Effects of Perceived Gender on Perception of Stereotype Conformity in
Micropublishing
A thesis submitted to the faculty of
San Francisco State University
In partial fulfillment of
The Requirements for
The Degree
Masters of Arts
In
Psychology: Research Psychology
by
Tameem Jonathan El-Bizri
San Francisco, California August 2011
1
Copyright by
Tameem Jonathan El-Bizri
2011
2
CERTIFICATION OF APPROVAL
I certify that I have read Effects of Perceived Gender on Perception of Stereotype
Conformity in Micropublishing by Tameem Jonathan El-Bizri, and that in my opinion
this work meets the criteria for approving a thesis submitted in the partial fulfillment
of the requirements for the degree: Masters of Arts in Psychology: Research
Psychology at San Francisco State University
Dr. Charlotte Tate
Professor of Psychology
Dr. Avi Ben-Zeev
Professor of Psychology
3
Effects of Perceived Gender on Perception of Stereotype Conformity in
Micropublishing
Jonathan Tameem El-Bizri
San Francisco, California
2011
ABSTRACT: Micropublishing, the primary process provided by social network
services, has become the most prevalent form of Computer Based Communication.
This experiment examined how gender, perceived within a micropublishing
environment, affects perception of personality.
After completing an intake survey, 29 participants were ostensibly directed to
subscribe to a fellow participant's micropublishing stream on the Twitter.com social
network service for two weeks. In actual fact, all participants received identical
messages, served from individual twitter accounts, with either a male or female
avatar photo. Upon completion, the participants rated their ostensible partners on a
subset of Prescriptive and Proscriptive Traits developed by Prentice and Carranza
(2002).
No significant results were found in the primary measure. Reasons for this finding
are discussed.
4
I certify that the Abstract is a correct representation of the content of this thesis.
Chair, Thesis Committee
Date
5
PREFACE AND ACKNOWLEDGEMENT
I would like to thank Professors Tate and Ben Zeev, Tarah Denehy, Katharine
Sorenson and the Spring 2011 Colloquium class for providing invaluable guidance,
direction and feedback in the refinement of this experiment.
iv
Table of Contents
Introduction .................................................................................................................................................... 1
Perception of presentation in Computer Mediated Communication ............................... 1
Gender in computer mediated communication ......................................................................... 2
Method .............................................................................................................................................................. 5
Participants .................................................................................................................................................. 5
Materials ...................................................................................................................................................... 5
Measures ..................................................................................................................................................... 5
Primary Measure. ............................................................................................................................... 5
Intake process. ..................................................................................................................................... 7
Daily process....................................................................................................................................... 10
Results ............................................................................................................................................................. 11
Exploratory Data Analysis ................................................................................................................. 11
Primary Measures ................................................................................................................................. 12
Secondary Measures ............................................................................................................................ 12
Discussion ...................................................................................................................................................... 13
Future directions .............................................................................................................................. 15
References ..................................................................................................................................................... 16
Appendix A: Tweets Sent ........................................................................................................................ 18
Tables............................................................................................................................................................... 20
Table 1 - Independent Means Test of Individual Trait Scores ........................................... 20
v
Table 2 - T-Test of Trait Groups, Female .................................................................................... 22
Table 3 - T-Test of Trait Groups, Male.......................................................................................... 23
vi
LIST OF TABLES
Table
Page
1.
Independent Means Test of Individual Trait Scores.............................22
2.
T-Test of Trait Groups, Female......................................................................24
3.
T-Test of Trait Groups, Male...........................................................................25
vii
LIST OF APPENDICES
Page
A. List of Tweets Sent................................................................................................... 20
viii
Introduction
The aim of this study is to explore how gender stereotypes operate in the realm of
web-based micropublishing streams. The term micropublishing stream refers to an
ongoing inventory of short text messages delivered via social networking and
micropublishing sites, such as Facebook, MySpace, Meebo or Twitter. These are
known colloquially on the various social network services as “Status Updates” or
“Tweets.”
Perception of presentation in Computer Mediated Communication
Computer mediated communication (CMC) comprises a rapidly growing proportion
of social interactions and is consequently the medium for a growing proportion of
social perceptions. Micropublishing has become the most prevalent form of CMC,
with the Nielsen media research company finding that one in every four minutes
online is spent on micropublishing aggregation sites, such as social networks
(Nielsen Wire, 2010). The same study found that U.S. individuals spend an average of
6 hours and 13 minutes a month publishing and consuming micropublishing streams
on social networking websites, as of May 2010. Additionally, of all U.S. households
with internet connectivity, 75% visited a social networking site that month, and that
55% of U.S. adults have one or more social networking accounts.
Baron (1998) states that the online world is “a communication domain with its own
linguistic structure and social consequences” (p.166). So far, much exploration of this
new domain has focused on the presentation of people interacting via CMC tools.
Specifically, research has focused on self-presentation on personal web pages (Hua
Qian & Scott, 2007) and in social network profiles(Salimkhan, Manago, & Greenfield,
2010), impression management on dating sites (Toma & Hancock, 2010), and
presentation behaviour when interacting in online spaces(Hammond, 2010).
1
However, interactions involve not only the presentation of oneself, but also the
perception of the presentations made by others, and much less attention has been
placed on this 'other half' of the interactive process. From the research performed, it
is clear that the impressions we form are not necessarily the ones we might form
from conventional interactions (Jacobson, 1999). In regards to certain traits of
character, people may be more accurate than when evaluating others, or during real
world interactions (Markey & Wells, 2002). It is clear that there are differences in
how people perceive others online. The process appears nuanced, and influenced by
routine misassumptions that endure after initial orientation to the new
communication form. With conflicting results, there is room for further study of the
process of personality conception online, to determine where these conceptions
spring from, and what biases we may bring to the situation.
Gender in computer mediated communication
"A preponderance of the literature indicates gender differences in written
communication," write Sussman and Tyson (2000). They found that gender based
differences in communication transcend differences between new and old mediums
of the written form. These differences have also been noted by Mazman (2011) in the
use of social networks. Researchers have observed a number of gender-specific
networking behaviors in online settings (Bond, 2009), despite a lack of differences
between the genders in regards to publishing and interaction frequency in
micropublishing environments (Berhane & Torey, 2011).
Men and women who utilize these communication mediums do so at similar levels of
engagement, but in markedly different ways. This has led some scholars to argue that
gender makes a difference (Heil & Piskorski, 2009). What sets genders apart in
regards to their interactions online? Gender schema theory may provide us with a
model to investigate this phenomenon. According to Sandra Bem’s gender schema
theory (Bem, 1974), people have a generalized tendency to understand and process
2
behaviors based on gender-linked associations that constitute the gender schema.
This schema works at the level of organizing information about oneself, as Bem
(1974) originally argued, and also seems to work in the manners by which people
understand each other. Since people present gender-associated behavior when
online, it is logical to assume that our conception of others is, in part, mediated by
their presentation of gender.
This assumption that gender schemas operate in people’s perception of others has
been borne out by research. Prentice and Carranza (2002) had participants indicate
the typicality and desirability of traits for men and women and the desirability of
traits for people in general. They found that the qualities that are expected from each
gender are also the ones that are attributed to each gender; gender stereotypes are
ascriptive as well as prescriptive.
One's online identity is typically comprised of the actions performed online along
with the information provided about oneself (Turkle, 1997, p. 177). Typically, this is
in the owner information for one’s social network or micropublishing account.
Frequently, gender identity is conveyed with an image that is supplied to represent
the self in visual form. I expect that manipulation of the gender presented by the
avatar and owner information of a micropublishing account will alter the perception
of the traits presented by the micropublishing entity according to the gender
stereotype expectations expressed by the participants.
Prentice and Carranza’s study also provides a framework for my investigation: a
subselection of the trait list they developed will be used as my primary measure. This
subselection comprises of traits with the largest differences between the genders in
expected typical behaviors and in the strength of positive or negative judgment
ascribed to the trait. This strength of negative or positive judgment is known as
proscriptiveness (negative) or prescriptiveness (positive).
3
Hypothesis 1
The gender identity presented by the avatar and owner information of a
micropublishing account will alter the perception of personality traits exhibited by
the publishing entity, as measured by the endorsed perception of personality traits
selected for their gender-association and prescriptiveness/proscriptiveness.
Additionally, I have an opportunity to search for potential sources for any correlation
that help to form such preconceptions. Research into entitativity – the perception of
human groups as abstractions, apart from their individual members – has shown that
“people who believe that human attributes are immutable... are more likely to
endorse social stereotypes and to explain [social stereotypes] with reference to
innate factors” (Bastian & Haslam, 2006, p. 228).
Hypothesis 2
Gender based differences in perception of the micropublishers personality will
covary with their endorsement of the strength of the micropublisher’s gender’s
entitativity.
4
Method
Participants
Twenty Nine participants (14 men, 15 women) were recruited from summer classes
at San Francisco State, along with online recruitment comprising of opportunity
sample from friends of friends of the investigator via http://facebook.com, along
with
posts
to
the
public
news
aggregation
site
http://reddit.com
and
http://twitter.com. The sample included 22 participants who identified as Caucasian,
3 as Asian, 1 as Latino, 1 East Indian, 1 Mixed Asian, and one participant that declined
to state a racial category.
Participants were required to be 18 years in age or older, have experience with social
networking sites and daily access to the internet for the duration of their
participation (two weeks).
Materials
Using software developed in Flash, PHP and the JavaScript programming
languages, along with an SQL database server, and making use of the Twitter
Application Programming Interface (API) and the Qualtrics survey administration
service, I developed a system for managing and delivering the experiment. The
system processes incoming participants, delivers the experimental medium through
Twitter.com’s micropublishing platform, and removes members who have completed
their participation in the experiment, directing them via email to the debriefing
survey and script.
Measures
Primary Measure.
My primary measure is a sub-set of Prentice and Carranza’s (2002) Measure of
Prescriptive and Proscriptive Gender Traits. For this study, I have used the 20 traits
5
for each gender where Prentice and Carranza found the largest difference between
the prescriptive/proscriptiveness ascribed to the trait for the respective gender as
compared to people at large. Since nine of the traits chosen presented a difference for
both genders, only 31 individual traits were actually used to provide 20 for each
gender.
During intake, experimental participants were asked to rate the following question
for each trait: "How socially valued do you think each one of the following
characteristics is in ADULT AMERICANS, on a scale from 1 (NOT socially valued) to 9
(VERY socially valued)?" performing this rating task for 'Adult American Males',
'Adult American Females' and 'Adult Americans'. During the outtake, they were asked
to "think of the personality of the person whose has been sharing their tweets" and
rate the same list of traits in regard to this.
Prentice and Carranza's original study used the term "valued" rather than "socially
valued." However, during pilot studies, multiple participants reported perceiving a
sexual charge in this term; they were unclear whether value was intended to imply
value as a mate (to themselves, or to a person attracted to the gender in question) or
value as a member of society. Consequently, I replaced the term with one considered
less ambiguous by the pilot group participants.
Secondary Measures.
Experimental participants performed an entitativity rating task: rating a list of
human groups, such as “members of an orchestra” or “women” on a 1 to 9 scale, with
1 meaning ‘not a group at all’, and 9 meaning ‘very much a group.’ The responses to
the human categories representing the male and female genders were the only
measure of interest: the remainder of the ten categories were diversionary. The ten
categories comprise of a modified subset of the category list used by Lickel, Hamilton,
and Sherman (2001) and Pickett and Perrott (2004).
6
Experimental participants also completed the Ambivalent Sexism Inventory (Glick &
Fiske, 1996), which measures sexist attitudes toward women as a target group, or the
Ambivalent Sexism Towards Men Inventory (Glick & Fiske, 1999), dependent on the
experimental group to which they were assigned. Specifically, participants who were
assigned to interact with a female gendered twitter account being administered the
Ambivalent Sexism Inventory, and participants assigned a male gendered twitter
account receiving the Ambivalent Sexism Towards Men Inventory.
Additional data on demographics, technology use and online social network activity
were also collected.
Intake process.
Participants were directed to a website ( http://quizitwithscience.com/twitterstudy
), which began the intake process and assigned them to either a yoking group or one
of two experimental groups. Assignment was dependent on establishing a minimum
of five yoking group members. Once this condition was fulfilled, assignment was
random between the experimental groups. Additionally, the yoking group, being
among the first members recruited for the study, were only active for the first two
weeks of the experiment, following which their work was reused.
Experimental group intake.
Members assigned to the experimental group were directed to a modified version of
Prentice and Carranza’s (2002) Measure of Prescriptive and Proscriptive Gender
Traits described in the Measures section above.
If they failed to complete all the directions within 24 hours, they received an
automated message reminding them to do so.
Once an experimental participant completed the intake script, they were informed
that they would be connected to another participant in the experiment, and
7
instructed to set up a reciprocal 'follow' from their twitter account to this
participant's. This instruction meant that they received any messages posted to
twitter from this other person in their micropublishing 'stream' (along with the
messages published by any other people that they are 'following' on the twitter
system). They are to follow this twitter stream for the duration of the experiment.
In actual fact, the twitter account they are interacting with is not a fellow participant,
but a surrogate for the experimental system. Messages were posted to the account by
the system from among those messages vetted by the yoking group.
One experimental group received messages from an account with a female avatar.
The other received messages from an account with a male avatar: this is my
experimental manipulation. Apart from the gender of the avatar, these accounts have
been set up to be as identical as possible, with only the gender of the account avatar
differing. All the accounts names start with the 'Jamie', followed by a three or four
digit number, such as "@Jamie445" and "@Jamie3425".
To maintain privacy and believability, each participant interacted with their own
experimental twitter account, rather than a single twitter account for each of the two
experimental groups: the twitter system would otherwise make the participants
visible to each other. The experimental twitter accounts were set up with privacy
enabled within the twitter system, so they cannot be viewed in searches or in public
twitter spaces. This was to ensure that participants did not come across messages
from experimental accounts other than their own in their everyday use of twitter.
Yoking group intake.
Members assigned to the yoking group were directed to the same Intake briefing and
questionnaire as the experimental groups, and then instructed in their daily task for
the experiment. When the yoking group members logged into the experimental site
to perform their task for the day, they were presented with a web page that
8
mimicked the look and feel of a Twitter account page, showing the messages that
were published to that account, alongside the account owners screen name, avatar
and information. In addition, a dialog box floating over the page, asked participants
to: "Please select the tweet that you think was most likely to be written by the person
who tweets are listed on this page." The dialog box also listed three tweets for the
participant to select from.
In actual fact, the messages listed on the page are those that were selected for use in
the experiment, and their selection during the yoking group task each day
determined the tweets published on subsequent days. (The messages the yoking
group viewed on the initial day of the experiment were selected by the
experimenter.) The purpose of this deception was to ensure that yoking groups
participants compared the messages they evaluated and selected from previous
experiences of online statements and behavior, rather than those of a person of their
own invention. This was intended to improve the believability of the twitter
messages selected. Like the experimental groups, the yoking group is divided into
male and female groups, with each seeing a male or female avatar on their task page,
respectively.
The messages that the yoking group participants vetted were selected from publicly
published tweets in the main twitter timeline, tweet tags local to San Francisco and to
the school of San Francisco, and other messages appropriate for the message stream
created. Effort was taken to make the messages believable while avoiding topics that
could be considered controversial or polarizing, that could consequently effect the
study. A complete list of the tweets used in the experiment, along with a timeline of
their publication is presented as the appendix. The yoking group members were
asked to perform the selection task five times each day, after which they are thanked
for their participation and reminded to return for next day’s task.
9
Daily process
Daily tally.
Each night, at 10 pm, the experimental system evaluated the activities of the day. The
system recorded which five of the fifteen messages presented to the yoking group
had received the most votes that day, marked these as ready for delivery and
determined the number of messages to publish on the following day, along with the
timing for those messages. The remaining messages were discarded.
The system randomly sends between 1 and 4 messages each day, setting a time
between 9am and 9pm to send them, and assigning a tweet to each time. The timing
and messages were then reviewed by the experimenter each night, to ensure that
tweets with time or date specific contexts (e.g. “Thank god it’s Friday"; "That was a
very long morning...”) were sent at an appropriate time. The system then prepared
the next set of 15 tweets for evaluation by the yoking group on the following day. At
this time, the system also administered the participants by removing those that had
completed their two weeks in the experiment, and sending them the debriefing
email; by reminding any new participants who have yet to 'follow' the surrogate
twitter account assigned to them to do so; and, finally by emailing a report of the
day's activity to the experimenter for review.
Once two weeks of messages have been sent, the system no longer assigns new times
for messages. Instead, it re-publishes the messages in the order, and at the daily
times already established. Since no participants is in the experiment for longer than
two weeks, this ensures that every participant is sent the same set of messages,
though not necessarily starting at the same point in the message queue. A list of the
messages sent, and the times they were sent is attached as Appendix A.
Message Delivery.
10
The next day, the system delivered the messages to the experimental groups on a
schedule described above. Additionally, it sent reminders to members of the
experimental group to visit their twitter stream. This is performed at a random time
each day. It was hoped that varying the time of the reminder would improve
responsiveness to it.
Conclusion of the experiment.
Participants who had completed their two week stint in the experiment were marked
in the database, so they would no longer receive tweets, and received an email
directing them to complete the concluding survey and debriefing. The concluding
survey asked participants to rate the micropublisher on the subset of items from
Prentice and Carranza’s (2002) Measure of Prescriptive and Proscriptive Gender
Traits in regards to the micropublisher, as described in the Measures section.
Results
Exploratory Data Analysis
Twenty Nine participants completed the experiment. Fifteen participants (7 women,
9 men) were in the male avatar condition and fourteen participants (8 women, 6
men) were in the female avatar condition.
No participant reported spending fewer than two hours online each day. The
majority of participants reported that they spend more than three hours. They
considered themselves 'experienced' or 'very experienced', compared to their peers,
with Social Network Services (M = 1.57, SD = 0.77) and with computers (M = 1.7, SD =
.952), Most of the participants were already users of the twitter platform, and 20%
considered it their primary social network service. Forty percent used it 'frequently'
in addition to additional platform.
11
In order to establish a baseline for the trait ratings that comprise the primary
measure, the median between the distribution for male and female ratings for each
trait was calculated and the individual participant’s ratings for each item was
normalized against this, giving each score as a difference from the mean. This
baseline allowed comparison of the differences between the scores in the two groups.
Primary Measures
An independent samples t-test of the 31 traits measures uncovers two traits with pvalues of less than .05: Business Sense (p = .025) and Helpful (p = .030). Stubborn also
approached significance (p = .054). However, considering the number of traits tested,
these results are too weak to be considered of any consequence. Type I error rate
increase across 31 comparisons requires an adjustment of only reporting effects less
than .0016 (.05 divided by 31).
To counteract the comparison-wise Type I error rate increase, the traits were
grouped into the eight categories originally defined in Prentice and Carranza's paper.
These eight categories are summarized in Table 1. Reliability analysis was performed
on each group. (Also see Table 1). Five of the eight trait groups presented a
Cronbach's alpha between 0.7 and 0.8, and can be considered suitable for averaging
into a composite score for the trade group.
The three that did not meet this criteria were Male Relaxed Proscriptions, Male
Intensified Proscriptions and Female Intensified Proscriptions. Removing Solemn from
Male Relaxed Proscription trait list increased the group's Cronbach's Alpha to 0.789.
The other two groups could not be made internally consistent in this manner, and
were dropped. However, a t-test of the six remaining qualifying trait groupings (see
Table 1) also showed no significant differences between the two experimental groups
on any of the groupings created.
Secondary Measures
12
Since evidence could not be found supporting the primary hypothesis, secondary
measures dependent on the primary measure could not be analyzed.
Discussion
I observed no significant effect from the primary measure of the experiment and
must retain the null hypothesis. I will now discuss the limitations of the experiment
that may have resulted in this lack of observable effect, presuming that an effect
existed to be observed.
Most likely, the effect I was trying to measure is simply too weak for the experimental
design and the sample size recruited. The fact that only three of the 31 individual
measures and none of the 6 combined scores even approached statistical significance
indicates that the comparisons suffered from low statistical power. To further
illustrate this, a power analysis to determine a priori sample size indicates that for an
effect size of d = .30 at 80% power (i.e., being able to detect this effect at this size 4
out of 5 times), I would need a total sample size of N = 352 (or 176 people in each
condition). A post hoc power analysis shows that power in my study was 13%-extremely low.
Could the experimental medium have been more believable? Maintaining the
environmental validity of the experiment hinged on convincing participants that they
are interacting with a real person. However, the experimental design, and the
environment in which it was performed put limitations on convincing the experiment
could be. Since the experiment relies on sending identical messages from two
accounts presenting a different gender, any references to gender had to be avoided in
the messages themselves. The very absence of gender cues these may have reduced
the environmental validity of the experiment. Additionally, the messages that were
sent had to be non-specific in nature, not only avoiding comments that would unveil
gender, but also avoiding any topics that might polarize participant opinion.
13
Messages also meeting with the requirements of the Institutional Review Board in
regards to treatment of human subjects and avoid any comments or messages that
could cause distress to the participants. Consequently, the message stream mainly
comprised of personal observations and comments on day to day life--not the most
stimulating reading--though well within a common use of the micropublishing
medium: "pointless babble" comprises 40% of messages published on the twitter
service (Pear Analytics, 2009).
Avoiding issues that might compromise the deception underlying the experiment
may have resulted in presenting the participants with a stilted, one-dimensional
personality. However, comments left during the experimental debriefing survey
suggest that the process succeeded in creating a believable, though less than genial
person. Participants that chose to comment mostly assumed the lack of meaningful
messages was due to the person's lack of experience with, or eloquence of use of, the
micropublishing medium. One participant indicated, "Clearly my partner was a
newbie on twitter." Another commented, "My matched account was boring and plain.
Shared empty tidbits of her daily life and I would not have followed her on normal
conditions." A third indicated, "I couldn't wait for the study to end so I wouldn't have
to read [her tweets] anymore. She is probably a nice person; she was clearly not
malicious or meanspirited, but she also was vapid, self-absorbed, and horribly dull.
However, this is pretty much the content of the majority of social networks."
When participant were asked, "Did you think that any of the messages you were
reading were fake messages?" most of the experimental participants responded that
they did think so. However 20% (seven participants) indicated that they did not, and
ostensibly assuming that the interaction they had was a genuine one, with another
participant in the study. Furthermore, due to the wording of the question, I do not
know how much of the interaction was unconvincing to other participants. Some
participants may have assumed that some of the messages were contrived and that
14
the interaction was otherwise genuine. Since the experiment was able to achieve a
level of environmental validity, as far as some of the participants were concerned,
any shortcomings in environmental validity may be slight. Nonetheless, believability
may still have had played a part in the lack of effect observed.
Future directions
Repeating the experiment with more participants is most obvious next step to take in
this line of inquiry. The effect I am measuring, if present, is too weak for examination
with the 29 participants we were able to recruit for this study. Other than the small
sample size, the weakest link in the experimental design appears to be the reliance
on misdirection to provide the experimental manipulation. Eighty percent of the
participants did not feel that the interaction they were having was genuine. An
experimental design that analyzed actual interactions between participants, rather
than a simulation of interaction, would provide a direct access to the phenomenon
being investigated.
Five years have passed since the inception of publically available micropublishing
services (Smith, 2011). In that time micropublishing has become a mainstay of
human communication, with more than 15% of the world's population interacting
with micropublishing streams on a daily basis (Nielsen Wire, 2010). This comprises
80% of all internet users, suggesting that as internet availability and usage increases,
micropublishing will become a universal form of human communication and
expression. As our society’s reliance on computer mediated communication in all its
forms increases, so will the relative amount of time people spend communicating and
interacting with others through them. And as a consequence, they will increasingly
define and transform people’s relationships and people themselves. Understanding
how people’s preconceptions affect interactions and how aspects of will likewise
increase in importance.
15
References
Baron, N. S. (1998). Letters by phone or speech by other means: The linguistics of
email. Language and communication, 18, 133–170.
Bastian, B. & Haslam, N. (2006). Psychological essentialism and stereotype
endorsement. Journal of Experimental Social Psychology, 42(2),228-235.
Bem, S. L. (1974). The measurement of psychological androgyny. Journal of
Consulting and Clinical Psychology, 42(2), 155-162.
Bond, B. J. (2009). He posted, she posted: Gender differences in self-disclosure on
social network sites. Rocky Mountain Communication Review, 6(2), 29-37.
Hammond, C. C. (2010). Self presentation through avatars: The influence of homophily,
identifying with the image, and social reflection on choice of avatar. ProQuest
Information & Learning, US.
Heil, B., & Piskorski, M. (2009). New Twitter research: Men follow men and nobody
tweets. Harvard Business Voices. Retrieved January 26, 2011, from
Harvardbusiness. org
Hua Qian, & Scott, C. R. (2007). Anonymity and Self-Disclosure on Weblogs. Journal of
Computer-Mediated Communication, 12(4), 1428-1451. doi:10.1111/j.10836101.2007.00380.x
Jacobson, D. (1999). Impression Formation in Cyberspace: Online Expectations and
Offline Experiences in Text-based Virtual Communities. Journal of ComputerMediated Communication, 5(1), 0–0.
Lickel, B., Hamilton, D. L., & Sherman, S. J. (2001). Elements of a lay theory of groups:
Types of groups, relational styles, and the perception of group entitativity.
Personality and Social Psychology Review, 5(2), 129.
Markey, P. M., & Wells, S. M. (2002). Interpersonal Perception in Internet Chat Rooms.
Journal of Research in Personality, 36(2), 134. doi:10.1006/jrpe.2002.2340
16
Mazman, S. G. (2011). GENDER DIFFERENCES IN USING SOCIAL NETWORKS. Turkish
Online Journal of Educational Technology, 10(2), 133-139.
Nielsen Wire. (2010, June 15). Social Networks/Blogs Now Account for One in Every
Four and a Half Minutes Online | Nielsen Wire. Nielsen Wire. Retrieved
January 26, 2011, from http://blog.nielsen.com/nielsenwire/global/socialmedia-accounts-for-22-percent-of-time-online/
Pickett, C. L., & Perrott, D. A. (2004). Shall I compare thee? Perceived entitativity and
ease of comparison. Journal of Experimental Social Psychology, 40(3), 283–
289.
Prentice, D. A., & Carranza, E. (2002). What women and men should be, shouldn’t be,
are allowed to be, and don’t have to be: The contents of prescriptive gender
stereotypes. Psychology of Women Quarterly, 26(4), 269–281.
Salimkhan, G., Manago, A. M., & Greenfield, P. M. (2010). The Construction of the
Virtual Self on MySpace. Cyberpsychology, 4(1), 1-18.
Smith, C. (2011, March 21). Twitter Turns Five! See The First Tweet Ever. Huffington
Post.
Retrieved
October
11,
2011,
from
http://www.huffingtonpost.com/2011/03/21/twitter-first-tweet-fifthanniversary_n_838280.html
Sussman, N. M., & Tyson, D. H. (2000). Sex and power: gender differences in
computer-mediated interactions. Computers in Human Behavior, 16(4), 381394. doi:10.1016/S0747-5632(00)00020-0
Toma, C. L., & Hancock, J. T. (2010). Looks and Lies: The Role of Physical
Attractiveness
in
Online
Dating
Self-Presentation
and
Deception.
Communication Research, 37(3), 335-351. doi:10.1177/0093650209356437
Turkle, S. (1997). Life on the Screen: Identity in the Age of the Internet. Simon &
Schuster.
17
Appendices
Appendix A: Tweets Sent
Tweet
now THAT was funny.
What I learned today: U'll never know something
until u ask.
I'm hungry. Again. #foodgoesrightthrume
Eyes gettin heavy.
Trying, trying, trying.................
Transmission dies, coworkers quit (3 of them)
vacation canceled. Story of my life.
Should be 30000 feet in the air instead of this
dealing with this
still. nothing.
Idk what to do with myself.....so bored.
Bad mood.
Facebook chat never works for me
Love how my phone said it was dying for 4 hours
today. Smh.
hmmmm. got something interesting brewing up
tonight.
Now what can I write that won't bore the crap out of
everyone? #idk
im in a good mood :)
warm chocolate cakee & a cold glass of milk not the
worst way to end the day
If I have so much stuff tosay, why can't I ever fit it
into a tweet?
I'm different, not weird. Sometimes, I forget.
I dropped my phone so bad at work today there's
hella scratches on my screen protector. thank God I
have that on!
everyone's asleep and I can't. great.
I just had a conversation that felt like it lasted 9
hours.
I wish I had the cash for an ipad, why does apple
keep making such cool stuff?
Lil ceasar: Nastiest pizza ever. Just say no.
I want an ipad. Even the one version would do.
Studying on my days off from work. Is there ever an
end?
i need somethin to do tonight...where my friends at?
just got home! i still wanna be out..but i enjoyed
myself
18
Day
Hour Minute
1
19
35
2
2
2
3
18
15
21
12
14
15
23
23
3
17
36
3
3
5
5
5
14
19
10
13
13
20
37
22
17
47
6
13
56
6
16
16
6
7
11
15
43
2
7
9
40
8
8
10
15
57
48
8
8
19
23
41
56
9
21
25
9
10
10
17
18
14
35
46
35
10
11
12
18
47
5
11
23
45
lmao.. life is funny..
OH:\Admit it... You wouldn't know it was your
friend's birthday if Facebook didn't alert you.\""
#AmyWinehouse? I saw it coming since that video of
her in europe. #kurtcobain all over again :(
#goplankintraffic - best twitter meme ever
watching a movie w/ my brother & sister. wish we
could see eachother more.
a hude spider just crawled out of my room....
hopefully that was the only one..
Monday again...
19
11
9
18
12
9
23
12
13
9
15
30
22
14
16
52
14
14
17
14
9
47
Tables
Table 1 - Independent Means Test of Individual Trait Scores
Independent Means Test of Individual Trait Scores
Levene's Test
for Equality of
Variances
t-test for Equality of Means
95% Confidence Interval
Sig. (2-
Trait
F
Sig.
T
Df
Mean
Std. Error
tailed) Difference Difference
of the Difference
Lower
Upper
Interest in children
.017
.898
.185
29
.855 .1000000
.5405404
-1.0055292
1.2055292
Loyal
.384
.540
.094
29
.926 .0625000
.6638297
-1.2951842
1.4201842
Sensitive
.179
.676
.588
29
.561 .3125000
.5315563
-.7746547
1.3996547
Friendly
.048
.829
-.777
29
.444 -.3333333
.4290683
-1.2108766
.5442099
7.957
.009
1.455
29
.156 .8250000
.5668843
-.3344085
1.9844085
.000
.983
-.266
29
.792 -.1625000
.6099819
-1.4100531
1.0850531
High Self-esteem
2.306
.140
.662
29
.513 .4083333
.6170452
-.8536657
1.6703324
Common Sense
.628
.434
.971
29
.340 .5333333
.5492593
-.5900282
1.6566948
Sense of humor
5.674
.024
-.803
29
.428 -.3583333
.4461172
-1.2707455
.5540789
.192
.665
.616
29
.543 .3916667
.6355357
-.9081498
1.6914832
Impressionable
1.449
.238
1.394
29
.174 .4500000
.3228376
-.2102771
1.1102771
Child-like
1.364
.252
29
.299 -.4083333
.3861807
-1.1981616
.3814949
Shy
.017
.898
.657
29
.516 .3625000
.5517035
-.7658604
1.4908604
Cynical
.094
.761
-.312
29
.757 -.1916667
.6141511
-1.4477466
1.0644133
Business Sense
.605
.443
-.657
29
.516 -.4291667
.6527787
-1.7642491
.9059158
Athletic
9.916
.004
-2.366
29
.025 -.9333333
.3944539
-1.7400821
-.1265845
Leadership ability
2.826
.104
-1.336
29
.192 -.5958333
.4460548
-1.5081179
.3164512
Self-reliant
.221
.642
-.176
29
.861 -.0791667
.4495361
-.9985713
.8402380
Dependable
.200
.658
-.575
29
.569 -.3333333
.5792550
-1.5180429
.8513762
Happy
1.867
.182
.595
29
.556 .2291667
.3849533
-.5581513
1.0164846
Helpful
.815
.374
.300
29
.766 .1708333
.5697090
-.9943524
1.3360191
Clean
.267
.609
2.280
29
.030 .8791667
.3855314
.0906664
1.6676669
Intelligent
Mature
Yielding
1.057
20
Emotional
.328
.571
.332
29
.743 .1125000
.3392356
-.5813148
.8063148
Rebellious
.221
.642
-.263
29
.795 -.1041667
.3963548
-.9148032
.7064699
Solemn
.032
.860
-1.037
29
.308 -.4375000
.4217612
-1.3000986
.4250986
1.039
.316
-.869
29
.392 -.5583333
.6425058
-1.8724052
.7557386
Stubborn
.402
.531
-1.048
29
.303 -.4500000
.4294142
-1.3282506
.4282506
Promiscuous
.671
.419
-2.009
29
.054 -.9583333
.4769972
-1.9339022
.0172355
Approval Seeking
.121
.731
.349
29
.730 .1791667
.5133695
-.8707919
1.2291252
3.432
.074
1.131
29
.267 .5916667
.5233051
-.4786123
1.6619457
Controlling
Superstitious
21
Table 2 - T-Test of Trait Groups, Female
T-Test of Trait Groups, Female
Levene's
Test for
Equality of
Variances
t-test for Equality of Means
95% Confidence Interval of the
Trait
F
Sig.
T
Df
Sig. (2-
Mean
Std. Error
tailed)
Difference
Difference
Difference
Lower
Upper
EnhPre
1.951 .173
-5.212
29
.000
-2.0220259
.3879532
-2.8154792
-1.2285725
RelPre
.504 .483
-1.878
29
.070
-.7747414
.4125339
-1.6184680
.0689853
EnhPre = Enhance Prescription
RelPre = Relaxed Prescription
22
Table 3 - T-Test of Trait Groups, Male
T-Test of Trait Groups, Male
Levene's Test for
Equality of
Variances
t-test for Equality of Means
95% Confidence Interval of the
Trait
F
Sig.
T
Df
Sig. (2-
Mean
Std. Error
tailed)
Difference
Difference
Difference
Lower
Upper
IntPre
.008
.929
-.556
28
.583
-.1949405
.3506008
-.9131136
.5232326
RelPre
.745
.395
1.206
28
.238
.4946429
.4102780
-.3457736
1.3350593
RelPro
.232
.634
-1.046
28
.304
-.3928571
.3754098
-1.1618492
.3761349
IntPro
1.040
.317
.193
28
.849
.0803571
.4174309
-.7747113
.9354256
IntPre = Intensified Prescription
RelPre = Relaxed Prescription
IntPro = Intensified Proscription
RelPro = Relaxed Proscription
23
24