1
The Influence of Dominance, Question Type, and Guilt on Linguistic Properties Associated with
Deception: An Examination of Criminal Interviews
Matthew L. Jensen*, Elena Bessarabova*, Bradley Adame*,
Judee K. Burgoon**, and Stanley M. Slowik***
* Center for Applied Social Research
University of Oklahoma
**Center for the Management of Information
University of Arizona
*** Stanley M. Slowik, Inc.
Oct. 17, 2010
Authors’ Note:
2
Abstract
This study examined the effect of guilt, dominance, and question type on linguistic
properties derived from video-taped criminal interviews conducted with 44 suspects accused of a
variety of crimes. A field experiment using a 2 (guilt: guilty/innocent) x 2 (dominance: high/low)
x 4 (question type) mixed-model repeated measures design was conducted. It was proposed that
guilt interacts with displays of dominance and question type to affect a variety of linguistic
properties. The data were partially consistent with the hypothesis. Implications, limitations, and
future research directions are discussed.
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The Influence of Dominance, Question, and Guilt on the Language of Innocent and Guilty
Criminal Suspects
To distinguish between guilty and innocent suspects in a criminal interview, investigators
have different techniques available to them. A few of these techniques such as reality monitoring
(RM) and criteria-based content analysis (CBCA) focus specifically on the properties of what the
suspect says (e.g., perceptual information) (Vrij, 2005, 2008). RM and CBCA are both founded
on the Undeutsch (1989) hypothesis positing that a statement describing an actual experience
differs in content and quality from a statement based on imagination. Employing these
techniques requires careful examination of details captured in interview transcripts to ascertain
whether the interviewee is reporting actual versus imagined events. These techniques have been
used successfully during criminal interviews to distinguish between truthful and fabricated
reports.
Recently, researchers have attempted to automate the analyses of textual features in
interactions where one individual is attempting to deceive another. Building on the success of
manual techniques (such as RM and CBCA), the approaches using automatically extracted
linguistic properties have been shown to be successful in identifying differences in statements
based on actual versus fabricated events or opinions . These differences in statements describing
actual versus fabricated events are evident across both mediated (Zhou, 2005; Zhou, Burgoon,
Nunamaker, & Twitchell, 2004; Zhou, Twitchell, Qin, Burgoon, & Nunamaker Jr., 2004) and
face-to-face interactions (Burgoon & Qin, 2006) as well as across statements from individuals
facing insignificant consequences such as small monetary rewards and statements taken as part
of criminal investigations with more serious consequences (Fuller, Biros, & Wilson, 2009).
Although differences between truthful and fabricated statements are evident across many
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contexts, the features differ based on context and they also tend to be unstable across samples
with significant differences in linguistic properties sometimes appearing (e.g., Burgoon & Qin,
2006) and sometimes not (e.g., Vrij, Mann, Kristen, & Fisher, 2007). This suggests other factors
may be influencing statements’ linguistic properties and occluding the effect of descriptions of
real or imagined events.
Specifically, research on automated linguistic analysis has yet to consider critical
elements of the interaction (such as the questions that are being asked) and interpersonal
characteristics of the interviewer and/or interviewee (such as dominance displays) that may
influence how real versus imagined events are portrayed through linguistic properties. If the
elements of an interaction and/or interpersonal characteristics have a moderating effect on
accounts of real versus imagined events, the results of linguistic analyses that do not include
these moderators may be unstable and lack discriminatory validity. This paper attempts an initial
foray into examining some of the most likely moderators that have been ignored in previous
research.
Two such relevant moderators are the specific questions asked during the interview and
the dominance relationship between interviewer and interviewee. onto examine these factors, we
conducteda field-experiment in which criminal suspects were interviewed and the interviews
were then transcribed. Then, using automated analysis (see Pennebaker, Francis, & Booth, 2001),
linguistic properties (quantity, complexity, certainty, immediacy, specificity, affect, and
diversity) were extracted, and the interaction between (1) interviewee dominance, (2) questions
being asked during the interview, and (3) interviewee innocence or guiltwere examined. The
objective was to determine whether taking these factors into account results in more accurate and
stable assessments of suspects’ statements in criminal interviews.
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Theoretical Background
A criminal interview is a dynamic, goal-oriented exchange where the interviewer
attempts to determine the interviewee’s involvement in a crime and the interviewee attempts to
appear innocent. During this exchange there is potential for misrepresentation, and the
interviewer must be able to distinguish between statements of real versus fabricated events.
Events of an interaction, such as the questions posed, affects the outcome of the interaction
including judgments of guilt or innocence (Buller, Strzyzewski, & Comstock, 1991). Yet, the
effects of specific questions or patterns of questions posed during an interaction have largely
been ignored (Vrij, Mann, & Fisher, 2006). In traditional studies (typically dealing with
deception detection) investigating how linguistic properties can be reviewed to separate real from
imagined events, the unit of analysis is typically the entire length or a large portion of an
interview; this approach obscures any effects from particular questions and the moderating
effects of these questions and other variables (e.g., interpersonal dominance). However, during
interviews, these within-interview factors are critical components on which the success of the
interviewer’s assessment rests (Buller & Burgoon, 1996).
Among the interpersonal factors affecting the outcome of an interaction is interpersonal
dominance. The level of dominance is one of two superordinate dimensions (along with
involvement) by which people understand their relationships with each other (Burgoon & Hale,
1984). Dominance is a relationally-based pattern of communication in which one individual
attempts to assert influence over his or her interaction partner (Burgoon & Dunbar, 2000; Dunbar
& Abra, in press; Dunbar & Burgoon, 2005). Dominance has been shown to have five behavioral
dimensions including influence, conversational control, focus and poise, panache, and selfassurance (Burgoon, Johnson, & Koch, 1998).
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In an interview setting (especially a criminal interview), there is a clear power inequality
between the interviewer and interviewee with the interviewer generally controlling the
conversation; and this inequality may inhibit dominance displays by the interviewee (Dunbar,
2004). Research on deception and dominance has revealed that deceivers are typically less
verbally and nonverbally dominant than their truth-telling counterparts (Burgoon, Buller, &
Floyd, 2001; Burgoon, Buller, Floyd, & Grandpre, 1996; Burgoon & Dunbar, 2000). However,
there is also evidence indicating that dominance fluctuates depending on the tactics the deceiver
employs (Dunbar et al., 2010; Zhou, Burgoon, Zhang, & Nunamaker, 2004). For example, in a
study where a deceiver had to persuade his or her interactional partner to accept something that
was untrue (e.g., hire a less qualified candidate, who happened to be the deceiver’s friend),
Dunbar et al. (2010) found that deceivers exhibited much more dominance than truth tellers, and
the deceiver’s dominance influenced linguistic properties of statements the deceiver and truth
teller exchange (specifically, deceivers produced more and more diverse arguments). Such
variation in dominance may be especially salient in criminal interviews because there may be
high incidence of anti-social tendencies among criminal populations (Kosson, Lorenz, &
Newman, 2006); these tendencies often manifest in attempts to dominate others for personal gain
(American Psychological Association, 2000).
In addition to the level of dominance potentially influencing the linguistic manifestation
of real versus imagined events, the topics discussed during the interview also may affect
linguistic properties. During the interview, an interviewer to a large degree has control over the
topic of the conversation because he or she is posing questions to which the interviewee must
respond. Some researchers have noted the variability in responses between different questions as
determined from automatically extracted linguistic properties and have limited their analyses to a
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single question or block of questions during an interview (e.g., Jensen, Meservy, Burgoon, &
Nunamaker, 2010). However, this approach does not take the differences based on specific
questions into consideration. Since in criminal interviews the difference between actual and
fabricated events may emerge across several questions, accounting for the fluctuations in
linguistic properties across questions becomes important. In sum, limiting the examinations of
interactions to one question or only examining aggregate summaries of linguistic features
obtained from several questions does not reflect the reality of criminal interviewing.
The utility of approaches that focus on multiple interview questions is particularly
pertinent for criminal interviews also because interviewing techniques that focus on specific
questions are widely taught among law enforcement personnel. This study examined three
questions drawn from a structured interview protocol, the Behavioral Analysis Interview (BAI;
(Inbau, Reid, Buckley, & Jayne, 2001) and one question that is commonly asked as part of a BAI
interview. Although some have criticized the BAI’s effectiveness (Vrij et al., 2006), the BAI is
frequently applied to situations where interviewers must discriminate between real and imagined
events, and its proponents claim to have taught the techniques to hundreds of thousands of law
enforcement professionals (John E. Reid and Associates). According to the developers of the
interview protocol, the structure and content of responses is expected to vary based on whether
the interviewee is describing real or fabricated events (Inbau et al., 2001). Thus, individual
questions should also influence linguistic properties of statements exchanged between an
interviewer and interviewee.
Further, it is likely that the differences in dominance exhibited by those who were later
exonerated of the crime as compared to those who plead guilty may affect the linguistic
properties produced in an interaction based on a question type being asked. For example, if a
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question inquires about some potentially verifiable information, those who have nothing to hide
with regard to the crime may respond to the question consistent with their baseline level of
dominance: Highly dominant people will be likely to use more rhetoric and influence tactics
trying to convince the interviewee of their innocence and, as a result, may be more likely to
produce a greater amount of content, be more complex, linguistically diverse, specific, more
certain, show greater immediacy, and greater affect (all of which are linguistic properties
derivable from interview transcripts). At the same time, low dominance individuals who have
nothing to hide may be more likely to exhibit the opposite pattern: When asked about potentially
verifiable information, they will be less likely to use overt persuasion. Instead, these individuals
may employ approaches they are more comfortable with, including simply denying involvement
in whatever the verifiable question inquires about. As a result, innocent low dominance people
may produce less text, less complexity, certainty, immediacy, specificity, affect, and diversity. In
sum, for individuals who have nothing to hide, substantial differences in the level of linguistic
properties are expected based on their level of dominance and depending on the type of question
being asked.
Conversely, individuals attempting to conceal their guilt may be less likely to follow their
natural levels of dominance. People who have something to hide may engage in information
control when asked about potentially verifiable information. In these circumstances they may be
trying ascertain what information the interviewer knows, and may attempt to appear as
forthcoming as possible without providing additional information about the events in question.
As a result, they may be less likely to be as voluminous as a high dominance truthful individual
because discussing too much information could reveal inconsistencies. Alternatively, these
individuals may also be careful not to say too little (as low dominance individuals with nothing
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to hide would) since they may perceive that saying too little would make them seem not
forthcoming enough. To test this relationship between dominance level, question type, and guilt
on linguistic properties, the following three-way interaction is proposed:
H1: Question type and level of dominance interact with guilt to influence linguistic
properties of (a) quantity, (b) complexity, (c) certainty, (d) immediacy, (e) specificity, (f)
affect, and (g) diversity.
Method
Participants
The corpus of 44 interviews was selected from a larger set of 101 interviews performed
by a single professional interviewer (male). The interviewer has performed over 17,000
interviews in criminal, civil, and employment contexts. The interviewees were all suspects in a
criminal investigation, and the interviews took place as part of actual investigations. Thirty seven
participants were male, and 7 were female. The majority of suspects were accused of sex-related
crimes (n = 27; 11.1% were female; 53.8% were guilty) including sexual assault (n = 16), rape (n
= 8), incest (n = 2), and sexual harassment (n = 1). Other crimes (n = 17; 23% were female;
52.9% were guilty) included 3 accusations of theft, 2 accusations of attempted homicide, 2
accusations of assault, and a single case of homicide, child abuse, forgery, road rage,
immigration violations, restraining order violation, menacing, burglary, battery, and fraud.
Design
The hypotheses were tested in a field experiment using a 2 (guilt: guilty/innocent) x 2
(dominance: high/low) x 4 (question type: here we need to provide the levels for question type)
mixed-model repeated measures design. Guilt and dominance were the between-subjects factors
and question type was a within-subjects factor. The dependent variables were derived from
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automated linguistic analyses of interview transcripts using linguistic properties and categories
developed by Zhou et al. (2004).
Instrumentation
Determination of guilt and innocence. The ground truth during criminal interviews can
be difficult to ascertain; therefore, statements from guilty parties cannot be labeled as deception
(because they may in fact be truthful when responding to some questions). Rather than
deception, the role of guilt in connection with dominance and question type was examined. To
determine guilt, three strict criteria were applied. The first was the result of the investigation and
in some cases prosecution (e.g., a suspect plead guilty or charges were dropped). For 24
interviewees in the guilty condition, 22 suspects confessed, and 2 were found guilty by a court of
law. The 20 innocent interviewees were exonerated during the investigation and had either all
charges dropped or no charges filed. The second criterion was the result of a polygraph test,
which was administered following the interview. The third criterion was the opinion of the
interviewer who administered the structured interview and had a chance to reconcile evidence
and interview statements. In determining guilt for each interviewee, all three criteria had to be
met: For example, only when the interviewer judged the interviewee as guilty, the interviewee
failed a polygraph test, and later pled guilty was this interview selected to be in the guilty
condition and to be included in further analyses. Cases where any criterion was not met were not
included in the analyses.
Dominance. Dominance was determined using two trained coders who were experienced
in coding dominance in dyadic interactions (α = .84). The coders were instructed to view the
relevant sections of the interview and rate the interviewee dominance. During initial coding, it
was noted that interviewees’ dominance did not fluctuate between interview questions.
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Therefore, a single dominance rating was provided across the four interview questions examined
in this study. Dominance was coded on a scale of 1 to 7 with the endpoints being submissive and
dominant. The ratings from both coders were averaged to produce a dominance score. The mean
dominance score was 3.43 (SD = 1.45). The dominance scores were then dichotomized into high
dominance (n = 21) and low dominance (n = 23) groups using a median split on the dominance
score. The difference between high and low dominance groups was significant t(42) = 9.92, p <
.001, with mean high dominance score at 4.69 (SD = 0.98) and the mean low dominance score at
2.28 (SD = 0.60).
Question Type. With the variety of crimes included in this sample, many different
questions were asked during interviews. To select questions suitable for analyses, three criteria
were used. First, due to our interest in questions typically asked during criminal interviews, only
questions commonly asked in BAI interviews (Inbau et al., 2001) were examined. Second, only
those questions were included that were asked in at least 90% of the interviews. Finally, to avoid
any confounding due to question order effects, we selected questions that were always asked in
the same order across all interviews. Based on this criterion, in each interview question 1
occurred before question 2, question 2 before question 3, and question 3 before question 4. For
interviews where questions were not asked, a mean substitution procedure was performed for
each of the linguistic properties to avoid listwise deletion during analysis due to missing data.
Note that mean substitution was only required for one case for questions 1 and 3, and four cases
for question 4. The selected questions are presented in Table 1 along with the number of
responses to each question and mean length of responses in seconds.
Linguistic Properties
Linguistic properties (e.g., word count, number of affective words, number of spatio-
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temporal words) were extracted from transcripts of interviews using the Linguistic Inquiry Word
Count [LIWC; (Pennebaker et al., 2001)] approach. These linguistic properties were further
combined into indexes based on the categories developed by Zhou et al. (2004). The following
indexes were formed: language quantity, complexity, certainty, immediacy, specificity, affect,
and diversity.
Prior to calculating indexes, the distribution of each dependent variable (here, linguistic
property) was examined for its approximate normality. If, as assessed by variable skewness, a
dependent variable appeared relatively non-normal, it was transformed.i Prior to
transformations, the dependent variables were first trimmed to a smaller value to control for
outliers. (Note that as a result of this procedure, none of the interviews were excluded from
analysis.) All transformations required a constant be added to the trimmed value because these
transformations cannot be performed on zero values. All of the analyses were performed on the
transformed variables.
Trimmed and transformed (as necessary) linguistic properties were used to form indices
by saving the first unrotated principal component. This is a commonly used procedure (see Afifi,
Clark, & May, 2004), which involves using principal component analysis and an unrotated onecomponent solution; standardized regression component scores are then calculated for each
participant. Because each linguistic property is weighted proportionally to its contribution to the
principal component, using these procedures produces a better index as compared to simple
summation or averaging of the items. Each index, index description, and the linguistic property
that contributed to the index are presented in Table 2.
Results
The means for each linguistic index from each question are listed in Table 3. To test the
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hypothesis that question type and level of dominance interact with guilt to influence linguistic
properties of (a) quantity, (b) complexity, (c) certainty, (d) immediacy, (e) specificity, (f) affect,
and (g) diversity, a mixed-model MANOVA was performed with dominance, guilt, and question
type used as independent variables, and the indexes of linguistic properties used as the dependent
variables. Although main effects were not hypothesized in this study, the results based on main
effects are briefly discussed below because a full factorial model was analyzed in this study.
Main Effects
Among the between-subjects effects, dominance exerted a significant influence on
quantity, F(1, 40) = 14.31, p = .001, partial η2 = .26, complexity, F(1, 40) = 5.59, p = .023,
partial η2 = .12, uncertainty, F(1, 40) = 5.25, p = .027, partial η2 = .12, diversity, F(1, 40) = 8.90,
p = .005, partial η2 = .18, and specificity, F(1, 40) = 8.50, p = .006, partial η2 = .18. The results
indicated that those who demonstrated higher levels of dominance during their interviews also
displayed more quantity, complexity, certainty, diversity, and specificity. However, two of these
variables were overridden by higher-order interactions, described below. Notably, guilt did not
yield a significant main effect on any of the linguistic indexes.
Before examining within-subjects effects, the data were first checked for the violations of
sphericity assumptions. Sphericity violations were found for immediacy, Mauchly’s W = .674,
2(5, N = 44) = 16.84, p < .05, and affect, Mauchly’s W = .596, 2(5, N = 44) = 20.01, p < .05.
Therefore, the degrees of freedom for within-subjects tests involving immediacy and affect were
adjusted using the Huynh-Feldt method (Meyers, Gamst, & Guarino, 2006); note that this
adjustment yields fractional degrees of freedom. The results indicated that the within-subject
effect of question exerted a significant influence on the linguistic indexes of immediacy, F(2.63,
105.06) = 13.84, p < .001, η2 = .26, and affect, F(2.61, 104.24) = 26.05, p < .001, partial η2 = .39.
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Interaction Effects
Because of violations of the sphericity assumption, all interaction effects are reported
using the Huynh-Feldt method for adjusting degrees of freedom. There was a significant
dominance by question type interaction on quantity, F(3, 120) = 4.74, p = .004, partial η2 = .11,
and, affect, F(2.61, 104.24) = 4.68, p = .006, partial η2 = .11. Finally, consistent with the study
hypothesis, there was a significant question type by dominance by guilt interaction on
complexity, F(3, 120) = 3.35, p = .021, η2 = .08, and specificity, F(3, 112.01) = 2.66, p = .051,
partial η2 = .06 (see Figures 1 and 2, respectively). The effect of the three-way interaction on all
other indexes of linguistic properties was not significant. Thus, the hypothesis in this study was
partially supported.
The significant three-way guilt by dominance by question interactions were further
examined using a two-step process involving series of MANOVAs. During the first step, a
separate MANOVA was performed within each question to isolate significant interactions
between dominance and guilt for each question. For this analysis, dominance and guilt were used
as independent variables and complexity and specificity were used as dependent variables.
Within question 4, there was a significant dominance by guilt effect on complexity, F(1, 40) =
5.08, p = .03, η2 = .11, and specificity, F(1, 40) = 5.52, p = .024, η2 = .12. However, the
dominance by guilt interaction was not significant for all other questions. These results indicate
that the three-way interaction was being manifested mainly in question 4.
To explore which groups were different from each other within question 4, two one-way
ANOVAs were performed, the first with complexity as the dependent variable and the second
with specificity as the dependent variable. Both ANOVAs used condition with four levels (i.e.,
high-dominance-guilty vs. low-dominance-guilty vs. high-dominance-innocent vs. low-
15
dominance-innocent) as the independent variable. Because the equal variance assumption could
not be made and to guard against type I error, post-hoc tests using Tamhane’s T2 (Ho, 2006)
were used to interpret differences in complexity and specificity across the four levels of the
independent variable. The results indicated that the low-dominance-innocent interviewees
exhibited much less complexity than high-dominance-innocent interviewees (p = .004), and the
difference in complexity between low-dominance-innocent interviewees and low-dominanceguilty interviewees approached significance (p = .07).
The pattern across the four levels within question 4 for specificity was very similar to that
of complexity. Again, using Tamhane’s T2, significant differences between conditions emerged.
The low-dominance-innocent interviewees exhibited much less specificity than high-dominanceinnocent interviewees (p = .014), less specificity than high-dominant-guilty interviewees (p =
.034), and the difference in specificity between low-dominance-innocent interviewees and lowdominance-guilty interviewees approached significance (p = .064). Taken together, these results
support H1(b) and H1(e).
Discussion
These study results underscore the need to consider interaction characteristics when
evaluating linguistic properties during interviewing. The level of dominance and the type of
questions being asked in conjunction with whether the interviewee was innocent or guilty clearly
impacted the linguistic properties of the interviewees’ statements. Note, however, that the main
effect of guilt on the linguistic properties was nonsignificant. Only when dominance and
question type were included as part of an interaction with guilt, did the significant differences in
specificity and complexity emerge.
These results have relevance for how automatically extracted linguistic properties should
16
be used during criminal and other types of interviewing. The types of questions asked and the
level of dominance demonstrated by the interviewee should be considered when judging question
responses. Both of these features are observable and should be taken into consideration by a
human observer doing manual assessment of the interview and should be included in an
automated decision model using linguistic properties. Indeed, it may be prudent, as
recommended by common interviewing guides (e.g., Inbau et al., 2001), to develop individual
models of linguistic responses for specific questions.
In addition, certain types of questions (e.g., question 3) did little to distinguish between
guilty and innocent interviewees based on linguistic properties, even when considering the level
of dominance. However, other types of questions may be more diagnostic. In this study, one
question, when an interviewee’s dominance was also taken into account, drew out differences
between guilty and innocent interviewees. Question 4 differs from other questions in that it is
potentially verifiable. In contrast, questions1 through question 3 elicit responses concerning
hypothetical motivations, feelings about the accusation, and opinions about consequences, all of
which cannot be verified. This characteristic of question 4 may have contributed to the effect that
was demonstrated in responses to this question, especially for the linguistic property of
specificity. Those who are trying to hide their guilt may be caught between disclosing too much
or too little specific information (e.g., spatio-temporal details); this task is much more difficult
when the information is potentially verifiable.
Finally, in this work we did not distinguish between potential sources of dominance, and
this point deserves discussion. During an interview, dominance may manifest for a variety of
reasons including the interviewee’s trait dominance, ambiguous power distribution between the
interviewer and interviewee (Dunbar, 2004), and as a tactic when deception is used for
17
persuasion (Dunbar et al., 2010). While there are many reasons why dominance may be
manifested during an interview, dominance remains observable; thus, the interviewer can note
when influence tactics are being employed and to what degree. Therefore, regardless of the
source, dominance should be used as part of the evaluation of an interviewee.
Limitations and Future Research Directions
There were a few limitations of this study, which merit discussion. First, much of the
research on which this work is based has centered on deception. We acknowledge that the
variable referred to in this study as guilt is not deception as all interviewees (even the guilty)
could mix truth and deception in response to the questions used in the analyses (particularly
question 1 through question 3). However, we note that all guilty interviewees denied being
guilty.
This study was an attempt to examine interaction processes within an ecologically valid
setting. In such settings, people are likely to mix truth and deception; thus, separating the two
may not always be possible. Therefore, in the circumstances like these, some type of proxy for
truth and deception may be needed. In the context of this study, we established several criteria to
determine criminal guilt, which was selected as such a proxy. As compared to altering a specific
message or part of a message, criminal guilt is likely to indicate a mindset, which is a broader
notion than a specific deception. Future research should systematically examine whether the
results of deception research might also apply to mindsets induced by criminal guilt.
Second, our sample is relatively small when crossed by two between-subjects variables.
Even though we employed a mixed design with a repeated measure, future research should
replicate this study using a larger sample.
Finally, the types of crime discussed during the interviews varied widely across our
18
sample. The type of crime may have affected the level of dominance displayed by the
interviewees and would certainly affect question responses. The extent of these effects are
unknown, nonetheless, we note them as potential limitations.
Conclusion
In an effort to investigate how automatically-extracted linguistic properties may be
applied to separating real from fabricated events, we have taken the first step in examining how
interactional factors such as questions during criminal interviews may moderate the effects of
suspect’s guilt or innocence. Our findings contribute to the literature in two important ways. First
because our data was gathered from actual criminal interviews, our study has a high degree of
ecological validity. The interview sample provided a unique opportunity to examine an
interaction previously unaddressed. Second, our findings highlight the impact that dominance
and interview questions have on the linguistic content of guilty and innocent interviewee’s
statements. Future research is required to further investigate interactional factors and exploring
methods that would allow distinguishing between those guilty of a crime from those who are
innocent. Taking moderating factors into account may lead to more stable ways to differentiate
between linguistic features of guilty and innocent interview statements.
19
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23
Table 1
BAI questions included in the analysis along with number of responses and response length
Question
N per question M Response Length (SD) in sec.
1. Why might someone want to do this to the victim?
43
42.5
(35.2)
2. How do you feel about this accusation?
44
45.6
(68.9)
3. What do you think should happen to the person who did this?
43
39.5
(25.8)
4. Did you ever do anything like this before?
40
28.3 (36.4)
24
Table 2
Description of the linguistic indexes
Index
Included Properties
Transformation
Quantity
Word count, Verb count, Function word count
(trimmed original variable + 25).1
Complexity
Words per sentence, six letter words
Trimmed original variable
Uncertainty
Modal verbs, certainty words, and tentative words
ln(trimmed original variable + 1)
Immediacy
First person references
Trimmed original variable
Diversity
Percent of unique words
None
Specificity
Spatio-temporal words, perception words
ln(trimmed original variable + 1)
Affect
Positive and negative affect words
Trimmed original variable
25
Table 3
Means and standard deviations for each index separated by question
Condition
Linguistic Index
Q1 M (SD)
Q2 M (SD)
Q3 M (SD)
Q4 M (SD)
Quantity
.54 (.71)
.61 (.70)
-.14 (1.12)
.63 (.99)
Dominance
Complexity
.45 (1.00)
-.09 (.95)
.04 (.77)
.60 (1.04)
– Innocent
Uncertainty
.44 (.78)
.53 (.88)
-.17 (.82)
.42 (1.10)
Immediacy
8.53 (6.30)
9.39 (3.86)
5.80 (5.36)
6.38 (5.46)
Diversity
72.35 (17.69)
65.99 (24.71)
74.14 (17.61)
74.08 (17.12)
Specificity
.23 (.98)
.07 (.99)
.11 (1.07)
.61 (1.24)
Affect
4.53 (3.70)
6.48 (5.26)
5.45 (3.83)
2.24 (1.64)
Quantity
.30 (1.12)
.39 (.45)
-.25 (1.36)
.09 (.76)
Dominance
Complexity
-.02 (1.03)
.52 (.66)
.22 (1.09)
.04 (1.03)
– Guilty
Uncertainty
.02 (.78)
.30 (.62)
-.05 (.70)
-.04 (.85)
Immediacy
11.64 (6.10)
13.43 (6.29)
6.39 (3.24)
4.45 (4.65)
Diversity
71.73 (21.82)
68.17 (10.12)
73.23 (19.04)
77.33 (17.37)
Specificity
.32 (.88)
.44 (.70)
.07 (.79)
.00 (.64)
Affect
4.95 (4.09)
7.19 (3.91)
4.34 (4.40)
1.40 (1.71)
Quantity
-.45 (1.15)
-.08 (.95)
.37 (.82)
-.84 (.37)
Dominance
Complexity
-.54 (.95)
.01 (1.35)
-.17 (1.22)
-.80 (.02)
– Innocent
Uncertainty
-.08 (1.48)
-.13 (1.11)
-.01 (1.44)
-.73 (.00)
Immediacy
9.00 (8.17)
12.69 (6.98)
5.25 (3.82)
.00 (.00)
Diversity
82.47 (21.80)
80.14 (16.66)
80.09 (18.99)
96.88 (8.84)
Specificity
.31 (1.32)
-.22 (.87)
-.23 (.90)
-.79 (.00)
Affect
2.94 (4.46)
10.75 (7.34)
3.23 (2.99)
.00 (.00)
High
High
Low
26
Low
Quantity
-.37 (.85)
-.68 (1.10)
.07 (.75)
-.11 (1.07)
Dominance
Complexity
-.06 (.94)
-.25 (.98)
-.07 (1.06)
-.07 (.97)
– Guilty
Uncertainty
-.32 (.93)
-.53 (1.00)
.17 (1.08)
.08 (1.11)
Immediacy
12.27 (10.76)
8.43 (7.44)
6.13 (3.92)
2.98 (4.24)
Diversity
81.73 (14.98)
79.11 (17.88)
80.24 (15.16)
67.61 (21.23)
Specificity
-.54 (.72)
-.21 (1.19)
-.01 (1.17)
-.07 (.97)
Affect
3.17 (3.98)
10.49 (6.30)
3.48 (3.95)
.93 (1.39)
Note: Recall that all multi-item indexes were formed by saving first unrotated principal
component.
27
Figure 1. A three way interaction of guilt by dominance by question on complexity.
28
Figure 2. A three way interaction of guilt by dominance by question on specificity.
Footnotes
i
An important assumption for the analyses based on the general linear
model is that the residuals of the dependent variables are approximately
normal (Bauer & Fink, 1983; Fink, 2009). Transforming data helps meet this
assumption.