The effect of topic of discussion on gendered language in computer-mediated communication discussion.

Empirical Methods in Communication

Application Assignment #1
Experimental Research
Overview: You will briefly answer five essay questions that require you to dissect an
experiment by Palomares and Lee, that was published in a 2010 issues of the Journal of
Language and Social Psychology. Your answers must be typed and double spaced. Provide
a 1½ inch margin on all sides of the page for written comments. Your paper must absolutely not
be longer than 1000 words; that is about 200 words per question. All word processors have a
word count feature. Please provide the word count of your essay (excluding your name and
other identifying information).
Due Date: May 10. Turn your paper in at the beginning of the class session. Electronic (email
attachment) submissions will not be accepted.
1. Palomares and Lee used a 2x2x2 between-subjects factorial design. (This kind of design
is referred to as “between-subjects” because each research participant was assigned to
only one experimental condition.) Explain why this design was necessitated by the
research hypotheses.
2. Experimental control is a critical feature of any experiment. If we wish to know the effects
of independent variables on dependent variables we must “hold constant” other variables
that would make causal inference challenging. What efforts were taken in this experiment
to achieve a high level of experimental control?
3. What efforts were made to determine if the manipulations of the independent variables
were valid?
4. Did this experiment measure any potential mediating variables? If so, what were these
variables? If no, why not?
5. The generalizeability of every experiment has limitations, and this study is no exception.
Briefly describe how our ability to generalize from the findings obtained are limited.
Your answers to these five questions will be equally weighted. Evaluation is based on the
thoughtfulness of your response and the quality of your writing.
Think of this essay as a take home exam. The work you submit must be your own. You may not
discuss this assignment or your ideas with other people. Doing so will be considered cheating.
Journal of Language and Social Psychology
29(1) 5–
© 2010 SAGE Publications
DOI: 10.1177/0261927X09351675
Virtual Gender Identity:
The Linguistic Assimilation
to Gendered Avatars
in Computer-Mediated
Nicholas A. Palomares1 and Eun-Ju Lee2
This research examined how individuals’ gendered avatar might alter their use of
gender-based language (i.e., references to emotion, apologies, and tentative language)
in text-based computer-mediated communication. Specifically, the experiment tested
if men and women would linguistically assimilate a virtual gender identity intimated
by randomly assigned gendered avatars (either matched or mismatched to their
true gender). Results supported the notion that gender-matched avatars increase
the likelihood of gender-typical language use, whereas gender-mismatched avatars
promoted countertypical language, especially among women. The gender of a partner’s
avatar, however, did not influence participants’ language. Results generally comport with
self-categorization theory’s gender salience explanation of gender-based language use.
gender-linked language, social identity, intergroup communication, message production,
stereotypes, prototypes
Gender-based communication is the focus of much scholarship. This work increasingly
emphasizes computer-mediated environments. Research, for example, has
examined how men and women communicate via e-mail (Colley & Todd, 2002), chat
groups (Koch, Mueller, Kruse, & Zumbach, 2005; Thomson, 2006), instant messages
1University of California, Davis, Davis, CA, USA
2Seoul National University, Gwanak-gu, Seoul, Korea
Corresponding Author:
Nicholas A. Palomares, Department of Communication, One Shields Avenue, University of California,
Davis, CA 95616, USA
Downloaded from at UNIV CALIFORNIA DAVIS on March 18, 2013
6 Journal of Language and Social Psychology 29(1)
(Fox, Bukatko, Hallahan, & Crawford, 2007), and other forms of computer-mediated
communication (CMC). One issue in this empirical arena is gender identity and its
manifestations in CMC. Scholars have argued, for example, that people perform masculinity
online as a means to reify their gender identities (Herrmann, 2007). Other
research has demonstrated that elevating the salience of gender identity prompted
women to reference emotions in e-mail more than men especially in mixed-sex interactions
(Palomares, 2008). Permeating this literature is a focus on the diverse, dynamic,
and sometimes transient nature of gender identity; how it differentially presents itself
in CMC given the circumstances; and the resultant communicative behavior of men
and women (Murachver & Janssen, 2007; Palomares, Reid, & Bradac, 2004).
Whereas the primary concern of this work is how a sex-consistent gender identity
affects communication, a relatively nascent interest is how people simulate a gender
identity online that they would not otherwise perform in offline settings (Herring &
Martinson, 2004; Hills, 2000; Rellstab, 2007). For example, a woman might pretend to
be a man in an online chat. The few instances of this research have studied strategic or
intentional portrayals of a different gender and focused on the communicative behaviors
people employ in these forgeries and if others can recognize a disingenuous gender
identity (Herring & Martinson, 2004; Hills, 2000; see also Thomson & Murachver,
2001); yet no known research has examined how more subtle cues might trigger the
enactment of a different gender identity online. We refer to this phenomenon as virtual
gender identity. Thus, we conducted an experiment to test if men and women would
linguistically assimilate a virtual gender identity intimated by (matched or mismatched)
gendered avatars representing them in text-based CMC. Specifically, our objective was
to determine if and how men’s and women’s gender-based language would emerge as a
function of gendered (i.e., masculine or feminine) avatars that represented them and
their interaction partner. In pursuit of this goal, we first review research on language
and gender, then present our theoretical orientation from which we deduce predictions,
and finally report an experiment that implemented and tested this rationale.
Language and Gender in CMC
Research traditionally has emphasized gender differences claiming that men and
women tend to use dissimilar language independent of the context, personal proclivities,
or interaction partners (e.g., Lakoff, 1975; Mulac & Lundell, 1980; Tannen,
1990). The empirical evidence is somewhat compatible with this claim. Consistent
with stereotypes, for example, meta-analyses demonstrated that women used more
affiliative speech (e.g., references to emotion) and less assertive speech (e.g., direct
language) than men (Leaper & Ayres, 2007). Over time, however, the focus has shifted
away from gender and onto alternative (i.e., extragender) influences, especially those
present in CMC. Whether in e-mail, newsgroup postings, blogs, discussion groups,
online chats, or other computerized settings, the language of men and women largely
depends on the specific circumstances and features of the technology and context (e.g.,
Colley & Todd, 2002; Fox et al., 2007; Herring, 1993; Huffacker & Calvert, 2005;
Palomares, 2004, 2008, 2009; Savicki, Kelley, & Ammon, 2002; Thomson, 2006). In
Downloaded from at UNIV CALIFORNIA DAVIS on March 18, 2013
Palomares and Lee 7
fact, the same aforementioned meta-analyses also found several factors that moderated
the gender effects often to an extent greater than gender alone (Leaper & Ayres,
2007). Language differences between men and women, thus, clearly exist, but they are
highly sensitive to extraneous factors that may increase, decrease, erase, or even reverse
the traditional gender-based patterns of use.
The emergence of three language features—references to emotion, apologies, and
tentative language—has been particularly vulnerable to contextual instability within
and across studies despite stereotypes and early conjectures that they are “feminine”
language forms. References to emotion, or language that includes any mention of a
feeling or emotion, have been indicted as typically associated with women’s language
(Mulac, Bradac, & Gibbons, 2001). Yet other research has shown that men reference
emotion more than women (Mulac, Seibold, & Farris, 2000), that men and women use
them equally (Thomson, 2006), and that their use depends on the salience of gender
identity and dyadic sex composition (Palomares, 2008). Examinations of apologies—
which some have construed as an indicator of politeness and a feminine language
style (Herring, 1993; Lakoff, 1975)—have yielded a similarly diverse array of differences
and similarities between men and women (O’Neill & Colley, 2006; Savicki,
Lingenfelter, Kelley, 1996; Tannen, 1990; Thomson, 2006). Tentative language signals
uncertainty, is typically associated with women (Herring, 1993; Lakoff, 1975), and like
apologies and references to emotion is contextually dependent (Brouwer, Gerritsen, &
De Haan, 1979; Carli, 1990; Palomares, 2008, 2009; S. A. Reid, Keerie, & Palomares,
2003; Tannen, 1990). We examined these three features because research frequently
employs them in CMC as stereotypically gender-based language forms.
Self-Categorization Theory
Notwithstanding inconsistent results among the three language features, an explanation
for the diverse collection of gender-based language manifestations is found in selfcategorization
theory (Turner, Hogg, Oakes, Reicher, & Wetherell, 1987).1 The basic
premise of the theory is that people mentally represent social groups as contextually
contingent prototypes or fuzzy sets of attributes that define in-group similarities in
contrast to out-group differences. People internalize the group prototype that is most
salient and relevant—a state called depersonalized. Prototypes operate not only to
describe but also to prescribe, such that depersonalization provides a normative selfdefinition
for how one should perceive and behave in a certain context.
When applied to gender and language phenomena (cf. Palomares et al., 2004), the
theory maintains that if people interact devoid of a gender distinction, then one’s gender
is irrelevant and gender-based language is unlikely to emerge; but if a gender categorization
is germane, then gender identity is applicable to one’s self-construal, and people
will behave according to the activated prototypical norms (Palomares, 2008;
S. A. Reid et al., 2003). Gender-relevant interactions, thus, increase the salience of
gender identity so that the prototype of intergender relations has significant consequences
for language use. Self-categorization theory has been relatively successful in
attempts to explain and predict a diverse array of linguistic behavior for men and women
Downloaded from at UNIV CALIFORNIA DAVIS on March 18, 2013
8 Journal of Language and Social Psychology 29(1)
in CMC. When sending an e-mail, for example, women referenced emotions significantly
more than men only if gender was salient because the prototype of gender salience
exploited supportiveness as a stereotypically feminine attribute (Palomares, 2008). We
formulated our expectations for the experiment based on self-categorization theory.
Performing Virtual Gender Identities
A limited number of studies have examined the online performance of a different gender.
The earliest scholarship highlighted intentional “gender swapping” on the Internet
(e.g., a man posing as a woman) and documented and described its natural occurrence.
People gender swap, for example, in text-based multiuser dungeons and similar online
groups for a range of reasons (Berman & Bruckman, 2001; Bruckman, 1993; Danet,
1996; Donath, 1999; McRae, 1995; Menon, 1998; Rheingold, 1993; Turkle, 1995; Van
Gelder, 1996). Assuming a different virtual gender identity has several sociological
and psychological implications (Herrmann, 2007; Kendall, 2000; E. M. Reid, 1991,
1995; Rellstab, 2007; Rodino, 1997) especially considering that a substantial portion
(40% to 60%) of online social-site members typically do so for some of their time
online (Roberts & Parks, 1999). Relatedly, Internet users can strategically ambiguate
their gender often via gender-neutral pseudonyms (Bechar-Israeli, 1995; Van Gelder,
1996). Gender equivocation, however, is more common among women than men
(Jaffee, Lee, Huang, & Oshagan, 1995; Jazwinski, 2001), likely because it assuages
gender biases that can occur in face-to-face interactions (Flanagin, Tiyaamornwong,
O’Connor, & Seibold, 2002; Koch et al., 2005). Research has also examined the detection
of real (Koch et al., 2005; Nowak, 2003; Thomson & Murachver, 2001) and false
(Herring & Martinson, 2004; Hills, 2000) gender identities in CMC.
Whereas most research on virtual gender identities has recorded its natural occurrence,
objectives, implications, and detection, recent examinations have studied the
communicative behaviors people manipulate when intentionally performing a false
gender. Such research has found that people seem to have control over macro forms of
communication (e.g., topic) more than molecular forms (e.g., tentative language). For
example, if told to pose as a different gender when interacting with an unknown partner
via e-mail, participants typically exploited gender-stereotypical topics while
having relatively little control over gender-typical syntactic and lexical choices (Hills,
2000). Likewise, in synchronous CMC, people successfully altered their topical content
when intentionally performing a different gender but ineffectively changed
molecular forms of communication; in fact, their molecular features actually gave
cues to their true gender despite their effective topic manipulations (Herring &
Martinson, 2004). Our experiment advances past research by not overtly instructing
people to communicatively perform a different gender identity. Instead, we manipulated
gendered avatars to test if people would automatically assimilate their language
to a virtual gender identity without explicit direction to do so.
A gendered (i.e., masculine or feminine) avatar can heighten the salience of gender.
Avatars are graphical self-representations in a computer-mediated environment that can
Downloaded from at UNIV CALIFORNIA DAVIS on March 18, 2013
Palomares and Lee 9
reveal social information in an otherwise cue-limited setting (Blascovich et al., 2002).
Interacting via avatars, for example, can impart levels of trust and intimacy similar to
an audio–video mode of mediated communication but more than text-only communication
(Bente, Rüggenberg, Krämer, & Eschenburg, 2008). Gender inferences of
anonymous others depend on their avatars even if avatar representations are known to
be arbitrary (Lee, 2007a). People prefer avatars that closely represent themselves over
less accurate digital representations, especially in terms of gender (Nowak & Rauh,
2005). In fact, avatars have behavioral consequences by inducing avatar-consistent communication:
In line with attractiveness stereotypes (cf. Langlois et al., 2000), intimacy
(e.g., self-disclosures) was greater for people represented by attractive than less attractive
avatars (Yee & Bailenson, 2007, Experiment 1). Likewise, in a second study that
capitalized on confidence stereotypes of tall people (cf. Young & French, 1996), participants
who assumed an avatar taller than their negotiation partner’s avatar were more
likely to decline their partner’s unfair offer than if their avatar was shorter. Given that
people heed avatars cognitively and behaviorally, a gendered avatar might affect
gender-based language because it yields a gender self-definition germane. According
to self-categorization theory, however, these linguistic consequences would depend
on the nature of the avatar and its ramifications for gender salience: A gendered selfrepresentation
in CMC will intimate the prototype for gender-based linguistic behavior.
Specifically, masculine avatars will implicate male-linked language norms, whereas
feminine avatars suggest female-typical language norms. As a result, people linguistically
assimilate to these communicative norms.
These effects, however, are likely more robust for women than men. Women tend
to be more responsive to gender salience than men are (Palomares, 2008; S. A. Reid
et al., 2003), and they tend to identify with their gender more strongly than men do
(Cameron & Lalonde, 2001). In fact, men were less likely than women to take a gendered
avatar into account when inferring an anonymous partner’s gender (Lee, 2007a).
Women also are more accurate when decoding others’ nonverbal communication and
are generally more sensitive to it than men are (Hall, 2006). Because women tend to
be particularly reactive to visual communicative stimuli and gender salience, we
expect a woman to use more stereotypically feminine language when her avatar is
consistent (i.e., feminine avatar) than inconsistent (i.e., masculine avatar) with her
true gender; yet the effect of this corresponding pattern for men will likely be less
extreme if it manifests at all. Thus, we present the following:
Hypothesis 1a-c: Women, but not men, use more gender-typical language—
(a) references to emotion, (b) apologies, and (c) tentative language—when
the gender of their avatar matches their true gender than when it mismatches.
We also tested the effects of a CMC partner’s gendered avatar because it too can
play an influential role in computerized interactions. In most circumstances, gender
differences are more likely in intergroup (i.e., mixed-sex) than intragroup (i.e., same-sex)
interactions. For example, women referenced emotion more than men when gender
Downloaded from at UNIV CALIFORNIA DAVIS on March 18, 2013
10 Journal of Language and Social Psychology 29(1)
was salient but chiefly in mixed-sex e-mail exchanges (Palomares, 2008); likewise,
gender differences in tentative language were present in intergroup but not intragroup
CMC (Palomares, 2009). Self-categorization theory accounts for such effects by
arguing that mixed-sex interactions render an intergender distinction more pertinent
than same-sex settings do, such that assimilation to the prototype of gender salience
becomes more likely (Hogg & Turner, 1987). We, therefore, might expect a partner’s
gendered avatar to affect gender-based language as well, which is analogous to other
research revealing partner-avatar effects for nongender groups. People in a virtual
environment, for example, maintained greater distance when encountering an avatar
of an ethnic minority than an avatar of an in-group member, especially if they held
implicit prejudice toward the minority out-group (Dotsch & Wigboldus, 2008).
Precisely predicting how another’s gendered avatar might interact with a gendered
graphical self-representation, however, is difficult because what constitutes “mixed
sex” is muddled when gendered avatars are introduced in CMC to represent anonymous
interactants. That is, whether people compare their true or virtual gender with
their partner’s gendered avatar can alter their inter-/intragroup determination. For
example, a woman who is represented by a masculine avatar when interacting with a
partner using a feminine avatar might consider the interaction to be intergroup if she
contrasts her and her partner’s avatars; whereas if she compares her partner’s avatar
with her actual gender, then she might conclude that the interaction is intragroup. In
fact, Lee (2007b) found that dyadic team members felt stronger group identification
when their avatars belonged to the same gender category (rather than different categories).
Such results suggest that perceptually salient, albeit explicitly arbitrary,
avatars can serve as a formative basis for an intra-/intergroup distinction. Nonetheless,
if and how self-other avatar comparisons have effects beyond fostering group
cohesion remains unclear in Lee’s study; that is, even when participants thought “My
partner and I are similar,” by virtue of the similar avatars, they might not have fully
embraced the specific identity represented in the avatars (“We are both masculine”),
especially considering that their avatar’s gender always mismatched their true gender
in the study. By examining social perceivers’ linguistic behavior as a function of their
own and their partner’s gendered avatars, the present study extends past work. Yet
given the difficulty gendered avatars present for ascertaining the inter-/intragroup
nature of an interaction in anonymous CMC, we ask this research question:
Research Question 1: Does the gender of a partner’s avatar influence (via either
main or interaction effects) gender-based language use?
Participants and Design
Participants were 157 undergraduates (74 men, 83 women) enrolled in communication
classes at a large, West Coast university. A 2 (participant’s gender: men vs. women) ×
Downloaded from at UNIV CALIFORNIA DAVIS on March 18, 2013
Palomares and Lee 11
2 (gender matching of participant’s avatar: match vs. mismatch participant’s true
gender) × 2 (gender of partner’s avatar: male vs. female) between-subjects design was
employed wherein participants completed a trivia game with an ostensible partner
both of whom were represented via gendered avatars in synchronous text-based CMC.
Avatar Manipulations
Two masculine and two feminine avatars manipulated self and partner representations.
An additional 50 undergraduate students (66% women) participated in a pretest to confirm
an effective manipulation of avatar gender. Participants first saw one of the four
cartoon characters and then indicated how feminine and masculine the character was on
10-point scales (1 = not at all masculine/feminine, 10 = very much masculine/feminine).
The femininity rating was reverse coded and then combined with the masculinity rating
to form a femininity–masculinity index (r = -.86, p < .001; range: 2-20). A 2 (participant
gender) × 2 (avatar gender) analysis of variance (ANOVA) established that male
characters were considered to be more masculine (M = 14.24; SD = 3.71) than female
characters (M = 5.16; SD = 1.95), F(1, 46) = 113.91, p < .001, hp
2 = .71. Furthermore,
one-sample t tests revealed that the attribution of masculinity to male characters was
significantly greater than the scale midpoint (11.00), t(24) = 4.37, p < .001, whereas
female characters were perceived as significantly less masculine (or more feminine)
than the scale midpoint, t(24) = -14.97, p < .001. There was no interaction between
participants’ and avatars’ gender, indicating that both men and women perceived the
avatars’ gender as intended, F < 1. The four avatars served to randomly manipulate
participants’ avatar gender and the partner’s avatar gender. A participant’s avatar was
never identical to his or her ostensible partner’s avatar in the main experiment.
Participants played a computerized trivia game with someone whom they believed to
be another study participant. To reduce participants’ suspicion about the purpose of the
experiment, they were first asked to choose a letter on the computer screen, ranging
from A to E, to determine the avatar (i.e., cartoon character) that would represent them
during the interaction. Unbeknownst to the participants, however, the character’s
gender was randomly predetermined to be either male or female regardless of their
true gender and the chosen letter. Once the participants’ avatar and their ostensible
partner’s avatar appeared on the computer screen, participants selected a number,
ranging from 1 to 10, to determine a set of questions to be asked during the game.
Regardless of the number chosen, however, the computer presented a fixed set of fastfood
trivia. For each multiple-choice question, participants indicated their initial
answer and confidence level and typed a comment to their partner. After participants
typed a comment, the participant’s and the partner’s characters showed their initial
responses, as illustrated in Figure 1. The partner’s responses were preprogrammed and
held constant across conditions, and their comments contained no apologies, tentative
Downloaded from at UNIV CALIFORNIA DAVIS on March 18, 2013
12 Journal of Language and Social Psychology 29(1)
language features, or references to emotion (e.g., “I have no clue,” “D seems too
obvious”). At this point, participants submitted their final answer and confidence
level, after which the computer presented the next question without revealing the correct
answer or the partner’s final response to the previous question. This procedure
was repeated for 12 unique questions that were held constant across all conditions.
Finally, participants were debriefed.
Language Coding
The comments that participants wrote to their ostensible partner during the trivia game
served as the source of gender-based language use. All comments formed a transcript
booklet with only a unique number identifying each participant’s transcript. Two
research assistants, who were blind to the design and hypotheses, underwent training
sessions where they learned definitions for, saw several examples of, and practiced
coding each language feature. Once well-trained and pretested for sufficient reliability,
the assistants individually coded all language features one at a time and then settled
disagreements via postcoding discussions. Across all language features the coders
agreed at a rate of at least 87% (Krippendorff’s as > .90).
The operationalizations of the three language variables were modeled after past
language and gender research (Palomares, 2008; S. A. Reid et al., 2003; Thomson &
Murachver, 2001). References to emotion were any mention of an emotion (e.g.,
happy, that should thrill you, mad, excited). Apologies were defined as a statement of
being sorry (e.g., I’m sorry, forgive me, I was wrong and won’t let it happen again).
Tentative language was defined as the combination of three unique language features
that indicate uncertainty and low confidence: hedges (e.g., might, pretty much, sort of,
Figure 1. Sample screen snapshot of avatars with responses: Participant with a male avatar
and a female partner character
Downloaded from at UNIV CALIFORNIA DAVIS on March 18, 2013
Palomares and Lee 13
maybe, probably), disclaimers (e.g., don’t trust me, but I’m not sure, I may be wrong,
who knows though) and tag questions (e.g., don’t you think? isn’t it? right?).
To ensure that the experimental task did not overtly favor one gender, we used fastfood
trivia whose gender neutrality was confirmed in previous studies (Lee, 2005).
Specifically, when asked to indicate how interested they were in the fast-food questions
(1 = not at all interested, 10 = very much interested), men (M = 2.89; SD = 2.42) and
women (M = 2.20; SD = 2.00) did not significantly differ, t(110) = 1.66, p = .10 (Lee,
2005, Study 1). In addition, participants directly rated how gender biased they thought
the questions were (1 = not at all gender biased, 10 = very much gender biased), and
the mean (M = 3.81; SD = 1.88) was significantly lower than the scale midpoint (5.5),
t(75) = -7.80, p < .001 (Lee, 2005, Study 3).
Hypothesis Tests
A series of 2 (participant gender) × 2 (participant avatar) × 2 (partner avatar) ANOVAs
was computed for (a) references to emotion, (b) apologies, and (c) tentative language.
One-tailed a priori contrasts tested any hypothesized differences (as indicated),
whereas two-tailed tests compared conditions when a difference was not expected or
when a possible difference was not explicitly hypothesized (Tabachnick & Fidell,
2007). Figure 2 displays the pertinent results.
References to emotion. A significant interaction emerged between participants’
gender and self-representation avatar, F(1, 149) = 5.64, p = .02, hp
2 = .03. No other
effects were statistically significant, all Fs < 1. Participants’ avatar had a greater
impact for women than men, which is consistent with Hypothesis 1a: Women used
more references to emotion when the character correctly represented their gender
(M = .84; SD = .99) than when it did not (M = .44; SD = .82), t(153) = 1.95, one-tailed
p = .03, hp
2 = .02; yet men’s references to emotion did not significantly vary across the
male (M = .54; SD = .82) and female (M = .87; SD = 1.10) avatars, t(153) = 1.50, p = .14.
When the interaction was decomposed within the self-representation conditions,
gender differences were more pronounced in the mismatched than matched avatar
condition. If participants’ character’s gender mismatched their true gender, then men
used more emotional references than did women, t(153) = 2.04, p = .04, hp
2 = .03;
when the avatar correctly represented their gender, women tended to reference emotions
more frequently than men, but this difference was not statistically significant,
t(153) = 1.39, p = .17.
Apologies. We found a significant interaction between participant’s gender and
avatar for apologies, F(1, 149) = 4.11, p = .04, hp
2 = .03. No other effects were statistically
significant, all Fs < 1. Hypothesis 1b received tentative support: Women were
Downloaded from at UNIV CALIFORNIA DAVIS on March 18, 2013
14 Journal of Language and Social Psychology 29(1)
more apologetic when the avatar matched their gender (M = .16; SD = .37) than when
it mismatched (M = .05; SD = .22), t(153) = 1.56, one-tailed p = .06, hp
2 = .02; whereas
men’s apologies did not statistically significantly differ across the two conditions
(match: M = .03, SD = .17; mismatch: M = .13, SD = .41), t(153) = 1.36, p = .18.
Within the matched self-representation condition, women used more apologies than
men, although this effect did not reach statistical significance, t(153) = 1.83, p = .07,
2 = .02. The same gender difference with mismatched avatars was not statistically
significant, t(153) = 1.08, p = .28.
Tentative language. There were no significant main or interaction effects on tentative
language use, all Fs < 1.84. Even though the interaction between gender and
self-representation failed to reach statistical significance, F(1, 149) = 1.75, p = .18, we
still tested Hypothesis 1c because omnibus tests are dispensable when specific predictions
exist (Rosenthal, Rosnow, & Rubin, 2000; Wilkinson & Task Force on Statistical
Inference, 1999). Supporting Hypothesis 1c, women were more tentative when a
female avatar matched their true gender (M = 1.20; SD = 1.25) relative to a mismatched
male character (M = .69; SD = 1.08), t(153) = 2.04, one-tailed p = .02, hp
2 =
.03. In contrast, men’s tentative language use was identical when the character either
correctly (M = 1.00; SD = .97) or incorrectly (M = 1.00; SD = 1.21) represented their
true gender. Comparing men and women within each self-representation condition,
however, yielded no significant effects, both ts < 1.20.
Figure 2. Effects of matched versus mismatched gendered avatars on gender-based
language use for men and women
Men Women Men Women Men Women
References to Emotion Apologies Tentative Language
Language Use
Match Mismatch
Participants’ Genered Avatar
Downloaded from at UNIV CALIFORNIA DAVIS on March 18, 2013
Palomares and Lee 15
Overall, the results suggest that a gender-matched avatar increases the likelihood of
gender-typical language use, whereas gender-mismatched avatars promote countertypical
language. That is, people not only communicatively perform gender when they
intentionally decide (Herring & Martinson, 2004) or are explicitly directed (Hills,
2000) to pose as a different gender, but they appear to adopt the language that conforms
to gendered norms that the visual cue of a gendered avatar intimates. A departure
from past studies, however, is that this linguistic assimilation to a virtual gender identity
is more likely among women than men: Past research did not demonstrate different
gender performances of male and female online users; rather, men and women alike
were able to change macro aspects of their communication (e.g., topic). Apparently,
men are capable of performing femininity communicatively when such acts are intentional
or explicitly researcher induced, but they are less likely to do so when the trigger
is a relatively subtle visual cue such as an avatar.
Self-categorization theory explains how the gender of a digitized self-representation
affects participants’ gender-based language: Because a gendered avatar implicated the
language appropriate for the context, people conformed to gender-based language
expectations. The theory suggests that such gender-based language norms were transmitted
via avatars that defined the prototype of gender salience. Specifically, a masculine
avatar implied male-typical language norms, just as a feminine avatar conveyed
female-typical language norms. Consequently, participants linguistically assimilated
to a virtual gender identity. Self-categorization theory also addresses how women are
especially more likely than men to use gender-typical language when the gender of
their avatar matches their true gender. That is, because women tend to be particularly
reactive to visual stimuli (Hall, 2006) and gender salience (Cameron & Lalonde, 2001),
they were more susceptible to gendered avatars than men were.
Our data also highlight a recent claim that gender-based language is highly dynamic
because of gender salience and its prototype. One should not ipso facto expect identical
or even highly similar patterns among all forms of gender-based language across contexts
(Palomares et al., 2004). We demonstrated that either countertypical or typical
gender-based language emerged depending on the prototype of gender salience induced
by avatars. In fact, although apologies and references to emotion manifested in the
same general pattern, tentativeness was only partly similar (see Figure 2). At the same
time, the relatively small effect sizes (<.04) of the current research seem to support the
gender similarities hypothesis that asserts men and women are primarily similar and
any differences between them are small and few (Dindia, 2006; Hyde, 2005). Metaanalyses
are compatible with this hypothesis (Hyde, 2005; Leaper & Ayres, 2007). The
overall differences we found between men and women were less frequent than the
moderator-produced effects. Taken together, even when some situational factors induce
gender differences in language style, the magnitude of such differences is relatively
small, suggesting that men’s and women’s linguistic behavior is more similar than different.
Moreover, given the small size of these effects, they are likely not readily
apparent in everyday interaction, as other research suggests (Mulac, 2006).
Downloaded from at UNIV CALIFORNIA DAVIS on March 18, 2013
16 Journal of Language and Social Psychology 29(1)
The current experiment also extends past work on the impact of gendered avatars
in CMC. In a sense, the findings that arbitrary gendered avatars shape people’s perceptions
of and behavioral responses to anonymous strangers in an otherwise cue-deprived
environment (e.g., Lee, 2007a, 2007b) are not surprising. That is, although participants
were likely to conform to a partner with a male avatar more than a partner with
a female avatar on male-oriented issues (Lee, 2007a) and identified more with a partner
whose avatar shared the same gender as their own avatar (Lee, 2007b), the absence
of individuating cues that would have enabled them to form more personalized, and
presumably more accurate, impressions about unknown partners likely fostered this
seemingly unreasonable reliance on random visual cues. However, our experiment
advances previous research by demonstrating that gendered self-representations significantly
modify individuals’ own language styles, which are supposedly more static
than perceptions of and conformity to complete strangers.
The level of automaticity of linguistic assimilation to a virtual gender identity is
unknown based on the current data; yet we imagine the process is relatively unconscious.
Herring and Martinson (2004) speculated and provided some evidence that
“unconscious use of gendered discourse styles can reveal one’s actual gender even
when one is [intentionally] performing a different gender (or trying not to give off any
gender cues)” (p. 427). In a similar study of the conscious performance of a different
gender, participants typically manipulated gendered topics successfully but less effectively
controlled gender-typical syntactic and lexical choices that tended to match
their true gender (Hills, 2000). Unlike this previous work, however, the focus of current
participants was likely on winning the trivia game rather than manipulating their
language. In addition, avatars were ostensibly assigned randomly. Consequently, participants
probably thought the gendered nature of the avatars was arbitrary and
peripheral to the game, as confirmed in postexperiment debriefings. Our findings that
people nonetheless altered their gender-based language in line with the relatively
subtle visual cues to gender identity, thus, seem to comport well with an automatic or
mindless argument.
That this process is unconscious is also consistent with other theorizing on genderbased
language. Mulac, Bradac, Palomares, and Giles (2009) distinguished between
gender-linked language stereotypes and schemata: Stereotypes about gendered communication
are accessible to conscious thought and focus on macro forms of communication
(e.g., topic), but schemata are implicit and primarily responsible for gender-based
language production. Coupling past research on the intentional or conscious performance
of gender that demonstrates accurate control over topics but failed control of
molecular behaviors such as language (Herring & Martinson, 2004; Hills, 2000) and
the current data that suggest an unconscious effect on language warrants a distinction
between gender-linked language schemata and stereotypes. Future research should
assess more directly the constituents and outcomes of gender-linked language stereotypes
versus schemata. Whereas this objective might be relatively straightforward for
stereotypes because their mental representations are explicit, doing so for schemata
might take some ingenuity. One possibility is to use a method similar to the assessment
of implicit prejudice (cf. Dotsch & Wigboldus, 2008).
Downloaded from at UNIV CALIFORNIA DAVIS on March 18, 2013
Palomares and Lee 17
Notwithstanding the previous theoretical supposition and implications, because we
did not directly measure nor manipulate gender salience (or any other cognitive processes),
our account of linguistic assimilation still awaits a more absolute empirical
validation. Clearly then, the absence of a gender salience measure is a limitation. Yet
to an extent, two points assuage this disadvantage. First, much other research demonstrates
that gender-based language is a function of gender salience (Palomares, 2004,
2008; S. A. Reid et al., 2003). Gender salience has even operated as a mediator of the
effects of a contextual stimulus on gender-based language (Palomares, 2009). In other
words, there is a clear cascading causal link from stimuli to gender salience to genderbased
language. Second, the feasibility of actually measuring gender salience is
questionable: Assessing gender salience might not have been practical or effective
because, as stated previously, we anticipate an unconscious process of the linguistic
assimilation to gendered avatars. In other words, unknown is the ability of an explicit measure
of gender salience to accurately gauge an unconscious process. Still, considering
that we cannot assume gender salience would have played the same causal role in the
current experiment as found in past research, some measure of gender salience might
have been useful herein.
Thus, employing an implicit measure of gender salience might allow a test of its
mediational effects thereby providing a more direct test of self-categorization theory’s
account. To do so, the linear order of effects should be well established by
measuring gender salience immediately before language production; although introducing
such a measure between the avatar assignment and language production
might prove pragmatically awkward if not difficult (at least in designs similar to the
current one). Another option is to implement gendered (i.e., masculine, feminine)
topics to heighten gender salience, which would yield subsequent language effects.
If gender salience was directly or indirectly manipulated somehow, then perhaps the
gender of the partner’s avatar would have also moderated language use. Thus,
researchers should seek to vary gender salience in ways other than using gendered
self-avatars because avatars seem to only subtly influence gender salience. Perhaps
when gender salience is unambiguously heightened, then partner–avatar or other
effects will emerge.
Another possible limitation is that the effects for women might have been more
robust than for men, not because of our proposed rationale, but because the three language
features examined are considered stereotypically feminine. In other words, if
features associated with men were also employed, then perhaps men would have displayed
more language variation depending on their gendered avatar. This issue,
however, is not likely a problem because other research has demonstrated that men’s
language can fluctuate in ways similar to women’s language variation. For example,
men and women alike used more references to emotion under certain conditions
(Thomson, Murachver, & Green, 2001). Likewise, men were more tentative than
women for feminine topics, just as women were more tentative than men for masculine
topics (Palomares, 2009). Admittedly though, the cues (i.e., gendered topic)
responsible for the language changes in Palomares were more explicit than in the current
research (i.e., avatars). Indeed, our rationale explicitly drew on the idea that
Downloaded from at UNIV CALIFORNIA DAVIS on March 18, 2013
18 Journal of Language and Social Psychology 29(1)
because avatars are subtle visual cues, to which women are likely more reactive or
sensitive than are men, linguistic assimilation would be greater for women than men.
Even so, future research might employ stereotypically masculine language features
(e.g., directives, references to quantity) along with feminine features when examining
men’s and women’s assimilation to gendered avatars to fully mitigate this concern.
The current article provided evidence that gender-based language use in CMC is
susceptible to the influence of arbitrarily assigned gendered avatars that represent
oneself, especially for women. In fact, prior to our work herein, extant research on
gender-based language production from a self-categorization theoretical perspective
had not included the influence of technological factors, such as avatars. That features
of CMC can change gender-based language is meaningful considering that gendered
forms of language are consequential for communicators: Tentative language encourages
judgments of incompetence and low status compared with direct styles (Carli,
1990; S. A. Reid et al., 2003), and references to emotion foster ratings of intelligence
and pleasantness (Mulac, 2006). Such language-effect outcomes are especially noteworthy
in CMC when other social cues are negligible and language plays a central role
in impression formation (cf. Walther, 1993, 1996). Additional research on the linguistic
assimilation to a virtual gender identity, therefore, would be advantageous to
increase the understanding of when, how, and why men and women communicate
similarly and differently.
Authors’ Note
An earlier version of this article was presented at the annual conference of the International
Communication Association in Chicago, 2009.
The authors thank Howie Giles and two anonymous reviewers for helping improve the effectiveness
of this article.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interests with respect to the authorship and/or publication
of this article.
This study was financially supported in part by the Institute of Communication Research,
Seoul National University.
1. The social identity model of deindividuation effects (SIDE model) is a specific version
of self-categorization theory focusing on CMC contexts (Postmes, Spears, & Lea, 1998,
Downloaded from at UNIV CALIFORNIA DAVIS on March 18, 2013
Palomares and Lee 19
2002), which is akin to other instantiations of the theory with a specific focus, such as the
self-categorization theory of social influence (Abrams & Hogg, 1990). Even though we
could have explicitly drawn on the SIDE model to the same avail, we chose to highlight
self-categorization theory because (a) doing so is consistent with past language and gender
research in CMC (cf. Palomares, 2004, 2008) and non-CMC contexts (S. A. Reid
et al., 2003) and (b) the SIDE model is deeply rooted in self-categorization theory and thus
would draw on the same explanatory mechanism to make identical predictions in the current
investigation (cf. Lea, Spears, & De Groot, 2001; Postmes & Spears, 2002).
Abrams, D., & Hogg, M. A. (1990). Social identification, self-categorization, and social influence.
European Review of Social Psychology, 1, 195-228.
Bechar-Israeli, H. (1995). From <Bonehead> to <cLoNehEAd>: Nicknames, play and identity
on Internet Relay Chat. Journal of Computer-Mediated Communication, 1(2), Article 2.
Retrieved April 25, 2008, from
Bente, G., Rüggenberg, S., Krämer, N. C., & Eschenburg, F. (2008). Avatar-mediated networking:
Increasing social presence and interpersonal trust in net-based collaborations. Human
Communication Research, 34, 287-318.
Berman, J., & Bruckman, A. S. (2001). The Turing Game: Exploring identity in an online environment.
Convergence, 7, 83-102.
Blascovich, J., Loomis, J., Beall, A. C., Swinth, K. R., Hoyt, C. L., & Bailenson, J. N. (2002).
Immersive virtual environment technology as a methodological tool for social psychology.
Psychological Inquiry, 13, 103-124.
Brouwer, D., Gerritsen, M. M., & De Haan, D. (1979). Speech differences between women and
men: On the wrong track. Language in Society, 8, 33-50.
Bruckman, A. S. (1993, August). Gender swapping on the Internet. Paper presented the meeting
of the Internet Society, San Francisco, CA. Retrieved April 23, 2008, from http://www
Cameron, J. E., & Lalonde, R. N. (2001). Social identification and gender-related ideology in
women and men. British Journal of Social Psychology, 40, 59-77.
Carli, L. L. (1990). Gender, language, and influence. Journal of Personality and Social Psychology,
59, 941-951.
Colley, A., & Todd, Z. (2002). Gender-linked differences in the style and content of e-mails to
friends. Journal of Language and Social Psychology, 21, 380-392.
Danet, B. (1996, February). Text as mask: Gender and identity on the Internet. Paper presented
at the conference on Masquerade and Gendered Identity, Venice, Italy. Retrieved April 25,
2008, from
Dindia, K. (2006). Men are from North Dakota, women are from South Dakota. In K. Dindia &
D. J. Canary (Eds.), Sex differences and similarities in communication (2nd ed., pp. 3-20).
Mahwah, NJ: Lawrence Erlbaum.
Donath, J. S. (1999). Identity and deception in the virtual community. In P. Kollock & M. Smith
(Eds.), Communities in cyberspace (pp. 29-59). London: Routledge.
Dotsch, R., & Wigboldus, D. H. J. (2008). Virtual prejudice. Journal of Experimental Social
Psychology, 44, 1194-1198.
Downloaded from at UNIV CALIFORNIA DAVIS on March 18, 2013
20 Journal of Language and Social Psychology 29(1)
Flanagin, A. J., Tiyaamornwong, V., O’Connor, J., & Seibold, D. R. (2002). Computer-mediated
group work: The interaction of member sex and anonymity. Communication Research, 29,
Fox, A. B., Bukatko, D., Hallahan, M., & Crawford, M. (2007). The medium makes a difference:
Gender similarities and differences in instant messaging. Journal of Language and Social
Psychology, 26, 389-397.
Hall, J. A. (2006). How big are nonverbal sex differences? The case of smiling and nonverbal
sensitivity. In K. Dindia & D. J. Canary (Eds.), Sex differences and similarities in communication
(2nd ed., pp. 59-81). Mahwah, NJ: Lawrence Erlbaum.
Herring, S. C. (1993). Gender and democracy in computer-mediated communication. Electronic
Journal of Communication, 3(2), Article 6. Retrieved June 11, 2008, from http://www.cios
Herring, S. C., & Martinson, A. (2004). Assessing gender authenticity in computer-mediated
language use: Evidence from an identity game. Journal of Language and Social Psychology,
23, 424-446.
Herrmann, A. F. (2007). “People get emotional about their money”: Performing masculinity in a
financial discussion board. Journal of Computer-Mediated Communication, 12(2), Article 8.
Retrieved April 18, 2008, from
Hills, M. (2000). You are what you type: Language and gender deception on the Internet. Unpublished
bachelor’s honors thesis, University of Otago, Dunedin, New Zealand. Retrieved April
17, 2008, from
Hogg, M. A., & Turner, J. C. (1987). Intergroup behavior, self-stereotyping and the salience of
social categories. British Journal of Social Psychology, 26, 325-340.
Huffacker, D. A., & Calvert, S. L. (2005). Gender, identity, and language use in teenage blogs.
Journal of Computer-Mediated Communication, 10(2), Article 1. Retrieved October 9, 2008,
Hyde, J. S. (2005). The gender similarities hypothesis. American Psychologist, 60, 581-592.
Jaffee, J. M., Lee, Y.-E., Huang, L.-N., & Oshagan, H. (1995, May). Gender, pseudonyms, and
CMC: Masking identities and baring souls. Paper presented at the annual conference of the
International Communication Association, Albuquerque, New Mexico. Retrieved October 9,
2008 from
Jazwinski, C. H. (2001). Gender identities on the World Wide Web. In C. R. Wolfe (Ed.),
Learning and teaching on the World Wide Web: A volume in the educational psychology
series (pp. 171-189). San Diego, CA: Academic Press.
Kendall, L. (2000). “Oh no! I’m a nerd!” Hegemonic masculinity on an online forum. Gender &
Society, 14, 256-274.
Koch, S. C., Mueller, B., Kruse, L., & Zumbach, J. (2005). Constructing gender in chat groups.
Sex Roles, 53, 29-41.
Lakoff, R. (1975). Language and women’s place. New York: Harper & Row.
Langlois, J. H., Kalakanis, L., Rubenstein, A. J., Larson, A., Hallam, M., & Smoot, M. (2000).
Maxims or myths of beauty? A meta-analytic and theoretical review. Psychological Bulletin,
126, 390-423.
Downloaded from at UNIV CALIFORNIA DAVIS on March 18, 2013
Palomares and Lee 21
Lea, M., Spears, R., & De Groot, D. (2001). Knowing me, knowing you: Anonymity effects on
social identity processes within groups. Personality and Social Psychology Bulletin, 27, 526-537.
Leaper, C., & Ayres, M. M. (2007). A meta-analytic review of gender variations in adults’ language
use: Talkativeness, affiliative speech, and assertive speech. Personality and Social
Psychology Review, 11, 328-363.
Lee, E.-J. (2005). Effects of the influence agent’s sex and self-confidence on informational
influence in computer-mediated communication: Quantitative vs. verbal presentation. Communication
Research, 32, 29-58.
Lee, E.-J. (2007a). Categorical person perception in computer-mediated communication:
Effects of character representation and knowledge bias on sex inferences and informational
social influence. Media Psychology, 9, 309-329.
Lee, E.-J. (2007b). Character-based team identification and referent informational social influence
in computer-mediated communication. Media Psychology, 9, 135-155.
McRae, S. (1995). Coming apart at the seams: Sex, text and the virtual body. Retrieved April
23, 2008 from
Menon, G. M. (1998). Gender encounters in a virtual community: Identity formation and acceptance.
Computers in Human Services, 15, 55-69.
Mulac, A. (2006). The gender-linked language effect: Do language differences really make a
difference? In K. Dindia & D. J. Canary (Eds.), Sex differences and similarities in communication
(2nd ed., pp. 219-239). Mahwah, NJ: Lawrence Erlbaum.
Mulac, A., Bradac, J. J., & Gibbons, P. (2001). Empirical support for the gender-as-culture
hypothesis: An intercultural analysis of male/female language differences. Human Communication
Research, 27, 121-152.
Mulac, A., Bradac, J. J., Palomares, N. A., & Giles, H. (2009). Exploring subjectivity in the genderlinked
language effect: A process model. In F. Gregersen, J. N. Jørgensen, M. Maegaard,
& P. Quist (Eds.), Language attitudes, standardization and language change (pp. 61-75).
Copenhagen, Denmark: Novus.
Mulac, A., & Lundell, T. L. (1980). Differences in perceptions created by syntactic-semantic
productions of male and female speakers. Communication Monographs, 47, 111-118.
Mulac, A., Seibold, D. R., & Farris, J. L. (2000). Female and male managers’ and professionals’
criticism giving: Differences in language use and effects. Journal of Language and Social
Psychology, 19, 389-415.
Murachver, T., & Janssen, A. (2007). Gender and communication in context. In A. Weatherall,
B. M. Watson, & C. Gallois (Eds.), Language, discourse and social psychology (pp. 185-205).
London: Palgrave Macmillan.
Nowak, K. L. (2003). Sex categorization in computer-mediated communication (CMC): Exploring
the utopian promise. Media Psychology, 5, 83-103.
Nowak, K. L., & Rauh, C. (2005). The influence of the avatar on online perceptions of anthropomorphism,
androgyny, credibility, homophily, and attraction. Journal of Computer-Mediated
Communication, 11(1), Article 8. Retrieved April 30, 2008, from
O’Neill, R., & Colley, A. (2006). Gender and status effects in student e-mails to staff. Journal
of Computer Assisted Learning, 22, 360-367.
Downloaded from at UNIV CALIFORNIA DAVIS on March 18, 2013
22 Journal of Language and Social Psychology 29(1)
Palomares, N. A. (2004). Gender schematicity, gender identity salience, and gender-linked language
use. Human Communication Research, 30, 556-588.
Palomares, N. A. (2008). Explaining gender-based language use: Effects of gender identity
salience on references to emotion and tentative language in intra- and intergroup contexts.
Human Communication Research, 34, 263-286.
Palomares, N. A. (2009). Women are sort of more tentative than men, aren’t they? How men and
women use tentative language differently, similarly, and counter-stereotypically as a function
of gender salience. Communication Research, 36, 538-560.
Palomares, N. A., Reid, S. A., & Bradac, J. J. (2004). A self-categorization perspective on
gender and communication: Reconciling the gender-as-culture and dominance explanations.
In S. H. Ng, C. N. Candlin & C. Y. Chiu (Eds.), Language matters: Communication, identity,
and culture (pp. 85-109). Kowloon, Hong Kong: City University of Hong Kong Press.
Postmes, T., & Spears, R. (2002). Behavior online: Does anonymous computer communication
reduce gender inequality? Personality and Social Psychology Bulletin, 28, 1073-1083.
Postmes, T., Spears, R., & Lea, M. (1998). Breaching or building social boundaries? SIDEeffects
of computer-mediated communication. Communication Research, 25, 689-715.
Postmes, T., Spears, R., & Lea, M. (2002). Intergroup differentiation in computer-mediated communication:
Effects of depersonalization. Group Dynamics: Theory, Research, and Practice,
6, 3-16.
Reid, E. M. (1991). Electropolis: Communication and community on Internet relay chat.
Unpublished manuscript, University of Melbourne. Retrieved April 25, 2008 from http://
Reid, E. M. (1995). Virtual worlds: Culture and Imagination. In S. Jones (Ed.), Cybersociety:
Computer-mediated communication and community (pp. 164-183). Thousand Oaks, CA: Sage.
Reid, S. A., Keerie, N., & Palomares, N. A. (2003). Language, gender salience, and social influence.
Journal of Language and Social Psychology, 22, 210-233.
Rellstab, D. H. (2007). Staging gender online: Gender plays in Swiss Internet Relay Chats.
Discourse & Society, 18, 765-787.
Rheingold, H. (1993). Virtual community: Homesteading on the electronic frontier. Reading,
MA: Addison-Wesley.
Roberts, L. D., & Parks, M. R. (1999). The social geography of gender-switching in virtual
environments on the Internet. Information, Communication & Society, 2, 521-540.
Rodino, M. (1997) Breaking out of binaries: Reconceptualizing gender and its relationship to
language in computer-mediated communication, Journal of Computer-Mediated Communication,
3(3), Article 3. Retrieved June 12, 2008, from
Rosenthal, R., Rosnow, R. L., & Rubin, D. B. (2000). Contrasts and effect sizes in behavioral
research: A correlational approach. New York: Cambridge University Press.
Savicki, V., Kelley, M., & Ammon, B. (2002). Effects of training on computer-mediated communication
in single or mixed gender small task groups. Computers in Human Behavior, 18,
Savicki, V., Lingenfelter, D., & Kelley, M. (1996). Gender language style and group composition
in Internet discussion groups. Journal of Computer-Mediated Communication, 2(3),
Article 5. Retrieved June 12, 2008 from
Downloaded from at UNIV CALIFORNIA DAVIS on March 18, 2013
Palomares and Lee 23
Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Boston: Allyn
& Bacon.
Tannen, D. (1990). You just don’t understand: Women and men in conversation. New York:
William Morrow.
Thomson, R. (2006). The effect of topic of discussion on gendered language in computer-mediated
communication discussion. Journal of Language and Social Psychology, 25, 167-178.
Thomson, R., & Murachver, T. (2001). Predicting gender from electronic discourse. British
Journal of Social Psychology, 40, 193-208.
Thomson, R., Murachver, T., & Green, J. (2001). Where is the gender in gendered language?
Psychological Science, 12, 171-175.
Turkle, S. (1995). Life on the screen: Identity in the age of the Internet. New York: Simon &
Turner, J. C., Hogg, M. A., Oakes, P. J., Reicher, S. D., & Wetherell, M. S. (Eds.). (1987).
Rediscovering the social group: A self-categorization theory. New York: Basil Blackwell.
Van Gelder, L. (1996). The strange case of the electronic lover. In R. Kling (Ed.), Computerization
and controversy (2nd ed., pp. 533-546). San Diego, CA: Academic Press.
Walther, J. B. (1993). Impression development in computer-mediated interaction. Western Journal
of Communication, 57, 381-398.
Walther, J. B. (1996). Computer-mediated communication: Impersonal, interpersonal, and
hyperpersonal interaction. Communication Research, 23, 3-43.
Wilkinson, L., & Task Force on Statistical Inference. (1999). Statistical methods in psychology
journals: Guidelines and explanations. American Psychologist, 54, 594-604.
Yee, N., & Bailenson, J. (2007). The Proteus effect: The effect of transformed self-representation
on behavior. Human Communication Research, 33, 271-290.
Young, T. J., & French, L. A. (1996). Height and perceived competence of U.S. Presidents.
Perceptual and Motor Skills, 82, 1002.
Nicholas A. Palomares (PhD, University of California, Santa Barbara, 2005) is an assistant
professor in the Department of Communication at the University of California, Davis. His
research examines the cognitive structures and processes involved in goal detection and genderbased
language use. His work has been published in various journals, such as Human
Communication Research, Communication Research, Communication Monographs, and the
Journal of Language and Social Psychology.
Eun-Ju Lee (PhD, Stanford University, 2000) is an associate professor in the Department of
Communication at Seoul National University, Seoul, Korea. Her research foci include social
cognition and social influence in computer-based communication. Her work has appeared in
many journals, such as Human Communication Research, Communication Research, the Journal
of Communication, and Media Psychology.
Downloaded from at UNIV CALIFORNIA DAVIS on March 18, 2013


Place an order today and get 13%Discount (Code GAC13)