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Being Honest About Dishonesty: Correlating Self-Reports and Actual Lying



Does everybody lie? A dominant view is that lying is part of everyday social interaction. Recent research, however, has claimed, that robust individual differences exist, with most people reporting that they do not lie, and only a small minority reporting very frequent lying. In this study, we found most people to subjectively report little or no lying. Importantly, we found self-reports of frequent lying to positively correlate with real-life cheating and psychopathic tendencies. Our findings question whether lying is normative and common among most people, and instead suggest that most people are honest most of the time and that a small minority lies frequently.
Being Honest About Dishonesty: Correlating Self Reports and Actual Lying
Rony Halevy, University of Amsterdam. Obtained an MSc at the University of
Bruno Verschuere, University of Amsterdam. Obtained a PhD at Ghent University
Shaul Shalvi, Ben-Gurion University of the Negev. Obtained a PhD at the University
of Amsterdam
Reference: Halevy, R., Shalvi, S, & Verschuere, B. (2014). Being Honest About
Dishonesty: Correlating Self Reports and Actual Lying. Human Communication
Research, 40, 54–72. DOI: 10.1111/hcre.12019
Contact details corresponding author: Bruno Verschuere, Department of Clinical
Psychology, University of Amsterdam, Roetersstraat 15, 1018 WB Amsterdam,
Netherlands. E-mail: Phone: +3125256799
Does everybody lie? A dominant view is that lying is part of everyday social interaction. It was
recently claimed however, that robust individual differences exist, with most people reporting
not to lie, and only a small minority reporting very frequent lying. In the current work we found
most people to subjectively report little or no lying. Importantly, we found self reports of
frequent lying to positively correlate with real life cheating and psychopathic tendencies. Our
findings question whether indeed lying is normative and common among most people, and
instead suggest that most people are honest most of the time, and that a small minority lies
Key words: Deception, cheating, pathological lying, individual differences, lying,
dishonesty, psychopathy, ethical decision making, morality, explicit attitude.
While condemned by society, lying is claimed to have survival value (King & Ford, 1988) and to
be a part of everyday social interaction. In a study based on a daily-diary methodology, students
reported telling on average two lies a day (DePaulo, Kirkendol, Kashy, Wyer & Epstein, 1996).
This finding was often replicated and widely cited (for a review see Vrij, 2000), leading to the
conclusion ´Everybody lies!´ (e.g., DePaulo, 2004).
The claim that everybody lies was, however, recently challenged. A mass survey of 1000
US citizens had shown large individual differences with respect to lying frequency (Serota,
Levine & Boster, 2010). The survey revealed an average amount of lies per day which was quite
similar to what has been found in previous studies: 1.65. However, the data was heavily skewed
– the few people who lied a lot pulled the overall sample mean upwards. The skewed distribution
was recently replicated by the same group in a sample of US high school students (Levine,
Serota, Carey & Messer, 2011), and in a large, representative UK community sample (Serota,
Levine & Burns, 2012). This line of work led to a competing conclusion 'only some lie - a lot'.
Everybody lies?
Growing literature, routed in social psychology, decision making and economics,
provides support to the claim that 'everybody lies'. This growing literature, focuses on situational
factors which lead people to lie more or less (see Bazerman & Tenbrunsel, 2011, Ariely, 2012).
For example, being in a dark room (Zhong, Bohns and Gino, 2010), benefiting a charitable cause
(Lewis et al. 2012) or other people (Gino, Ayal, & Ariely, 2013), reading a text encouraging a
deterministic beliefs (Vohs & Schooler, 2008), depleting self-control (Gino, Schweitzer, Mead &
Ariely, 2011), and having no time to think (Shalvi, Eldar & Bereby-Meyer, 2012), are all
claimed to increase lying. By implication, if only situational factors tempt people to lie or be
honest, very little room is left for individual difference to explain dishonesty.
Serota and colleagues' (2010) findings however, challenges this viewsome participants
in their sample claimed to have lied a lot, others very little. If self-reported dishonesty is
somewhat related to actual dishonesty, then the skewed distribution with large variance in
people's tendency to lie suggests individual difference may play a larger role in determining
human deceptive communications. Indeed, initial recent work revealed that people who
chronically tend towards attempting to achieve positive outcomes (rather than to avoid negative
ones from happening) are more likely to lie due to their reduced fear of the risks involved in such
behaviors (regulatory focus; Gino & Margolis, 2011), and that individual differences on
religiousness predict people's dishonesty: religious people seem to lie less on tasks commonly
evoking dishonesty (Shalvi & Leiser, 2013; Fischbacher & Utikal, 2011).
The current work squarely fits the debate by assessing the role individual difference play
in predicting dishonest behavior. According to Serota et al. (2010), individual differences play a
major (but neglected) role in this field, and most lies in our society are told by a small number of
prolific liars. Finding out if individuals difference are also predictive to human deceptive
communications, will help us better understand dishonesty in society. If everybody lies, then lies
can be seen as a practical tool of communication, and research should focus on situational factors
nudging people to act dishonestly (Thaler & Sunstein, 2008). If however, some individuals lie
more than others, while the general population is honest most of the time, it might be useful to
try and understand the specific characteristics of those individuals who succumb to such
behaviour. If a small group is responsible for most of the lies told in our society – we want to be
able to distinguish these people from the rest of the population.
Importantly, the evidence suggesting individual difference in dishonesty are chiefly based
on self-reported evidence. But to what extent people respond honestly when asked about their
dishonesty? And how reliable are the results of such a large survey (Merckelbach, Giesbrecht, &
Smeets, 2010)? No work we are familiar with has assessed the relation between individual's self-
reported dishonesty and their actual dishonest tendencies. The current work fills exactly this
The correlates of frequent liars
As far as self-reported dishonesty goes, recent work suggests most lies are told by a small
number of ´prolific liars' (Serota et al., 2010; 2012; Levine et al. 2011). This raises the question –
who are these people? To address this issue, we explored which individual differences predict
frequent lying, in both self-report and real-life cheating. The real life deception task we use
keeps the likelihood of getting caught and expected punishment constant (and set to zero). Thus,
meaningful differences in the amount of dishonesty reflect individual differences in one's
willingness to bend the task's rules in order to secure personal financial gain.
While the empirical evidence concerning frequent lying is scarce, we derived predictions
from the literature on one condition that is considered an extreme case of frequent lying:
Pathological lying, individuals who repeatedly and compulsively tell false stories (Poletti, Borelli
& Bonuccelli, 2011). It was suggested that pathological liars do not need any external motivation
in order lie (Dike, Baranoski & Griffith, 2005). While all pathological liars lie frequently, we do
not propose all frequent liars to be pathological liars. However, since lying frequently is a
defining characteristic of pathological lying, theories regarding pathological liars seem to
provide valuable insight to the potential profile of frequent liars.
First, it was suggested that pathological liars do not link negative affect to lying (Grubin,
2005). The lack of negative attitude associated with lying can be seen either as a predictor of
frequent lying, namely that some people lie more since they do not consider deception to be a
negative act, or as a way of justifying an existing behavior by adopting a less negative attitude
towards it (Shalvi et al., 2011). Accordingly, we expect frequent liars to show a less negative
explicit attitude towards deception.
Further, pathological liars show diminished moral reasoning abilities, proposed to lead
them to a difficulty in distinguishing right from wrong (Healy & Healy, 1916). We explore the
possibility that frequent liars show deficits in moral reasoning. If indeed such deficits in moral
reasoning are associated with frequent lying, related personality traits, known to be associated
with moral deficits, should also correlate with frequent lying. Such personality traits include
psychopathic personality, which was found to be correlated with self reports of lying in a daily
diary paradigm within a normal population (Kashy & DePaulo, 1996). We expect frequent liars
to show elevated psychopathic traits.
In sum, the current work seeks to (1) examine whether everybody lies (DePaulo et al.,
1996) or most people report not to lie and a few people report lying very frequently (Serota et al.,
2010), (2) find a relation between self-reports regarding lying habits and real life cheating, and
(3) explore the affective, personality, and cognitive correlates that are associated with frequent
lying. To do so, we first used a large survey of more than 500 participants, aimed at investigating
the distribution of self-reports regarding lying frequency, as well as some of the correlates of
frequent lying. Following, a sub-sample was assembled based on their reports on the lying
frequency questionnaire, in order to further investigate correlates of frequent lying, and measure
real life deception.
Study 1
The lying frequency questionnaire was administered as part of a large battery of
questionnaires to all first year psychology students at the University of Amsterdam. In this study
we investigated the lying frequency distribution in order to see whether the skewed distribution
found by Serota et al. (2010) can be replicated. In addition, we examined the possibility that the
skewed distribution on the Lying Frequency Questionnaire is caused by random or deviate
responding, namely – that people scoring high on the questionnaire were simply trying to give
weird answers, or were answering the questionnaire randomly, without giving attention to the
questions. Finally, if indeed the Lying Frequency Questionnaire measures actual lying behavior,
we expect it would positively correlate with participants' Psychopathic tendencies.
Subjects. N = 527 (372 or 71% female) first year psychology students from the
University of Amsterdam (M age = 19.7 years; SD = 2.56 years). Not all subjects completed all
questionnaires. Hence, a different N is reported for each measurement, see Table 1.
Measurements. Lying frequency questionnaire. A Dutch translation of the
questionnaire used by Serota et al. (2010) was used. The questionnaire started with a short, non-
evaluative description of lying as used by Serota and colleagues:"We are interested in truth and
lying in everyday communication. A frequently used definition of lying is intentionally
misleading anyone. Some lies are big, others are small. Some are totally false claims, others are
partial truths with some important details made up or left out. Some lies are obvious, others are
subtle. Some lies are told for good reason. Some lies are selfish, other lies are told in order to
protect others. We are interested in all these different kinds of lies. To get a better understanding
of lying, we ask a lot of people how often they lie." (p. 6)
After the description, subjects were asked to indicate how many times in the last 24 hours
they lied to different people (Family members, Friends or other people you know, People you
work with or know as business contacts, People you do not know but might see occasionally
such as store clerks, and Total strangers) using different communication types (face to face and
non-face to face). A total of 5 (target of lie) x 2 (communication type) estimates were given and
summed into one lying frequency index.
Youth psychopathic trait inventory. (YPI; Andershed, Kerr, Stattin & Levander, 2002).
The YPI is a self reported questionnaire designed to measure traits of psychopathic personality.
The YPI consists of 50 items that can be divided into ten subscales: dishonest charm,
grandiosity, lying, manipulation, callousness, un-emotionality, remorselessness, impulsiveness,
thrill seeking and irresponsibility. Respondents are requested to rate to what degree each of the
items apply to them on a 4-point Likert scale (1 = does not apply at all to 4 = applies very well).
The Dutch version of the YPI was found reliable and valid using a sample of non-referred Dutch
adolescents (Hillege, Das & Ruiter, 2010) and a community based sample of adults (Uzieblo,
Verschuere, van den Bussche & Crombez, 2010). In the current study, the YPI total score and the
YPI lying scale (YPI LIE) were used.
Multidimensional personality questionnaire- brief form. (MPQ BF, Patrick, Curtin &
Tellegen, 2002, See Eigenhuis, Kamphuis & Noordhof, 2012, for the Dutch version). This is a
general self report measure of personality, measuring a range of discrete trait dispositions. In the
current study, two measurements for inconsistent response patterns were used: the Variable
Response Inconsistency scale (VRIN) and the True Response Inconsistency (TRIN). The VRIN
scale consists 21 content-matched item pairs, keyed in the same direction. The VRIN score
increases as these item pairs are answered in an opposite directions, and is hence measuring
random answering patterns. The TRIN scale consists of 16 content matched item pairs, keyed in
an opposite direction, so that frequent 'true' or 'false' answers is indicative 'yea-saying' or 'nay-
saying', respectively" (Patrick et al., 2002), and is hence measuring deviate response patterns. In
addition, a selection of MPQ-subscales has been used as a measure of psychopathic traits
(Benning, Patrick, Blonigen, Hicks & Iacono, 2005). Here we used the Dutch version of the
MPQ-based psychopathy scale (Van Schagen, Verschuere, Kamphuis, Eigenhuis, & Gazendam,
Three subjects were excluded from the Lying Frequency Questionnaire, one from the YPI
questionnaire and two from the MPQ questionnaire for submitting their answers too quickly (i.e.,
the duration was less than 2.5 SD from the mean duration, suggesting lack of attention to the
Lying frequency questionnaire. Lying frequency distribution is presented in Figure 1. The
lying frequency distribution was skewed, SK = 4.76, SE = 0.11. An average of 2.04 lies per day
was found (SD=3.85) with 41% of the subjects telling no lies, 51% telling 1-5 lies, and 8%
telling 6 lies or more. Together, 5% of the subjects told 40% of all reported lies. Since the
variables were not normally distributed, Spearman's rho tests were used1. Descriptive statistics
and correlation with the Lying Frequency Questionnaire are presented in Table 1.
Psychopathic traits. Positive correlations were found between the Lying frequency
questionnaire and YPI total score (r = .31, p < .01), YPI LIE scale (r = .30, p < .01) and MPQ
psychopathic trait measure (r = .21, p < .01).
Inconsistence response patterns. We used the VRIN and TRIN scales to test the
possibility that random or inconsistent responses are associated with higher levels of reported
lying. The Cut-offs suggested by Patrick et al. (2002) was used to divide subjects to valid and
invalid respondents (a deviation of 3 SD’s from the mean in VRIN; a deviation of 3.21 SD’s
from the mean in TRIN; or a deviation of 2 SD’s from the mean in VRIN and a deviation of 2.28
SD’s from the mean in TRIN was considered as invalid). Nine subjects were categorized as
invalid respondents. An independent t-test was used to compare Lying Frequency Questionnaire
score of valid (M = 2.06; SD = 3.88) and invalid respondents (M = 1.33; SD = 1.22). The
difference was not significant, t (495) = 0.56, p = .58, obtaining no evidence for a link between
inconsistence response patterns and lying frequency.
In study 1, using a self-report questionnaire for lying frequency, we replicated the skewed
lying frequency distribution reported by Serota et al. (2010; 2012). Many of the lies were told by
a small part of the population; only 5% of all participants were what we may label ´frequent
liars´, and were responsible for 40% of all reported lies. Moreover, using two inconsistency
scales (MPQ VRIN and MPQ TRIN), we found no indication for a link between lying frequency
and random or deviate response patterns. This finding provides tentative support for the validity
of a self-report tool for lying frequency.
It is important to note that the internal consistency (measured using Cronbach’s Alpha) of
the Lying Frequency Questionnaire was modest (= 0.67). However, given the nature of the
questionnaire, measuring lying to various people, high internal consistency is not necessarily
expected. That is, one may lie (a lot) to some (e.g., friends) but not to others (e.g., family). In
addition, most people answer most items on this scale, with 0 or 1, rendering high internal
consistency less relevant.
Results further revealed the expected positive correlation between lying frequency and
psychopathic tendencies (e.g., the YPI total score and the MPQ psychopathic trait measurement).
Furthermore, as expected, Lying Frequency was associated with a self-reported subscale of lying
habits (e.g. the YPI LIE scale). While the correlation between the Lying Frequency
Questionnaire and the YPI LIE score was modest (r = .30), we note that the two measurements
are quite different from each other and seem to tap on different kinds of lies. The YPI LIE scale,
administered in a context of psychopathic trait measurement, is more tuned to measuring
egocentric lies, whereas the Lying Frequency Questionnaire, which starts with a paragraph
emphasizing the prevalence of deception in society, captures a larger variety of lies.
Taken together, the results of study 1 replicated the skewed lying frequency distribution
(Serota et al. 2010; Serota et a., 2012; Levine et al., 2011). Moreover, initial information
regarding the characteristics of the small minority of frequent liars is provided. We found that
frequent lying is associated with psychopathic tendencies. We were not able, however, to address
the affective aspect of frequent lying and the relation to moral deficits in the mass survey used in
Study 1. In addition, as mentioned above, one of the main goals of the current project was
assessing the relation between self reported lying frequency and real life deception, measured by
incentivized behavior. In Study 2, we invited subjects to the lab to address these issues.
Study 2
To further investigate the correlates of frequent lying, a sample of Study 1 participants
was invited to the lab. To assess moral development, a Dutch version of Defining Issues Test
(DIT-2; Rest, Narvaez, Thoma & Bebeau, 1999, see van Goethem et al., 2012 for the Dutch
version) was used. This task is based on presenting subjects with moral dilemmas and asking
them to rate and rank different statements as important or unimportant to the decision how to act
in such a situation. In addition, we administered the Feeling Thermometer (Jung & Lee, 2009),
assessing explicit attitude towards deception by asking subjects to rate the pleasantness of words
related to truth or deception.
We further used two different tasks in which subjects had the opportunity to privately
cheat for financial profit, allowing us to investigate the relation between self-reported lying
frequency and real life cheating. The first task was an adaptation of the Die Under Cup task
(Shalvi et al., 2011 adapted from Fischbacher & Heusi, 2008) in which participants receive a
regular die inside a paper cup, with a small hole enabling them to privately roll the die under the
cup and see the outcome. Participants are instructed to roll and report the outcome to determine
their pay (higher numbers equal higher pay). While results of the die rolls are truly private, lying
can be analyzed on the aggregate level comparing the empirical distribution of reported die rolls
to the theoretic distribution of an honest die roll (Shalvi et al., 2011). Here, we made a first
attempt to use this task in order to classify subjects according to their individual (dis)honesty.
Subjects were asked to engage in multiple trials of rolling the die and reporting the rolled
outcomes. We classified participants as dishonest if their reported average of die rolls was
statistically unlikely (see similar approach in Greene & Paxton, 2009).
The Die Under Cup task was found very useful when investigating real life cheating,
since it is very simple to explain, and the subjects can easily understand that no one but them will
know if they cheated. However, classifying subjects into honest and dishonest in this task is
based solely on statistics, meaning that extremely lucky subjects will be falsely classified as
being dishonest. To address this limitation, we administered a second cheating task, the Words
task (Wiltermuth, 2011), in which a clear-cut discrimination between honest and dishonest
subjects is possible. In this task, subjects are asked to solve word jumbles in the order in which
they are presented, and are paid according to their reported success in a consecutive order. As
participants are asked to report only the number of correct answers they had, not their actual
answers, they could lie about the number of correct answers and gain more money. Critically, the
third word presented was unsolvable, so any subject reporting solving more than two words, can
be classified as dishonest.
Sampling. Subjects were invited to the lab for a follow-up study according to their score
in the Lying Frequency Questionnaire. To ensure enough variance in the Lying Frequency
variable, low lying frequencies were under-sampled and high lying frequencies were over-
sampled. From the initial sample, we invited 50 subjects scoring 0 (~25% of the sample), 50
subjects scoring 1 (~40% of the sample), 63 subjects scoring 2 (100% of the sample), 72 subjects
scoring 3-5 (100% of the sample) and 35 subjects scoring 6 and above (100% of the sample). In
total, 270 invitations were sent. Thirty-three subjects responded to the invitation and 31 were
eventually scheduled. An additional sample of 20 subjects was recruited without screening, from
the psychology department and other faculties in the University of Amsterdam. Subjects
participated in the experiment for either money or course credit.
Subjects. N = 51 (37 female) participants from the University of Amsterdam (M age =
21.1 years; SD = 6.16 years).
Procedure. The experimenter was blind of the subjects’ Lying Frequency Questionnaire
score throughout the scheduling and testing phases. Lying Frequency Questionnaire and YPI
were administered again. In addition, the following measures were used2:
Feeling thermometer (FT). (Jung & Lee, 2009). Subjects were presented with 6 words
related to deception and 6 words related to honesty, and were asked to rate the words as pleasant
or unpleasant on an 11-points Likert scale (1=unpleasant, 11=pleasant). The average score for lie
words was subtracted from the average score for truth words. A high score indicates a bigger
difference between truth and lie words, e.g. a more negative attitude towards deception.
Defining issues test 2 (DIT2). (Rest et al., 1999). Subjects were presented with three
paragraph-long moral dilemmas and were asked what the main character should do. Next,
subjects were asked to rate 10 statements as important/unimportant for the decision on a 5-points
Likert scale and then rank these statements. Answers were used to calculate an index score of
moral development (known as N2 index; higher score indicate higher moral development; for
details regarding the computing procedure see Rest, Thoma, Narvaez & Bebeau, 1997).
Die Under Cup. A modification of the paradigm by Shalvi and colleagues (Shalvi et al.,
2011) was used. Subjects received a covered cup with a die inside it, and a small hole in the lid.
They were informed that they will be paid according to their reported scores, €0.02 for each
point they rolled in each of the trials, meaning €0.02 when rolling '1', €0.04 when rolling '2', and
so on. In each trial, subjects were requested to roll the dice three times, check the outcome every
time, but to report only the outcome of the first roll by typing the outcome into the computer.
The task included 60 trials, so in total each subject rolled the dice 180 times but only reported
and were paid for the 60 rolls they reported. A participant who wishes to maximize profit (or a
very lucky honest one) may thus report rolling 6 in all 60 trials earning €7.20 in total, which is
well above the expected value if reporting honestly.
Words task. (Wiltermuth, 2011). Subjects were presented with 9 scrambled words in
Dutch, and had 5 minutes to solve them (the solutions were: potlood [pencil], bloem [flower],
taguan [taguan], sokken [socks], steen [stone], kleur [color], rusten [rest], koekje [cookie] and
ijsberg [iceberg])P
P. Subjects were paid an extra €0.5 for each word they solved, but the
instructions indicated that the words should be solved in the order in which they appeared, noting
“if you successfully unscramble the first three words but not the forth you will only be paid for
the first three words, even if you successfully unscramble the fifth, sixth and seventh words”.
When the time was up, subjects were requested to indicate how many words they were able to
solve in a row, knowing that the number they indicate will be used to calculate the payment.
After reading the instructions, the experimenter verified that subjects understood the instructions.
Crucially, and unknown to the participants, the third word was extremely rare and difficult to
solve. This word could only be unscrambled to spell “taguan”, a large nocturnal flying squirrel.
Given this design, any reported score of three and above could be considered as cheating,
enabling a clear cut between honest and dishonest subjects.
Confession question. After receiving their payment, subjects were asked to answer the
following question on a small piece of paper and put it in a sealed box: “We are interested in the
way people perform in the Die Under Cup task. The money you earned is yours and will not be
taken away. We want to ask you a short question regarding your performance in this task. Out of
the 60 times you had to report your score, how many times did you report a higher score than
you actually rolled?“ Any report of above zero was considered as a confession of dishonesty.
The different measurements’ descriptive statistics and correlations with Lying Frequency
Questionnaire are presented in Table 2.
Lying frequency questionnaire. Results distribution is presented in Figure 2.
YPI. As predicted, and replicating the results of study 1, a positive correlation was found
between the Lying Frequency questionnaire and both YPI total score, r = .38, p < .01, and the
YPI LIE score, r = .31, p < .05.
DIT. No significant correlation was found between the DIT N2 score and the Lying
Frequency Questionnaire (r = .17, p > .05).
FT. Subjects generally rated words related to truth (M = 8.87, SD = 1.19) as more
pleasant than words related to deception (M = 2.47, SD = 1.23), t (49) = 20.55, p < .001, d =
3.05. A difference score (average for truth words minus average for lie words) was calculated for
each subject and correlated with the Lying Frequency Questionnaire score. A non significant
negative trend was found in the predicted direction – frequent liars showed a slightly less
negative attitude towards deception (r = -.15, p = .08).
Die under cup. The average score in the Die Under Cup task was 3.63€ (SD = 1.67) and
the distribution of responses deviated from the theoretical symmetric (honest) distribution, χ²=
23.73, p < .001, indicating that in general, some cheating took place. As expected, the average
die roll score was positively correlated with the Lying Frequency Questionnaire, (r = .39, p <
.01). The more people self-reported lying, the more they earned in the Die Under Cup task. We
further classified subjects as Honest and Dishonest according to the likelihood that their reported
scores were honest. Taking an alpha of 10%, 15 subjects (29% of the sample) showed a score
which was higher than the score predicted by chance. A One Way ANOVA was used to compare
the scores of the two groups (honest [n = 36] and dishonest [n = 15] subjects). For the Lying
Frequency Questionnaire and the YPI total score, results revealed a significant difference
between males and females, with males scoring higher on both measurements; hence gender was
entered as a covariate. A significant difference was found for the Lying Frequency score, F (1,
49) = 9.45, p < .005, η ² = 0.14, with dishonest subjects scoring higher (M = 3.93, SD = 3.49)
than honest subjects (M = 1.58, SD = 1.93). A significant difference was also found for the
Confession question, with dishonest subjects scoring higher (M = 7.00, SD = 11.92) than honest
subjects (M = 0.69, SD = 1.75), F (1, 49) = 9.83, p < .005, η ² = 0.17. A non-significant
difference in the expected direction was found for the FT score, with dishonest subjects scoring
lower (M = 5.61, SD = 2.30) than honest subjects (M = 6.74, SD = 2.10), F (1, 49) = 2.85, p =
.09, η ² = 0.06. No difference was found for the Words score, F (1, 49) = 2.68, p = .11, YPI total
score, F (1, 49) = 1.07, p = .31, YPI LIE score, F (1, 49) = 0.54, p = .46 or DIT score, F (1, 49) =
0.64, p = .42.
Confession question. Twelve out of 51 subjects (23.5 %) confessed to some degree of
over-reporting (e.g. dishonesty). Confessions were positively correlated with the Die Under Cup
score, r = .49, p < .001, validating that high scores on the Die Under Cup task are related to
deception. Furthermore, confessions were also positively correlated with the Lying Frequency
Questionnaire (r = .35, p < .05). We used the Confession question to divide subjects to honest
and dishonest. One Way ANOVA's were used to compare self-proclaimed honest vs. dishonest
subjects. For the Lying Frequency Questionnaire and the YPI total score, males scored higher
than females, hence gender was entered as a covariate. Dishonest subjects scored higher (M =
3.75, SD = 3.19) than honest subjects (M = 1.82, SD = 2.87) in the Lying Frequency
Questionnaire, F (1, 49) = 6.82, p < .05, η² = 0.12. No significant differences were detected
between honest and dishonest subjects in the YPI total score, F (1, 49) = 1.4, p = .244, YPI LIE
score, F (1, 49) = 1.46, p = .23, DIT score, F (1, 49) = 1.31, p = .72 or FT score, F (1, 49) = 2.07,
p = .16.
Words task. Twelve out of 51 subjects (23 %) reported solving more than 2 words, and
were classified as dishonest. Six of them were also classified as dishonest based on the Die
Under Cup total score. A positive correlation was found between the Words task score and the
Lying Frequency Questionnaire (r = .39, p < .01). One way ANOVA’s were used to compare the
scores of the honest vs. dishonest subjects. For the Lying Frequency Questionnaire and the YPI
total score, a significant difference was found between males and females, with males scoreung
higher in both measurements. Hence gender was entered as a covariate. A non-significant
difference in the expected direction was found in the Lying Frequency Questionnaire between
dishonest (M = 3.58, SD = 2.47) and honest subjects (M = 1.87, SD = 2.65) subjects, F (1, 49) =
3.7, p = .06. A significant difference was found in the Die Under Cup task between dishonest (M
= 3.82, SD = 0.33) and honest subjects (M = 3.58, SD = 0.27), F (1, 49) = 6.08, p < .05, η² =
0.12. Dishonest subjects also scored higher in the YPI LIE scale (M = 8.92, SD = 2.58, as oppose
to M = 7.54, SD = 1.80), F (1, 49) = 4.2, p < .05, η² = 0.08. Finally, dishonest subjects scored
higher on the YPI total score (M = 98.17, SD = 15.88, as oppose to M = 87.18, SD = 14.97), F (1,
49) = 4.7, p < .05, η² = 0.07. No significant difference was found in the FT score, F (1, 49) =
2.31, p = .13 and the DIT score, F (1, 49) = 0.68, p = .41.
The correlation between lying frequency and psychopathy identified in Study 1 was
replicated in Study 2. The Lying Frequency Questionnaire results were positively correlated with
both the general psychopathy score (YPI total score) and the psychopathy-lying scale (YPI Lie
scale). In addition, frequent lying was non-significantly associated with a slightly less negative
attitude towards lying.
Importantly, people who reported to lie more often were also more likely to cheat in tasks
allowing them to make a quick profit. Dishonest subjects in both Die Under Cup and Words
tasks showed higher scores on the Lying Frequency Questionnaire. These tasks are good proxies
to lies in real life, since the participant makes the decision to lie, and a (financial) reward is
attached to lying.
An additional interesting finding was that in the Words (but not the Die Under Cup) task,
dishonest subjects also scored higher in the YPI total score and YPI LIE score. Perhaps the clear
cut between honest and dishonest subjects in the Words task also affects the way subjects
conceive the task. In the Die Under Cup task, subject can over report occasionally, or by just a
bit. On the Words task, on the other hand, subjects have one moment in which they make the
decision to cheat. It was shown before that a violation of quantity is the most common form of
deception, perhaps because it requires less effort (Levine et al., 2002). Over-reporting on the Die
Under Cup task can be considered as a violation of quantity, while cheating in the Words task is
more of a quality violation. Hence it is reasonable that psychopathic tendencies are related to
deception in the Words task, a more effortful form of deception.
No correlation was found between lying frequency and the DIT score, and dishonest
subjects in the die task did not score lower on the DIT task. These findings indicating that
frequent lying is not related to a deficiency in moral judgment, as suggested for pathological liars
(Healy & Healy, 1916). It seems that frequent liars in the present study displayed normal moral
judgment, and are capable of discriminating right from wrong, but simply make the decision to
lie. A similar claim, of being able to discriminate right from wrong and yet choosing wrong, was
recently made for psychopathic inmates (Cima, Tonnaer, & Hauser, 2010).
Finally, a strong correlation was found between the Confession question and results on
the Die Under Cup task. First, this finding validates the idea that a high score on the Die Under
Cup task is generally related to dishonesty and not luck. Second, this questionnaire enabled
another classification of honest and dishonest subjects based on self-reports. Using this
classification as well revealed that dishonest subjects scored higher on the Lying Frequency
Questionnaire. It seems like frequent liars do not only cheat more, they are also more willing to
admit it.
One limitation of the study is the fact that as opposed to the results of study 1 and
previous studies (Andershed et al., 2002; Hillege et al., 2010), the internal consistency of the YPI
Lie scale in Study2 was modest (.55). This may be due to the combination of the small amount of
items in this scale (see Streiner 2003) and the smaller sample used in study 2.
General Discussion
Results obtained in two studies (1) replicated the distribution of lying frequency
described by Serota et al. (2010), and validate the self-report tool as means to assess dishonesty,
(2) revealed that people reporting high lying frequency are also more likely to cheat for personal
profit, and finally (3) showed a correlation between lying frequency and psychopathic tendencies
and a trend of a less negative attitude towards deception.
We found a skewed lying frequency distribution, replicating the findings of Serota and
colleagues (2010). While the claim that everybody lies is becoming widely accepted, with some
claiming all people lie between 10 and 200 times a day (see Meyer, 2011), the findings presented
here provide a somewhat different picture most people report not to lie at all.
Results further support the validity of the Lying Frequency Questionnaire: We did not
find a relation between the Lying Frequency Questionnaire and random answering pattern or a
tendency for deviate responses. Given the large sample size, it is not likely that the lack of
findings is due to power issues. Using G*Power 3 (Faul, Erdfelder, Lang, & Buchner, 2007), we
calculated the magnitude of effect that could be detected with this sample size and the
conventional value of .80 for minimal statistical power. This analysis showed that our sample
was large enough to detect an effect size of only .07.
Positive Relation Between Self-Reports and Actual Dishonesty
Another support for the validity of the questionnaire was found by the correlation
between self-reported frequent lying and real life deception in the lab in two separate tasks
enabling profitable deception: the Die Under Cup and the Words tasks. We further used these
tasks to classify subjects as honest and dishonest, and found that dishonest subjects score higher
on the Lying Frequency Questionnaire. The finding, linking self-reported lying and real life
cheating, gives support to the validity of the Lying Frequency Questionnaire as a measurement
for deception.
Correlates of Frequent Liars
Finally, the data enabled a better understanding of the different correlates of frequent
lying. With respect to the personality correlates of frequent lying, a correlation between lying
frequency and psychopathic tendencies was found. This correlation was of medium strength, 0.2-
0.3, and significant in both studies using two different measures. It seems as if at least part of the
variance in lying frequency is explained by psychopathic tendencies. It has been claimed before
that antisocial individuals are inclined to lying frequently (Dike et al., 2005), and psychopathy,
as seen from the subscales of the YPI, is partially defined by lying habits. In addition, Kashy and
Depaulo (1996) found that manipulative individuals (another personality trait that characterize
people with psychopathic tendencies), tend to lie more. The stable correlation between
psychopathic tendencies and lying habits therefore corroborates clinical observations and
theoretical claims about psychopathy.
With respect to the affective aspect, a non-significant trend of negative correlation was
found between lying frequency and explicit attitude towards deception. This means that similarly
to the assumptions about pathological lying (Grubin, 2005), people who lie frequently might
conceive deception as slightly less negative. Questions have been raised regarding the relation
between attitude and behavior (Chen & Bargh, 1999). With respect to deception, implicit but not
explicit attitude was claimed to predict behavior (Jung & Lee, 2009). Given this claim, the trend
found here is quite interesting. This finding is coherent with the finding of Serota et al. (2012)
that prolific liars find lying to be a more acceptable behavior. A less negative attitude towards
lying might pre-dispose some individuals to frequent lying. It is also possible that people who
find lying less negative are the ones who will admit lying frequently in a questionnaire, while
others who lie just as much but consider lying to be a negative act, choose to under-report their
lying rates.
However, it seems that frequent liars do not have a moral deficit; no correlation was
found between the Lying Frequency Questionnaire or the Die Under Cup task and the DIT2. We
investigated the possibility that the lack of findings is due to power issues. According to
G*Power 3 (Faul et al., 2007), in order to find a correlation of .17 (the correlation found between
DIT2 and the lying frequency questionnaire) as significant, a sample of N = 212 was needed
(with α = .05). While our sample size does not allow completely ruling out the possibility, moral
deficiency is unlikely to be an important factor.
Implications of the Current Findings
Taken together, our findings contribute to the developing debate regarding the role of
individual differences in lying behavior. We provide solid evidence showing that both self-
reports regarding lying frequency and cheating in the lab are correlated and associated with
certain individual characteristics. These evidences strengthen the need to continue investigating
the role of individual differences in deceptive communication, as clearly such differences matter.
While situational factors are likely to play a role in the decision to lie or cheat, as lying or
cheating is easier or more appealing in some situations, it seems like some personality traits
make some of us more prone to deceptive behavior than others.
The fact that individual differences play a role in deceptive behavior, is of great interest
to economic and (ethical) decision making research. The small minority of frequent liars may be
less susceptible to manipulations used in this line of work. For example, recent work revealed
that people lie more when they can secure opportunities to win positive outcomes. Yet, some
individuals cheat to the maximum extent possible regardless of such situational considerations
(Shalvi, 2012). It was suggested before that self-concept maintenance can explain the extent to
which people lie (Mazar, Amir & Ariely, 2008). According to this theory, "people behave
dishonesty enough to profit, but honestly enough to delude themselves of their integrity" (Mazar
et al., 2008, p. 633). Perhaps the desire to maintain an honest self-concept explains decision
making only in non-frequent liars, more than in frequent liars. Simply put, frequent liars may be
less concerned with maintaining an honest self image.
Understanding the characteristics of a liar is of great relevance to the field of human
communication. It was claimed that a truth bias exists in human communication – that is, people
tend to believe others, regardless of their actual honesty (Levine, Parks & McCornack, 1999).
According to this point of view, assessing the reliability of every claim we come across is
ineffective, and we are hence prone to believe in what we hear. This bias is reasoned to provide a
useful tool in most social interactions (as most social interactions are claimed to be honest).
However, the usefulness of this heuristic obviously depends on the chance that the individual in
front of us is telling the truth. If we are all liars – the truth bias will be a non-adaptive
mechanism. If a small group in the population is prone to very frequent lying, the truth bias
could be misleading only when communicating with these specific individuals, but adaptive
when communicating with others.
Our research may also provide a new motivation for research on pathological lying. The
knowledge regarding pathological lying is mostly limited to theory, clinical observation, and
anecdotal evidence. Our study provides the much needed tools to start the empirical investigation
of pathological lying and how they are qualitatively and quantitatively different from frequent
liars. The study of frequent liars seems valuable for shaping new hypotheses concerning the
nature and development of pathological lying.
The current research reveals individual differences play a role in deceptive
communication, and provide a better understanding of the elements distinguishing frequent liars
from the rest of the population. The emerging profile of a frequent liar is of a person which has
higher scores on psychopathic traits measures and is more prone to cheating in a lab task. Future
research should further craft the characteristics of this interesting population and investigate the
causal directionality of the uncovered correlations. Further promising direction is gaining better
understanding to the developmental pathway that may lead some individuals to become frequent
liars, or even pathological liars. Gaining such knowledge may help identifying the frequent liars
among us. It may also be valuable to craft interventions aimed at these populations to stop lying
and begin telling the truth.
Andershed, H., Kerr, M., Stattin, H., & Levander, S. (2002). Psychopathic traits in non-referred
youths: A new assessment tool. E. Blaauw & L. Sheridan (Eds.). Psychopaths - current
international perspectives (pp. 131-158). The Hague: Elsevier.
Ariely, D. (2012). The Honest Truth About Dishonesty: How We Lie to Everyone, Especially
Ourselves. New York, NY: HarperCollins.
Bazerman, M. H. & Tenbrunsel, A. E. (2011). Blind Spots: Why We Fail to Do What’s Right and
What to Do About It. Princeton: Princeton University Press.
Benning, S. D., Patrick C. J., Blonigen D. M., Hicks B. M., & Iacono W. G. (2005). Estimating
facets of psychopathy from normal range personality traits: A step towards community
epidemiological investigations. Assessment, 12, 3–18. doi:10.1177/1073191104271223
Chen, M., & Bargh, J. A. (1999). Consequences of automatic evaluation: Immediate behavioral
predispositions to approach or avoid the stimulus. Personality and Social Psychology
Bulletin, 25, 215-224. doi:10.1177/0146167299025002007
Cima, M., Tonnaer, F., & Hauser, M. D. (2010). Psychopaths know right from wrong but don’t
care. Social Cognitive and Affective Neuroscience, 5, 59–67. doi:10.1093/scan/nsp051
DePaulo, B.M. (2004). The many faces of lies. A. G. Miller (Ed.). The social psychology of good
and evil (pp. 303–236). New York, NY: Guilford Press.
DePaulo, B. M., Kirkendol, S. E., Kashy, D. A., Wyer, M. M., & Epstein, J. A. (1996). Lying in
everyday life. Journal of Personality and Social Psychology, 70, 979-995.
Derogatis, L. R. (1975). How To Use the System Distress Checklist (SCL-90) in Clinical
Evaluations, Psychiatric Rating scale, Self-Report Rating Scale. Nutley, NJ: Hoffmann-
La Roche.
Dike, C. C., Baranoski, M., & Griffith, E. E. H. (2005). Pathological lying revisited. Journal of
the American Academy of Psychiatry and the Law, 33, 342-349.
Eigenhuis, A., Kamphuis, J. H., & Noordhof, A. (2012). Development and validation of the
Dutch brief form of the multidimensional personality questionnaire (MPQ-BF-NL).
Assessment. Advance online publication . doi: 10.1177/1073191112444920.
Faul, F., Erdfelder, E., Lang, A. G., & Buchner, A. (2007). G*Power 3: A flexible statistical
power analysis program for the social, behavioral, and biomedical sciences. Behavior
Research Methods, 39, 175-191. doi:10.3758/BF03193146
Fischbacher, U. & Heusi, F. (2008). Lies in disguise, an experimental study on cheating. TWI
Working Paper 40, Thurgau Institute of Economics, University of Konstanz .
Fischbacher, U. & Utikal, V. (2011). Disadvatageous lies. Thurgau institute of economics,
Research paper series, 71, 6.
Gino, F., Ayal, S., & Ariely, D. (2013). Self-serving altruism? The lure of unethical actions that
benefit others. Journal of Economic Behavior and Organization. Special Issue on
“Deception, Incentives and Behavior.” Forthcoming.
Gino, F., & Margolis, J. D. (2011). Bringing ethics into focus: How regulatory focus and risk
preferences influence (un)ethical behavior. Organizational Behavior and Human
Decision Processes, 115, 145-156.
Gino, F., Schweitzer, M. E., Mead, N. L., & Ariely, D. (2011). Unable to resist temptation: How
self-control depletion promotes unethical behavior. Organizational Behavior and Human
Decision Processes, 115, 191-203. doi:10.1016/j.obhdp.2011.03.001
Greene, J. D., & Paxton, J. M. (2009). Patterns of neural activity associated with honest and
dishonest moral decisions. PNAS, 106, 12506-12511. doi: 10.1073/pnas.0900152106
Grubin, D. (2005). Getting at the truth about pathological lying. The Journal of the American
Academy of Psychiatry and the Law, 33, 350-353.
Healy, W., & Healy, M. T. (1916). Pathological lying, accusation, and swindling. The Yale Law
Journal, 25, 252-293. doi: 10.2307/786422
Hillege, S., Das, J., & de Ruiter, C. (2010). The youth psychopathic traits inventory:
Psychometric properties and its relation to substance use and interpersonal style in a
Dutch sample of non-referred adolescents. Journal of Adolescence, 33, 83-91.
Jung, K. H., & Lee, J. (2009). Implicit and explicit attitude dissociation in spontaneous deceptive
behavior. Acta Psychological, 132, 62-67. doi: 10.1016/j.actpsy.2009.06.004
Kashy, D. A., & DePaulo, B. M. (1996). Who lies? Journal of Personality and Social
Psychology, 70, 1037-1051. doi:10.1037//0022-3514.70.5.1037
King, B. H., & Ford, C. V. (1988). Psaudologia fantastica. Acta Psychiatry, 77, 1-6.
Levine, T. R., Lapinski, M. K., Banas, J., Wong, N. C. H., Hu, A. D. S., Endo, K., Baum, K. L.,
& Anders, L. N. (2002). Self-construal, self and other benefit and the generation of
deceptive messages. Journal of Intercultural Communication Research, 31, 29–47.
Levine, T. R., Park, H. S., & McCornack, S. A. (1999). Accuracy in detecting truths and lies:
Documenting the “veracity effect.” Communication Monographs, 66, 125–144. doi:
Levine, T. R., Serota, K. B., Carey, F., & Messer, D. (2011). Teenagers lie a lot. Proceedings
from the 61st Annual International Communication Association Conference Boston,
Lewis, A., Bardis, A., Flint, C., Mason, C., Smith, N., Tickle, C., Zinser, J., Drawing the line
somewhere: An experimental study of moral compromise, Journal of Economic
Psychology (2012), doi:10.1016/j.joep.2012.01.005
Mazar, N., Amir, O., & Ariely, D. (2008). The dishonesty of honest people: A theory of self-
concept maintenance. Journal of Marketing Research, 45, 633-644.
Merckelbach, H., Giesbrecht, T., Jelicic, M., & Smeets, T. (2010). Kretenzers in surveys: het
probleem van onzorgvuldige respondenten. Tijdschrift voor Psychiatrie, 52, 663-669.
Meyer, P. (2011, October). Pamela Meyer: How to spot a liar [video file]. Retrieved from:
Patrick, C. J., Curtin, J. J., & Tellegen, A. (2002). Development and validation of a brief form of
the multidimensional personality questionnaire. Psychological Assessment, 14, 150-163.
Poletti, M., Borelli, P., & Bonuccelli, U. (2011). The neuropsychological correlates of
pathological lying: Evidence from behavioral variant frontotemporal dementia. Journal of
Neurology, 258, 2009-2013. doi:10.1007/s00415-011-6058-1
Rest, R. J., Narvaes, D., Thoma, S. J., & Babeau, M. J. (1999). DIT2: Devising and testing a
revised instrument of moral judgment. Journal of Educational Psychology, 91, 644-659.
Rest, R. J., Thoma, S. J., Narvaes, D., & Babeau, M. J. (1997). Alchemy and beyond: Indexing
the defining issues test. Journal of Educational Psychology, 89, 498-507.
Serota, K. B., Levine T. R., & Boster F. J. (2010). The prevalence of lying in America: Three
studies of self-reported lies. Human Communication Research, 36, 2-25.
Serota, K. B., Levine, T. R., & Burns, A. (2012). A few prolific liars: Variation in the prevalence
of lying. Proceedings from the 98th Annual Convention of the National Communication
Association. Orlando, Florida.
Shalvi, S. (2012). Dishonestly increasing the likelihood of winning. Judgment and Decision
Making, 7, 292-303.
Shalvi, S., Dana, J., Handgraaf, M. J. J., & Dreu, C. K. W. (2011). Justified ethicality: Observing
desired counterfactuals modifies ethical perceptions and behavior. Organizational
Behavior and Human Decision Processes, 115, 181-190.
Shalvi, S., Eldar, O., & Bereby-Meyer, Y. (2012). Honesty requires time (and lack of
justifications). Psychological Science, 23, 1264-1270. doi: 10.1177/0956797612443835
Streiner, D. L. (2003). Starting at the beginning: An introduction to coefficient alpha and internal
consistency. Journal of Personality Assessment, 80, 99-103.
Thaler, R. H. & Sunstein, C. R. (2008). Nudge – Improving Decisions About Health, Wealth, and
Happiness. New York,NY: Penguin Group.
Uzieblo, K., Verschuere, B., Van den Bussche, E., & Crombez, G. (2010). The validity of the
Psychopathic Personality Inventory-Revised in a community sample. Assessment, 17,
334–346. doi:10.1177/1073191109356544
Van Goethem, A. A. J., van Hoof, A., van Aken, M. A. G., Raaijmakers, Q. A. W., Boom, J., &
Orobio de Castro, B. (2012). The role of adolescents’ morality and identity in
volunteering: Age and gender differences in a process model. Journal of Adolescence, 35,
509-521. doi:10.1016/j.adolescence.2011.08.012
Van Schagen, S., Verschuere, B. J., Kamphuis, J. H., Eigenhuis, A., & Gazendam, F. (2012).
The Multidimensional Personality Questionnaire (MPQ) as a measure of psychopathic
traits. Unpublished manuscript, University of Amsterdam.
Vohs, K. D., & Schooler, J. W. (2008). The value of believing in free will: Encouraging a belief
in determinism increases cheating. Psychological Science, 19, 49−54. doi:
Vrij, A. (2000). Detecting Lies and Deceit. Chichester, England: Wiley.
Wiltermuth, S. S. (2011). Cheating more when the spoils are split. Organizational Behavior and
Human Decision Processes, 115, 157-168. doi: 10.1016/j.obhdp.2010.10.001
Zhong, C., Bohns, V. K., & Gino, F. (2010). A good lamp is the best police: Darkness increases
self-interested behavior and dishonesty. Psychological Science, 21, 311–314. doi:
1. An additional analysis computing Pearson correlations while controlling for gender
effects revealed the same pattern of results. Gender is thus not discussed further.
2. We additionally administered the Symptom Check-List (SCL 90, Derogatis 1975) and an
extension for the Lying Frequency Questionnaire including open questions regarding the
last lie told, but these did not produce any meaningful results and are thus not discussed
further. Information about these scales and their results are available from the
corresponding author.
3. These words were selected from a larger sample of words, after piloting the words with
10 native Dutch speakers. The unsolvable word was not solved by any of the subjects,
and the solvable words were solved by all 10 subjects.
(A) Occurrence (B) Number of Lies in the past 24 hours
Figure 1. Lying Frequency Questionnaire distribution – Study 1.
Figure 2. Lying Frequency Questionnaire distribution – Study 2.
Table 1
Descriptive statistics and correlations with lying frequency – Study 1.
M (SD)
Spearman's rho correlation
with Lying Frequency
Lying Frequency
2.04 (3.85)
2.72 (1.73)
-0.23 (1.75)
MPQ psychopathy
11.65 (5.19)
8.01 (2.55)
88.55 (17.73)
Note * p < 0.05, ** p < 0.01
Table 2
Descriptive statistics and correlations with lying frequency – Study 2.
M (SD)
Spearman's rho correlation with
Lying Frequency Questionnaire
Lying Frequency
2.27 (2.68)
7.86 (2.1)
YPI total score
89.76 (15.75)
Die Under Cup Mean
3.63 (0.3)
Confession question
2.55 (7.1)
Words task
2.8 (1.9)
6.4 (2.2)
35.16 (17.20)
Note. * p < 0.05, ** p < 0.01.

Supplementary resource (1)

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... Neuroticismo y Psicoticismo comparten desconfianza paranoide y una actitud favorable a mentir. Según Halevy et al. (2014), la ausencia de una actitud negativa hacia la conducta de mentir podría ser un buen predictor de la mentira frecuente. ...
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Objective: Scientific tests confirm that certain personality features predict frequent lying. However, lying as a multidimensional construct still lacks a validated measure of dispositional deception to understand this heterogeneous behavioural pattern. Method: The ATRAMIC test was created in order to conceptualize and offer an objective and operational measure of lying as a dispositional trait. We wanted to know what factors from ATRAMIC (the variables of "Propensity to Lie", Personality, Attitudinal and the Sincerity and Social Desirability scales), of the EPQ-R and the IPDE-77, could predict the number of lies per day, in 475 adults of the general population aged 18 to 65 years (Mage = 36.97 years; SD = 13.39). Results: 52.65% of the participants reported that they lied one to three times a day. The ATRAMIC factors correlated more with neuroticism and psychoticism than with EPQ-R extraversion, suggesting different behavioural correlates associated with lying. Logistic regression shows that the variables that best predict the tendency to lie are "Recognition and Acceptance of Lying", "Paranoid Mistrust", "Empathy" and "Neuroticism". As the scores on these variables increase, the more likely it is that the individual will report lying daily. The variable "Acknowledgment and Acceptance of Lying" doubles the probability of lying daily. Conclusions: It is presented as a "dispositional trait" that underlies "the basic personality traits” that define liars. Keywords: predisposition to lie; personality; recognition and acceptance of lying; dispositional trait; frequency of lying.
... Notwithstanding, to best understand the phenomenon, triangulating research methods when studying collaborative dishonesty is important. Doing so can be inspired by work on individual (dis)honesty and moral behavior examined in contextualized settings employing experience sampling techniques (Hofmann et al., 2014) and self-reported behavior (DePaulo et al., 1996;Halevy et al., 2014;Serota et al., 2010), examining diverse samples (Cohn et al., 2014;Rahwan et al., 2019), analyzing archival data (Ferrali, 2020;Gould & Kaplan, 2011), and conducting field experiments with measures that closely resemble everyday rule-violating and (dis)honest behavior (Cohn et al., 2019;Vranka et al., 2019; see a review by Larkin et al., 2021;Pierce & Balasubramanian, 2015). Indeed, Azar et al. (2013) take the first steps toward studying collaborative dishonesty in contextualized settings, examining whether groups of restaurant diners return extra change they have wrongly received. ...
Although dishonesty is often a social phenomenon, it is primarily studied in individual settings. However, people frequently collaborate and engage in mutual dishonest acts. We report the first meta-analysis on collaborative dishonesty, analyzing 87,771 decisions (21 behavioral tasks; k = 123; nparticipants = 10,923). We provide an overview of all tasks used to measure collaborative dishonesty, and inform theory by conducting moderation analyses. Results reveal that collaborative dishonesty is higher (a) when financial incentives are high, (b) in lab than field studies, (c) when third parties experience no negative consequences, (d) in the absence of experimental deception, and (e) when groups consist of more males and (f) younger individuals. Further, in repeated interactions, group members' behavior is correlated-participants lie more when their partners lie-and lying increases as the task progresses. These findings are in line with the justified ethicality theoretical perspective, suggesting prosocial concerns increase collaborative dishonesty, whereas honest-image concerns attenuate it. We discuss how findings inform theory, setting an agenda for future research on the collaborative roots of dishonesty. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
It is often suggested that we are equipped with a set of cognitive tools that help us to filter out unreliable testimony. But are these tools effective? I answer this question in two steps. Firstly, I argue that they are not real-time effective. The process of filtering, which takes place simultaneously with or right after language comprehension, does not prevent a particular hearer on a particular occasion from forming beliefs based on false testimony. Secondly, I argue that they are long-term effective. Some hearers sometimes detect false testimony, which increases speakers’ incentives for honesty and stabilizes the practice of human communication in which deception is risky and costly. In short, filtering prevents us from forming a large number of beliefs based on false testimony, not by turning each of us into a high-functioning polygraph but by turning the social environment of human communication into one in which such polygraphs are not required. Finally, I argue that these considerations support strong anti-reductionism about testimonial entitlement.
Prior work suggests those who lie prolifically tend to be younger and self-identify as male compared to those who engage in everyday lying, but little research has developed an understanding of prolific lying beyond demographics. Study 1 ( N = 775) replicated the prior demographic effects and assessed prolific lying through situation-level (e.g., opportunistic cheating) and individual-level characteristics (e.g., dispositional traits, general communication patterns) for white and big lies. For these two lie types, prolific lying associated with more opportunistic cheating, the use of fewer adjectives, and being high on psychopathy compared to everyday lying. Study 2 ( N = 1,022) replicated these results and observed a deception consensus effect reported in other studies: the more that people deceived, the more they believed that others deceived as well. This piece develops a deeper theoretical understanding of prolific lying for white and big lies, combining evidence of situational, dispositional, and communication characteristics.
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The goal of this research was to develop an implicit association test for honesty-humility (IAT-HH) and assess its validity. Construct validity was established by conducting correlations and confirmatory factor analysis. A model with honesty-humility facets measured by two related but distinct method factors (self-report vs. implicit) showed superior fit, suggesting that our IAT-HH captured important substantive variance, but also IAT-specific variance. We further investigated criterion-related validity in relation to academic behaviour of Belgian university students (N = 161), using both desirable and undesirable criteria. Our results suggest that IATs of honesty-humility may show measurement overlap with self-reports. However, the IAT-HH could neither predict self-report nor behavioural academic criteria. Predicting behaviour with the IAT-HH appears to be complex and is discussed.
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People not only seek to avoid losses or secure gains; they also attempt to create opportunities for obtaining positive outcomes. When distributing money between gambles with equal probabilities, people often invest in turning negative gambles into positive ones, even at a cost of reduced expected value. Results of an experiment revealed that (1) the preference to turn a negative outcome into a positive outcome exists when people's ability to do so depends on their performance levels (rather than merely on their choice), (2) this preference is amplified when the likelihood to turn negative into positive is high rather than low, and (3) this preference is attenuated when people can lie about their performance levels, allowing them to turn negative into positive not by performing better but rather by lying about how well they performed.
When confronted with an ethical dilemma, most of us like to think we would stand up for our principles. But we are not as ethical as we think we are. InBlind Spots, leading business ethicists Max Bazerman and Ann Tenbrunsel examine the ways we overestimate our ability to do what is right and how we act unethically without meaning to. From the collapse of Enron and corruption in the tobacco industry, to sales of the defective Ford Pinto and the downfall of Bernard Madoff, the authors investigate the nature of ethical failures in the business world and beyond, and illustrate how we can become more ethical, bridging the gap between who we are and who we want to be.Explaining why traditional approaches to ethics don't work, the book considers how blind spots like ethical fading--the removal of ethics from the decision--making process--have led to tragedies and scandals such as the Challenger space shuttle disaster, steroid use in Major League Baseball, the crash in the financial markets, and the energy crisis. The authors demonstrate how ethical standards shift, how we neglect to notice and act on the unethical behavior of others, and how compliance initiatives can actually promote unethical behavior. Distinguishing our "should self" (the person who knows what is correct) from our "want self" (the person who ends up making decisions), the authors point out ethical sinkholes that create questionable actions.Suggesting innovative individual and group tactics for improving human judgment,Blind Spotsshows us how to secure a place for ethics in our workplaces, institutions, and daily lives.
G*Power (Erdfelder, Faul, & Buchner, 1996) was designed as a general stand-alone power analysis program for statistical tests commonly used in social and behavioral research. G*Power 3 is a major extension of, and improvement over, the previous versions. It runs on widely used computer platforms (i.e., Windows XP, Windows Vista, and Mac OS X 10.4) and covers many different statistical tests of the t, F, and chi2 test families. In addition, it includes power analyses for z tests and some exact tests. G*Power 3 provides improved effect size calculators and graphic options, supports both distribution-based and design-based input modes, and offers all types of power analyses in which users might be interested. Like its predecessors, G*Power 3 is free.
In three experiments, we propose and find that individuals cheat more when others can benefit from their cheating and when the number of beneficiaries of wrongdoing increases. Our results indicate that people use moral flexibility to justify their self-interested actions when such actions benefit others in addition to the self. Namely, our findings suggest that when people's dishonesty would benefit others, they are more likely to view dishonesty as morally acceptable and thus feel less guilty about benefiting from cheating. We discuss the implications of these results for collaborations in the social realm.
In a study by Shalvi, Dana, Handgraaf, and De Dreu (2011) it was convincingly demonstrated that psychologically, the distinction between right and wrong is not discrete, rather it is a continuous distribution of relative ‘rightness’ and ‘wrongness’. Using the ‘die-under-the-cup’ paradigm participants over-reported high numbers on the roll of a die when there were financial incentives to do so and no chance of detection for lying. Participants generally did not maximise income, instead making moral compromises. In an adaptation of this procedure in a single die experiment 9% of participants lied that they had rolled a ‘6’ when they had not compared to 2.5% in the Shalvi et al. study suggesting that when the incentive is donation to charity this encourages more dishonesty than direct personal gain. In a follow-up questionnaire study where sequences of three rolls were presented, lying increased where counterfactuals became available as predicted by Shalvi et al. A novel finding is reported where ‘justified’ lying is more common when comparative gains are higher.An investigation of individual differences revealed that economics students were much more likely to lie than psychology students. Relevance to research on tax evasion, corporate social responsibility and the ‘credit crunch’ is discussed.
We present experimental evidence on the existence of disadvantageous lies. Literature so far assumes that people do not lie to their monetary disadvantage. However, some people have preferences for appearing honest. If the utility gained from appearing honest outweighs the monetary payoff gained from an advantageous lie or the truth, people will tell a disadvantageous lie.