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Psycholinguistic Markers of Therapeutic Rupture Types

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This study was designed to further understand rupture events that counselors encounter during a counseling session that ultimately impact the quality of the therapeutic alliance. We employed a cross-sectional analysis of a linguistic corpus created from mock counseling transcripts embedded in a website administered by a peer-reviewed expert in the psychology field and three video recorded sessions of Carl Rodgers, Fritz Pearls, and Albert Ellis. The content of the corpuses was analyzed using Linguistic Inquiry and Word Count software. The results showed a significant difference between she/he words, or third-person singular pronouns, and certainty words when comparing withdrawal and mixed rupture corpuses with a confrontation rupture corpus. In addition, we found a significant differences between positive emotion words and discrepancy words when comparing a rupture-infused psychotherapy corpus to a general psychotherapy corpus. Several implications for counseling and research are provided in response to these findings. Keywords: corpus linguistics, therapeutic alliance, alliance rupture, rupture event, LIWC
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Psycholinguistic Markers of Therapeutic Rupture Types
Justin Jacques1
Cass Dykeman1
1Oregon State University
This study was designed to further understand rupture events that counselors
encounter during a counseling session that ultimately impact the quality of the
therapeutic alliance. We employed a cross-sectional analysis of a linguistic corpus
created from mock counseling transcripts embedded in a website administered by a
peer-reviewed expert in the psychology field and three video recorded sessions of Carl
Rodgers, Fritz Pearls, and Albert Ellis. The content of the corpuses was analyzed using
Linguistic Inquiry and Word Count software. The results showed a significant difference
between she/he words, or third-person singular pronouns, and certainty words when
comparing withdrawal and mixed rupture corpuses with a confrontation rupture corpus.
In addition, we found a significant differences between positive emotion words and
discrepancy words when comparing a rupture-infused psychotherapy corpus to a
general psychotherapy corpus. Several implications for counseling and research are
provided in response to these findings.
Keywords: corpus linguistics, therapeutic alliance, alliance rupture, rupture
event, LIWC
When an individual has a significant injury or is diagnosed with a serious
disease, they search for the most well-known and highly qualified healthcare
professional using the latest technologies to ensure they get the best treatment.
However, in the field of counseling, there are few ways to identify a counseling
superstar. If counselors could easily determine who was a counseling prodigy, the way
educators train counselors would change drastically, and counseling outcomes would
improve substantially. New software provides a way through the veil of words that
compose a counseling session transcript to catch a glimpse at quality. With the aid of
computer programs, researchers may be able to assess the skill of a counselor by
examining the linguistic and psychological processes underlying their conversations with
clients. Furthermore, this methodology may allow researchers to identify top
counselors. This includes clinicians who could detect the most important components of
effective counselingnamely, ruptures in the therapeutic alliance. The present study
explored the therapeutic alliance by examining the linguistic and psychological features
of the three rupture marker types: confrontation, withdrawal, and mixed. The study also
identifies when these three rupture types occur, their frequency, and what this means
for the quality of the counselor’s relationship to the client and ultimately the proficiency
of the counselor.
This preprint has been prepared for review with a peer-reviewed journal. Comments are
welcomed and can be directed to the first author at
There were two primary goals for the present study. First, we attempted to
target gaps in the extant literature on alliance ruptures that occur in counseling sessions
by helping the reader understand an area of ambiguity (understanding the linguistic and
underlying psychological processes of rupture types) and investigating additional
research questions (Farooq, 2017). Corpus linguistics research can examine a variety of
topics using an assortment of corpora. These examinations have included everything
from profanity use as a marker of distinction to understanding when second language
learners implement the nuances of social dimensions in speaking tasks (Gablasova et al.,
2017; McEnery, 2004). Concerning counseling research, few studies have appeared
regarding the linguistics of any aspect of therapeutic alliance. Second, this study
contributes to the counseling profession by disrupting current practice (Tadajewski &
Hewer, 2011). Research has also found that counselors do not improve their
effectiveness through practical cumulative experience over their career (Chow et al.,
2015) or understand why specific counselors can create and sustain a stronger
therapeutic alliance than can their colleagues. The findings of this research could enable
counselors and counselor educators to gain insight into this key practice dynamic and
develop strategies to improve their therapeutic bond and efficacy (Newhill et al., 2000).
In sum, the present study may provide counselors with additional insight into the subtle
nuances of the three rupture marker types (withdrawal, confrontation, and mixed) to
assist the repair of therapeutic alliance ruptures.
In the review of the literature on therapeutic alliance ruptures, eight themes
emerged: (a) definition of alliance ruptures, (b) features of withdrawal rupture type, (c)
features of confrontation type, (d) features of mixed rupture type, (e) research on the
linguistics of alliance, (f) linguistic processes and alliance ruptures, (g) psychological
processes and alliance ruptures, and (h) broad psycholinguistic processes and alliance
ruptures. After these eight themes are reviewed, the research questions that guided this
are stated.
The existing literature provides a range of definitions of a therapeutic alliance
rupture varying in terms of severity continuum, frequency, and type. The more severe
ruptures on the continuum consist of impasses, empathic failures, dysfluencies,
disturbances, disruptions, deteriorations, vicious circles or cycles, and breakdowns
(Muran & Safran, 2016). Subtle ruptures include weakenings, miscoordinations, strains,
misattunements, misalliances, breaches, and enactments. In terms of frequency, a
counselor may experience a single rupture, a few ruptures, or numerous ruptures within
a counseling session, as therapeutic alliance ruptures vary in their presentation. In
addition to severity and frequency, alliance ruptures can be classified by rupture type.
Researchers have divided therapeutic alliance ruptures into three subtypes or
marker types: withdrawal, confrontation, and mixed rupture markers (Muran & Safran,
2016). Research has divided therapeutic alliance ruptures into three subtypes or marker
types: withdrawal, confrontation, and mixed rupture markers (Safran & Muran, 2016).
Construct validity of this tripartite model of ruptures has been supported by two
articlesSafran and Muran (2006, 2019), which clearly outline the definitions of the
therapeutic alliance rupture types and posit limitations to these proposed constructs.
Concerning this model, Safran et al. (2011) meta-analysis gave evidence that identifying
and repairing ruptures were positively related to a beneficial therapeutic outcome.
Given a review of the research supporting the construct validity of this tripartite, an
examination of each part is warranted.
Withdrawal rupture markers consist of behaviors that indicate a disconnection
in the counseling relationship, evidence of the client moving away from the counselor,
and a subtle alliance rupture event (Muran & Safran, 2016). The client most commonly
communicates their disconnect from the counselor by displaying behaviors that
represent disengagement from either the counselor, some aspect of counseling, or an
affective state. Salient examples include abstract talk, storytelling, minimal responses,
topic shifts, and silences. Additionally, withdrawal ruptures create distance by moving
the client away from the counselor and toward independence (Muran & Safran, 2016).
Examples include overt denial or a client’s words not matching their affect.
Furthermore, withdrawal ruptures are subtle and consist of difficult-to-identify words
and behaviors. Taking a self-critical stance in a counseling session or appeasing a
counselor are examples. In general, withdrawal rupture markers are difficult to detect
because the client’s covert language and behavior represent a disconnection and
distancing process.
In direct contrast to withdrawal ruptures are confrontation ruptures.
Confrontation rupture markers are detectable breaks in the counseling relationship
between the counselor and client and occur in an antagonistic manner. The primary
characteristic of a confrontation rupture marker is the explicit communication of
frustration or discontentment by the client to the counselor. In addition, these rupture
types consist of movements against the counselor with an antagonistic posture and a
bid for relational control. Confrontation ruptures may also include coercions such as
being excessively friendly or even seductive (Eubanks et al., 2018). Salient examples
include concerns or conflicts about: (a) the course of counseling, (b) the procedure of
counseling, (c) the limits of counseling, (d) the activities of counseling, (e) a counselor in
session activities, and (f) the counselor (Eubanks et al., 2015). Because of their direct
nature, confrontation rupture markers are easier to identify and are more obvious to
the counselor, but they still signify an injury to the counseling relationship.
A final rupture marker type, called a mixed rupture, is a combination of the
confrontation and withdrawal rupture marker types. Mixed ruptures are composed of
elements from both withdrawal and confrontation rupture markers. Within a mixed
rupture, the client moves away and against the counselor concurrently, or in a sequence
of verbal interactions. An example is when a client overtly disagrees with their counselor
about an interpretation they have made, which represents a confrontation, but then
immediately retracks their claim to reduce tension, in an act of withdrawal. This
example highlights that clients often exhibit a combination of confrontation and
withdrawal markers at times when they are upset about some aspect of counseling but
at the same time want to avoid conflict with their counselor (Eubanks et al., 2015).
Mixed rupture markers have the most complex presentation of the three rupture types
and may be the most difficult to identify. Hence, it may be beneficial to utilize
instruments that can assist counselors in identifying the various rupture marker types
effectively and efficiently.
A common way to identify the various rupture markers is through the analysis of
counseling session transcriptions. Recently, linguistic researchers have utilized software
programs to code and subsequently evaluate counseling sessions (Perez-Rosas et al.,
2017). These automated programs can reliably and efficiently code and summarize large
amounts of transcription-based data in a matter of minutes. These tools assist linguists
in their research endeavors and counselors in their session transcription coding tasks
(Perez-Rosas et al., 2017). Two noteworthy software programs that can perform
counseling session transcription analysis are Language Inquiry and Word Count (LIWC;
Pennebaker et al., 2015c) and Antconc (Anthony, 2017). These software platforms can
uncover and code underlying counseling and client linguistic processes through the
analysis of gram patterns, which are parallel linguistic patterns between counselors’ and
clients’ speech, as well as context-dependent metafeatures. Metafeatures are random
framework-like elements that exist behind language. They are a collection of more
common aspects of language that are identified by grouping trait occurrences from
automatically annotated transcription data. Metafeatures may enable researchers to
uncover significant relationships amongst words within a set of interactions (Chen et al.,
2016) in this instance, a counseling session. As metafeatures are discovered and their
covert significances understood, they can be employed to analyze counseling session
conversations and subsequently assess the strength of the alliance between the
counselor and client. Additionally, associated metafeatures could help researchers
identify dialog cycles between client and counselor. Linguistic-oriented computer
programs such as LIWC and Antconc are linguistic devices that can help social scientists
more precisely evaluate gram patterns and their hidden metafeatures, which are
numerous in the semantic dialogues that make up the therapeutic alliance.
There exist several linguistic variables that may presage alliance rupture. A
salient example of a linguistic variable that can be measured is pronoun use. Simmons et
al. (20005) found that when couples were asked to rate their marriages for an
interviewer, the more they used “we,” the better their marriage. Additionally, research
has shown that using the pronoun “you” in conversation predicts lower relationship
quality (Tausczik & Pennebaker, 2010). Other studies have found that second-person
pronoun use (e.g., you, your) was negatively related to the quality of a relationship
(Simmons et al., 2008). Because therapeutic alliance is analogous to a marriage or a
close relationship, assessing pronoun use could indicate the quality of the alliance and
help target alliance ruptures. Emotional language use is another linguistic variable
helpful in identifying alliance ruptures. In written discourse, negative emotion words
(e.g., hurt, ugly, nasty) are used in writing about negative events (Kahn et al., 2007).
Analyzing negative emotion words in verbal discourse may signal negative events and
reveal ruptures in the therapeutic alliance, specifically when the counselor and client
are talking about their counseling relationship.
An additional linguistic variable that can help identify a therapeutic rupture is
word count. Word count analysis can illuminate who is controlling the conversation and
how engaged the parties are in the discourse (Tausczik & Pennebaker, 2010). In a study
of social hierarchy, Sexton and Helmreich (2000) found that individuals of high status
tend to speak more frequently and openly and make more statements that involve
others. Conversely, low-status speakers have the propensity to use language that is
more self-focused and cautious. In the therapeutic relationship, where status tends to
be more equal and the client typically speaks more often than the counselor, identifying
unequal status and power dynamics through discourse analysis could help identify
alliance rupture. Word count is also helpful in assessing level of engagement (Tausczik &
Pennebaker, 2010) and may help identify when a client is subtly or overtly disengaged
from a counseling session signifying a rupture. Finally, the number and frequency of
acquiesces and positive emotion words are helpful in measuring levels of agreement
(Tausczik & Pennebaker, 2010). Assessing the level of agreement in a counseling session
could, in turn, point to a positive alliance or detect a subtle rupture, depending on the
There are numerous psychological processes that signify alliance events and
outcomes. Pennebaker et al. (2015b) included the following variables as categories in
their linguistic-based measure of psychological processes: time orientation, positive
emotion, negative emotion, anxiety, anger, and sadness. A foundational study
conducted by Mergenthaler and Bucci (1999) discovered that by analyzing three
categories of wordsemotional tone, abstraction, and referential activitythey could
predict successful outcomes in therapy. To discern emotional tone, Mergenthaler and
Bucci tracked the flow of the therapeutic discourse, targeted the patients’ verbal form,
and looked for expressions of emotional experience. Another study looking at the
negative emotion variable embedded in a psychological process measure found that
better therapy outcomes were associated with a reduction in negative emotion words
over the course of treatment when working with clients diagnosed with a personality
disorder (Arntz et al., 2012). These studies support the use of linguistic methodologies
to identify psychological processes that presage.
Given the aforementioned, three research questions were developed to guide
this study. These questions were:
RQ1: What is the use rate of linguistic and psychological processes of rupture-
infused psychotherapy by type?
RQ2: Do linguistic and psychological processes differ by rupture type in rupture-
infused psychotherapy? If so, how do they differ?
RQ3: Does the use rate of linguistic and psychological processes in rupture-
infused psychotherapy type differ from the use rate of these processes in
general psychotherapy?
This study utilized a synchronic corpus linguistic design (Brezina, 2018). The
corpus was created by transcribing mock counseling vignettes obtained from the
“clinical tools” subheading on a publicly available website created by nationally
recognized experts in alliance ruptures and three video recorded sessions of Carl
Rodgers’, Fritz Pearls’, and Albert Ellis’ work from the Three Approaches to
Psychotherapy films (Shostrom, 1965). The study involved 14 variables. The four
linguistic process variables examined were: first-person singular pronouns, first-person
plural pronouns, third-person singular pronouns, and third-person plural pronouns. The
10 psychological process variables explored were: negative emotion, anger, sadness,
anxiety, positive emotion, discrepancy, certainty, differentiation, tentative, and
causation. The unit of analysis was single words (Bjekić et al., 2014).
An a priori power analysis χ2 test square test was conducted using G*Power 3.1 (Faul et
al., 2009). The proper effect size for such a test was Cohen’s w (Rosnow & Rosenthal,
2003). The effect size input for this power analysis was drawn from an online
environment study of gender discourse (Sullivan et al., 2015). The input parameters
were: (a) test family - χ2 tests; (b) statistical test - goodness-of-fit tests: contingency
tables; (c) type of power analysis - a priori: compute required sample size - given α,
power, and effect size; (d) w = 0.60; (e) power (1-β error probability) = 0.90; (f) α =
.0001; and (g) degrees of freedom (Df) = 2. The G*Power 3.1 output suggested a sample
size of 67 with an actual power of 0.91.
Register, Scope, and Sources
Biber (2012) detailed the existence of four main registers in English: (a)
conversation, (b) fiction, (c) news reportage, and (d) academic prose. The texts of the
present study fall within the conversation register with the subregister being
psychotherapy conversation. In building the corpus, researchers used transcriptions
from mock counseling vignette videos that highlighted the three types of alliance
ruptures (confrontation, withdrawal, and mixed) based on the constructs from Muran
and Safran’s seminal psychotherapy research (Muran & Safran, 2016). The videos were
obtained from their website ( under the “clinical
tools” tab (Muran et al., n.d.). Dr. Muran, who is a nationally renowned expert in
ruptures, described the process of making the videos for the website:
For the videos on our website, we simply asked our students to bring and play
difficult moments (some based on their readings, some based on their own
clinical experience). We specifically invited withdrawal, confrontation and mixed
rupture events and kept it to one take to promote authenticity and spontaneity:
Only the initial rupture marker and case formulation were discussed in advance.
The students were familiar with our definitions and principles. (C. J. Muran,
personal communication, June 18, 2021)
These publicly available mock counseling sessions were transcribed for the creation of
the corpus. This included the transcription of three counseling vignettes that highlighted
confrontation ruptures, two vignettes that highlighted withdrawal ruptures, and four
vignettes that highlighted mixed ruptures. The resultant confrontation rupture corpus
contained three transcripts, 1,995 tokens, and 387 types; the resultant withdrawal
rupture corpus contained two transcripts, 2,384 tokens, and 393 types; and the
resultant mixed rupture corpus contained four transcripts, 3,148 tokens, and 513 types.
Additionally, a reference corpus was created to establish a base rate for
psychological processes within a therapy session. The corpus was created by combining
the transcriptions of the emblematic and influential counseling sessions with Carl
Rogers, Fritz Pearls, and Albert Ellis (Shostrom, 1965). The resultant reference corpus
contained three transcripts, 13,584 tokens, and 1,410 types.
Nine mock counseling vignette videos (three confrontation, two withdrawal,
and four mixed) were disembedded from the website (Muran et
al., n.d.) using standard downloading protocol and were subsequently converted into
MP4 files. The files were then uploaded to and electronically
transcribed. Transcripts were subsequently converted into Word documents after being
manually checked for transcription accuracy. Additionally, three video recorded sessions
of Carl Rodgers, Fritz Pearls, and Albert Ellis working with clients were transcribed from
the Three Approaches to Psychotherapy films (Shostrom, 1965). The electronic files
were then converted into .txt files using AntFileConverter (Anthony, 2017), and three
distinct corpuses were created by combining the respective .txt files of each rupture
type vignette videos (confrontation, withdrawal, or mixed). Spelling and word-related
errors were identified and corrected, and the corpora were further cleaned for non-
ASCII characters and diacritics using After the three rupture type corpuses were
preprocessed, they contained 1,995 (confrontation), 2,384 (withdrawal), 3,148 (mixed),
and 13,584 (Carl Rodgers, Fritz Pearls, and Albert Ellis) words.
Linguistic Inquiry and Word Count (LIWC; Pennebaker et al., 2015c) was the software
program used in this analysis. Within LIWC, there are multiple subscale measures that
can be utilized to analyze a corpus. The validity and reliability of LIWC have been well
established (Pennebaker et al., 2015a).
Linguistics Processes
The LIWC linguistics processes employed were first-person singular (I, me, my, mine),
first-person plural ( “we,” “us,” “our,” and “ours”), third-person singular ( “she,” “her,”
and “him”), and third-person plural pronouns (“they,” “their,” and “they’d”)
(Pennebaker et al., 2015b).
Psychological Processes
The LIWC psychological processes employed were: negative emotion (e.g., “hurt”),
anger (e.g., “hate”), sadness, (e.g., “lonely”), anxiety (e.g., “worried”), positive emotion
(e.g., “happy”), discrepancy (e.g., “should”), certainty (e.g., “always”), differentiation
(e.g., “hasn’t”), tentative (e.g., “maybe”), and causation (e.g., “because”; Pennebaker et
al., 2015b).
Linguistic Inquiry Word Count (LIWC)
The most recent edition of the LIWC software is from 2015 (Pennebaker et al., 2015c).
The default LIWC2015 dictionary is composed of almost 6,400 words, word stems, and
sect emoticons. The program is also composed of 90 analyzable output variables, which
focus mainly on psychological processes. The variables are scored by comparing the
percentage of words being analyzed to a dictionary of words in categories and
subdictionaries (Smith-Keiling & Hyun, 2019).
Data Analysis
For RQ1 (use rates), raw and normalized frequency rates (i.e., percentage of all
words) are reported for all variables across all three types of alliance ruptures
(confrontation, withdrawal, and mixed). Normalized frequencies (e.g., relative
frequencies) were calculated by taking the percentage of the total words of a variable
(e.g., she/he words were .0015 percent of confrontation corpus) as calculated by the
LIWC software and dividing that number by 100 (basis for normalization) and then
multiplying the result by the total number of tokens (Brezina, 2018). In terms of RQ2,
(type differences), the log likelihood test (G2) was employed (Rayson & Garside, 2000).
The effect size was calculated using the Bayes information criterion (BIC) with
interpretation guidelines from Wilson (2013). If overall significant type differences were
encountered, post hoc pairwise comparisons were conducted using the G2 test
(McDonald, 2014). Concerning RQ3, differences between the rupture and general
counseling corpuses were assessed by means of G2 and BIC. All analyses were
conducted using R with a preset alpha level of .001. Additionally, due to the large
number of tests, a Bonferroni correction was utilized to set a family-wise error rate to
control for type one errors.
Regarding RQ1, the raw and normalized count of linguistic and psychological
processes for confrontation, withdrawal, and mixed rupture type corpuses can be found
in Table 1.1. In terms of RQ2, differences in the linguistic and psychological processes
category among the rupture types can be reviewed in Table 1.1 For those linguistic and
psychological processes where significant differences did occur, the pairwise post hoc
analyses can be inspected in Table 1.2. Concerning RQ3, linguistic and psychological
processes that distinguished the rupture corpus from the baseline counseling corpus can
be examined in Table 1.3.
The aim of this study was to explore the linguistic and psychological processes
that take place in mock counseling session vignettes that consisted of an alliance
rupture event. In this section the potential reasons for the obtained results are
discussed. After this discussion, limitations, practice implications, and researched
implications are addressed. RQ1 looked at the level of use of linguistic and psychological
processes known to be related to each of the respective alliance rupture types
(confrontation, withdrawal, and mixed). The probable explanations for these obtained
results are addressed in the discussions in the findings for RQ2 and RQ3.
Regarding RQ2, two of the 14 variables returned a significant result. The first of
these was the third-person singular pronoun variable. Two likely explanations for this
result should be considered. First, research has found that third-person pronouns have
been associated with self-monitoring and general social awareness in verbal discourse
(Pennebaker et al., 2003). This could be one explanation as to why she/he words were
found to be significantly more common in withdrawal and mixed ruptures compared to
confrontation ruptures, as a client or counselor may be more subtlety aware of
themselves and their relationship with the other in these events. A second explanation
is that pronoun use also indicates the focus of the speaker’s attention (Kacewicz et al.,
2014) and in the case of third-person singular pronouns, a focus on others. This aligns
with withdrawal rupture events and mixed events (which include withdrawal rupture
events), where the client is subtly moving away from the counselor (Muran & Safran,
2016) and may include an “other focus” by the client to hide the misattunement. Both
findings seem plausible, but the first finding appears more likely as confrontation
ruptures may be other focused but are, by definition, disagreements.
The second significant result was for the certain variable. Two likely
explanations for this result should be considered. The first explanation is that "certain
words” may represent the speaker’s perceived sense of power from a psycholinguistic
perspective. Research has shown that those who use certainty language (e.g., always,
never) in verbal discourse are viewed as more powerful (Adkins & Brashers, 1995; Han &
Lind, 2017; Hart & Childers, 2004) and are more committed to the truth of what they are
asserting (Holmes, 1982). This finding makes logical sense in the context of a
confrontation rupture where a client is directly expressing anger or dissatisfaction
toward the counselor (Muran & Safran, 2016). This subsequent certainty language
employed by the client or counselor may reflect their commitment to their perceived
truth and be seen by the observer (counselor or client) as an act of power. A secondary
explanation is that research shows they increase the persuasiveness of a message
(Corley & Wedeking, 2014). This makes logical sense in that a confrontation rupture is
where a client is moving toward the counselor in a direct manner (Eubanks et al., 2016)
or bringing up a concern or complaint regarding the counselor or some aspect of
counseling in a targeted manner (Muran & Safran, 2016). According to Corley and
Wedeking, certainty language is often used to ensure compliance. Within a
confrontation rupture event, the client may want the counselor to comply with their
request after directly confronting them about their personal concerns about the
counselor or some aspect of the counseling. Both findings may be plausible, but the
second finding appears more likely as clients often want the counselor to comply with
their requests regarding some goal or task related to counseling process. However,
because the BIC was under 2, at 1.52, caution should be taken in drawing strong
conclusions unless this finding can be replicated or other evidence is presented, as the
results are weak and may have occurred by chance.
In the matter of RQ3, two explanations deserve consideration for each finding.
First, pertaining the finding of a decreased use of positive emotion words for rupture-
infused psychotherapy (general psychotherapy comparison), the use of positive emotion
words has been shown to help stabilize emotionality because an individual is able to
shift attention away from self (Lyons et al., 2006). This would also make logical sense in
the context of an alliance rupture event where both the client and counselor may be
using less positive emotion words as there is disharmony in either of the party’s internal
states, as overt or covert conflict is occurring in the counseling session. Second, positive
emotion words have been shown to correlate to social coping. This finding makes sense
in the context of a rupture event (confrontation, withdrawal, or mixed), as the client and
even the counselor may not be using social coping (Chung & Pennebaker, 2012) to
sustain social norms. Both findings are plausible, but the second finding is more
convincing as social coping to sustain social norms is necessary in communication and
appears very important in the context of an alliance rupture event.
Regarding the second finding, there are two plausible explanations for the
obtained result of significantly lower use of “discrepancy words.” First, this possibly
counterintuitive finding (the reader might expect that rupture events in counseling
discourse would be more cognitive in nature) could be explained by the fact that
therapy is inherently composed of cognitive mechanisms, or language which includes
discrepancy words (Lee et al., 2011). This includes counselors and clients discussing their
thoughts (cognitions) about causes, consequences, or conflict about a discussion topic
(Chung & Penebaker, 2012). Therefore, both the rupture infused corpus and the general
psychotherapy corpus may have higher than usual cognitive content or discrepancy
language than other verbal discourse. Second, the lower number of discrepancy words,
a subset of cognitive processing words, in the rupture infused corpus compared to the
general psychotherapy corpus could be explained by an unproductive moment in
therapy. Ruptures have been defined as missattunements between the client and
counselor (Muran & Safran, 2016), and in those segments of therapy discourse the
conversation may be less cognitive in nature, on average, than in baseline or useful
therapy where the counselor and client are attuned. This explanation aligns with
research showing that highly helpful or productive moments in therapy include greater
proportion of cognitive words and specifically words indicating insight (McCarthy et al.,
Second, an increased frequency of discrepancy words such as “should” or
“would” has been shown to be accompanied by an overall decrease or weakening in
clout (certainty) and confidence in written text (Moore et al., 2021). The findings by
Moore et al. also align with the statistically significant finding of discrepancy words,
which shows lower certainty (clout) and less confidence by the client or counselor. In
the context of rupture events, clients may be less certain and confident about the
counseling process and may communicate this through subtle passive communication in
the case of a withdrawal rupture. Or, they may display a lack of certainty or confidence
in a direct confrontation with the counselor in the case of a confrontation rupture
event. Equally important, the counselor may reciprocate in one of the three rupture
events with a plethora of discrepancy words, consciously or unconsciously
communicating their own decrease in certainty or confidence through verbal discourse.
Both justifications seem to have merit, but the first explanation is more plausible, as the
cognitive nature of psychotherapy conversations around conflict, both active and
passive, is very apparent.
Two limitations to the present study should be noted. The first limitation is
sample size. Having a larger sample would allow for a more in-depth exploration of the
vocabulary related to each of the rupture types and allow for additional experiments on
a larger number of areas. In order to identify the underlying linguistic and psychological
processes that occur in a rupture event, studies would require a much larger corpus and
one that would separate the clients’ and counselors’ discourses. Additionally, there are
some significant limits regarding our capacity to generalize the findings from this study
to other alliance rupture discourse datasets. First, we utilized a corpus of a mock
counseling session, which included professional counselors and actors. Although this set
of discourses are very similar to an actual counseling session, there are likely significant
differences between the discourse of a session that included playacting with a counselor
and an actual counseling session discourse. A second limitation is that analyses focus on
whole sessions rather than specific rupture events. Results may have varied if the
discourse around each rupture event had been isolated for a study.
There are four implications for counseling practice that can be drawn from the
obtained results. First, the finding of she/he words that indicates the necessity of
supervisors and counselors to remain keenly aware of increased use of third-person
singular pronouns by their clients (and their own use) is important. This is because third
person singular pronoun use may indicate self-monitoring and increased general social
awareness in verbal discourse (Pennebaker et al., 2003) and may indicate a withdrawal
or mixed rupture event. Furthermore, supervisors and counselors should be aware that
pronoun use point to the focus of the speaker’s attention and specifically third-person
singular pronoun use (she/he words) is “other focused” (Kacewicz et al., 2014). This may
signal that a client in the context of a withdrawal or mixed rupture may be using
deflection of attention to create distance in a subtle manner as they are covertly moving
away from the counselor or some aspect of the counseling process.
Second, the finding of certainty words necessitates that supervisors and
counselors remain keenly aware of increased certainty language in counseling session
discourse. This finding is important because it may indicate that the client or the
counselor believes the other is in a position of power, are more committed to certainty
(Adkins & Brashers, 1995; Han & Lind, 2017; Hart & Childers, 2004; Holmes, 1982), and
that a rupture event is occurring. Additionally, because there is evidence that using
certainty language increases the persuasiveness of their message, supervisors and
counselors need be aware of this type of discourse (in the context of a confrontation
rupture event) to call out and work through a potential rupture in the therapeutic
alliance with a client.
Third, the finding of infrequent use of positive emotion words in the rupture
infused psychotherapy corpora indicates the necessity of supervisors and counselors to
remain keenly aware of a decrease of positive emotion language in counseling session
discourse. This is important because it may indicate that a participant is feeling less
emotionally stable (Lyons et al., 2006), may not be coping socially, and be less able to
sustain the social norms of conversation (Chung & Pennebaker, 2012). Therefore, a
decrease in positive emotion words may signify a subtle disconnect in the therapeutic
alliance, which counselors could become skilled at recognizing and repairing.
A final implication regarding the finding of decreased use of discrepancy words
in the rupture infused psychotherapy corpus is that this may be an indicator that the
discourse may be less cognitive in nature than the average psychotherapy conversation.
This lack of discrepancy words, which are cognitive in nature, may indicate that a
rupture event is occurring, and that the session or segment is not as therapeutically
useful (McCarthy et al., 2017) because the counselor and client are not attuned, and
helpful cognitive insights are not occurring. Supervisors and counselors who recognize a
decrease in discrepancy or cognitive language can be aware that a rupture event may
have occurred, attempt to disembed from the event, and subsequently tend to the
discord in the counseling relationship.
Two recommendations for further research should be noted. First, because the
results demonstrate that the linguistic and psychological process that underlies
language between three types of alliance rupture types may be distinguishable,
researchers have a new opportunity to further explore rupture-specific counseling
session discourse. Specifically, it may be important for researchers to continue this line
of inquiry and study each rupture type (confrontation, withdrawal, and mixed) and their
discourse in actual counseling sessions. Second, expanding on the results of this study
would allow researchers to further examine alliance ruptures but in a more granular
manner through analyzing rupture marker events in counseling session segments.
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Table 1.1 Rup
ture Type Descriptive Statistics (RQ1) and Results from Inferential Analyses (RQ2)
Rupture Type Descriptive Statistics (RQ1) and Results from Inferential Analyses (RQ2)
Actual Count
Expected Count
BIC Desc.
Very Strong
Very Strong
Very Strong
Very Strong
Very Strong
Very Strong
Very Strong
Very Strong
Very Strong
Note. Confrontation n = 1,986, withdrawal n = 2,367; mixed n = 3,136; adjusted error rate
for 14 comparisons was p < .00007; G2 for that error rate = 19.09. A negative BIC indicates
support for the null hypothesis.
Table 1.21 Post Hoc Pairwise Rupture Comparisons
Post Hoc Pairwise Rupture Comparisons
Corpus 1
Corpus 2
Raw Ct.
Raw Ct..
BIC Descript.
Very Strong
Note. The critical value for G2 at p < .001 is 10.83. Withdrawal n = 2,367; confrontation n =
1,986; mixed n = 3,136.
Table 1.32 Rupture Versus Reference Corpus Results (RQ3)
Rupture Versus Reference Corpus Results (RQ3)
Raw Ct
(Norm count)
Raw Ct
(Norm count)
184.04 (2.46)
Very Strong
172.99 (2.31)
497.91 (3.67)
Very Strong
371.84 (4.97)
518.26 (3.82)
9.99 (0.13)
55.62 (.41)
Note. The critical value for G2 at p < .001 is 10.83. Rupture n = 7,489; reference n = 13,567.
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