Lie Detection Among College Aged Adults
By: Alex Perry, Julian Pritcher, Jessica Scarbrough, and Ilana Ruben
Introduction:
The goal of this study was to find out if college aged young adults were more
likely to accurately detect lies from their peers or older individuals who are 30 and over.
Going into this study we believed that there would be a discrepancy between which age
group the participants thought lied the most. We predicted that participants would be
more accurate in detecting lies from their peers than from older adults. Our study also
analyzed if age stereotypes played a role in respondents’ answers.
In order to test our research question we conducted an online survey that was
distributed via email and social media. The survey included eight videos of both peers
and older adults telling truths and lies. Participants were asked to decided if the
individual was lying or telling the truth, and what cues helped them come to their
decision.
Literature Review:
Nonverbal communication plays a crucial role in sending and receiving deceptive
messages. Communicative behavior, particularly nonverbal cues, function as signals of
deception or truth telling (Vrij 2006). Thus, People can use nonverbal cues to detect
deception, such as kinesics, oculesics, and proxemics to detect deception. People in our
survey were able to use nonverbal cues to detect such deception.
People strategically manipulate nonverbal cues when they are trying to deceive
others (Daly). For example, children eventually learn to look their parents in the eyes
when lying to them. There is little prior research on how the strategic manipulation of
cues changes when people are talking into a video camera. We suspected that people will
still try and strategically manipulate cues, but to a lesser extent.
People have many stereotypes of the typical liar. Americans associated deception
with 18 different behaviors, reporting that liars avert gaze, touch themselves, move their
feet and legs, shift their posture, shrug, and speak quickly (Zuckerman). In addition, Liars
shift their posture, they touch and scratch themselves, liars are nervous, and their speech
is flawed. These beliefs are common across the globe (Globe). It is clear from prior
research that people look for specific cues when detecting deception. Liars know that
these stereotypes exist, and strategically manipulate their cues to reduce the chances of
deception detection.
It is also important to note how familiarity with the deceiver can affect detection.
The familiarity between the sender and receiver is another critical factor that influences
behavior during deceptive interactions (Guerrero Chapter 46.) Informational familiarity
deals with background information and knowledge gathered from past interactions with a
particular sender or receiver. Behavioral familiarity concerns knowledge about the sender
or receiver’s normal patterns of behavior. Relational familiarity deals with how well
people know one another (Burgoon). In our study, there may have been some familiarity
between our respondents and those who are deceiving. Depending upon the level of the
relationship, this familiarity could have had some impact on our results. There is little
prior research on how viewing deception via video camera affects the findings. In
addition, a truth bias could certainly occur.
A truth bias occurs when people believe that someone is honest unless something
suggests otherwise. We tend to have a truth bias in most interactions, but this bias is
especially strong when interacting with people we know well and like (Guerrero).
Research has found this to be true. Findings were consistent with the truth bias
hypothesis. Partners in well-developed relationships demonstrated a strong truth bias,
resulting in greater judgments of truthfulness and (somewhat) lower detection accuracy
(Stiff). People recognize people in our study may thus be inclined to believe their friends
are telling the truth, regardless of the cues.
Unpremeditated lies are more easily detected than planned lies, because of the
advantage rehearsal gives. Planning a lie ahead of time makes the liar less likely to have
to pause for words and more free to control tone of voice and other potential leaks
(Goleman). However, deception in the real world is rarely premeditated (Daly). Because
of this, we allowed two of the people filmed in our survey to plan their lies and the other
two made them up on the spot. Prior research suggests that those who planned their lies
will be detected at a lesser rate than those who spontaneously made them up.
People are not very successful in detecting lies. The percentage of lie detection in
most studies ranges from 45% to 60%, while 50% is expected by chance alone (Guerrero,
Chapter 48). In a study where police officers were asked to detect deception, they had an
accuracy rate of only 49%. The detectives reported being very confident in their
responses (Vrij, 1999). Thus, we expected that our accuracy rate for detecting deception
would be around fifty percent. We were also interested to find out how confident college
students are in their responses, so we also measured the degree of confidence participants
felt when trying to distinguish truth from lies.
In an article published by The Journal of Language and Social Psychology a
study was published about how age-related physical qualities affect age stereotyping.
Participants listened to voice recordings of individuals aged 22-79 and their perceptions
of how old the speaker was and their perception of traits associated with that speaker
were recorded. It was found that there is a positive social value associated with aging
voices. In situations of actual or perceived age, participants perceived these speakers to
significantly wiser. This study also confirmed age stereotypes when it found that speakers
perceived to be older were seen as significantly less powerful and engaged than younger
speakers. The speakers’ age was also associated with less significant but still notable
negative impressions of their warmth. With the increase in the speakers’ age there was a
significant decrease in the participants willingness to interact and approach the speaker.
These findings confirm pre existing ideas about age stereotypes and their influence on
social interactions (Montepare, 2014). For this reason, we believe that our participants
may be influenced by age stereotypes when responding to our survey.
Rationale and Formally Stated Hypothesis:
For this study our hypothesis was, college students/young adults, ages 17-23, are
more likely to detect lies from their peers rather than older adults, 30+. We decided to
do an online survey to maximize our response rate. For our chosen sample of college
aged individuals an online survey that individuals could complete on their own time and
anonymously, was the best option. The survey was distributed via email, listserv, and
facebook to each group members’ social networks. By including videos of individuals
telling a lie or truth, we were able to create a more life-like scenario despite the survey
being conducted online and not in person.
The survey consisted of four different individuals describing two different images
they had in front of them, or had just looked at. Those taking the survey could not see the
pictures, forcing them to use cues from the videos to figure out whether the person was
lying or telling the truth. Since there were two individuals over 30 and two from 17 to 23
years old, we believed age would factor into people’s decision.
To analyze this data we used basic statistics like mean, median and mode to
quantify our results. We coded them so that truths were represented by “1” and lies are
represented by “2.” We also utilized the Likert Scale so we could more accurately
measure a greater variance in responses.
We also asked questions about each age group after the video portion of the
survey. The categories were trustworthiness, credibility, power, how wise and how
honest. We used a Likert Scale for these questions to gauge if our participants had any
preexisting age stereotypes and how that influenced their perceptions of if the individual
was lying or not.
Procedures and Methods:
Sample
A sample is a specific subset of the population, which we use to then make a
statement about that population. In order for a sample to be representative of a
population, we typically go through a process called the sampling process. Our sampling
process assured that our sample was a convenient sample. A convenient sample is used
by researchers when they have constraints on time or money. In a convenient sample, the
researcher simply takes a sample of individuals that is convenient. Due to time and
monetary constraints, we were forced to bypass random sampling and instead use
convenient measures. This unfortunately assured that our sample did not appropriately
represent the population. In this study, our population was people living in the United
States aged seventeen to twenty-three. For a sample to accurately reflect the population,
samples have to have two characteristics. Every member of the population has to have a
known, equal and nonzero chance of being selected into the sample. In addition, once you
choose one individual from the population into the sample, that probability cannot
change. In our study, the people in our population did not have a known, equal, and
nonzero chance of being selected into the sample.
We had about 50 responses to the survey, 48 of which were college aged students
and 2 of which were above 40 years of age. The average age of the participants was 21.9
years old. The sample was mostly white, and almost even in regards to gender. However,
there were ultimately more women than men.
Measurement
The survey itself consisted of two adults ranging from the age of thirty to sixty
and two students aged seventeen to twenty-three. We chose who to film and include in
our survey based on what was convenient for us. We each filmed one of our friends or
relatives who fit under the age criteria we needed. Each of these individuals were filmed
on camera telling one truth and one lie. Each individual was given two pictures to look at,
and lied about one while telling the truth about the other. We handpicked the pictures out
of the New Yorker magazine, finding sets of pictures that were similar in content. A table
has been provided below of the pictures that we included. As seen below, pictures
included Waldo at a bar, people talking in jail, a 3-D Thanksgiving, a cat and a man
having dinner, a cow at the doctor’s office, a piano with fish in it, a flight attendant on an
airplane, and a dog at a dinner table. We randomly determined who would be talking
about what pictures. Each individual was instructed to look at one picture and describe it
truthfully in detail, and then look at the other picture and make up a detailed lie about the
picture. Two individuals were given a minute to prepare their lies, while two individuals
were instructed to lie spontaneously. The individuals described these pictures while being
videotaped by video cameras or iphones. As an example, person two was given a picture
of a cow next to a doctor and fish in a piano. He described in detail what he was
observing in the first picture, saying “I’m looking at a picture of a doctor and a cow. The
cow is seated at a table and has a bell around his neck, and there are a couple awards on
the wall behind him.” Person two then looked at the picture of the fish in the piano, and
said, “I’m looking at a picture of a lion seated in a safari next to a tour guide. There is a
couple people behind him and a jeep parked to the right.” To collect the data, we created
a survey that asked questions based on the videos of people lying or telling the truth.
Copies of the pictures used can be found in the Figures section of the report. The survey
is as follows:
Do you think the person in the video is telling a truth or a lie? Yes or No.
How Confident are you in your answer? Scale of 1 to 7, 1 is very unsure while 7
is very confident.
What did they say or do that helped you come to your decisions? (Open-ended)
We then repeated this process seven more times, showing seven more videos and then
asking the above questions after each video. We then asked some additional questions to
help explain our findings.
How trustworthy do you think the age group of 17 to 23 is? 1 is very untrustworthy, 7 is
very trustworthy.
How trustworthy do you think the age group of 40-60 is? 1 is very untrustworthy, 7 is
very trustworthy,
How wise do you think the age group of 17-23 is? 1 is very unwise, 7 is very wise.
How wise do you think the age group of 40-60 is? 1 is very unwise, 7 is very wise.
How credible do you think the age group of 17-23 is? 1 is very unsound, 7 is very
credible.
How credible do you think the age group of 40-60 is? 1 is very unsound, 7 is very
credible.
How powerful do you think the age group of 17-23 is? 1 is very weak, 7 is very powerful.
How powerful do you think the age group of 40-60 is? 1 is very weak, 7 is very powerful.
How honest do you think the age group of 17-23 is? 1 is very dishonest, 7 is very honest.
How honest do you think the age group of 40-60 is? 1 is very dishonest, 7 is very honest.
We then asked some demographic questions
What is your age? fill in the blank
What is your gender? Female, Male
Are you a Penn State Student? Fill in the blank
What is your major? fill in the blank
What is your ethnicity? fill in the blank
How outgoing are you? 1 is very shy, 7 is very outgoing
DId you recognize anyone in the survey? If so, who? fill in the blank
Procedures for Gathering Data
To obtain the participants for our sample, we sent out our survey through many
different means. The main means of obtaining participants was by sending our survey to
various Penn State club and organization list serves. We collected data from fifty
individuals. These individuals were composed of college students and non-college
students aged seventeen to twenty-thirty. These individuals came from a multitude of
groups, including the Penn State Club Floor Hockey Team, interns who work for Penn
State Football, members of the Alpha Xi Delta sorority, THON organizations and
committees, members of the CAS 470 Nonverbal Communication class at Penn State,
Penn State Men’s and Women’s club Lacrosse, friends on Facebook, residents of South
Halls, residents of the apartment complex Town Square in State College, friends who we
directly reached out to in person or via the phone, and random participants we found in
the HUB-Roberson Center on Penn State’s campus. As noted above, it is obvious from
methods of obtaining respondents not everyone in the population had an equal, nonzero,
known chance of being selected. Due to this reason, our sample was convenient. In turn,
our sample was a non-probability sample.
Results:
The survey responses were collected on a Google form. We ran basic statistical
methods looking at the mean, median and mode from the sample and their responses.
Figure 1. shows how often a participant thought that the individual in the video was
telling the truth or lying. On average participants were right 51% percent of the time
when looking at both their peers and adults, demonstrating that less than half of the
responses in Figure 1. were incorrect. However, the average confidence that participants
had in their responses was 4.8 on a scale from 1 to 7, meaning that most were slightly
confident in their replies, despite their accuracy being only slightly better than chance
overall. Figure 2. shows a line graph that depicts the average level of confidence that
participants had when responding to the eight different videos. There was significant
amount of variance, however as stated earlier the average was 4.8.
When comparing how participants did in detecting lies from their peers and
adults, the results didn’t agree with our hypothesis. On average, participants answered
correctly 44% of the time when trying to detect lies from their peers. Their responses
were less accurate than chance, and below the overall average. When trying to detect lies
in adults, the participants were right 58% of the time. Figure 3. is a table showing the
averages in accuracy/correctness of responses for each of the eight videos used in the
study. There was incredible variance in general accuracy, however the mean
demonstrated a greater overall accuracy when detecting lies in adults (persons 3 and 4).
When looking at the data for stereotypes that was asked at the end of the survey,
those between the ages of 40-60 were rated more highly on all aspects. Adults were seen
as more credible, honest, powerful, trustworthy, and more wise. Figure 4. reports the
averages that we found. The greatest difference in perception was in perceived wiseness.
These stereotypes don’t seem to have come into play in the manner we were expecting,
because participants were better at detecting lies in adults.
What was interesting, but not surprising, was that the responses to what cues
helped participants come to their answers were very diverse, and contradictory.
Participants often cited where the individual in the video looked, how often they
hesitated, how confident they seemed in their presentation of the information, and
how/what gestures they used as the cues that helped them determine who was lying and
who was not. However, given their accuracy being about that of chance, and that many
responses were contradictory, it seems that in general people have no idea what indicates
a sign of lying.
Discussion:
Restatement of hypothesis
We believed going into this study that there would be a difference in which age
group the participants thought were the liars and which were telling the truth. We
predicted that participants would be better at detecting lies from their peers than from
adults older than them. Our study also analyzed if age stereotypes played a role in
respondents’ answers.
Implications of Theory
We wanted to study this topic because there is little pre existing research. There is
research on lie detection and age stereotypes separately, but we couldn't locate a study
that combines the two and analyzes the differences in lie detection of young adults when
dealing with peers and adults significantly older than themselves. We felt that this topic
was important enough to merit a study. As college students in CAS classes it would be
helpful and interesting to us to find out how our peers respond to their own age group and
other age groups, like their professors, when it comes to lie detection and trustworthiness.
We felt that delving into this topic could spark a discussion about a need for a theory
based on our hypothesis. Research on this topic needs to be advanced, as mentioned
before there is limited existing research on this topic, which made things difficult for us
to get an idea about how to conduct our study on this topic.
The implications of our results demonstrate that despite stereotypes that view
adults in a more positive light, young adults are better at detecting lies in adults than in
their peers. Our hypothesis was based on the notion that peers would be around eachother
more and therefore better able at detecting lies in peers. As well as, because there were
positive stereotypes in relation to honesty, trustworthiness, and credibility related to older
individuals, we felt that that would result in a more automatic trust and belief in what the
older adults were saying. This not what happened, and did not support our hypothesis.
This line of study may actually support research that peers believe their friends more
readily. The implications of our study may even expand upon the research and
demonstrate that we more readily believe our peers as a whole, and notice more lies in
those who are not in our group. Given these findings, we believe that this line of study
should be expanded.
Implications for Practice
The implications that relate to practice and the practical world include being more
aware that we more carefully and accurately scrutinize the statements made by adults.
However, it is more important to recognize that young adults are less accurate at
discerning the truth from their peers. What this suggests is that when we are interacting
with peers and friends more effort should be put forth to analyze the statements we are
told.
In addition to more carefully scrutinizing what our peers tell us, it seems that the
reasons people gave for answering the way they did were all over the place. No one has a
clear idea what the “signs” of lying are, however people seem confident that they do and
answered incorrectly based on their notions of what “signs” or cues indefinitely indicate
lying. Common answers were based on where the individual in the video looked, how
often they hesitated, how confident they seemed in their presentation of the information,
and how/what gestures they used. The implications of this suggest that either better
education of what cues actually follow a lie would be helpful to young adults, or working
to negate the notion that there are concrete signs of lying, because the responses
demonstrated a lack of knowledge and consensus.
Limitations
Our research had some very large limitations. From the start of this study our
sample and collection of participants was inherently flawed. As stated above, in order for
a sample to be representative of a population, every member of the population has to have
a known, equal, and nonzero chance of being selected into the sample. In addition, once
you choose one individual from the population into the sample, that probability cannot
change. Our method for collecting participants assured that our sample was not random,
instead making it a convenient sample. We did what was convenient for us to collect
data. Because of this, our data cannot be generalized to the population of college students
across the United States. Our sample was a non-probability sample, which means our
findings are not representative.
Our survey also had some flaws. The lies and truths that our participants watched
were not viewed in person. There has not been a lot of research done on the effect
viewing lies has on participants, but people may be more or less accurate in detecting lies
when viewing lies via video playback than in person. As explained above, we thought
this was the most efficient way to collect responses, but the videos did limit responders
ability to focus on cues other than vocalics.
Research has shown that when people focus on vocalic cues, they will be more
accurate in detecting lies, so some of our participants may have been more accurate in
detecting lies for this reason (Scherer).
In addition, a fairly large percentage of our respondents recognized one of the
people who we filmed for our survey. 33% of our respondents, or 17/51 respondents,
recognized one person who was included in our survey, but no respondents recognized
more than one person. 17.6% of our respondents recognized person 2, this means that
person 2 would be most affected by the truth bias. The truth bias occurs occurs when
people believe that someone is honest unless something suggests otherwise. The
limitations should not have been all that crippling to the survey, because in the worst
case scenario, only one out of four people would have been recognized.
There has been little research conducted on the accuracy of lie detection between
age groups. However, other research has show patterns between age groups. For example,
people are more likely to stand closer to their peers (Hecht).
Our survey could also be flawed due to the lies themselves. Some people based
their decisions off the description of the picture itself. For example, somebody said
“Waldo doesn’t drink,” and immediately decided that the fact that the story portrayed
Waldo sitting at a bar meant the story was a lie. For the most part, the responses were not
that impactful in the grand scheme of our research. Usually about five to ten people per
video included the content of the lie as the reason they predicted the speaker was not
telling the truth.
Lastly, the limitations of our study relate to the statistical analysis and or lack
there of conducted. Our research did not look at correlations, regression, ANOVA, or any
other statistical means of assessing the significance of our findings.
Suggestions For Future Research
There are many suggestions for future research. First and foremost, when
conducting this study future researchers should collect a random sample. Our sample was
not sufficient, thus our data cannot be trusted. Future researchers should also conduct
their results through face to face methods. Face to face methods may provide better and
more accurate results. Researchers could also measure the difference that exists between
detecting lies face to face versus on video cameras. If this study is recreated, researchers
should be sure that respondents do not recognize people in the survey, this would ensure
that no truth bias is present in the results.
Figures:
Figure 2. Participant confidence, per video, throughout the survey.
Figure 3. Average accuracy per video.
Accuracy of responses.
1A
2A
3A
4A
1B
2B
3B
4B
Correct
72%
63%
82%
26%
52%
33%
46%
78%
Wrong
18%
37%
18%
74%
48%
67%
54%
22%
Figure 4. Differences in stereotype responses. Responses were rated on a 1-7 scale.
Stereotype:
Trustworthy
Wise
Credible
Powerful
Honest
17-23 rating
3.55
3.12
3.82
4.53
3.41
40-60 rating
5.00
5.30
5.08
5.43
4.71
Figures 5-12 are the pictures used by the individuals we filmed lying or truth telling for
our study.
Figure 5
Figure 6.
Figure 7.
Figure 8.
Figure 9.
Figure 10.
Figure 11.
Figure 12.
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