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Influence of Communication Partner's Gender on Language

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Forty participants (20 male) had 3-minute conversations with trained male and female communication partners in a repeated-measures, within-subject design. Eighty 3-minute conversations were transcribed and coded for dependent clauses, fillers, tag questions, intensive adverbs, negations, hedges, personal pronouns, self-references, justifiers, and interruptions. Results suggest no significant changes in language based on speaker gender. However, when speaking with a female, participants interrupted more and used more dependent clauses than when speaking with a male. There was no significant interaction to suggest that the language differences based on communication partner was specific to one gender group. These results are discussed in context of previous research, communication accommodation theory, and general process model for gendered language.
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Language and Speech
http://las.sagepub.com/content/early/2014/09/17/0023830914549084
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DOI: 10.1177/0023830914549084
published online 17 September 2014Language and Speech
Adrienne B. Hancock, Holly Wilder Stutts and Annie Bass
Transgender Communication Therapy
Perceptions of Gender and Femininity Based on Language: Implications for
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DOI: 10.1177/0023830914549084
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Language
and Speech
Perceptions of Gender and
Femininity Based on Language:
Implications for Transgender
Communication Therapy
Adrienne B. Hancock
The George Washington University, USA
Holly Wilder Stutts
The Lab School of Washington, USA
Annie Bass
Boston University, Boston
Abstract
Recent research presents a picture of diminishing gender differences in language. Two
experiments examined whether language use can predict perceptions of gender and
femininity; one included male and female speakers telling a personal narrative, the other
also included male-to-female transgender speakers and analyzed an oral picture description.
In each experiment, raters read transcribed samples before judging the gender and rating
the femininity of the speaker. Only number of T-units, words per T-unit, dependent
clauses per T-unit, and personal pronouns per T-unit emerged as different between gender
groups. As none of the variables were strongly correlated with perceptual judgments,
regression analysis was used to determine how combinations of linguistic variables predict
female/feminine ratings. Results from these two studies demonstrate that gender-related
differences in language use for these two contexts are limited, and that any relationship
of language to perceptions of gender and femininity is complex and multivariate. This
information calls into question the utility of training key language features in transgender
communication therapy.
Keywords
Transgender, transsexual, gender perception, language
Corresponding author:
Adrienne B. Hancock, PhD, George Washington University, 2115 G Street, NW, Washington, DC 20052, USA.
Email: hancock@gwu.edu
549084LAS0010.1177/0023830914549084Language and SpeechHancock et al.
research-article2014
Original Article
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2 Language and Speech
1 Introduction
1.1 Gender differences in language
Prior to the 1960s, the field of sociolinguistics examined social roles with the assumption that
research findings based on men applied to everyone (Kramer, 1977). However, with the rise of the
feminist movement, this assumption was called into question, and sociolinguistics began to exam-
ine gender differences, in terms of real differences as well as differences that are widely believed
to be true, regardless of their actual existence (Kramer, 1977). Robin Lakoff’s (1973) foundational
work “Language and Woman’s Place” shaped the direction of research in gender language differ-
ences in the 1970s, and is still referenced in almost every gender language study to date. Based on
her own insight, Lakoff identified the variables which she determined were representative of wom-
en’s language, calling the combination of these variables the “female register.” In terms of lexical
differences, Lakoff describes women’s use of more specific color terms (e.g., mauve, lavender),
“weaker” particles (e.g., less offensive expletives such as oh fudge), and empty adjectives (e.g.,
divine, cute). Regarding syntax and suprasegmentals, Lakoff describes women’s use of tag ques-
tions (a question added onto the end of a declarative indicating uncertainty, e.g., They like ice
cream, don’t they?), rising intonation in declarative answers to questions, and the use of more
polite requests as opposed to commands. Soon after publishing this paper, Lakoff (1975) supple-
mented these characteristics of women’s speech with additional variables of hedges (e.g., sort of,
kind of, maybe), intensive use of the word so (e.g., That dog is so cute), and excessively correct
grammar (Lakoff, 1975). Although she provides no empirical research regarding the perception or
actual presence of these variables, Lakoff’s (1973, 1975) assertions served to spark discussion
regarding differences between the language of men and women, and set the stage for further
research to be conducted.
In 1973 Hirschman presented what she called preliminary evidence of gender differences in
language behavior, though they were not entirely consistent with Lakoff’s concept of feminine
register. She examined six mixed- and same-gender dyads of four college students during 10-
minute paired discussions about controversial topics. She reported that the women used more fill-
ers (e.g., um, you know) and personal references (e.g., we, you, I) than men used, fewer third person
references (e.g., he, they) and interruptions, but equal number of qualifiers (what Lakoff called
hedges, in addition to uncertainty verbs, e.g., I think, I wonder) (Hirschman, 1994). Crosby and
Nyquist (1977) performed an empirical study of Lakoff’s hypothesis, conducting three studies in
which the “female register” linguistic markers were measured. The first study observed conversa-
tions between two speakers of the same sex, while the second and third observed same- and mixed-
sex dyads at an information booth and police station, respectively. Results were inconsistent,
finding that the female register was used significantly more in women’s speech than men’s speech
when in same-sex conversation, but no significant difference between genders occurred in infor-
mation booth interactions. The female register was used by men and women inquiring at the police
station, confirming the author’s hypothesis that feminine register was used by people based upon
roles during conversation and not necessarily status or gender. Therefore, one’s perceived role may
influence his or her language use.
The 1980s and 1990s saw an expansion of research in gender language differences, and the
Gender-Linked Language Effect (GLLE) was more thoroughly explored (Mulac, 2006). Statistical
modeling analyses were used to relate the frequencies of linguistic variables to perceptions of gen-
der, as well as to attributes of socio-intellectual status, aesthetic quality, and dynamism (Mulac &
Lundell, 1986; Mulac, Wiemann, Widenmann, & Gibson, 1988). The models identified clusters of
variables that, when weighted together accordingly, can discriminate between male and female
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Hancock et al. 3
samples fairly accurately. For example, Mulac and Lundell’s (1986) discriminant analyses of tran-
scripts from a picture description task resulted in eight variables indicative of male speakers:
impersonals, fillers, elliptical sentences, units (i.e., words or vocalized pauses), justifiers, refer-
ences to pictures, geographical references, and spatial references; and nine more indicative of
females: intensive adverbs, personal pronouns, negations, verbs of cognition, dependent clauses
with subordinating conjunctions understood, oppositions, pauses, tag questions, and mean length
of sentence. Another study (Mulac et al., 1988) investigated language used during a problem-
solving task and resulted in a different model; it overlaps slightly with Mulac and Lundell’s (1986)
model but mostly consists of additional variables: interruptions, directives, and fillers/conjunctions
starting the sentence were indicative of male participants and questions; justifiers, intensive
adverbs, personal pronouns and adverbs starting the sentence were indicative of females. The only
conflicting result in these two particular models is for justifiers; they were attributed to males by
Mulac and Lundell (1986) but to females in the problem-solving task used by Mulac et al. (1988).
Variables measured and consequently included in models were not consistent in all studies, but
generally authors concluded there was great overlap between male and female language, with some
key features able to support the idea of a GLLE in most contexts. Personality characteristics were
hypothesized for some language clusters, allowing generalizations about females being “relatively
unassertive, sensitive, formal and person-oriented… [while] males were relatively informal, con-
cerned with holding the floor, and thing-oriented” (Mulac & Lundell, 1986, p. 96). Appendix A
defines some of the commonly used variables and notes whether the variable was associated with
males, females, or both in previous research.
Recent research presents a picture of diminishing gender differences in language use as earlier
findings are not replicated and in some cases contradicted; this is possibly due to new contexts or
changes over time in societal language norms. Furthermore, results from more context-based anal-
yses indicate that several linguistic variables differ between men and women only in certain con-
texts, with differences changing between conversation and writing as well as according to the
formality of the situation or characteristics of the communication partner (Hancock & Rubin, in
press; Hannah & Murachver, 1999; Leaper & Ayres, 2007). In 2001, Thomson and Murachver
found no difference between males and females in use of questions in e-mails, yet in a context of
professional role-play Mulac, Seibold, and Farris (2000) found higher use of questions in men and
more directives in women. Also contradictory to previous evidence, Mulac et al. (2000) found
more references to emotion in men’s language, and Mehl and Pennebaker (2003) found no signifi-
cant difference in reference to emotion in conversation. Newman, Groom, Handelman, and
Pennebaker (2008) found that number of words and mean sentence length did not vary between
genders when considering all communication contexts or conversational context in isolation. For
example, self-references were used more frequently by women in the more functionally applied
conversational context.
Mulac and Lundell (1986) categorized males’ picture descriptions as “relatively empirical (with
a) thing-oriented perspective” whereas female discourse was characterized by “a general lack of
assertion or, alternatively, a higher degree of interpersonal sensitivity” (p. 95). Therefore, it was
somewhat surprising when naive observers reading transcripts were unable to identify the gender
of the speaker but were able to discriminately rate socio-intellectual status, aesthetic quality, and
dynamism. The language variables were used to develop strong statistical models of prediction for
these three characteristics. Similarly, Mulac, Bradac, and Gibbons (2001) were able to relate gen-
der to four dimensions of culture. Several variables were categorized as a feature of either males
(direct, succinct, personal, instrumental) or females (indirect, elaborate, contextual, affective), but
whether some of the same language variables could be used in a combination to develop a model
to predict perceptions of speaker gender was not reported. Therefore, the utility of gender-related
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4 Language and Speech
language differences is unclear. As observed, differences are often minimal and mediated by other
factors such as communication activity, relationship of speakers, group size and gender composi-
tion (Leaper & Ayres, 2007); therefore, the effect of language on listener/reader perception is
questionable.
1.2 Clinical applications of gendered language research
Knowledge of how contemporary men and women use language and how the language used influ-
ences the way people perceive a speaker’s gender can be applied by speech–language pathologists
(SLPs). In recent decades, the scope of speech–language pathology services has grown to include
communication therapy for transgender (TG) individuals. This therapy has evolved from simply
raising vocal pitch and now often addresses several aspects of speech and language (Hancock &
Garabedian, 2013). Goals are usually established based on normative gender data for that area. For
some areas, particularly in language use, normative research cannot provide effective guidelines
for treatment because of insufficient or inconsistent findings.
With the emergence of the TG population in SLPs’ caseloads, the initial and most salient com-
municative change to address was pitch (Gelfer & Schofield, 2000; Wolfe, Ratusnik, Smith, &
Northrup, 1990), and then also resonance and intonation (Carew, Dacakis, & Oates, 2007; Dacakis,
2002; Gelfer & Schofield, 2000; Hancock, Colton, & Douglas, 2014). Although less consistent,
research has also examined the effects of volume, vocal quality, articulation, and rate on gender
differentiation, and as a result these components may be included in communication therapy for
TG speakers (Adler, Hirsh, & Mordaunt, 2012; Andrews & Schmidt, 1997; Fitzsimons, Sheahan,
& Staunton, 2001; Oates & Dacakis, 1983; Van Borsel, Janssens, & De Bodt, 2009; Van Borsel &
Maesschalck, 2008). Perhaps the least addressed communication variable in the TG communica-
tion literature is language. If language is targeted in therapy, SLPs rely on sociolinguistic literature
for guidance. Hooper, Crutchley, and McCready (2012) recommend a variety of language therapy
targets based on sociolinguistic research findings; however, many findings are of limited value
because generalization is restricted to highly specific communicative contexts or particular age
groups. In addition to research regarding observed gender differences, SLPs must rely on research
regarding the influence of variables on perceived gender in order to guide their TG clients to be
perceived as their desired gender. This balance of observed gender differences and influence on
perception has proven useful in creating appropriate therapy targets for TG voice (e.g., pitch), but
sufficient research has yet to be conducted to allow for this same balance when targeting language.
Therefore, the influence of language on perception of gender is of great interest to TG individuals
who work with SLPs to sound and communicate in such a way that they are perceived and accepted
as their desired gender.
1.3 Purpose
The current study examined the nature of language differences between men and women, as well
as how well particular language measures can predict gender and femininity perceptions. The first
study included female and male groups telling a personal narrative; the second experiment added
a group of male-to-female transgender women and analyzed a picture description sample. Inclusion
of the transgender group provided an opportunity to measure language on a gender continuum,
rather than in two discrete gender categories, and also directly examines the impact of language on
perception of gender for transgender women. Two contexts were used because the literature sug-
gests varying formality or constraints on language may impact conclusions about gender differ-
ences. Specifically, we aimed to:
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Hancock et al. 5
1. Evaluate differences between gender groups for frequency of T-units (a measure of sen-
tence length for spoken discourse) or any of the following variables (per T-unit): negations,
dependent clauses, judgmental adjectives, progressive verbs, intensive adverbs, justifiers,
hedges, qualifiers of time/quality, qualifiers of quantity, fillers, personal pronouns, self-
references, words. (See Appendix A for operational definitions.)
2. Determine the value – if any – of the above variables for predicting gender judgments or
femininity ratings by readers naive to the speaker’s gender.
2 Method
2.1 Experiment 1
2.1.1 Materials. The purpose of presenting written transcription of spoken narratives was to control for
confounding influence of speakers’ voice characteristics (e.g., pitch) and disguise the speakers’ gender.
The Child Language Data Exchange System (CHILDES, Carnegie Mellon University, Pittsburgh, PA;
MacWhinney, 2000) includes software to transcribe audio recordings of narratives (CHAT software)
and code each sample for predetermined language variables (CLAN software). CHAT was selected
because it was developed with the intention of providing a more accurate method for transcription and
analysis compared to doing so by hand. CLAN is also beneficial in that it automates a portion of data
analysis by performing functions such as frequency counts of specific words and coded variables.
A customized computer program was used to collect the perceptual data. This program dis-
played typed narratives, selection boxes for readers to make gender judgments for each speaker,
and visual analog scales (which correlated to a point value of 0 to 1000) for the readers to rate each
speaker’s femininity. This program also displayed instructions and a practice narrative and rating.
The program was linked with Microsoft Access software to record each reader’s participant ID,
age, and gender, as well as their ratings for each narrative.
2.1.2 Participants. A total of 40 narratives regarding a previous injury or illness were collected.
Thirty narratives (19 by females, 21 by males) were downloaded from the TalkBank AphasiaBank
database, an online databank of audio recordings and transcripts for language research (http://talk-
bank.org/). An additional 10 male narratives were collected by the researchers to supplement those
downloaded from TalkBank. To mimic the protocol used by TalkBank researchers, the topic of a
previous illness or injury was used in Experiment 1. This sample size was chosen to attempt to
provide statistical power while remaining within the practical means of the researchers.
Selection criteria for all speakers (collected from TalkBank and otherwise) required that speak-
ers self-report to be a minimum of 18 years old, have at least 12 years of completed education, be
native English speakers, have no neurological, psychological, or cognitive disorders, have no hear-
ing loss that affects speech, and have no expressive language disorder.
Forty readers (20 male, 20 female) participated in Experiment 1 by judging femininity and gen-
der. The mean age of the readers was 22 years (ranging from 18–31), with a standard deviation of
2.75 years. Selection criteria required that all readers self-report to be a minimum of 18 years old,
have a minimum of 12 years of education completed, be native English speakers, have no neuro-
logical, psychological, or cognitive disorders, have no current reading disability, and have no his-
tory of a language disorder. Readers were recruited through flyers posted on the George Washington
University (GWU) campus, recruitment in courses taught at GWU, and word of mouth. They read
narratives and provided a gender judgment and a femininity rating for each narrative. They received
US$15 for their participation. All materials were approved by The George Washington University’s
Institutional Review Board.
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6 Language and Speech
2.1.3 Procedures for collecting narratives. A total of 21 males (11 downloaded from TalkBank, 10
collected by the researchers) and 19 females (all downloaded from TalkBank) were asked to talk
about an illness or injury. Of the narratives downloaded from TalkBank, 25 included audio record-
ings and transcriptions, while five included only audio recordings. For those that did not include
transcriptions, researchers used the audio recordings to transcribe the narrative in CHAT (see sec-
tion 2.1.4.: Procedures for transcribing narratives). Demographic speaker data was collected from
TalkBank to ensure that all speakers met selection criteria.
Supplemental narratives were collected from individuals who were unfamiliar with the study.
Each speaker completed a medical history form to ensure that selection criteria were met and signed
an informed consent, and was asked to talk for three to five minutes about an illness or injury. This
was recorded with a digital voice recorder and collected outside of the clinical setting.
2.1.4 Procedures for transcribing narratives. Audio recordings of narratives were transcribed using
CHAT software. CHILDES provides a manual of transcription protocol, which was followed to
ensure the accuracy of each transcription (MacWhinney, 2000). Using this protocol, running
speech is segmented by pauses, and each segment is linked to audio that can be played back, for
the entire language sample or for an individual segment. Additionally, this protocol allows for dif-
ferential transcription of relevant speech components, such as fragments, abbreviated speech,
lengthy pauses, and quotations.
In order to prevent biased ratings based on gender-indicative content rather than the language of
the speaker, words that were clearly indicative of the gender of the speaker were changed. This
included pronouns, names (e.g., Sarah), and titles (e.g., husband/wife) when referring to a spouse,
and gender preferential sports (e.g., football), activities (e.g., fishing) or jobs (e.g., construction).
Words were changed in a way that did not affect the presence of a language variable being meas-
ured in this study (e.g., football was changed to ball rather than a game so as not to change the
number of words spoken).
Inter-rater reliability was assessed for narrative transcription. All narratives used in this study
(those transcribed by TalkBank researchers as well as those transcribed by researchers for this
study) were checked by a speech–language pathology graduate student, who checked the corre-
spondence between the audio recordings of the narratives against the typed transcripts, and marked
any discrepancies. Point-by-point agreement was 99.6%, and any discrepancies marked by the
graduate student were examined by the first author to make the final determination.
2.1.5 Procedures for coding narratives. Narratives were coded using CLAN software and guidelines.
Definitions for each linguistic variable were determined using previous research (e.g., Mulac et al.,
2001), and were further clarified through discussion among researchers (see Appendix A).
Inter-rater and intra-rater reliability were assessed for the linguistic coding of transcribed narra-
tives. Two researchers compared codes for the same 12 narratives (30% of all narratives) for all
variables, with the exception of separation into T-units. T-units were coded by two researchers for
every narrative and point-by-point agreement was originally 93%. Following discussion, 100%
were agreed upon. Eight linguistic variables originally selected based on their occurrence in previ-
ous literature of gendered language were eliminated due to insufficient occurrences (i.e., questions,
tag questions, directives, and oppositions) or inability to establish a reliable definition (i.e., ellipti-
cals, sentence initial adverbs, references to emotion, and locatives). Point-by-point agreement for
the remaining linguistic variables in Appendix A was 80% for the 12 transcripts (30% of all the
transcripts) used to measure inter-rater reliability. Each researcher also re-coded eight (20%) of
the narratives at least one week after initial coding. Point-by-point agreement was 90% and met the
80% criterion (Johnson & Pennybacker, 1993).
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Hancock et al. 7
2.1.6 Procedures for collecting perceptual data. Each transcribed narrative was formatted to a reada-
ble paragraph, with punctuation determined based on the perceived intent of the speaker as well as
readability. Dashes were placed after word fragments and repeated words. Commas were placed
after pauses when the pause correlated with a grammatically appropriate comma, and wherever
necessary to maintain readability of the narrative. Periods were placed at the end of a thought
whether or not this correlated with a pause in speech, due to the tendency in spoken language to
string together thoughts without pausing. This paragraph version of the narrative was entered into
the computer program to be used by the readers.
Readers completed a questionnaire to ensure that selection criteria were met, and signed an informed
consent form. Using the previously described custom computer program, they read brief instructions
followed by a practice narrative, in which they read an exemplar paragraph created by the researchers
and provided ratings, after which they were asked if they had any questions regarding their task.
Participants then read a total of 48 narratives. Forty of these were from different speakers, and eight
were repeated to assess intra-reader reliability. After reading a narrative, readers provided a gender
judgment (whether they perceived the speaker to be male or female) and a femininity rating (a visual
analog scale anchored by “feminine” and “masculine” anchor terms). Each reader received US$15 for
their participation in the study. Each participant took approximately one hour to complete the study.
To prevent a potential reading order effect, half of the readers received narratives 1–48 in con-
secutive order, while the other half received narratives 25–48 first, followed by 1–24. To assess
intra-reader reliability, eight of the narratives (20%) were randomly selected to be repeated and
distributed within the rest of the narratives. They were positioned semi-randomly, with the research-
ers only controlling to ensure that half of the repetitions were in narratives 1–24, and half were in
narratives 25–48, and to ensure that the initial reading was not immediately followed by the repeti-
tion of that narrative.
2.2 Experiment 2
2.2.1 Participants. A total of 35 narratives were collected. Ten men, 12 women, and 13 transgender
women (male-to-female; hereafter MtF) each described the same picture. Selection criteria for all
speakers required that speakers self-report to be a minimum of 18 years old, have at least 12 years
of completed education, be native English speakers, have no neurological, psychological, or cogni-
tive disorders, have no hearing loss that affects speech, and have no expressive language disorder.
The selection criteria were set due to the potential for each of these factors to differentiate the
language of the speaker from typical adults. Speakers were given US$10 for their participation in
a larger study, of which this picture description task was a portion.
Forty readers (20 male, 20 female) participated in this study by judging femininity and gender.
The mean age of the readers was 22 years (ranging from 18–31), with a standard deviation of 2.75
years. Selection criteria required that all readers be a minimum of 18 years old, have a minimum of
12 years of education completed, be native English speakers, self-report no neurological, psycho-
logical, or cognitive disorders, no current reading disability, and no history of a language disorder.
Readers were recruited through recruitment in courses taught at George Washington University.
They read narratives and provided a gender judgment and a femininity rating for each narrative.
They were given US$10 for their participation. All materials were approved by the institution’s IRB.
2.2.2 Procedures for collecting narratives. A total of 10 men, 12 women, and 13 transgender women
(MtF) were asked to describe Norman Rockwell’s painting “The Waiting Room” for 20–30 seconds.
Each speaker completed a medical history form to ensure that selection criteria were met and signed
an informed consent form. Two of the narratives were omitted from rating because of their outlier
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8 Language and Speech
characteristics: one female speaker talked about a subject unrelated to the prompt, and one MtF
speaker used unusual language (e.g., “ochre-colored bench”) and provided only a very short sample
(i.e., 35 words). Thus, analyses are based upon 10 men, 11 women, and 12 MtF women.
2.2.3 Procedures for transcribing narratives. Audio recordings of all narratives were transcribed by
the authors, using CLAN software. A speech–language pathology graduate student checked the
correspondence between the audio recordings of the narratives and the typed transcripts, and
marked any discrepancies. Discrepancies marked by the graduate student were rechecked by the
first author to make the final determination.
2.2.4 Procedures for coding narratives. Narratives were coded using CLAN software. The same lin-
guistic variables from Experiment 1 were used. Inter-rater and intra-rater reliability were assessed
for the coding of transcribed narratives. A sample of six narratives (18%) were coded for T-units
by two researchers and point-by-point agreement was originally 91.4%, with 100% agreed upon
following discussion. One researcher then coded T-units for the remaining 33 narratives, and a
second checked them; 98% reliability was obtained, with 100% agreed upon after discussion. Six
(18%) of the transcripts were coded for the other linguistic variables by two people for measure-
ment of inter-rater reliability. Point-by-point agreement for linguistic variables was 87%, with
100% agreed upon after discussion. In order to achieve consistency, the definitions of the linguistic
variables were clarified further. Appendix A is the final definitions used to code the data included
in this paper. Point-by-point agreement was then 92%.
2.2.5 Procedures for collecting perceptual data. Each transcribed narrative was formatted to a reada-
ble paragraph form, with punctuation determined based on the intent of the speaker as well as
readability. This paragraph version of the narrative was entered into the computer program to be
used by the readers. Readers completed a questionnaire to ensure that selection criteria were met,
and signed an informed consent form. Using the previously described computer program, they read
brief instructions followed by a practice narrative, in which they read an exemplar paragraph and
provided ratings, after which they were asked if they had any questions regarding their task. Par-
ticipants then read a total of 40 narratives. Thirty-three of these were from different speakers, and
seven were repeated to assess intra-reader reliability.
After reading a narrative, readers provided a gender judgment (whether they perceived the
speaker to be male or female) and a femininity rating (a sliding scale anchored by “feminine” and
“masculine” terms). Each reader received US$10 for their participation in the study. Each partici-
pant took approximately 30 minutes to complete the study.
To prevent a potential reading order effect, half of the readers received narratives 1–40 in con-
secutive order, while the other half received narratives 21–40 first, followed by 1–20. To assess
intra-reader reliability, six of the narratives (18%) were randomly selected to be repeated and dis-
tributed within the rest of the narratives. They were positioned semi-randomly, with the researchers
only controlling to ensure that half of the repetitions were in narratives 1–20, and half were in
narratives 21–40, and to ensure that the initial reading was not immediately followed by the repeti-
tion of that narrative. The practice paragraph was repeated at the end.
3 Results
3.1 Group comparisons
A post-hoc power analysis using n = 40, α = .05, and groups = 2 (e.g., Experiment 1) revealed .69
power for large effect sizes, .34 power for medium effect sizes, and .09 for small effect sizes.
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Hancock et al. 9
Another power analysis using n = 33, α = .05, and groups = 3 (e.g., Experiment 2) revealed less
than .50 power for all effect sizes.
A one-way ANOVA revealed statistically significant differences between males and females in
Experiment 1 for number of T-units, F(1,39) = 8.67, p = .006; words per T-unit, F(1,39) = 6.21,
p = .017; dependent clauses, F(1,39) = 7.36, p = .010; and personal pronouns, F(1,39) = 4.49, p =
.041; with females always having fewer than males. The effect sizes of the former three variables
were large (i.e., eta2 > .14). See Table 1.
For Experiment 2, an ANOVA revealed significant between-group difference for T-units,
F(2,32) = 3.36, p = .048; words per T-unit, F(2,32) = 4.57, p = .019; and dependent clauses,
F(2,32) = 3.86, p = .032. Bonferroni post-hoc comparison tests revealed a significant difference
between the female and the MtF group for the significant variables: T-units (p = .045), words per
T-unit (p = .017), dependent clauses (p = .050). For each of these variables, males were not signifi-
cantly different from either group; the mean of the male group was between the MtF and female
group means. MtF speakers used the least T-units but the most words per T-unit and dependent
clauses. These three variables had large effect sizes (i.e., eta2 > .14). See Table 2.
3.2 Prediction of gender perception
3.2.1 Experiment 1. Fifty-three percent (21/40) of the gender judgments were accurate. Seventy-
one percent (15/21) of males’ samples were judged to be male (three were undecided) and 31%
(6/19) of the females’ narratives were judged to be female. All 14 linguistic variables were first
entered into a multiple linear regression model to describe how the variables predicted the gender
judgment score given by raters. The fit of the model was not statistically significant, F(14, 25)=
1.24, p = .31. Therefore, forward, backward, and step-wise regression modeling was used to select
variables to predict gender judgment. The model with the best fit indicated the sample was more
likely to be judged as a female’s with an increase in dependent clauses, justifiers, qualifiers of
quantity, fillers, and personal pronouns and a decrease in progressive verbs, qualifiers of time and
quality, and words per T-unit, F(8,31) = 2.29, R2 = .37, p = .047.
Table 1. Means (and standard deviations) of language variables for Experiment 1 by gender group.
Linguistic variable Males (n=21) Females (n=19) F p eta2
T-units 29 (15) 16 (11) 8.67* .006 .19 a
Negations .18 (.11) .15 (.14) .49 .488
Dependent clauses .29 (.17) .16 (.08) 7.36* .010 .16 a
Judgmental adjectives .14 (.12) .16 (.08) .47 .496
Progressive verbs .24 (.15) .21 (.12) .57 .455
Intensive adverb .20 (.16) .22 (.17) .21 .649
Justifier .05 (.04) .03 (.04) 3.85 .057
Hedges .18 (.10) .14 (.13) 1.24 .272
Qualifiers of time/quality .12 (.11) .09 (.09) .75 .392
Qualifiers of quantity .34 (.16) .31 (.13) .46 .502
Fillers .49 (.24) .50 (.44) .01 .927
Personal pronouns 1.42 (.36) 1.21 (.24) 4.49* .041 .10 b
Self-references 1.03 (.32) 1.15 (.51) .77 .386
Words per T-unit 11.6 (2.1) 9.70 (2.8) 6.21* .017 .14 a
Note: variables are analyzed in a ratio of variable per T-unit.
*= Statistically significant at the p < .05 level.
a = large effect size b = medium effect size.
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10 Language and Speech
All 14 linguistic variables were then entered into a multiple linear regression model to describe
how the variables predicted the femininity score given by raters. The fit of the model was not sig-
nificant, F(14,25) = 1.19, p = .338. Therefore, forward, backward, and step-wise regression mod-
eling was used to select variables to predict femininity rating. The model with the best fit included
the same eight variables that were included in the model to predict gender judgment (i.e., increase
in femininity rating with an increase in dependent clauses, justifiers, qualifiers of quantity, fillers,
and personal pronouns and a decrease in progressive verbs, qualifiers of time and quality, and
words per T-unit), F(8,31) = 2.17, R2 = .36, p = .059.
3.2.2 Experiment 2. Forty-five percent (15/33) of the gender judgments were accurate. Fifty per-
cent (5/10) of males’ samples were judged to be male, 36% (4/11) of the females’ narratives were
judged to be female, and 50% (6/12) of the MtF speaker samples were judged to be female. The
speaker’s gender and measures of all linguistic variables were entered into a multiple linear
regression model to describe how the variables predicted the gender judgment score given by
raters. The fit of this model was not statistically significant, F(14,18) = 2.18, p = .06. Total T-units
had a strong linear correlation with words per T-unit (r = –.88 ) and was therefore removed to
avoid redundancy, but the resulting model was still not significant, F(13,19) = 2.35, p = .05.
Therefore, forward, backward, and step-wise regression modeling was used to select variables to
predict gender judgment. The model with the best fit indicated the sample was more likely to be
judged as a female’s with an increase in dependent clauses, intensive adverbs, qualifiers of quan-
tity, fillers, personal pronouns, and a decrease in progressive verbs, hedges, and self-references,
F(8,24) = 4.20, R2 = .58, p = .003.
The speaker’s gender and measures of all linguistic variables were entered into a multiple linear
regression model to describe how the variables predicted the femininity score given by raters. The
fit of this model was not statistically significant, F(14,18) = 1.77, p = .126. Total T-units had a
Table 2. Means (and standard deviations) of language variables for Experiment 2 by gender group.
Linguistic variable Males (n=10) Females (n=11) MtF (n=12) F p eta2
T-units 5.4 (1.8) 6.5 (1.6) 4.7 (1.3) 3.36* .045 .18a
Negations 0.4 (.97) 0 0.1 (.3) 1.49 .241
Dependent clauses 2.5 (1.3) 1.4 (1.1) 2.6 (1.1) 3.86* .032 .20a
Judgmental adjectives 0.5 (.5) 0.1 (.3) 0.3 (.7) 1.66 .207
Progressive verbs 2.6 (1.6) 1.3 (1.1) 2.1 (1.4) 2.60 .091
Intensive adverb 0.4 (.5) 1.3 (1.1) 0.8 (.8) 2.68 .090
Justifier 1.0 (.8) 1.0 (.8) 1.5 (1.7) .64 .536
Hedges 4.4 (3.2) 4.4 (2.4) 3.1 (2.2) .95 .399
Qualifiers of time/quality .5 (1.1) 0.7 (.7) 0.3 (.5) .77 .471
Qualifiers of quantity 3.3 (2.1) 3.9 (1.6) 2.6 (1.7) 1.59 .222
Fillers 1.8 (1.4) 1.9 (1.6) 3.3 (2.8) 1.91 .166
Personal pronouns 3.4 (2.3) 3.3 (1.7) 2.3 (1.3) 1.19 .318
Self-references 0.1 (.3) 0.3 (.5) 0.5 (.5) 2.19 .130
Words per T-unit 12.7 (4.3) 9.8 (2.6) 14.2 (3.5) 4.57* .019 .23a
Note: variables are analyzed in a ratio of variable per T-unit.
*= Statistically significant at the p < .05 level.
a= large effect size.
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Hancock et al. 11
strong linear correlation with words per T-unit (r = –.88) and was therefore removed, but the
resulting model was still not significant, F(13,19) = 1.93, p = .093. Therefore, forward, backward,
and step-wise regression modeling was used to select variables to predict femininity score. The
model with the best fit indicated femininity score increased with an increase in dependent clauses,
intensive adverbs, qualifiers of quantity, and personal pronouns and a decrease in progressive
verbs, hedges, and self-references, F(7,25) = 3.74, R2 = .51, p = .007. With the exception of fillers
in the gender judgment model, the models for gender judgment and femininity rating in Experiment
2 included the same variables.
3.3 Summary
Four of the 14 variables differentiated males from females (T-units, words per T-unit, dependent
clauses, personal pronouns). Unexpectedly, the MtF speakers tended to be more distinct from the
females than even the males were; this was to a significant extent for number of T-units, words per
T-unit, and dependent clauses.
Raters’ judgment of a speaker’s gender based on reading a transcript of a spoken narrative
was at chance levels in both experiments (53% and 45%). Regression models for perception
of gender and femininity were similar within each experiment. Four variables were common
to all four models: dependent clauses, progressive verbs, qualifiers of quantity, and personal
pronouns. In narrative context (Experiment 1), justifiers, qualifiers of time/quality, and words
per T-unit were also in the models predicting gender and femininity; whereas in a picture
description context (Experiment 2) intensive adverbs, hedges, and self-references were the
additional variables. No models were strengthened by total number of T-units, negations, or
judgmental adjectives.
4 Discussion
4.1 Gendered language
Overall, our studies do not provide strong evidence for language differences between males and
females. Compared to males, females used significantly lower numbers of four of the 14 variables
measured, and these differences occurred in only one of the two contexts (personal narratives). Our
female subjects used significantly fewer T-units, words, dependent clauses, and personal pronouns
than males during picture description.
Although females were expected to talk more (i.e., use more T-units and more words per T-unit)
and more elaborately (i.e., use more dependent clauses) than males, they consistently – and signifi-
cantly in Experiment 1 – used fewer of each of these variables. Considering females are character-
ized as preferring to talk about personal matters and affiliate with the listener, it is surprising that a
more “feminine style” did not occur in a personal narrative (Mulac & Lundell, 1986; Mulac et al.,
2001). Perhaps the topic of the personal narrative (i.e., an injury), was masculine and therefore
influenced use of gendered language (Palomares, 2009).
Although females unexpectedly used fewer dependent clauses and personal pronouns than men
did in Experiment 1, these variables were included in the perceptual models with positive coeffi-
cients – indicating an increase in use would actually increase perception of femininity. This sug-
gests a mismatch between actual and stereotypical gender differences. For practical applications
(e.g., transgender communication strategies), the impact of language use on perception of gender
is of utmost value.
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12 Language and Speech
4.2 Language and perception of gender
Gender prediction accuracy was near chance in both experiments. None of the individual variables
alone were robust predictors of perceived gender or femininity. Only through regression modeling,
a statistical method for examining the value of using combinations of variables to predict an out-
come, were good prediction models found. As expected, models for predicting gender and feminin-
ity were similar within each experiment in that they included the same eight linguistic variables
with the same coefficient signs. However, the eight variables were not consistent across experi-
ments (i.e., different language tasks). The variation in linguistic variables included in the models
for these experiments affirms the importance of considering context and associated influences
when examining how language could predict perceptions (Crosby & Nyquist, 1977; Hancock &
Rubin, in press; Hannah & Murachver, 1999).
An important note about interpreting regression models is warranted. In order to influence
these outcomes (in this study, perceptions), one must address each linguistic variable in the
model by using it more or less frequently, as determined by the unstandardized coefficient (see
Table 3). The magnitude of the coefficient indicates to what extent changing the frequency of
the variable will change perception, whereas the sign of the coefficient indicates whether an
increase or decrease of the variable’s frequency will increase the likelihood of the outcome. It
would be inappropriate, for example, to say that if all four models include dependent clauses
then a person who is male-to-female transgender should use more dependent clauses in order to
pass as female or more feminine. Instead, all variables included in the model for the context of
interest would need to be changed to the direction and extent indicated by the coefficient. With
this understanding in mind, we proceed cautiously to examine the linguistic variables in light
of previous literature. The direction of the coefficients (i.e., whether an increase in the variable
is associated with an increase or decrease in femininity score) was not always as predicted
based on previous literature.
Table 3. Unstandardized coefficients for variables included in final regression models for gender rating
and femininity score outcomes in each experiment.
Experiment 1 Experiment 2
Gender rating Femininity score Gender rating Femininity score
Negations
Dependent clauses .73 347.52 .05 24
Judgmental adjectives
Progressive verbs −.33 −94.60 −.04 −23.26
Intensive adverb .06 27.69
Justifier 1.01 508.61
Hedges −.03 −15.94
Qualifiers of time/quality −.51 −173.28
Qualifiers of quantity .37 116.50 .05 24.77
Fillers .16 98.32 −.02
Personal pronouns .12 70.95 .04 16.43
Self-references −.06 −74.76
Words per T-unit −.03 −17.04
Notes: Variables are analyzed in a ratio of variable per T-unit. Mean gender ratings for each sample could range 0–1,
with 1 as more female. Mean femininity score for each sample could range 0–1000, with 1000 as more feminine.
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Hancock et al. 13
4.3 Variables associated with increased perception of femininity
First, we consider the variables with a positive coefficient, indicating an increase in female/femi-
nine scores with increasing use of these variables: dependent clauses, personal pronouns, intensive
adverbs, qualifiers of quantity, and justifiers. The first three are consistent with the literature and
the conceptualization of female speech to be complex, descriptive, and affiliative (Hirschman,
1994; Mulac & Lundell, 1986).
All the perceptual models in this study included positive coefficients for qualifiers of quantity.
This is in contrast to studies that examined written communication and attributed qualifiers of
quantity to men because they were used in a direct speaking style focused on external attributes of
objects, as opposed to a more affective speaking style focusing on internal feelings (Mulac, Studley,
& Blau, 1990; Mulac et al., 2001).
Greater use of justifiers associated with an increase in perception of femininity was somewhat
surprising, given Mulac and Lundell’s (1986) report of justifier use as indicative of male speakers
in their multiple regression analysis of picture descriptions. However, justifiers have been used by
females more than males in other contexts (e.g., problem-solving tasks, Mulac et al., 1988) and
people may perceive speakers who justify to be more submissive and therefore associate them with
a female stereotype.
4.4 Variables associated with decreased perception of femininity
Next, the variables with a negative coefficient, indicating an increase in female/feminine scores
when frequency decreased were: progressive verbs, self-references, hedges, qualifiers of time/
quality, and words per T-unit. Previous literature reports females using fewer progressive verbs in
a verbal context, compared to men (Mulac & Lundell, 1994; Mulac, Lundell, & Bradac, 1986);
however, the other variables with negative coefficients were not necessarily expected to contribute
to a masculine perception.
In Experiment 2, using self-references decreased the perceived femininity. This is consistent
with Mulac et al.’s (2001) assertion that “I” references are associated with male culture and “reflect
an ego-centric orientation” (p. 144). However, Newman et al. (2008) found women to use more
self-references than men in conversational contexts and explained this as a reflection of females’
tendency to attend to personal aspects of conversation. However, those findings may be dependent
upon a conversation partner (Hancock & Rubin, in press), which would not be an issue in the cur-
rent study’s contexts of personal narrative and picture description.
There is some discrepancy in the literature about hedges. According to Lakoff and theories sug-
gesting women’s language reflects a submissive status, hedges or uncertainty words are more
expected from women (Lakoff, 1975). Crosby and Nyquist (1977) found women more likely than
men to use hedges when requesting information. However, several studies, including this one,
found no distinction between genders for number of hedges, sometimes called uncertainty modi-
fiers (e.g., Hirschman, 1994; Mulac & Lundell, 1986; Mulac & Lundell, 1994; Mulac et al., 1986;
Mulac et al., 1988). To our knowledge, no previous studies have indicated that hedges would be
associated with gender judgment of male and low femininity rating, as it was in Experiment 2.
Qualifiers were examined in previous gender language research in terms of the subcategories of
qualifiers of certainty or references to quantity (Mulac & Lundell, 1994). However, qualifiers can
also include those qualifying time and relative quality, which are unexamined to date. As a result,
the current study included qualifiers of time and relative quality to obtain information about gender
differentiation as reflected in all qualifiers. This has proven interesting because qualifiers of quantity
were associated with female/feminine scores while qualifiers of time and quality were associated
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14 Language and Speech
with male/masculine scores (in Experiment 2). However, in future studies it may be valuable to
further separate qualifiers of time and quality into “relative” and “absolute” categories, because rela-
tive qualifiers, such as some, usually, or nearly, indicate uncertainty, while absolute qualifiers, such
as all, always, or never indicate certainty. Such differentiation could provide insight on the occur-
rence and influence of qualifiers, and contribute to the discussion of whether relative qualifiers
might be considered hedges, and whether they are more frequently used by men or women.
4.5 Variables with neutral effect on perception of femininity
Finally, because no models were strengthened by the total number of judgmental adjectives or
negations, we can conclude that these variables were not valuable in these experiments, even in
combination with other language variables, for predicting these perceptual outcomes. This is not to
say they may never be of interest. Previous studies concluding that judgmental adjectives or
phrases are predominately used by males measured written communication (Mulac & Lundell,
1994; Mulac et al., 1990), which may explain why judgmental adjectives were not significant in
this study of oral language. Also, the finding that negations did not contribute to any models of
perception may be explained by the fact that few negations were used at all in these samples; none
of the 11 females in Experiment 2 used even one negation, despite previous findings that females
use more negations than males use (Mulac & Lundell, 1986).
Fillers were associated with an increase in female/femininity scores in models using Experiment
1 results, but a small negative coefficient was required in the gender judgment model of Experiment
2. This may not be surprising considering the literature is also mixed regarding how this variable
is used by men and women (Hirshman, 1994; Mulac & Lundell, 1986; Mulac & Lundell, 1994;
Mulac et al., 1986; Mulac et al., 1990). Studies have attributed fillers to females, as Experiment 1
does, but they involved the writing of 4th graders (Mulac et al., 1990), the public speaking of uni-
versity students (Mulac et al., 1986), and a small study comparing two females and two males in
conversational contexts (Hirschman, 1994). Experiment 2 and Mulac and Lundell (1986) both
indicate males use more fillers – and they both used description tasks. Thus, the tendency for a
particular gender to use more fillers is another clear example of the effect of context.
5 Limitations
There was a low frequency of occurrence of many linguistic variables. This may have been due to
the narrowness of the topic as well as the length of the language sample. Although longer or addi-
tional samples may increase the statistical power of the multiple linear model, the feasibility of
meeting these requirements was beyond the scope of the design of the current study in that it would
be unreasonable for the readers to evaluate 40 samples longer than those used in this study, or more
than 40 samples. Future studies may be designed to accommodate this potential limitation by
including several rater groups.
Internal validity may also be affected by low statistical power, low percentages of females
judged as females, and constraints of the linguistic variables selected for measurement. Variables
which were not reported previously occurred minimally in the current study’s language samples
(i.e., questions, tag questions, directives, and oppositions), or were too complex to code reliably
(i.e., ellipticals, sentence initial adverbs, references to emotion, and locatives), were not included
in the analysis. It is possible that linguistic variables not measured account for additional gender
language differences or would increase the percentage of samples correctly judged to be from a
female speaker. External validity is constrained by the culture of the speakers and raters in this
sample. For example, raters were largely university students and staff.
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Hancock et al. 15
Over the last several decades, gender-based language patterns have not been robust, to say the
least. One factor contributing to the lack of compelling evidence is that linguistic variables exam-
ined vary among studies and the actual coded features and interpretations often differ. Methodologies
have changed with the continual development of technology to use for data collection and analysis.
Furthermore, language use itself may have evolved, further complicating the current application of
available research.
6 Implications for transgender speakers and future directions
The primary purpose of including an MtF group in the second experiment was to gain insight
into to the clinical utility of any differences between male and female language use. It was
hypothesized that, if there were gender differences, the MtF group would adopt language more
similar to the female group, with the average scores of the MtF falling between the male and
female group scores. Instead, they did not statistically differ from males and in fact often the
MtF group mean was closer to the male mean than the female mean. Perhaps not surprisingly,
with few observable differences between the language of male and female gender groups, accu-
racy of gender prediction after reading transcripts was near chance. The lack of measured and
perceptible differences calls into question the utility of training key language features in
transgender communication therapy.
However, other literature has found greater gender differences and also found perception of gen-
der to be influenced by language. Palomares (2004, 2009) recently suggested that perception of
gender may be influenced by the recipient’s stereotypes and gender identity schema in addition to
language choices made by the speaker. Therefore, continued research in this area could investigate
what the speaker could do in order to capitalize upon (or minimize) the recipient’s gender schema
so that language choices could be used to influence perception of gender. This would be particularly
valuable for the written communication of transgender people, such as email (Palomares, 2004).
7 Conclusions
These two experiments demonstrate that gender-related differences in language use are limited,
and that any relationship of language to perceptions of gender and femininity is complex and mul-
tivariate. Specific linguistic variables may be relatively more predictive than others, but none
emerge as clearly useful for influencing how people perceive gender or femininity.
Acknowledgements
The authors wish to acknowledge Amy Lopez for her contributions to linguistic analysis and Xian Sun and
Liyi Jia for assistance with statistical analysis.
Funding
The research received no specific grant from any funding agency in the public, commercial, or not-for-profit
sectors.
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Appendix A
Definitions and citations for linguistic variables
1) Narrative level
a) T-Unit: one main clause plus any subordinate clauses or nonclausal structures attached
to or embedded in the main clause. An index of syntactic complexity and useful as a
common measurement base for other analyses (Hunt, 1965; Shadden 1998).
b) Words per T-unit: the number of total words divided by the number of total T-units
in a language sample. (Mulac & Lundell, 1986, F+; Mulac et al., 1986, F+). This is
sometimes termed mean length of utterance.
c) Negation (NO): a statement of what something is not. (Mulac & Lundell, 1986, F+;
Mulac et al., 1986, F+.) This can consist of:
i. Turning an affirmative statement into its opposite denial (e.g., You don’t feel like
looking, I am not the winner).
ii. Negating a noun or verb using a negative adjective (e.g., There is no winner), a
negative pronoun (e.g., Nobody is the winner), or a negative adverb (e.g., I never
was the winner).
2) Phrase level
a) Dependent clause: a phrase that contains a subject and verb but cannot stand alone as
a full sentence. A dependent clause usually begins with a subordinating conjunction
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18 Language and Speech
(e.g., that, which, who, whose, when, because, even though, whether, while) (Mulac &
Lundell, 1986, F+). Dependent clauses can appear in various forms:
i. Relative pronouns (e.g., I liked the dog who lived next door, I saw the man
whose smile annoyed me, She had a pimple which wouldn’t go away).
ii. Adverbial clauses (e.g., Be careful when leaving the house).
iii. Non-finite clauses (e.g., I wanted to be loved).
b) Progressive verb: verb presented in the -ing form (Mulac et al., 1986, M+) (e.g., She
was running).
3) Word level: semantic (meaning)
a) Justifier: a reason is given for a previous statement (Mulac & Lundell, 1986, M+;
Mulac et al., 1988, F+) (e.g., I left because I was hungry).
b) Hedges: (Crosby & Nyquist, 1977, F+)
i. Modifiers that indicate a lack of confidence in, or diminished assuredness of, the
statement. A word or phrase that changes how absolute or certain a statement is
(e.g., sort of, somewhat, kind of, probably, about).
ii. Verb phrase indicating lack of certainty or assuredness (e.g., I wonder if, it looks
to be, I think, I guess, it seems to be).
c) Qualifier of time or relative quality
i. Qualifier of time indicates when or how often something occurs (e.g., occasion-
ally, usually, sometimes, always, never, recently).
ii. Qualifier of relative quality compares the item at hand to something else, on
a scale of degree (e.g., happier, coolest, worst nightmare, more satisfied, least
hungry).
d) Qualifiers of quantity: reference to amount or quantity. Answers the questions How
much? How many? Includes nominal or ordinal numbered quantities and qualifiers of
quantity (Mulac & Lundell, 1994, M+; Mulac et al., 1990, M+) (e.g., all, most, none,
each, any, some, first, below 32, 6–8 weeks).
4) Word level: syntactic (function)
a) Intensive adverb: expresses how complete a quality is. As with all adverbs, must mod-
ify a verb, adjective, phrase, clause, or another adverb (Mulac & Lundell, 1986, F+;
Mulac et al., 1986, F+; Mulac et al., 1986, M+; Mulac et al., 1988, F+) (e.g., very,
really, quite, entirely, a little, a bit, pretty, more).
b) Judgmental adjective: rather than purely describing, judgmental adjectives evaluate
and comment on the item under discussion. Many judgmental adjectives can be easily
categorized as either good or bad (Mulac & Lundell, 1994, M+; Mulac et al., 1986,
M+; Mulac et al., 1990, M+) (e.g., funny, horrible, annoying, nice, bothersome, ugly,
fancy, cosmopolitan, old; NOT tired, bright, blurry, loud).
i. “He’s scared” is not a judgmental adjective, but “he’s scary” is.
c) Personal pronoun: word that refers to a person, place, thing, or idea (Mulac & Lundell,
1986, F+; Mulac et al., 1988, F+). Includes subject pronouns (e.g., I, you, he, she, it,
we, they) and object pronouns (e.g., me, you, him, her it, us, them).
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Hancock et al. 19
d) Self-reference: First person singular pronoun (e.g., I, me, my, mine, myself) (Mulac
et al., 2001, M+; Newman et al., 2008, F+).
e) Filler: Word or phrase used without apparent semantic intent. Repetitions of fillers are
counted independently (Mulac et al., 1986, F+; Mulac & Lundell, 1986, M+; Mulac
et al., 1988, M+) (e.g., like, er, I you know uh um went to bed).
Note: F+ study results attributed this variable to females; M+ study results attributed this variable
to males.
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