
because the candidates in the positive skew group will each seem to be higher on the desired
traits as they ranked higher in the initial pool.
Results and Brief Discussion
Table 10 shows information on the average chosen candidates for both groups. Table 10
shows that on average, the positive skew group chose a cheaper candidate who exhibited the
desirable traits a lower percentage of time. These results were tested with a 2 (between: group) X
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5 (within: personality trait) mixed model ANOVA. As expected there was the main effect of
group, which was highly significant (F [1,116] = 11.38, p = .001). There was a main effect of
personality trait suggesting that, independent of condition, in some personality domains people
chose higher more expensive and more desirable candidates (F [4,464] = 10.97, p < .001),
consistent with the results of Study 4 where some traits attracted higher bonuses than others.
There was no personality X group interaction (F [4,464] = 1.50, p = .20). The results supported
the hypothesis that participants who saw a positive skewed pool of candidates would choose a
cheaper candidate who was lower on the desirable personality traits.
General Discussion
Five studies show that a person’s placement within a reference group affects how their
personality is judged. Further, judgments appear to be based on two processes; where the target’s
behavior ranks within the reference group, and where their behavior falls within the range of the
other reference group member’s behavior. Both principles appear to be approximately equally
weighted and together can provide near perfect prediction of personality ratings (with other
individual difference factors held constant). Further, each principle was equally as applicable to
personality judgments on any of the Big Five domains. Such findings support the key reference
group theory prediction, with all other factors being equal, that personality can be very strongly
predicted if three pieces of information are known: (a) the reference group used the by the rater,
(b) the target’s position in the reference group, and (c) the cognitive processes used to compare
the target to the reference group.
Establishing rank and range as the principles of reference group effects on personality
judgments is consistent with RFT. Given that RFT also strongly predicts psychophysical
judgments (which originally lead to the development of the model), the current research suggests
that the same cognitive principles may be used to make personality judgments as are used to
judge such non-social stimuli as tone, weight, and size. This goes someway towards Sternberg
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and Grigorenko’s (2001) call for a more unified psychology with greater integration between the
research on basic judgements (as in psychophysics) and more social processes (such as
judgement of personality).
Implications and Future Directions
Key implications and future directions emerge from the current work, particularly
regarding the role of reference group judgments in the self-report of personality judgments, the
interpretation of cross-cultural research, and understanding personality processes in applied
settings. First, the research focused on judgments of others. Future investigation should consider
whether range and rank principles explain how reference group effects influence judgments of
one’s own personality.
Second, the research may provide an explanation of why cross-cultural research
encounters problems with self-report (Peng et al., 1997), where mean level differences in
personality traits emerge from behavioral indices (Heine et al., 2008) and the ratings of cultural
experts (Heine et al., 2002), but not from the aggregated self-reports of people completing
questionnaires in different countries (Terracciano et al., 2005). The current results would predict
exactly this pattern of results; if people living in two or more cultures ranked themselves (and
others) relative to people within their culture, then when aggregated the mean level of the traits
in different cultures would be identical (as difference in rank position would average out). People
in different cultures may, however, behave very differently, and this should be identified by
behavioral indices. Similarly, experts may be expected to identify difference in the cultures as
they have experience of both cultural reference groups (as Heine, et al.’s., 2002, bi-national
students were able to give different ratings of depending on which national reference group they
were asked to focus).
Two testable hypotheses arise from this theorizing. It can be directly tested whether
Heine’s (2002) results can be accounted for in terms of their participants’ behavior changing
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rank and range position based on the reference group on which they were focusing. A further
hypothesis is based on the range principle. Differences in the shape of the distribution of trait
behavior in a given culture could be expected to lead to different mean levels of personality
ratings. As in the Study 4 test of range, if people cluster towards the high end of a behavioral
distribution (as in a negatively skewed distribution), the group overall should attract higher
ratings than a positively skewed distribution where people clustered at the low end of the range.
This would be unaffected by aggregation over multiple self-reports. Indeed, it may be the case
that (a) where no cross-cultural differences in mean level of self-reports have been observed, the
cultures that were compared had the same shaped distribution (so the range principle could not
apply), and (b) where differences have been observed, one or other culture had a more positively
or negative skewed distribution. That this process may occur is supported by a recent crosscultural study which showed that the shape of the culture’s distribution of the number of deaths
through disaster was related to sensitivity towards human fatalities within that culture (Olivola &
Sagara, 2009).
Third, the results have implications for occupational settings, where judgments of
personality are explicitly used for hiring decisions (Anderson & Shackleton, 1993). Often
making an relative comparison may be desirable – such as when promoting the most able
candidate from within the organization. Problems, however, occur when people are under the
impression they are making objective individual judgments when they are being influenced by
the employees’ range and rank position amongst other employees. Studies 4 and 5 illustrate
different two cases where this may occur —when different sized bonuses are given for the same
amount of work depending on the performance of others, and when more money is paid for the
same (and quite average candidate) because he or she seems better than they objectively are
through rank and range comparison. Further research needs to test this more directly in
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36
occupational environments, as well as other applied settings (such as schools and medical
settings) where the effects may also occur.
Potential Limitations
The results were based solely on self-report. This is partially mitigated by three factors:
(1) Self-reports are actually the key outcome here, with the focus on understanding personality
judgments, (2) the studies are wholly experimental, and thus don’t suffer from the common
method variance problem which occurs with the association of multiple self-report scales, and
(3) Study 5 included behavioral intentions; future work using objectively rated behavioral
responses is nevertheless encouraged. In common with the RFT paradigm, in each of the studies
the stimuli were presented simultaneously or in rapid succession, and it may be questioned how
this would generalize to the real world where other people are frequently encountered in
isolation. However, the studies do appear to give a good approximation of the social world (and
expectations of reference group theory) where the same group of people would often be seen
with high frequency, quite like the experimental tasks (and as in Study 4, environments such as
the workplace often involve a self-contained small group of people who are frequently seen
together). A complementary body of contemporary work in cognitive psychology has also
recently shown how the rank based judgments of the RFT paradigm (as in the current studies)
generalize to real world situations where stimuli are presented in isolation; briefly, people
retrieve a context of people from memory, and compare the target to that context using a series
of ordinal comparisons leading to ranking (Olivola & Sagara, 2009; Stewart et al., 2006).
A more general concern of the whole RFT paradigm is that the results don’t represent
phenomenological reality but are somehow simply artifacts of the experimental design (such as
through leading participants to make certain responses through experimental instructions or
response options). Thus people may not naturally use range and rank comparisons, but can be
induced to do so in tightly controlled experiments. Whilst this is an issue for the paradigm,
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37
several design features of the current study suggest these potential issues don’t affect the current
results. First, the results were observed across a variety of stimuli and forms of presentation,
including people described by natural language probability words (Studies 1 and 2) and
percentages (Study 3). Thus the results cannot be an artifact of any specific stimuli. Second, the
results were invariant across response scale, which ranged from 7 points anchored with
agreement terms (in common with personality scales; Studies 1, 2, and 3), a 990 point allocation
task (Study 4), and a dichotomous decision making process (Study 5). Thus the results cannot be
attributed to the form of response participants were requested to give. Third, the results still
emerged when participants were specifically instructed not to make relative judgments. Thus the
results cannot be attributed to the tasks instructions leading people to make certain responses.
Given that the results cannot be attributed to the form of stimuli presentation, response option, or
task instruction, we have confidence we are observing a genuine effect in person perception.
A related concern about the RFT paradigm is that participants are not actually engaging
in the task (and rating personality) but are simply rating the size of stimuli (e.g., the size the
probability words or the percentages). Whilst this is a concern when rating abstract stimuli (such
as social events or size of shapes) it is hard to see how this concern would apply here, as
probabilities and percentages have an absolute, objective value which can be transformed to the
rating scale with simple mathematics. Thus if people were simply rating the size of the
percentages they should not have provided relative judgments, which would have counted
against the hypothesis. Further, in considering this issue, Wood et al. (in press) suggest that there
is evidence that this problem is not occurring when people don’t use the full rating scale when
this wouldn’t necessarily have been logically expected. For example in Study 4, if participants
were simply be rating the size of the stimuli they would be expected to use the full 990 point
scale (including the highest and lowest options); in fact participants assigned moderate bonuses
to even people showing the least desirable behavior (giving on average between £69.42 and
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38
£81.31), and even the most desirable behavior didn’t lead to the maximum bonuses (giving on
average between £686.84 and £745.47). It is quite plausible that people would think that very
low bonuses would be insulting to workers and the highest stimuli (75%) clearly doesn’t
represent perfect worker behavior. Regardless of the participant’s motivation in allocating these
amounts, such results are not consistent with participants simply rating the size of the stimuli. It
is also not clear how this issue could have lead to the results in Study 5, where participants select
a single option rather than rating each stimulus. Thus we are confident that this issue cannot
explain the present results.
Conclusion
This research supports the rank and range principle as the mechanisms underlying
reference group effects on personality judgments. The research is consistent with RFT and
approach that has emerged from psychophysics. The results hold additional implications for
understanding the self-report of personality, cross-cultural differences, and behavior in
occupational settings.
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39
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Table 1
Probability Word Stimuli Used in Study 1 and 2.
Studies 1 and 21
Unimodal
Bimodal
1. No chance (definitely) (0%)
1. No chance (0%) (definitely)
2. Small chance (14.43%) (very likely)
2. Almost impossible (3.75%) (almost
certain)
3. Slightly more than a small chance (quite likely)
3. Highly unlikely (7.11%) (very certain)
4. A bit less than an even chance (a bit more…)
4. Small chance (14.43) (very likely)
5. An even chance (50%)
5. An even chance (50%)
6. A bit more than an even chance (a bit less…)
6. Very likely (81.53%) (small chance)
7. Quite likely (slightly more than a small chance) 7. Very certain (89.78%) (highly unlikely)
8. Very likely (81.53%) (small chance)
8. Almost certain (92.32%) (almost
impossible)
9. Definitely (100%) (no chance)
9. Definitely (100%) (no chance)
Study 2: Skew Comparisons
Positive Skew
Negative Skew
1. No chance (0%)
1. No chance (0%)
2. Almost impossible (3.75%)
2. Small chance (14.43%)
3. Highly unlikely (7.11%)
3. Slightly more than a small chance
4. Quite unlikely
4. Quite likely
5. Small chance (14.43%)
5. Very likely (81.53%)
6. Slightly more than a small chance
6. Pretty Certain
7. Quite likely
7. Very Certain
8. Very likely (81.53%)
8. Almost definitely
9. Definitely (100%)
9. Definitely (100%)
Note: 1 In the Study 2 modality comparisons, very certain was replaced with pretty certain and
almost certain was replaced with very certain. Words in brackets indicate the opposite terms used
for the negative skewed descriptions in Study 1. Target people are bold.
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Table 2
Mean Personality Ratings For the Target People In The Positive and Negative Skew Groups,
Study 2
Stimuli
Condition
Target
Associated
Person
Likelihood
Positive
Negative
Skew
Skew
M
SD
M
SD
Extroversion
1
Small Chance
2.49 0.88
1.97 0.59
2
Very Likely
4.56 0.59
4.08 0.75
Openness
1
Small Chance
2.72 0.73
1.87 0.58
2
Very Likely
4.42 0.63
4.08 0.67
Neuroticism
1
Small Chance
2.81 0.70
1.92 0.67
2
Very Likely
4.47 0.78
4.13 0.67
Conscientiousness
1
Small Chance
2.63 0.76
2.00 0.57
2
Very Likely
4.51 0.55
4.13 0.53
Agreeableness
1
Small Chance
2.65 0.75
1.95 0.61
2
Very Likely
4.56 0.59
4.24 0.68
46
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47
Table 3
Mean Personality Ratings For the Target People In The Unimodal and Bimodal Groups, Study 2
Stimuli
Condition
Target
Associated
Person
Likelihood
Unimodal
M
SD
Bimodal
M
SD
Extroversion
1
Small Chance
1.87 .66
2.43 .69
2
Even Chance
3.21 .62
3.03 .29
3
Very Likely
4.49 .56
4.05 .41
Openness
1
Small Chance
1.87 .62
2.49 .61
2
Even Chance
3.15 .43
3.08 .28
3
Very Likely
4.62 .54
4.05 .41
Neuroticism
1
Small Chance
1.85 .75
2.38 .64
2
Even Chance
3.08 .53
3.05 .41
3
Very Likely
4.51 .60
4.00 .47
Conscientiousness
1
Small Chance
1.85 .54
2.43 .56
2
Even Chance
3.10 .64
3.05 .41
3
Very Likely
4.38 .82
4.05 .41
Agreeableness
1
Small Chance
1.97 .67
2.43 .65
2
Even Chance
3.00 .76
3.08 .28
3
Very Likely
4.46 .56
4.14 .42
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Table 4
Stimuli Used in Studies 3 and 4.
Study 3 (Skew
Comparisons)
Positive
Negative
Skew
Skew
1. 9%
1. 9%
2. 10%
2. 26%
3. 12%
3. 36%
4. 14%
4. 43%
5. 17%
5. 48%
6. 20%
6. 52%
7. 24%
7. 55%
8. 29%
8. 58%
9. 36%
9. 60%
10. 46%
10. 62%
11. 63%
11. 63%
Study 3 (Modality
Comparisons)
Unimodal
Bimodal
1. 9%
1. 9%
2. 23%
2. 12%
3. 27%
3. 15%
4. 30%
4. 19%
5. 33%
5. 23%
6. 36%
6. 36%
7. 39%
7. 49%
8. 42%
8. 53%
9. 45%
9. 57%
10. 49%
10. 60%
11. 63%
11. 63%
Study 4 (Test of Range)
48
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HM
Positive
Skew
Negative
Skew
1. 35.4%
1. 10.7%
2. 36.6%
2. 30.9%
3. 38.9%
3. 42.8%
4. 41.3%
4. 51.2%
5. 44.8%
5. 57.1%
6. 48.4%
6. 61.9%
7. 53.1%
7. 65.5%
8. 59%
8. 69.0%
9. 67.3%
9. 71.4%
10. 79.1%
10. 73.8%
11. 99.1%
11. 75%
Study 4 (Test of Rank)
Unimodal
Bimodal
1. 10.7%
1. 10.7%
2. 27.4%
2. 14.3%
3. 32.1%
3. 17.9%
4. 35.7%
4. 22.6%
5. 39.3%
5. 27.4%
6. 42.8%
6. 42.8%
7. 46.4%
7. 58.3%
8. 50.0%
8. 63.1%
9. 53.6%
9. 67.8%
10. 58.3%
10. 71.4%
11. 75%
11. 75%
Note: HM = High Mean.
49
A cognitive
Table 5
Mean Personality Ratings Of The Target People In The Positive and Negative Skew Groups,
Study 3
Group
Positive
Negative
Skew
Skew
Trait
M
SD
M
SD
Extroversion
4.47 1.31
2.93 1.14
Openness
4.33 1.30
2.66 1.00
Neuroticism
4.63 1.07
3.26 1.29
Conscientiousness
4.13 1.17
2.37 1.15
Agreeableness
4.17 1.21
2.85 1.13
50
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51
Table 6
Improvement in Fit (Chi Squared) Through Using a Full RFT Model Rather than Rank or Range
Only Models, Study 3.
Trait
df
RFT
RFT
vs.
vs.
Rank
Range
Only
Only
Positive Skew
Extroversion
30 279.00 197.40
Openness
30 267.38 222.35
Neuroticism
30 243.69 235.19
Conscientiousness 29 301.78 180.74
Agreeableness
29 322.02 178.34
Negative Skew
Extroversion
26 209.80 189.20
Openness
26 144.65 286.97
Neuroticism
26 253.38 197.11
Conscientiousness 25 187.04 195.17
Agreeableness
25 166.41 212.53
Unimodal
Extroversion
29
85.60 215.80
Openness
29
91.12 173.23
Neuroticism
29
78.68 186.20
Conscientiousness 29
89.06 198.78
Agreeableness
68.85 173.16
28
Bimodal
Extroversion
32
82.00 186.00
Openness
31
94.03 152.87
Neuroticism
32
87.80 176.85
Conscientiousness 29
78.47 133.83
Agreeableness
78.03 187.92
Note: All p < .001.
30
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52
Table 7
Mean Personality Ratings Of the Target People In The Unimodal and Bimodal Groups, Study 3
Stimuli
Condition
Target
Assoc.
Unimodal
Bimodal
Person
%
M
M
SD
SD
Extroversion
1
23%
1.90 0.62
2.73 0.72
2
36%
3.55 1.02
3.27 0.84
3
49%
5.31 1.26
4.06 0.93
Openness
1
23%
1.86 0.69
2.85 0.94
2
36%
3.48 1.06
3.45 0.83
3
49%
5.31 1.26
4.33 0.82
Neuroticism
1
23%
1.90 0.67
3.09 1.18
2
36%
3.66 0.97
3.79 1.22
3
49%
5.31 1.23
4.55 1.03
Conscientiousness
1
23%
1.90 0.72
2.67 0.99
2
36%
3.59 1.12
3.21 0.96
3
49%
5.28 1.28
4.15 1.03
Agreeableness
1
23%
2.10 0.77
2.73 0.94
2
36%
3.59 1.05
3.36 1.14
3
49%
5.24 1.24
4.00 1.06
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Table 8
Mean Bonuses (£) Given To the Target People In The Unimodal and Bimodal Groups, Study 4
Point
Condition
Target
Assoc.
Person
%
Unimodal
M
SD
Bimodal
M
SD
Conscientiousness
1
27.4%
167.95 105.46
227.87 123.52
2
42.8%
332.76 147.33
358.11 159.17
3
58.3%
559.22 174.11
491.66 165.95
Agreeableness
1
27.4%
180.00 110.59
215.89 133.51
2
42.8%
335.26 158.15
335.21 147.66
3
58.3%
536.59 190.54
476.13 189.62
Openness
1
27.4%
194.34 117.29
217.08 118.13
2
42.8%
355.13 147.41
352.84 157.43
3
58.3%
571.85 184.14
476.66 189.61
Extroversion
1
27.4%
180.39 109.88
228.66 96.23
2
42.8%
334.87 152.40
361.13 140.54
3
58.3%
527.64 168.96
471.00 178.52
Emotional Stability
1
27.4%
169.79 104.59
211.29 105.44
2
42.8%
324.55 154.07
335.71 152.03
3
58.3%
510.74 189.14
444.29 193.07
53
A cognitive
Table 9
Mean Amount of Bonuses (£) Awarded In The Positive and Negative Skew Groups, Study 4
Group
Positive Skew
Trait
M
SD
Negative Skew
M
SD
Conscientiousness
4645.16 1704.01
5644.16 1305.57
Agreeableness
4488.89 1914.76
5017.99 1605.23
Openness
4353.34 1952.22
5516.23 1307.51
Extroversion
4316.84 2275.20
5160.89 1315.09
Emotional Stability
4033.05 2283.16
4794.64 1550.75
54
A cognitive
55
Table 10
Average Behavior and Wages of the Chosen Candidate, Study 5.
Associated Behavior (%)
Trait
Positive
Negative
Skew
Skew
M
SD
M
SD
Associated Wages (£)
Positive Skew
Negative Skew
M
SD
M
SD
Extroversion
55.33 18.68
66.81 17.70
15919.67
2001.19
17148.71
1897.41
Openness
55.33 19.70
64.89 17.11
15919.67
2110.44
16943.73
1833.67
Neuroticism
61.35 17.74
70.39 15.98
16563.66
1901.44
17533.06
1712.92
Conscientiousness 62.95 18.85
73.26 15.10
16737.05
2019.35
17840.53
1618.27
Agreeableness
66.80 16.10
16613.20
1923.62
17148.71
1726.33
61.81 17.95
A cognitive
Figure Captions
Figure 1. Mean ratings (and 95% CI) of the target people, Study 1.
Figure 2. Mean ratings (and 95% CI) of the target people, Study 2.
Figure 3. Results of the modeling, Study 3. The lines represent predicted values, the symbols
represent mean responses for each of the nine rated people in each condition.
56
A cognitive
5
4.5
4
3.5
3
2.5
Unimodal
Bimodal
2
1.5
Small Chance Even Chance
Very Likely
57
A cognitive
5
4.5
Extraversion
Openness
5
Unimodal
4.5
Bimodal
5
Unimodal
4.5
Bimodal
4
4
4
3.5
3.5
3.5
3
3
3
2.5
2.5
2.5
2
2
2
1.5
1.5
1.5
Small Chance
Even Chance
Very Likely
Conscientiousness
Small Chance
5
5
Unimodal
4.5
Even Chance
Very Likely
Agreeableness
Unimodal
4.5
Bimodal
4
4
3.5
3.5
3
3
2.5
2.5
2
2
1.5
Bimodal
1.5
Small Chance
Even Chance
Very Likely
Small Chance
Even Chance
Very Likely
Neuroticism
Unimodal
Bimodal
Small Chance
Even Chance
Very Likely
58
A cognitive
1.0
.9
.8
.7
.6
.5
.4
.3
.2
.1
.0
Conscientiousness
1.0
.9
.8
.7
.6
.5
.4
.3
.2
.1
.0
Agreeableness
Personality Rating
Neuroticism
Personality Rating
1.0
.9
.8
.7
.6
.5
.4
.3
.2
.1
.0
Personality Rating
Openness
65
60
55
50
45
40
35
30
25
20
15
10
5
65
60
55
50
45
40
35
30
25
20
15
10
5
65
60
55
50
45
40
35
30
25
20
15
10
5
65
60
55
50
45
40
35
30
25
20
15
10
5
65
60
55
50
45
40
35
30
25
20
15
10
5
% of Time Exhibiting Trait
% of Time Exhibiting Trait
% of Time Exhibiting Trait
% of Time Exhibiting Trait
% of Time Exhibiting Trait
Neuroticism
1.0
.9
.8
.7
.6
.5
.4
.3
.2
.1
.0
Conscientiousness
1.0
.9
.8
.7
.6
.5
.4
.3
.2
.1
.0
Agreeableness
Personality Rating
1.0
.9
.8
.7
.6
.5
.4
.3
.2
.1
.0
Personality Rating
Openness
Personality Rating
1.0
.9
.8
.7
.6
.5
.4
.3
.2
.1
.0
Personality Rating
Extraversion
Personality Rating
1.0
.9
.8
.7
.6
.5
.4
.3
.2
.1
.0
1.0
.9
.8
.7
.6
.5
.4
.3
.2
.1
.0
Personality Rating
Extraversion
Personality Rating
1.0
.9
.8
.7
.6
.5
.4
.3
.2
.1
.0
65
60
55
50
45
40
35
30
25
20
15
10
5
65
60
55
50
45
40
35
30
25
20
15
10
5
65
60
55
50
45
40
35
30
25
20
15
10
5
65
60
55
50
45
40
35
30
25
20
15
10
5
65
60
55
50
45
40
35
30
25
20
15
10
5
% of Time Exhibiting Trait
% of Time Exhibiting Trait
% of Time Exhibiting Trait
% of Time Exhibiting Trait
% of Time Exhibiting Trait
59