Running head: INTERPERSONAL BEHAVIOR AND THE IIDL
Article In Press – Journal of Research in Personality, 4/17/2014
Subject to final copy-editing changes
Predicting Interpersonal Behavior Using the
Inventory of Individual Differences in the Lexicon (IIDL)
Nicolas A. Brown & Ryne A. Sherman
Florida Atlantic University
Author Notes
Nicolas A. Brown, Florida Atlantic University;
Ryne A. Sherman, Florida Atlantic University.
All statistical analyses were conducted using R (R Development Core Team, 2014).
Correspondence regarding this article may be addressed to Nicolas Brown, Department of
Psychology, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431. E-mail:
[email protected].
We thank Patrick Markey for his invaluable guidance calculating the relevant circumplex
statistics, however, any errors or omissions remain our own.
Word Count = 3138 (excluding tables, figures, and references)
INTERPERSONAL BEHAVIOR AND THE IIDL
2
Abstract
Personality psychology relies on well-validated measures of individual differences to describe
and predict behavior. A newer comprehensive measure, the Inventory of Individual Differences
in the Lexicon (IIDL) has been developed, but its ability to predict actual behavior has not been
examined. The present article uses the IIDL to predict directly observed behavior, as categorized
by the Interpersonal Circumplex (IPC). Video recorded interviews with participants in a
laboratory setting were coded for directly observable behavior. Forty-eight IIDL items had
meaningful associations with the IPC. Most importantly, 25 items provided unique predictive
information above and beyond a factor-level measure of personality. This suggests that
comprehensive measures of personality should be considered for their additive validity in
predicting interpersonal behavior.
Keywords = scale validation, personality assessment, behavior, comprehensive measures
INTERPERSONAL BEHAVIOR AND THE IIDL
3
Personality psychology has a venerable history of investigating the ways in which
individuals differ from one another. Indeed, it was over 75 years ago that Allport and Odbert
(1936) searched Webster’s Dictionary and identified 17, 953 words that could ostensibly
describe individuals. In the intervening years, there has been no shortage of personality measures
developed and validated to advance this goal. Many of these measures, however, were developed
on the premise that personality can be described with only a few factors—a so-called “essential
trait approach” (Funder, 2013).
This approach also reflects the generally accepted notion that personality is structured
hierarchically, with broad factors at the highest level subsuming numerous lower-order facets.
For instance, the Five-Factor Model (e.g., McCrae & Costa, 1997) and the Big Five (Goldberg,
1990) assert that personality can be summarized with five general factors—Openness,
Agreeableness, Extraversion, Neuroticism, and Conscientiousness. These general factors can
also be decomposed into lower order facets, such as Order (Conscientiousness), Anxiety
(Neuroticism), and Warmth (Extraversion), to name a few. More recent studies of lexicons
across different languages revealed evidence for a sixth factor—Honesty-Humility (Ashton et al.,
2004) yielding the so-called HEXACO model of personality, with their respective facets (Ashton
& Lee, 2007).
An alternative approach to measuring individual differences is to consider nuanced
dimensions of personality through a “many-trait” or comprehensive approach (Block, 1978;
Funder, 2013; Peabody, 1987; Wood, Nye, & Saucier, 2010). In contrast to essential-trait
measures of personality, comprehensive measures are designed such that each item taps a
separate personality characteristic. A benefit of comprehensive measures is that factor analyses
INTERPERSONAL BEHAVIOR AND THE IIDL
4
extracting five or six factors often produces the factors identified in the Big Five and HEXACO
models of personality (e.g., Lanning, 1994; McCrae, Costa, & Busch, 1986).
One recently developed tool for comprehensive personality assessment is the Inventory
for Individual Differences in the Lexicon (IIDL; Wood et al., 2010). As the name implies, the
IIDL follows the lexical tradition—the notion that important individual differences will be
encoded in language—(Goldberg, 1981). Like other lexically based instruments, the origins of
the IIDL can be traced back to Allport and Odbert’s (1936) initial search of Webster’s Dictionary.
Allport and Odbert, and others since, noted that many person descriptive words found in
dictionaries are too ambiguous, colloquial, difficult to comprehend, or simply unused in the
modern lexicon (e.g., Goldberg, 1981; Norman, 1967, Saucier, 1997). To this end, Saucier
(1997) developed a list of 504 person descriptors intended to maintain breadth in content, while
also containing words more common in everyday language. Wood and colleagues (2010)
selected Saucier’s (1997) common person descriptors as a basis for the IIDL in an attempt to
sample a wide variety of individual differences while also minimizing subjective decisions about
what content to include. A cluster analysis of these words resulted in 61 pairs of synonyms that
measure a wide variety of individual characteristics from physical attractiveness and health to
honesty and dominance (see Wood et al., 2010).
Since its introduction, the IIDL has been used to investigate various research questions
including person perception (Wood, Harms, & Vazire, 2010) and the stability of perceived
desirability of traits (Wood & Wortman, 2012). However, no research has examined the ability
of the IIDL to predict directly observed behavior, which is arguably the most important criterion
for any measure of personality (Baumeister, Vohs, & Funder, 2007; Colvin & Funder, 1991;
Epstein, 1979; Funder, Furr, & Colvin, 2000; Furr, 2009).
INTERPERSONAL BEHAVIOR AND THE IIDL
5
Interpersonal Behavior
To obtain a holistic view of personality, researchers must measure three key ingredients:
persons, situations and behavior (Funder, 2006). Personality psychologists have been very
successful in developing personality inventories (e.g., the IIDL; Wood et al., 2010), and work on
taxonomies specifying the psychologically important features of situations is rapidly increasing
(Kelley, Holmes, Kerr, Reis, Rusbult, Van Lange, 2003; Rauthmann et al., under review;
Wagerman & Funder, 2009; Yang, Read, & Miller, 2006). The study of directly observed
behavior and the tools to measure it, however, has not garnered as much attention (Baumeister et
al., 2007; Furr, 2009).
Despite these shortcomings, one measure of directly observed behavior that exists is the
Riverside Behavioral Q-sort (RBQ; Funder et al., 2000; Furr, Wagerman, & Funder, 2010). The
RBQ contains 68 items designed to describe a wide-range of interpersonal behaviors including
talkativeness, warmth, and likability, to name a few (Funder et al., 2000). After viewing an
interaction with a participant, which are typically recorded, trained observers rate the degree to
which the participant exhibited that behavior using a Q-sort. The result is a profile of the
participant’s behavior, which can then be correlated with another variable of interest, such as a
personality trait (e.g., Funder et al., 2000; Furr et al., 2010; Nave, Sherman, Funder, Hampson, &
Goldberg, 2010).
The RBQ can also be used to construct eight broadly important interpersonal behaviors
(Markey, Funder, & Ozer, 2003) derived from Leary’s (1957) Interpersonal Circumplex (IPC;
Wiggins, Trapnell, & Phillips, 1988).1 The IPC was introduced by Leary (1957) to summarize
interpersonal behaviors in a circular fashion along two dimensions – dominance and warmth.
1
To save space, the list of RBQ items and their respective IPC octants are available as
Supplementary Materials.
INTERPERSONAL BEHAVIOR AND THE IIDL
6
Since its introduction, the IPC has been revised numerous times (e.g., Strong et al., 1988,
Wiggins, 1982), though researchers generally agree upon its basic elements. Wiggins and
colleagues (1988) proposed that interpersonal styles could be conceptualized by eight
combinations of behavior: Assured-Dominant, Arrogant-Calculating, Cold-Hearted, AloofIntroverted, Unassured-Submissive, Unassuming-Ingenuous, Warm-Agreeable, and GregariousExtraverted. Previous research using the RBQ to measure the IPC octants has examined the
complementarity of interpersonal behavior in adults (Markey et al., 2003) and as validation of an
Interpersonal Circumplex personality measure (Markey, Anderson, & Markey, 2012).
The Present Research
The present research has two goals. The first goal is to investigate the ability of the IIDL
to predict behavior as categorized by the IPC. As noted previously, to our knowledge, no studies
have examined the correspondence between directly observed behavior and the IIDL. Secondly,
the present article explores whether the IIDL provides incremental validity above factor level
measures of personality such as the HEXACO-60 (Ashton & Lee, 2009). Stated differently, if a
comprehensive measure of personality (i.e., the IIDL) provides additional ability to predict
behavior above and beyond essential-trait measures, this would it point to the necessity and
importance of such comprehensive measures of personality.
Participants
Two-hundred and eighteen participants were recruited from introductory psychology
courses at Florida Atlantic University.2 Two participants were excluded: one participant’s
personality questionnaire data was lost due to a computer error, and one participant’s videotaped
interview was deleted by accident. The final sample was comprised of 74 males, 141 females and
2
A pilot study analyzed data from 60 participants from a different and unrelated study that
included the IIDL and the RBQ. The small sample size prevented our ability to draw strong
conclusions and warranted a study more fully aimed at this question.
INTERPERSONAL BEHAVIOR AND THE IIDL
7
1 did not indicate (M age = 18.64 years, SD = 1.86). Due to missing responses on the IIDL, Ns
for certain analyses (i.e., pairwise deletion was used when possible) may be as low as 212. The
ethnic breakdown for the sample was 47% Caucasian, 23% Hispanic/Latino/a, 19% AfricanAmerican, 8% Other, 2% No Response, and 1% Asian. Participants were compensated with
partial course credit.
Measures
Inventory of the Individual Differences in the Lexicon. The Inventory of the
Individual Differences in the Lexicon (IIDL; Wood et al., 2010) is a 61-item measure of
individual differences derived from the lexicon. Items are presented as adjective pairs, for
example, “outgoing, extraverted,” “messy, sloppy,” and “crabby, grouchy.” Participants rated
each item on a 7-point Likert-type scale ranging from 1 (very uncharacteristic, untypical of me)
to 7 (very characteristic of me, typical of me).
HEXACO-60. The HEXACO-60 (Ashton & Lee, 2009) is a 60-item measure of the
HEXACO model of personality (i.e., the Big Five plus an Honest-Humility factor). Participants
rated each item using a 5-point Likert-type scale ranging from 1 (strongly disagree) to 5
(strongly agree). The means (SD/ alpha) for the scales were: Honesty-Humility = 3.33 (0.56
/ .63), Emotional Stability = 3.29 (0.67 / .76), Extraversion = 3.56 (0.63 / .80), Agreeableness =
3.31 (0.64 / .76), Conscientiousness = 3.59 (0.57 / .75) and Openness = 3.20 (0.65 / .74).
Riverside Behavioral Q-sort. The Riverside Behavioral Q-sort Version 3.11 (RBQ;
Funder et al., 2000; Furr et al., 2010) comprises 68 items characterizing a participant’s
observable behavior. Example items include, “Exhibits social skills,” and “Dominates the
situation.” Research assistants used a Q-sort computer program to rate the degree in which each
RBQ item was characteristic of the behavior exhibited in that situation using nine categories (1 =
INTERPERSONAL BEHAVIOR AND THE IIDL
8
extremely uncharacteristic, 9 = extremely characteristic) forming a forced-choice, quasi-normal
distribution. Because data collected using Q-sorts may be susceptible to item order effects
(Serfass & Sherman, 2013a), we calculated two statistics for the 24 RBQ items: the order effects
on variance (i.e., the correlation between the item number and the standard deviation for each
item) and item placement (the average absolute distance from the midpoint which reflects the
tendency for a coder to place an item near the midpoint.) Both effects were medium, r = -.39
(variance) and r = -.40 (placement).
Procedure
Laboratory assessment. Participants visited the laboratory and were video recorded
during a “getting to know you” interview completed as part of a larger study on personality and
situations. The video camera was in plain sight and directly pointed at the participant. Research
assistants prompted participants to talk about themselves by asking questions such as, “Tell me a
little about yourself,” “What do you like most about yourself?” and “Can you tell me about an
experience that you have in your life that would really describe who you are as a person?”
Participants were permitted to talk as long as they wanted. The average length of time to conduct
the interviews was 3 minutes (range = 1 to 11 min). Upon completing the interview, participants
completed the HEXACO-60 and the IIDL, and other individual difference measures not relevant
to this study. Over the next seven days these participants completed experience sampling-type
reports of their situations and behavior, but these data too were not relevant to this study.
Behavioral coding. Four undergraduate research assistants, from a pool of 14,
independently viewed and rated each video-recorded interview for directly observable behavior
using the RBQ. Because research assistants were involved in multiple aspects of running the
study, raters were not permitted to code a video where they conducted the interview. The
INTERPERSONAL BEHAVIOR AND THE IIDL
9
reliability of the four ratings for each video were analyzed by calculating the profile correlations
to ensure quality ratings were obtained. Fifteen ratings (1.7%) with low agreement were dropped
and re-coded by a different rater. Ratings from the four research assistants were averaged to form
a composite. The average item level agreement (ICC1; Shrout & Fleiss, 1979) for the 24 RBQ
items relevant to the IPC was .13 (SD = .12), resulting in an average composite reliability of .29
(SD = .28).
To investigate interpersonal behavior within the IPC framework, the three RBQ items
that represent the interpersonal behaviors of an IPC octant (Markey et al., 2003) were averaged
to create a composite for each IPC octant. The means (SD / alpha) for the octants were: AssuredDominant = 4.30 (0.68 / .54), Arrogant-Calculating = 4.47 (0.57 / .40), Cold-Hearted = 3.41
(0.52 / .38), Aloof-Introverted = 4.55 (1.11 / .84), Unassured-Submissive = 4.41 (0.61 / .51),
Unassuming-Ingenuous = 5.40 (0.35 / .05), Warm-Agreeable = 6.88 (0.70 / .67), and GregariousExtraverted = 5.64 (0.89 / .68).
Results
Predictive validity
To address our first research question—Is the IIDL related to directly observed behavior
organized by the IPC?—we correlated each IPC octant composite derived from the RBQ with the
IIDL. Next, using these zero-order correlations, we calculated the angle, elevation, and
amplitude for each IIDL item using the structural summary method (e.g., Gurtman, 1992,
Gurtman & Pincus, 2003; Wright, Pincus, Conroy, & Hilsenroth, 2009). These values are
reported in Table 1. For completeness, we have also included these statistics for the relationship
between the HEXACO factors and the IPC. The angle is the area on the circumplex with which
the IIDL item is most strongly associated. For example, the IIDL item, “great, wonderful” has an
angle of 23, indicating it corresponds maximally to the Warm-Agreeable octant. Amplitude is a
INTERPERSONAL BEHAVIOR AND THE IIDL
10
measure of the IIDL item’s discriminant validity, or the degree to which the item correlates with
a particular octant in the circumplex, but not others. Elevation indicates the mean level of the
profile (i.e., the average r across all of the octants). The R2 is a goodness-of-fit statistic of how
well the summary statistics (i.e., angle, elevation, and amplitude) capture the correlation between
the IIDL and the octant (Wright et al., 2009). Hence, higher R2 values indicate that the IIDL item
performs better at predicting a particular interpersonal behavior.
---Insert Table 1 about here--Incremental validity
To address our second research question—What does the IIDL contribute above and
beyond a factor-level measure of personality?—we calculated semi-partial correlations between
each IPC octant and each IIDL item, controlling for the six HEXACO factors.3 These semipartial rs indicate the degree to which these IIDL items provided unique information that was not
captured by the HEXACO. Next, we calculated the structural summary statistics using these
semi-partial correlations. The results appear in the far right columns of Table 1. Figure 1
graphically displays the angular location of: (a) each IIDL item correlated with the IPC (left
panel) and (b) each IIDL item correlated with the IPC controlling for the HEXACO (right panel).
As is customary with IPC research, only IIDL items that showed a good fit (an R2 of ≥.70) are
displayed.
---Insert Figure 1 about here--Discussion
The present study sought to address two research questions: (1) What is the
correspondence between a comprehensive measure of personality, the IIDL, and directly
3
The semi-partial rs and their respective p-values are available as Supplementary Materials.
INTERPERSONAL BEHAVIOR AND THE IIDL
11
observed behavior? And (2) does the IIDL provide incremental predictive validity of directly
observed behavior over-and-above that provided by an essential-trait measure of personality?
Based upon the structural summary fit statistics presented in Table 1, there is ample
evidence that the IIDL is predictive of interpersonal behavior. Indeed, 48 of the 61 IIDL items
had strong patterns of associations with the IPC. Further, 25 IIDL items predicted interpersonal
behavior even controlling for the six HEXACO factors.4 These associations provide empirical
evidence that the IIDL is an adequate measure for predicting behavior.
It should be noted that the behaviors underlying one IPC octant– Unassuming-Ingenuous
–did not strongly correspond to participants’ self-reported IIDL. This finding is not necessarily a
shortcoming of the IIDL, but instead likely a consequence of the context in which we observed
the participants’ behaviors. Because the participants were asked to speak (and not the
interviewers), the behaviors that comprise the Unassuming-Ingenuous octant were rarely
displayed. That is, participants did not have an opportunity to “seek advice” or “appear interested
in what someone else had to say” due to the one-way structure of the interview. Indeed, as noted
in the methods section, raters were unable to reliably code (alpha = .05) the behaviors that
underlie the Unassuming-Ingenuous octant.
Despite these rather convincing results supporting the IIDL as predictive of behavior,
future studies should expand the contexts in which participant behavior is observed and
connected to personality as measured by the IIDL. For instance, Markey and colleagues (2003)
analyzed behavioral data from a larger study where participants were observed in unstructured,
competitive, and cooperative situations with another participant serving as the interaction partner.
Increasing the number of the contexts and interaction partners present could benefit researchers
4
We conducted the same analysis controlling for the subfacets of the HEXACO. This analysis
yielded virtually identical results (also available as Supplementary Materials.)
INTERPERSONAL BEHAVIOR AND THE IIDL
12
in two ways. First, measurement of the Unassuming-Ingenuous octant might become more
reliable as those interpersonal behaviors become relevant to the interaction under observation.
Second, researchers can investigate whether the pattern of correlations between the IIDL and
directly observed behavior is stable across a diverse set of situations.
With respect to our second research question, does the IIDL contribute to the
understanding of personality above “essential-trait” measures of personality, the amplitudes
based on semi-partial correlations presented in Table 1 suggest the answer is somewhat mixed.
For some behaviors, the IIDL provides unique predictive information that is not covered by the
HEXACO factors or facets. For example, individuals who act cold-hearted tend to rate
themselves as tired, exhausted, and not traditional or conventional. The HEXACO factors (or
facets) could not by themselves explain these additional relationships. However, other behaviors,
such as those underlying the Aloof-Introverted octant, do appear to be covered sufficiently by the
factor-level measure. Given the long existence (or persistence?) of comprehensive measures of
personality (e.g., Block, 1978) and their increasing frequency of use (e.g., Block & Block 2006;
Fast & Funder, 2008; Luminent, Bagby, Wagner, Taylor, & Parker, 1999; Mehl, Gosling, &
Pennebaker, 2006; Schimmack, Oishi, Furr, & Funder, 2004; Serfass & Sherman, 2013b;
Sherman, Nave, & Funder, 2013; Watson, 2001; Wood et al., 2010; Wood & Wortman, 2012) it
is essential to examine how such comprehensive measures either complement or provide
incremental validity over existing essential-trait measures such as the Big Five Inventory (BFI;
John & Srivastava, 1999) and the HEXACO (Lee & Ashton, 2004). The results of this study
indicate that, when it comes to behavioral prediction, although essential-trait measures of
personality are quite good, sometimes comprehensive measures of personality can offer
additional predictive information. Because it is generally accepted that personality is structured
INTERPERSONAL BEHAVIOR AND THE IIDL
13
hierarchically, an advantage of comprehensive measures is that they may be also used as factorlevel measures by way of extracting the first five or six factors if desired. Although many factorlevel measures do include subscales that assess lower-order aspects of personality, they cannot
be used to assess more nuanced aspects of personality. As the present research suggests,
comprehensive measures such as the IIDL seem to go beyond even these facet-level measures.
INTERPERSONAL BEHAVIOR AND THE IIDL
14
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INTERPERSONAL BEHAVIOR AND THE IIDL
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Table 1.
Structural Summary of IIDL Items and HEXACO factors
Zero-order correlations
IIDL Item
Angle Amplitude Elevation
admirable, impressive
57
.14
-.03
affectionate, loving
352
.11
.00
.15
-.01
afraid, scared
264
angry, hostile
183
.17
.00
.20
-.03
attractive, good-looking
61
awkward, clumsy
272
.16
.02
.30
.01
bashful, shy
257
beautiful, pretty
32
.15
-.03
.17
-.02
bold, assertive
102
brave, adventurous
96
.20
.00
calm, relaxed
294
.12
.01
competent, capable
150
.05
-.02
.09
.02
conceited, egotistical
175
confident, self-assured
66
.25
-.04
controlling, dominant
119
.14
.00
courteous, polite
348
.09
.00
crabby, grouchy
211
.15
.00
.07
.01
creative, imaginative
35
cruel, abusive
171
.13
.00
dependable, reliable
16
.05
-.01
disorganized, messy
267
.09
.03
enthusiastic, excited
33
.21
-.02
.23
-.02
exciting, fascinating
51
feminine, unmasculine
336
.17
-.02
funny, amusing
39
.14
.00
giving, generous
10
.19
-.03
great, wonderful
23
.17
-.02
happy, joyful
359
.24
-.02
R2
.85
.68
.86
.88
.90
.75
.98
.75
.91
.96
.83
.64
.57
.95
.80
.75
.94
.70
.94
.76
.74
.93
.97
.68
.91
.84
.97
.93
Semi-partial correlations
Angle
Amplitude Elevation
194
.02
-.01
272
.06
.01
257
.05
-.02
154
.08
-.02
68
.08
-.03
296
.08
.01
271
.15
.00
36
.04
-.03
142
.08
-.01
132
.08
.01
287
.12
.01
205
.06
.00
217
.08
.01
87
.09
-.02
112
.04
.00
323
.05
.01
212
.03
-.02
142
.02
.01
182
.09
.00
6
.04
-.01
309
.01
.01
18
.05
.00
70
.07
-.01
342
.10
-.02
18
.04
.01
14
.12
-.02
349
.04
-.01
315
.12
.00
R2
.07
.73
.57
.91
.77
.49
.93
.31
.79
.84
.89
.88
.61
.81
.25
.41
.34
.12
.90
.60
.05
.40
.51
.67
.42
.81
.69
.93
INTERPERSONAL BEHAVIOR AND THE IIDL
healthy, well
intelligent, smart
kind-hearted, caring
lonely, lonesome
loud, noisy
lucky, fortunate
narrow-minded, close-minded
ordinary, average
outgoing, sociable
pleasant, agreeable
positive, optimistic
practical, sensible
prominent, influential
radical, rebellious
rude, inconsiderate
sad, unhappy
selfish, self-centered
short, little
skilled, skillful
slim, slender
stable, well-adjusted
tense, anxious
thankful, grateful
tired, exhausted
touchy, temperamental
traditional, conventional
truthful, honest
undependable, unreliable
unfriendly, cold
wealthy, well-to-do
weird, strange
well-liked, likeable
youthful, young
54
61
17
215
51
55
236
237
56
6
25
323
47
62
173
225
137
239
75
230
144
210
313
220
194
10
23
298
162
70
261
43
10
.14
.09
.13
.16
.12
.17
.05
.14
.34
.21
.22
.07
.27
.07
.12
.17
.08
.04
.18
.03
.09
.09
.10
.22
.10
.09
.11
.02
.11
.15
.04
.23
.22
21
-.01
-.01
-.01
.00
.01
.00
.00
.02
-.02
-.01
-.02
.02
-.04
.01
-.01
.00
.01
.00
-.05
.02
.00
.01
.00
.03
.02
-.04
-.01
.00
-.01
.01
.01
-.03
-.01
.92
.82
.84
.80
.87
.86
.55
.92
.97
.95
.91
.68
.96
.38
.86
.95
.76
.31
.92
.32
.83
.88
.68
.89
.91
.70
.94
.17
.87
.89
.22
.92
.93
89
140
11
143
48
62
275
275
71
356
357
319
40
31
182
242
90
175
96
209
193
141
257
213
162
352
339
49
94
76
51
43
2
.03
.01
.05
.04
.04
.08
.05
.04
.14
.12
.07
.08
.13
.05
.06
.01
.08
.04
.06
.02
.12
.03
.09
.14
.06
.08
.07
.04
.07
.07
.03
.08
.12
.01
.01
.01
-.02
.01
.01
.00
.01
.00
.00
.01
.02
-.02
.00
-.02
-.02
-.01
-.01
-.03
.02
.02
.00
.01
.02
.01
-.03
.00
-.02
-.02
.02
.00
-.01
.00
.28
.04
.81
.31
.51
.70
.56
.32
.92
.98
.81
.79
.91
.24
.50
.14
.68
.46
.66
.13
.89
.47
.68
.72
.84
.65
.76
.30
.51
.71
.20
.79
.96
INTERPERSONAL BEHAVIOR AND THE IIDL
HEXACO
Honesty-Humility
Emotionality
Extraversion
Agreeableness
Conscientiousness
Openness
289
314
46
323
91
329
.15
.14
.32
.15
.13
.04
22
.01
.00
-.03
-.01
-.04
.00
.88
.69
.93
.86
.93
.40
INTERPERSONAL BEHAVIOR AND THE IIDL
23
Figure 1.
Circular plots of each IIDL item correlated with the IPC (left panel) and each IIDL item correlated with the IPC controlling for the HEXACO
(right panel).