AEJMC Archives

AEJMC Archives


View:

Next Message | Previous Message
Next in Topic | Previous in Topic
Next by Same Author | Previous by Same Author
Chronologically | Most Recent First
Proportional Font | Monospaced Font

Options:

Join or Leave AEJMC
Reply | Post New Message
Search Archives


Subject: AEJ 96 ReavyM CTM Coorientation model in group communication on the Internet
From: Elliott Parker <[log in to unmask]>
Reply-To:AEJMC Conference Papers <[log in to unmask]>
Date:Mon, 23 Dec 1996 05:20:07 EST
Content-Type:text/plain
Parts/Attachments:
Parts/Attachments

text/plain (1181 lines)


            Use of the Coorientation Model in Studying
            Group Communication on Internet Communication
 
 
              Abstract
 
                When Chaffee and McLeod introduced coorientation, they asserted
that the model could be generalized to more than two persons.  They later
tempered their position, noting that such analysis could prove problematic.
Recent research into small group interaction has shown that early fears about
polarization as reification may have been specious.  This paper argues in favor
of the general model as a tool in studying communication.  Data gathered from a
Q study of attitudes regarding journalism on the Internet are used to illustrate
advantages of the model-- particularly the interpersonal/group/mass
communication environment of the Internet.  Suggestions for future research
follow.
               [Use of the Coorientation Model in Studying Group Communication
on the Internet]
 
 
 
 
 
 
 
 
 
 
 
 
         Use of the Coorientation Model in Studying
         Group Communication on the Internet
 
 
              Matthew M. Reavy
              Assistant Professor
              Manship School of Mass Communication
              Louisiana State University
              Baton Rouge, LA 70803-7202
              tel.-- 504-388-2095
              fax -- 504-388-2125
              e-mail -- [log in to unmask]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
              [Submitted to Communication Theory and Methodology Division of
AEJMC, 1996]
                When Chaffee, et al. (1969) introduced their general model of
coorientation more than a quarter century ago, they asserted that lessons
learned in the study of dyadic communication environments could be generalized
to more than two persons.  Building upon previous work (Chaffee & McLeod, 1967;
Chaffee & McLeod 1968), they posited that an individual member of a group could
coorient with that or another group.  Such coorientation, they argued, would
necessitate the construction a "'generalized other', his internal reification of
the other members of the group taken as a unit" (Chaffee, et al. 1969: 3).  If
this representation were cognitively operative for the individual member, then
one could perform coorientation analysis utilizing the two-person model.
                The researchers later tempered their position on generalizing the
model to more than two persons, noting that such analysis could prove
problematic (Chaffee 1971; McLeod, et al. 1972).  They specified a number of
criteria that must be addressed in order to produce a valid measurement of
coorientation in the group environment, including proper labeling of the groups
and special attention to sampling and measurement procedures (McLeod & Chaffee
1972).  The researchers also specifically and consistently warned against using
polarization (Carter 1962) as an expression of reification.
                Recent research into social-psychological aspects of small group
interaction (cf., Kiesler et al. 1984; Hogg 1992; Reicher 1984; Spears et al.
1990) has shown that early fears about polarization as reification may have been
specious.  Researchers now believe that polarization produces precisely the type
of reification deemed necessary for generalization of the dyadic coorientational
model (Hogg 1992; Turner 1982) .  Moreover, they suggest that the such
reification is intensified within computer-mediated communication environments
(Siegel et al. 1986).
                This paper examines the literature of computer-mediated
communication and social-psychological effects, arguing in favor of the general
model of coorientation as a tool in studying the group communication
environment.  Data gathered from a Q study of attitudes regarding journalism on
the Internet are used to illustrate the advantages of the coorientation model in
investigating inter-group processes-- particularly the interpersonal/group/mass
communication environment of the Internet.  Suggestions for future research
follow.
              BACKGROUND
                Computer-mediated communicators and social psychologists have
long recognized the tendency toward disinhibiting or antisocial behavior online
(Siegel et al. 1986; McGuire et al. 1987).  Gaffin and Heitk tter (1994) warn,
                   Something about online communications seems to make
                   some people particularly irritable. Perhaps it's the
immediacy and
                   semi-anonymity of it all. Whatever it is, there are whole
classes of
                   people you will soon think seem to exist to make you
miserable.
 
              Such hostile behavior in CMC environments, often referred to as
"flaming," has become a somewhat common phenomenon.  Categories of flames have
even been identified, including the Spelling Flame, the Read-the-Manual Flame,
the Clueless-Newbie Flame and the Science-Skeptic Flame to name a few (Barger
1993).  What these all have in common is that each is a personal attack in
response to the content of a message that has been posted or the way in which it
was posted.
                Some have argued that such antinormative behavior is a necessary
byproduct of the CMC environment (Martin, et al. 1992).  One problem lies in the
fact that CMC's largely text-based environment lacks the social contextual clues
of face-to-face communication (Kiesler & Sproull 1992). CMC participants report
that the lack of social cues inhibits expressiveness (Kraut et al. 1992),
resulting in a feeling of social isolation (Taha & Calwell 1993).  This feeling
of isolation lessens the group's normative influence, as described by Deutsch
and Gerard (1955).  Normative influence denotes pressure upon participants to
conform to group norms.  It is brought about by an individual desire to be
socially accepted.   Such influence derives additional power from the perceived
ability of a group to coerce the individual with threats or punishment.  Without
such normative influence, an individual is much more likely to exhibit behavior
that goes against the norms of that group.
                Normative influence is only one factor in promoting group
conformity.  Informational influence also plays a role (Deutsch & Gerard 1955).
Informational influence flows from a subjectively valid reason to agree with the
group (Abrams & Hogg, 1990).  When an individual's beliefs concur with the
group's, or when that individual accepts and internalizes the group's beliefs,
that individual is obviously more likely to concur with the group and conform to
group norms.  Information influence is therefore a potential factor in any group
communication environment.
                Where normative and informational influence fail to affect an
individual's behavior in small groups, self-regulatory processes can provide a
valve to check the flow of antinormative acts.  Hogg (1992: 85) describes three
self-regulatory processes and the effect that they have upon behavior in the
small group environment: self-definition, self-portrayal and lack of self-focus.
Self-definition refers to attention given to the private self.  The individual
who has private self-awareness (SA) exhibits behavior that is independent of
group attitudes, but consistent with personal attitudes.  Self-portrayal
indicates an attention to the public self.  This public SA generally manifests
itself as conformity to group norms and behavior that is socially acceptable.  A
lack of self-focus indicates the absence of both private and public SA.
Individuals who lack self-focus also lack self-regulation.  Their behavior in a
small group environment is generally unplanned, disinhibiting and antinormative.
                The lack of focus on the self, reflecting a low public SA as well
as a low private SA, was a primary indicator of deindividuation in early
studies.  Deindividuation (DIV) refers to the loss of personal identity in a
group situation, primarily as a result of the loss of self-awareness when one
becomes "psychologically absorbed" by the group (Diener, 1980).  Research has
shown that DIV correlates positively with increased group size and gender
similarity (Diener et al. 1980), greater liking for the group (Singer et al.
1967) and an elevated sense of group unity (Duval & Wickland 1972).  Anonymity
and impulse have also been show to be primary causes of deindividuation
(Zimbardo 1969).  Because DIV was understood to reduce introspection and lead to
a kind of dehumanization, early CMC scholars believed it could be to blame for
the antinormative behavior phenomenon in online communications (Siegel et al.
1986).
                However, deindividuation in the CMC environment has not been
without its detractors.  Many findings have been contradictory.  For example,
DIV predicts a reduced self-awareness (Diener 1980), but studies have found that
use of a computer for communication has heightened self-consciousness (Siegel et
al. 1986; Matheson & Zanna 1990).  Others, including Spears et al. (1990)
contend that the computer creates an "individuating" rather than deindividuating
communications environment.  Such studies have led others to conclude that the
concept of DIV is "half-baked" (Prentice-Dunn 1991) and that the public-private
SA distinction is a "fallacy" (Wicklund & Gollwitzer 1987).
                Deindividuation also contradicts the well-documented phenomenon
of polarization.  Polarization refers to the tendency of small groups to produce
decisions that are much more extreme than the average of members' opinions prior
to the discussion.  Both normative influence, sometimes referred to as "cultural
values" (Sanders & Baron 1977), and information influence, sometimes called
"persuasive arguments" (Burnstein & Vinokur 1977; Vinokur & Burnstein 1974) have
been cited as potential causes of group polarization.  Others have suggested
that normative and informational influence combine to produce polarizing
tendencies.  For example, one would imagine that normative influence would fade
after the discussion group disbanded, but that informational influence would
persist.  Davis et al. (1974) found that the pregroup to group mean-shift
persists outside the discussion environment, indicating that informational
influence exists.  However, they also found that the difference between
pre-group and group means does decrease after the discussion has ended,
indicating the existence of a lessening normative influence.
                DIV would appear to work against polarization. If normative
influence is reduced, as DIV theory suggests, then polarization should also be
reduced.  But studies have shown that CMC groups produce decisions that are
actually more polarized than those reached by face-to-face groups (Siegel, et
al. 1986).  Reicher (1984) attempted to account for contradictions within the
DIV by reconceptualizing deindividuation in order to account for social and
normative factors.  He argued that, rather than weaken social norms,
deindividuating conditions strengthen those norms by enhancing group saliency.
As a person becomes involved with a group, that person comes to identify with
that group.  Group membership becomes salient to them, and they become more
susceptible to normative influence.
                This reconceptualization of DIV involves a question of
prominence.  Building upon Reicher's work, Spears et al. (1990) found that
polarization increased as group saliency was enhanced and decreased as
individual saliency was enhanced.  Lea and Spears (1991) argue that these
findings account for the polarizing effect brought about by increased private SA
in the CMC environment.  As group saliency increases, the individual identifies
more with the group.  Increased private SA then helps the individual move the
self more in the direction of the group.  In a condition of heightened
individual saliency, increased private SA would prompt the individual to hold on
to personal standards whether or not they agreed with group norms.
                Reicher (1984) suggests that DIV works by altering identity away
from self and toward the group.  Antinormative behavior is a side effect,
perhaps temporary, of this move from personal to group saliency.  In his
research, Reicher makes extensive use of the social identity theory originated
by Tajfel (1978) and Turner (1982).  Social identity theory maintains that
people get a sense of who they are from the groups they belong to.  Individuals
may therefore have an array of possible identities, a personal identity and
variety of social identities depending upon the various groups to which they
belong.
                A group can be thought of as two or more persons who adopt the
same social identification.  The question "Who am I?" is resolved with a series
of short answers: where I live, where I work, where I attend church, what I
enjoy doing and what I don't enjoy.  Each of these answers tends to involve
identification with a group.  It is important to note that identity groups do
not have to involve face-to-face communication, nor does the group necessarily
have to possess a structure.  Thus, it is not at all unlikely to find
individuals who may identify with groups of people whom they have never met.
                People who identify with groups get a sense of their own identity
from those groups.  One consequence of this process is a phenomenon known as
"referent informational influence" (Turner 1982).  Referent information
influence compels an individual to begin matching his or her attitudes and
behavior to that of a stereotypical group member.  Such influence is generally
not active at all times.  Individuals identify with many different groups.  A
journalist who is also a Christian will typically act somewhat differently at
church and in the newsroom.  And that person's answers to certain questions will
vary greatly depending on whether he or she is being asked to answer as a
journalist, as a Christian or as an individual.
                Turner (1985) takes the concept of social identity even further,
refining it to highlight self-categorization and self-stereotyping as the
psychological foundation of group behavior.  Self-categorization theory asserts
that group membership occasions an active cognitive process by which individuals
intensify aspects of the self that belong to the same category as the group.
Categorization of self and others helps individuals derive subjective
significance from their experiences by allowing them to abstract meaning that
conforms to stereotypical prototypes -- sets of characteristics by which a given
individual defines various forms (Hogg 1992).  Individuals categorize themselves
according to the prototypical group norm and undergo a process of
depersonalization by which they and other group members come to be seen as
representations of the self-defined stereotype.
                Reicher (1984) argues that the process of self-stereotyping
strengthens rather than destroys identity.  But the identity that it strengthens
is social identity, and it does so at the expense of personal identity.
Discarding Diener's (1980) distinction between personal identity and the lack of
identity, Reicher replaces it with individual vs. group saliency.  He writes,
"deindividuation gains its effects by altering the relative salience of personal
and social identity and consequently by manipulating adherence to personal
standards or norms" (Reicher 1984: 342).  The environment that creates such a
phenomenon generally possesses two important characteristics: immersion in a
group and visual anonymity.
                The process of deindividuation, therefore, is the process of
conformity, which Hogg and Abrams (1988) propose occurs in five stages:
self-categorization, whereby the individuals categorize themselves according to
a group; discovery, where individuals uncover ingroup norms; stereotyping, where
individuals accept the newly discovered norms as a behavioral prototype;
self-stereotyping, where individuals assign the prototype to themselves; and
conformity, where individuals begin to display behavior that they believe
matches the group norm.
                As members of the group categorize themselves according to the
perceived prototypical norm, their behavior and attitudes begin to move toward
that norm as well.  Because a prototypical norm tends to be an extreme rather
than the average of members' true positions (Abrams et al. 1990), members
gradually become polarized in their views.  Several studies have applied
self-categorization theory in this manner to explain group polarization.   Hogg
et al. (1990) looked at polarization as an ingroup reaction to outgroup
behaviors.  They found that an ingroup confronted by a risky outgroup will move
toward caution, while an ingroup confronted by a cautious outgroup will move
toward risk.  A group confronted by both risky and cautious outgroups conforms
to its pretest mean.  McGarty et al. (1992) found that ingroups tended to become
more polarized as the ingroup prototype grows more extreme over time.
                These findings reconcile DIV with both group polarization and
antinormative behavior.   They suggest that individuals create a prototypical
norm for each group that they identify with.  That prototype lies in the general
direction of their attitude, but tends to be much more extreme than their own
position.  Individual attitudes and behaviors move in the direction of that
group norm as individual saliency decreases and group saliency increases.  The
group norm consequently shifts even further outward from the pregroup mean.  As
members of a group undergo this change, the group's attitude becomes
increasingly polarized and decisions made by that group become more extreme.
                The protoypical norm generated by polarization represents the
"generalized other" of Chaffee et al. (1969) in terms of both in-group and
out-group reification.  Moreover, group members' attitudinal shift toward the
prototype validates such refication as individuals become what they have
psychologically beheld.  Members not only react to the out-group as what Chaffee
and McLeod would call a "concrete and homogeneous entity" (1972: 65), they help
transform psychological reality into social reality.
              METHODOLOGY
                This study examines two distinct facets of the Internet
communication environment: attitude and orientation.  These are the brick and
mortar of behavior.  In a sense, attitude is behavior.  As Stephenson (1953) has
observed, behavior that is subjective, such as thinking or dreaming, is no less
behavior than that which is observable and therefore deemed objective.  Thus,
our attitudes paint as much a picture of our self as do our more overt
behaviors.  However, our attitudes do not arise out of a vacuum, especially when
those attitudes concern others.  What I think of you may be influenced to a
great extent by how I expect you to react to me.  That expectation is in turn
influenced by my perception of your attitude, your orientation toward me
(McLeod, et al. 1972).  Our mutual orientations or "coorientation," create an
integrative framework (Patterson, 1994) for our behavior in a communication
environment.
                In conceiving of the communication environment as an active
system strongly influenced by attitude and perception of attitude, this study is
predisposed to a research strategy involving the use of Q Methodology and
Coorientation Analysis.  Although distinct in conception, these two
methodologies have been employed together in the past (cf., Stegall, 1985;
Lipschultz, 1991).  Q Methodology, developed by William Stephenson (1953),
examines attitude as a collection of judgments about specifics, such as
statements in a conversation.  Rather than look at these judgments in isolation,
Q Methodology permits the researcher to view them in relation to one another
within the subject's self-referential belief system.  Thus, the researcher can
seek out what Crumley (1966) calls "broad attitudes of the mind."
                This study, therefore, uses Q Methodology to uncover attitudinal
patterns among online journalists and Internet users and coorientation analysis
to make a comparison among those patterns.  Q technique permits researchers to
scientifically study subjectivity by examining the manner in which individuals
communicate their point of view (McKeown & Thomas, 1988).  Individuals are
presented with a sample of opinion statements generally derived from a
qualitative analysis of conversations about the subject under study.
Individuals then sort those statements according to a specific set of
instructions.  The resulting sort is self-referential, in the sense that
subjectivity is always self-referential (Brown, 1980), and representative of
the individual's own frame of reference, because each statement is ranked in
relation to all other statements.
                Once individual viewpoints are in hand, the researcher analyzes
the data to uncover correlations between persons (rather than tests as is done
in R-Methodology).  Factor analysis is employed on the correlation matrix to
uncover factors representing various points of view or attitudes.  Each
individual has a loading on each factor representing his or her degree of
association with others on that factor.  Each statement is given a score that
depicts its association with each factor, enabling the researcher to create a
representative Q sort for each factor.  The final stage of analysis usually
entails the interpretation of the various factors.
                We can see, then, that each participant's placement of each
statement in a Q sort represents his or her opinion of that statement.  The
entire Q sort represents his or her attitude toward the topic under study.  The
factors derived from statistical analysis represent groups of participants who
have the same attitude -- a shared viewpoint or attitudinal pattern.
                Q Methodology reveals subjective attitudes, which makes it ideal
for this study.  The general coorientation model proposed by Chaffee and McLeod
(1967) serves as the research tool for examining these attitudes in a
communications context by focusing upon cognitive transactions between
communicators or groups of communicators.  It postulates two distinct states of
cognition between communicators: 1) each communicator knows what he or she
thinks; and 2) each has an estimate of what the other thinks.  These two states
of cognition allow for five separate measurements.  One can ascertain:
              1) the similarity between A's and B's attitude toward a subject
(Agreement);
              2) the similarity between A's attitude and A's perception of B's
attitude (Congruency I);
              3) the similarity between B's attitude and B's perception of A's
attitude (Congruency II);
              4) the similarity between A's perception of B's attitude and B's
true attitude (Accuracy I);
              5) the similarity between B's perception of A's attitude and A's
true attitude (Accuracy I).
 
              Together these five measurements permit one to explore the
communication relationship shared by A and B.
              Q Sample
                Q Methodology constitutes an objective exploration of subjective
phenomena.  It places the focus of research upon the correlation of persons
rather than of traits, as is common in traditional R methodology.  Emphasis
likewise shifts from the sampling of persons to the sampling of stimulus items,
which can range from art objects (Stephenson, 1936) to human body segments
(Brown, 1972) but generally consist of written statements.  Because Q explores
subjectivity, these statements deal with opinion rather than fact.  They are
also self-referential, communicating individual points of view bound to an
internal frame of reference (McKeown & Thomas, 1988).
                The need to maintain the self-referential quality of stimulus
items helps account for the fact that Q generally maintains tighter control in
sampling stimuli than other methodologies.  Rather than personally generate
items for use in a tool, the Q researcher samples stimuli from a population of
statements on a given topic and within a common frame of reference.  For
example, one might draw a sample from more than 2,000 statements collected made
by Jung concerning his conception of psychological types (Stephenson, 1939).
Because they are taken from the actual concourse of communication on the topic
under study, these statements should have more inherent meaning to the
participants than would a similar set of stimuli provided by the researcher.
                This study employed a hybrid, structured sample of 48 statements.
Statements were collected from conversations involving journalism and the
Internet that appeared on the Usenet newsgroup alt.internet.media-coverage or in
private e-mail responses during a one month period from April 1 to April 31,
1995.  Additional statements on the topic were derived from an exploratory
Internet-journalism attitudinal study conducted in December 1994.[1]
                Three postgraduate students at the University of Missouri, two
active journalists and one former journalist pretested the Q sample.  All were
familiar with and had access to the Internet.  The researcher was present during
each test.  Responses led to subtle refinements in participant instructions.  No
corrections were suggested for any statements, nor did the test subjects express
serious difficulty in conducting the sort.
              P Sample
                Although Q researchers devote much of their attention to sampling
stimuli, the sampling of persons also deserves some methodological
consideration.  Unlike R methodology, Q does not require special random sampling
of persons (McKeown & Thomas, 1988).  Q studies do not exhaust the range of
available opinion regarding a specific topic.  Their results are not meant to be
generalized in the sense that an attitude noted in n percent respondents
suggests an attitude in n(q) percent of the population.  Rather they represent
what Stephenson, in his philosophical credo, termed "'general implications' that
the same conditions will hold true elsewhere" (1961: 5).  R methodological
statistics are not required in selection of the P sample because the researcher
does not attempt R methodological statistical inferences.
                The Q researcher is free to select subjects according to
pragmatic considerations.  That is, anyone will do (McKeown & Thomas 1988).  The
researcher may also select subjects according to theoretical considerations.
This generally occurs when the researcher has some interest in determining
attitudes within a certain strata of the population.  Above considerations
suggested that the P sample for this study be drawn in four subsets: online
journalists, Internet users, online journalists who have posted to a.i.m-c and
non-journalists who have posted to a.i.m-c.
                The term Internet users here refers to members of electronic
discussion lists and those who have posted to Usenet newsgroups.  This
sub-population of the Internet was chosen for several reasons.  Given that
individuals in this sub-population generate the bulk of all online
conversations, they are the most likely to be affected by journalists using the
Internet as a source of news.  They are therefore more likely to take an
interest in media coverage of the Internet.  Because they are already
participating in conversations on the Internet, they would also be more likely
to have at least seen online conversations about journalism.
                The final sample of journalists included 18 radio journalists, 21
television journalists, 6 magazine journalists and 105 newspaper journalists
from eight countries.  The final sample of Internet users included 64 discussion
list members and 86 newsgroup posters from six countries.  Each was contacted by
electronic mail in May 1995 and asked to take part in a study of journalism and
the Internet.  Within two weeks 55 journalists and 43 Internet users replied
that they were interested in participating.
                Two additional samples were drawn from the Usenet newsgroup
alt.internet.media-coverage during May 1995.  A random number generator was used
to select 14 journalists and 14 Internet users from among those who had posted a
message to the newsgroup between April 15 and April 30, 1995.  Subjects were
contacted by electronic mail and asked to participate in the study.  Six
journalists and six Internet users replied that they would be interested in
taking part in the study.
                Returns were halted on July 3, 1995.  The final P-sample
consisted of 41 individuals -- 21 journalists and 20 Internet users.  Of the 21
journalists, six were a.i.m-c posters, five were broadcast journalists and ten
were print journalists.  Of the 20 Internet users, four were a.i.m-c posters and
16 were non-a.i.m-c posters.
              The Q Sort
                The data-gathering mechanism of Q methodology is the Q sort.
Rather than allowing subjects to consider each item separately, as R
methodological approaches do, Q methodology requires participants to view each
statement in relation to all other statements.  Participants sort the items into
a distribution specified in instructions with the statements.  The result
roughly resembles a standard distribution with the bulk of the items at the
center and fewer items at the extremes.
                Subjects who agreed to take part in this study were sent a
package by standard postal mail.  The package contained a brief letter of
thanks, instructions for the sort, a form to assist in recording the sort, a
series of demographic questions, 48 statements and 11 markers to assist in the
sort process itself.  The instructions asked subjects to read the statements,
separating them into three piles: "Like My Opinion," "Unlike My Opinion" and
"Neutral."  They were then asked to spread out the Markers that had been
provided according to the following scheme:
              +5        +4      +3      +2      +1      0       -1      -2      -3      -4      -5
              The next instruction asked that they take "Like My Opinion" pile,
select the four statements most like their own point of view and place them
under the +5 marker.  They were then asked to pick up the "Unlike My Opinion"
pile, remove the four statements most unlike their opinion and place them under
the -5 marker.  Subjects were instructed to continue sorting back and forth,
resorting to the "Neutral" pile when one of the other two ran out, until they
were left with eight statements, which they were to place under the 0 marker.
They were then asked to record the results.
                Subjects were asked to place the statements back in one pile and
take a brief break.  When they were able to return, they were asked to sort the
statements again from another's perspective.  Journalists were asked to sort the
statements as they thought a "typical" Internet user would sort them.  Internet
users were asked to sort the statements according to how they felt a "typical"
journalist would sort them.  When they had recorded the results of the second
sort, subjects were asked to complete several demographic questions and return
the answers via electronic mail along with the results of their sorts.  Two
electronic reminders were sent out to participants in mid-June.  Responses were
cut off seven weeks after the initial request was mailed.
                Q methodology employs factor analysis to examine data generated
by the Q sort.  Factor analysis attempts to reduce a complex array of variables
to a smaller set of (hypothetical) underlying variables.  Whereas R
methodological factor analysis looks for clusters of traits, Q methodology
employs factor analysis to look for clusters of persons.  The result of each
person's sort is correlated with every other person's sort to generate an N   N
correlation matrix.  Intercorrelations among persons are then used to generated
factors -- hypothetical variables that account for some measure of variance in
the sample.  In the case of Q, these factors are actually clusters of people who
share the same hypothetical attitude.  The degree to which they share that
attitude is represented by their loading on that factor.  The higher the
loading, the closer they are to the hypothetical attitude.
                Factors representing hypothetical attitudes can be rotated in
order to examine the data from a different perspective.  Without changing the
data itself, the researcher can view it from several different angles and alter
the hypothetical variables, the factors, so that they take in as many persons as
possible.  It is a search for a simple solution or for the solution that best
addresses the theory being explored.
                This study made use of the QUANL program of factor analysis.
QUANL, which was specifically designed for Q methodology, generates the N   N
correlation matrix and derives relevant factors according to information
supplied by the user.  It rotates the factors in order to find a simple
solution, one that maximizes purity in each of the factors.  Finally, in the
WRAP (Weighted Rotational Analysis Procedure) phase, the program derives z
scores for each factor, generates prototypical sorts based upon descending z
scores and contrasts z scores between the various factors.
                Data were run through QUANL several times.  In each case a
bipolar splitting criterion of 40 percent was specified.  Splitting criterion
refers to the percent of a factor that must be negative in order for the factor
to be split into two separate factors.  Although QUANL defaults to 25 percent,
program documentation indicates that "experienced Q researchers recommend 40
percent instead" (QUANL, p. 3).  The 40 percent figure was also selected because
of interest in determining whether or not rotation would reveal journalists and
Internet users to be loaded at polar opposites on the same factors. A minimum
eigenvalue[2]_ factoring criterion of 1.00 was specified.  QUANL used the
varimax method of factor rotation.  The initial run consisted of all first-order
and second-order sorts.  Two secondary runs consisted of journalists' first
order sorts and Internet users' first-order sorts.
              ANALYSIS
                Q Methodological studies typically employ a qualitative analysis
of rotated factors generated by correlation analysis.  However, as this study
involves an examination of the coorientational model in a small group
environment, discussion of the factors will be here dispensed with.  We proceed
instead to examine the coorientation data.
                As originally conceived, the coorientation model deals with
dyadic communication (cf., Stamm & Pearce 1971, 1974).  Studies utilizing the
model generally involve two logically related individuals, such as husband/wife
pairings (Chaffee & McLeod 1968) or university public relations
personnel/reporters covering those universities (Stegall, 1985).  Measurement in
such survey-type studies involves a relatively straightforward process of
comparing the rating responses for each individual and squaring the differences
to yield distance measures.  In Q/Coorientation studies of individuals,
appropriate sorts are referenced in the correlation matrix to measure the degree
of agreement, accuracy and congruency.
                McLeod and Chaffee (1972) stipulated that group coorientation
requires that the groups involved meet two criteria:
                   they must be psychologically meaningful to the
                   population of people under study; and they must suggest
boundaries for
                   the groups such that the researcher can tell objectively who
should be
                   included and who should not (p.67).
 
              Later research into coorientation with one's own group has adhered
to these restrictions (cf., Steeves 1984); however, this study argues against
both criteria.  First, the researcher can objectively define the boundaries of
any group simply by altering the operational definition of that group.  This
also addresses another of Chaffee and McLeod's concerns, the relative ease with
which the researcher can sample subjects (of significantly less concern in Q
methodology).  Second, the boundaries of any group and the psychological meaning
of that group go hand-in-hand.  Psychological meaning is a subjective concept.
Many groups have psychological meaning to individuals, but that meaning will
vary from one person to another.  Indeed, the degree to which that meaning
varies can tell much about not only the group, but also about the communication
environment that spawns meaning. This study holds, with Stephenson (1953), that
psychological meaning is a subject of scholarly study rather than a necessary
precondition of it.
                In addition to the issue of who rates whom, Chaffee and McLeod
expressed concern about what it is that is rated-- the content.  They approached
the problem in typical R methodological style:
                   Any list of topics is open to question and perhaps the
                   best guideline is to over-sample topics to make sure some are
relevant to
                   all persons under study.  It is generally advisable to check
the
                   assumption of relevance by permitting "don't know" (1972:
69).
 
              The Q methodological approach addresses these concerns in two
ways: 1) by sampling statements from the participants' own communication
concourse, thus ensuring some degree of relevance; and 2) by having the option
of "don't know" inherent in the sort method.  Because statements are sorted in
relation to one another along a bipolar continuum, statements at either end of
the distribution will have more meaning for participants.  Statements that have
little or no meaning for participants, whether because they were worded
improperly or because the subject fails to grasp their import, generally appear
at the center of the completed sort.  Their low z scores have meaning, but
generally do not play a large role in helping to define the resulting factor.
                Chaffee and McLeod asserted that the use of scaling procedures,
such as allowing individuals to sort items along a continuum, could be easily
adapted to coorientation analysis:  "Presumably the person could sort the items
in terms of how he felt others would sort them, thus setting up the
possibilities for coorientation analysis"  (1972: 74).  They argued that one
could then assess agreement by comparing modal sorting patterns between groups.
However, because Q methodology usually employs a forced sort, determining the
modal patterns of sorts is not feasible.  Analysis generates prototypical sorts
that generally do not fall into the forced-sort pattern.
                Several Q researchers have attempted to use the means of
statement scores or  mean correlations (cf. Butler, 1972) in an effort to
compare factors generated by Q analysis.  However, McLeod and Chaffee (1972)
argued that techniques like summation and averaging contain the "hidden
assumption" that the group co-oriented with is seen as a collection of
individuals rather than as a reification of the prototype individual.  Although
indexing mean correlations and averaging statement scores are of some value in
assessing the overall picture, the two can produce widely varied results.  For
example, individual correlations between second-order sorts for journalists and
Internet users in the present study range from -.6159 to +.6144.  Taking the
mean correlation of these sorts yields a group correlation of -.1046.  On the
other hand, correlating factors derived from the mean statement scores of those
same second-order sorts results in a group correlation of  +.9477.  Neither
measurement appears to be in line with data derived from factor rotation.
                This study argues that the generalized group member is not
represented by the mean of statement scores, but rather by the prototypical sort
depicted in the collection of z scores for a given factor.  Such a prototypical
sort results not in an "average" of viewpoints, but rather a factor that
maximizes group agreement.  The comparison of these prototypes would therefore
be a correct approach to the coorientation of data derived utilizing Q
techniques.
                In order to accomplish this, four primary runs were conducted to
generate single factor solutions for: 1) journalists' first-order sorts; 2)
journalists' second-order sorts; 3) Internet users' first-order sorts; and 4)
Internet users' second-order sorts.  Eight secondary runs, each specifying
single factor solutions, were made as follows: 1) a.i.m-c journalists'
first-order sorts; 2) a.i.m-c journalists' second-order sorts; 3) non-a.i.m-c
journalists' first-order sorts; 4) non-a.i.m-c journalists' second-order sorts;
5) a.i.m-c Internet users' first-order sorts; 6) a.i.m-c Internet users'
second-order sorts; 7) non-a.i.m-c Internet users' first-order sorts; 8)
non-a.i.m-c Internet users' second-order sorts.
                In each case, QUANL's single factor solution generated a
descending array of z scores for each item representing that items standard
deviation.  The resulting arrays were placed into individual Q sorts, beginning
with the lowest (negative) z scores in the -5 column and working to the highest
z scores in the +5 column.  These 12 prototypical Q sorts were then run through
QUANL as a whole to generate a 12 x 12 correlation matrix that was used as the
basis for coorientation analysis.
                Examining the various factors resulting from analysis of the
combined array can provide qualitative insight into the communications
environment of journalists and Internet users.  However, in order to fully
explore the relationship between the two groups it is necessary to examine the
data from a different perspective, one that permits the researcher to isolate
each group's attitudes and perceptions independent of the other group's
predictions. The coorientation model suggests a methodology for achieving this
goal.
              Non-newsgroup Participants
                In dyadic coorientation involving Q data, one can simply examine
the correlation matrix generated by QUANL in order to determine agreement,
congruency and accuracy (Stegall, 1985).  However, the use of coorientation in
groups implies the reification of a generalized other.  In order to define this
generalized other, the first-order sorts of journalists and Internet users were
separated and factor analyzed.  Analysis was performed in such a way as to
generate a single, prototypical sort for journalists (J1) and another for
Internet users (N1).  Similar analysis of second-order sorts produced
prototypical sorts representing the predictions of each group (J2 and N2).  The
prototypical sorts were then analyzed with QUANL in order to generate a
correlation matrix depicting relationships among the sorts (See Table 1).
               ------------------------------------------------------------
               Table 1   N-J CORRELATION MATRIX
 
                        J1       N1       J2       N2
               J1     1.0000    .6932    .1477    .5455
               N1      .6932   1.0000    .4227    .1932
               J2      .1477    .4227   1.0000   -.5205
               N2      .5455    .1932   -.5205   1.0000
               ------------------------------------------------------------
                The correlation matrix depicted above provides the data necessary
to determine measurements for the coorientation model (See Table 2).  Agreement
refers to the correlation between the first-order statements of journalists and
Internet users--the degree to which their prototypical sorts overlap.
Congruency refers to the correlation between the first- and second-order sorts
of each group--the degree to which they believe the other group's viewpoint
resembles their own or "perceived agreement."  Accuracy refers to the
correlation between one group's second-order sort and the other's first-order
sort--their degree of success in predicting the other's viewpoint.
               ------------------------------------------------------------
               Table 2   N-J COORIENTATION MEASURES
               Measurement                   Score                      t
               Agreement(N1-J1)      +.6932                     6.466 (p < .05)
               Congruency(N1-N2)             +.1932                     1.313 (n.s.)
               Congruency(J1-J2)             +.1477                     0.950 (n.s.)
               Accuracy(J2-N1)       +.4227                     3.139 (p < .05)
               Accuracy(N2-J1)       +.5455                     4.351 (p < .05)
               ------------------------------------------------------------
                The coorientation model enables us to see that journalists and
Internet users in this study possess opinions about journalism and the Internet
that tend to agreed far more than either group believes.  The significantly high
accuracy measures and insignificant congruency scores suggest that each group
correctly predicted the other's differentiating opinions rather than those
indicating consensus.  Had the groups correctly predicted consensus items,
accuracy and congruency would both be positive and significant.
                In addition to analyzing the communications environment among
journalists and Internet users as a whole, this study was also interested in
examining the environment among those who post messages to the
alt.internet.media-coverage Usenet newsgroup.  Separate analyses produced
prototypical sorts for the attitudes of a.i.m-c Internet users (AN1) and a.i.m-c
journalists (AJ1), as well as their predictions of the other group's attitudes
(AN2 and AJ2).  The correlation matrix showing the relationship between these
groups can be seen in Table 3.
               ------------------------------------------------------------
               Table 3   a.i.m-c CORRELATION MATRIX
 
                           AJ1          AN1             AJ2             AN2
               AJ1              1.0000   .4727   .4705     .5386
               AN1               .4727  1.0000   .5705     .1273
               AJ2               .4705   .5705  1.0000    -.1795
               AN2          .5386        .1273      -.1795    1.0000
               ------------------------------------------------------------
                This correlation matrix produced the coorientation measurements
is shown in Table 4.
               ------------------------------------------------------------
               Table 4   a.i.m.c COORIENTATION MEASURES
               Measurement                       Score          t
               Agreement(AN1-AJ1)       +.4727          3.611 (p < .05)
               Congruency(AN1-AN2)      +.1273          0.820 (n.s.)
               Congruency(AJ1-AJ2)      +.4705          3.611 (p < .05)
               Accuracy(AJ2-AN1)                +.5705          4.705 (p < .05)
               Accuracy(AN2-AJ1)                +.5386          4.239 (p < .05)
               ------------------------------------------------------------
                The coorientation model cogently illustrates the apparent belief
among a.i.m-c journalists that they are themselves "typical" Internet users.
Hence, there is a high degree of congruency between their two sorts.  On the
other hand, the congruency measurement for a.i.m-c Internet users indicates that
they see the viewpoint of journalists as very unlike their own.  Both groups
were very accurate in predicting the other's viewpoint, but it is apparent that,
where the journalists accurately predicted similarities, the Internet users
accurately predicted differences.  This coincides with the a.i.m-c Internet
users opinions concerning Internet coverage and quoting discussed in the
previous chapter.
              Non-newsgroup Participants
                Given that a.i.m-c participants appeared to be less in agreement
than non-a.i.m-c participants, additional analysis was performed using on the
"pure," non-a.i.m-c sorts (PN1, PJ1, PN2, PJ2).  The resulting correlation
matrix is illustrated in Table 5.
               ------------------------------------------------------------
               Table 5   NON-a.i.m-c CORRELATION MATRIX
 
                           PJ1       PN1       PJ2       PN2
               PJ1              1.0000     .7364     .2182     .4364
               PN1               .7364    1.0000     .3705     .2909
               PJ2               .2182     .3705    1.0000    -.5114
               PN2          .4364     .2909    -.5114    1.0000
               ------------------------------------------------------------
                The correlation matrix is transposed into coorientation
measurements in Table 6.
               ------------------------------------------------------------
               Table 6   NON-a.i.m.c COORIENTATION MEASURES
               Measurement                       Score          t
               Agreement(PN1-PJ1)       +.7364          7.244 (p < .05)
               Congruency(PN1-PN2)      +.2909          2.055 (p < .05)
               Congruency(PJ1-PJ2)      +.2182          1.457 (n.s.)
               Accuracy(PJ2-PN1)              +.3705            2.701 (p < .05)
               Accuracy(PN2-PJ1)              +.4364            3.230 (p < .05)
               ------------------------------------------------------------
                The "pure" prototypes of non-a.i.m-c posters show significantly
more agreement than those of a.i.m-c posters (t = 1.826, p < .05).  Both
journalists (-.20) and Internet users (-.10) also show less accuracy than
a.i.m-c posters in predicting the attitudes of one another, but the difference
falls short of statistical significance.
              CONCLUSIONS AND FUTURE RESEARCH
                This study has argued that the general coorientational model
proposed by Chaffee et al. (1969) has value in studying communication
interactions between small groups.  We began by noting that coorientation
researchers had tempered earlier remarks regarding the applicability of
coorientation to groups of more than two persons.  Certain limitations were
placed upon such practice, including 1) the assumption of an internal
reification of the group; 2) the assumption that such a reification was
cognitively operative for the individual; 3) the proper labeling of the groups
so that they are psychologically meaningful to the individual; 4) the existence
of boundaries clearly delineating who should and who should not be included in a
given group; and 5) special attention to sampling and measurement procedures,
including not only who is sampled but what is sampled in terms of the topics
studied.
                We argued that proper labeling of groups and the existence of
objectifiable group boundaries were, in retrospect, of little conceptual value
in studying such groups.  Psychological meaning is a subjective concept.  Many
groups have psychological meaning to individuals, but that meaning will vary
from one person to the next.  The degree to which meaning varies can tell much
not only about the group, but also about the communication environment that
spawns such meaning.  Therefore, the researcher is justified in objectively
defining the boundaries of any group simply by altering the operational
definition of that group and, thereby, the population from which p sampling
occurs.
                Q Methodology was used to address the problem of which content
would be rated and how it would be rated.  The "assumption of relevance" (McLeod
& Chaffee 1972: 69) was addressed in typical Q style by sampling statements from
the participants' own communication concourse, thus ensuring some degree of
relevance, and by having the option of "don't know" inherent in the sort method.
Factor analysis was used to generate prototypical sorts that appeared to conform
to the data more closely than either the modal patterns suggested by McLeod and
Chaffee (1972) or the mean correlations that have used by other researchers.
                Perhaps most importantly, this study addresses the question of
reification that has thwarted earlier research into group coorientation.
Research since the general coorientational model appeared suggests that
reification is a necessary by-product of group communication, particularly in
computer-mediated communication environments.  CMC is, by its very nature,
deindividuating.  That is, it simultaneously forces a condition of anonymity
upon users and enables them to act more impulsively than they might otherwise.
Although scholars had originally thought that deindividuation would work against
polarization, subsequent studies showed that groups of individuals communicating
via computers produce decisions that are actually more polarized than those
reached by face-to-face groups.  As an individual communicates with others in a
computer-mediated environment, the saliency of that group tends to increase.
Individuals become more susceptible to normative influence--one of the primary
catalysts of polarization.
                The normative influence of a.i.m-c became particularly evident
during the course of this study.  On July 3, the day that the final Q sort
arrived via electronic mail, Time magazine ran a cover story entitled simply
"Cyberporn."[3]  The article's main author, Philip Elmer-DeWitt, is a frequent
contributor to alt.internet.media-coverage.  In the weeks following the story,
he was reviled by other a.i.m-c posters, who felt that the article was
"sensational" (Lizard, 1995) and even "damaging" (Gryn, 1995)  One referred to
DeWitt as a "bonehead" (Finkelstein, 1995) and another called he and Time
management "a bunch of blithering idiots" (Dhesi, 1995).  Some messages
castigating Elmer-DeWitt specifically mentioned pressure tactics.  Typical of
the group was Seth Finkelstein, who warned, "You won't be able to live it down
on the Net until you honestly face up to what you did" (1995).  Elmer-DeWitt
appear to be affected to some degree by the pressure applied against him.  He
continued posting messages to the newsgroup well after the initial Time article
ran.  Speaking of the constant berating he took at the hands of a.i.m-c members
he wrote, "I can try to answer these questions, or I can prostrate myself and
beg forgiveness, but I'm not sure I can do both at the same time" (Elmer-DeWitt,
1995).
                Deindividuation can encourage polarization provided that the
group is salient to individual members.  The saliency of a.i.m-c to some members
became apparent in postings related to the Time article.  As Tristan Louis told
one reader interested in learning more about those who posted to a.i.m-c, "I'm
sure you'll hear from them in one way or another. Our little group is
particularly vocal" (1995).  Another regular poster and a study participant
responded to the same post:
                   ... the major Rimm lambasters and lambastees, but not
                   necessarily the major players of a.i.m-c over time.  I'm an
Internet
                   consultant (the lovely undefinable job), a programmer, a
                   pseudo-revolutionary, a general malcontent, and the enemy of
the media,
                   even though I talk to its representatives with wild abandon
(Phillips,
                   1995).
 
              As these messages show, a.i.m-c members see themselves as a group
with players major and minor in terms of their contributions to the whole.
                Given that a.i.m-c is a salient group, small group theory
suggests that increased self-awareness in the computer-mediated communication
environment should produce a polarizing effect among participants.  As
individuals come to identify with a group, they begin to gain a sense of
identity from that group.  The resulting referent informational influence
compels them to match their own behavior to that of the stereotypical group
member by intensifying aspects of their selves that conform to the stereotype.
The resulting conformity toward a polarized endpoint is precisely the kind of
attitude observed in a.i.m-c Internet users within this study, making
coorientation useful in analyzing this kind of communication environment.
                There is, of course, a limitation inherent to classic
coorientation.  One must actually communicate with members of the other group.
Some have referred to the option for communication in groups as a kind of
"outside-dyad" task (cf., Pavitt & Cappella 1979).  This has value in specific
experiments, but must be closely guarded against confounding by other variables.
For example, one might say "boy scouts," "gay boy scouts," "minority boy
scouts," "young boy scouts," or "older boy scouts" and subjects can make use of
an availability heuristic (Tversky & Kahneman 1973) to create a reification of
that group whether or not they know, let alone have communicated with any
individuals who belong to that group.  Although non-a.i.m-c journalists in this
study likely had contact with other Internet users, non-a.i.m-c Internet users
had virtually no contact with journalists who used the Internet.  These groups,
therefore, served more as a control group with which to compare data from
a.i.m-c members.  The heretofore unmentioned difficulty of communication
necessity is the underlying problem in using classic coorientation to examine
certain group communication.  One assumes that all members of Group A have
communicated with all members of Group B.  Of course, this will rarely be the
case.
                Future research might do well to consider this, as well as
possible implications for use of the coorientation model as a heuristic in
exploring mass communication and the use of information availability in the
construction of certain schema (cf. Graber 1988).  Certainly one might argue
that mass media audience members gain their sense of who media personnel are
from mass communication or some combination of mass and interpersonal
communication.  With regard to the Internet, one might also argue that users
create internal reifications of certain groups based upon online communications
by those groups.  The general coorientation model has served as a valuable tool
for studying the communication process in a variety of fields.  The time has
come to reexamine its use in light of opportunities offered by emerging media
technologies such as the Internet.
 
               [1]   The unpublished, exploratory study referred to, Predicting
Use of the Internet By Journalists, was conducted by the author during the 1994
CAR Trek conference sponsored by Investigative Reporters and Editors and the
National Institute for Computer-Assisted Reporting.  Items were paraphrased from
suggested ethical guidelines for journalists, which had been discussed on the
Internet during the spring and summer of 1994.
 
               [2] _  The eigenvalue represents the sum of the squared factor
loadings.
               [3]   Elmer-Dewitt, P.  (1995).  "Cyberporn."  Time.  p. 38ff.
              Works Cited
 
              Abrams, D. & Hogg, M.  (1990).  Social identification,
                   self-categorization and social influence, in W. Stroebe and
M. Hewstone (Eds.),
                   European Review of Social Psychology, Vol 1, Chichester:
Wiley, 195-228.
 
              Abrams, D., Wetherell, M., Cochrane, S., & Hogg, M.  (1990).
                   Knowing what to think by knowing who you are:
Self-categorization and the
                   nature of norm formation, conformity and group polarization.
British Journal
                   of Social and Applied Psychology, 29: 97-119.
 
              Barger, J.  (1993).  Flamology.  Available at
                   http://www.mcs.net/~jorn/html/flamers.html.
 
              Brown, S.  (1972).  A fundamental incommensurability between
                   objectivity and subjectivity.  In S. Brown and D. Brenner
(Eds.)  Science,
                   Psychology and Communication.  New York: Teachers College
Press, 57-94.
 
              ------.  (1980).  Political Subjectivity.  New Haven: Yale
                   University Press.
 
              Burnstein, E. & Vinokur, A.  (1977).  Persuasive argumentation
                   and social comparison as determinants of attitude
polarization.  Journal of
                   Experimental Social Psychology, 13: 315-332.
 
              Butler, J.  (1972).  Self concept change in psychotherapy.  In S.
                   Brown and D. Brenner (Eds.)  Science, Psychology and
Communication.  New York:
                   Teachers College Press, 141-171.
 
              Carter, R.  (1972).  Stereotyping as a process.  Public Opinion
                   Quarterly, 26: 77-91.d
 
              Chaffee, S.  (1971).  Pseudo-Data in Communication Research.
                   Paper presented at the Association for Education in
Journalism meeting, 1971.
 
              Chaffee, S., & McLeod, J.  (1967).  Communication as
                   coorientation: Two studies.  Paper presented at the
Association for Education
                   in Journalism meeting, 1967.
 
              ------.  (1968).  Sensitization in panel design: A coorientation
                   experiment.  Journalism Quarterly, 45: 661-669.
 
              Chaffee, S., McLeod, J., & Guerrero, J.  (1969).  Origins and
                   implications of the coorientation approach in communication
research.  Paper
                   presented at the Association for Education in Journalism
meeting, 1969.
 
              Crumley, W.  (1966).  A Study of the Attitudes About Mass
                   Communication.  Unpublished dissertation, University of
Missouri.
 
              Davis, J., Kerr, N. Sussman, M. and Rissman, A.  (1974).  Social
                   decision schemes under risk.  Journal of Personality and
Social Psychology, 34:
                   1177-1187.
 
              Deustch, M. and Gerard, H.B.  (1955).  A study of normative and
                   information influences upon individual judgment.  Journal of
Abnormal and
                   Social Psychology, 51: 629-36.
 
              Dhesi, R.  (1995). Re: TIME Cover on Cyberporn.  Message posted
                   to alt.internet.media-coverage, July 26, 1995.
 
              Diener, E.  (1980).  Deindividuation: The absence o
                   self-awareness and self-regulation in group members.  in P.
Paulus (Ed.),
                   Psychology of Group Influence,  Hillsdale, NJ: Erlbaum,
209-42.
 
              Diener, E., Lusk, R., DeFour, D., & Flax, R.  (1980).
                   Deindividuation: Effects of group size, density, number of
observers and group
                   member similarity on self-consciousness and disinhibited
behavior.  Journal of
                   Personality and Social Psychology, 29: 449-459.
 
              Duval, S. and Wickland, R. (1972).  A Theory of Objective
                   Self-Awareness.  New York: Academic Press.
 
              Elmer-DeWitt, P.  (1995).  Re: More PEDagogy (was Re: TIME Cover
                   on Cyberporn).  Message posted to
alt.internet.media-coverage, July 26, 1995.
 
              Finkelstein, S.  (1995).  More PEDagogy (was Re: TIME Cover on
                   Cyberporn).  Message posted to alt.internet.media-coverage,
July 26, 1995.
 
              Gaffin, A. & Heitk tter, J.  (1994).  The Big Dummy's Guide to
                   the Internet. Available at
http://alpha.acast.nova.edu/bigdummy/bdg_toc.html.
 
              Graber, D.  (1988).  Processing the News: How People Tame the
                   Information Tide.  2nd ed.  New York: Longman.
 
              Gryn, M.  (1995).  Re: TIME Cover on Cyberporn.  Message posted
                   to alt.internet.media-coverage, July 23, 1995.
 
              Hogg, M.  (1992).  The Social Psychology of Group Influence: From
                   Attraction to Social Identity.  New York: New York University
Press.
 
              Hogg, M., & Abrams, D.  (1988).  Social Identifications: A Social
                   Psychology of Intergroup Relations and Group Processes.
London and New York:
                   Routledge.
 
              Hogg, M., Turner, J., & Davidson, B.  (1990).  Polarized norms
                   and social frames of reference: A test of the
self-categorization theory of
                   group polarization.  Basic and Applied Social Psychology, 11:
77-100.
 
              Kiesler, S., Siegel, J. & McGuire, T.  (1984).
                   Social-psychological aspects of computer-mediated
communication.  American
                   Psychologist, 39: 1123-1134.
 
              Kiesler, S., & Sproull, L.  (1992).  Group decision making and
                   communication technology.  Organizational Behavior and Human
Decision Processes
                   , 52: 96-123.
 
              Kraut, R., Galegher, J., Fish R., & Chalfonte, B. L.  (1992).
                   Task requirements and media choice in collaborative writing.
Human-Computer
                   Interaction, 7: 375-407.
 
              Lea, M. & Spears, R.  (1991).  Computer-mediated communication,
                   deindividuation and group decision-making.  International
Journal of
                   Man-Machine Studies, 34: 283-301.
 
              Lipschultz, J.  (1991).  A comparison of trial lawyer and news
                   reporter attitudes about courthouse communication.
Journalism Quarterly,
                   ,750-763.
 
              Lizard.  (1995).  Re: TIME Cover on Cyberporn.  Message posted to
                   alt.internet.media-coverage, July 23, 1995.
 
              Louis, T.  (1995).  Re: WHO'S WHO on a.i.m-c ?  Message posted to
                   alt.internet.media-coverage, July 23, 1995.
 
              Matheson, K., & Zanna, M.  (1990).  Computer-mediated
                   communications: The focus is on me.  Social Science Computer
Review, 8: 1-12.
 
              McGarty, C., Turner, J., Hogg, M., & David, B.  (1992).  Group
                   polarization as conformity to the prototypical group member.
British Journal
                   of Social Psychology, 31: 1-19.
 
              McGuire, T., Kiesler, S., & Siegel, J.  (1987).  Group and
                   computer-mediated discussion effects in risk decision-making.
Journal of
                   Personality and Social Psychology, 52: 917-930.
 
              McKeown, B., & Thomas, D.  (1988).  Q Methodology.  Newbury Park,
                   Calif.: Sage.
 
              McLeod, J., Becker, L., & Elliott, W.  (1972).  Exploration of
                   methodological problems in coorientation research.  Paper
presented to the
                   Association for Education in Journalism meeting, 1972.
 
              McLeod, J. & Chaffee, S.  (1972).  The construction of social
                   reality.  In Tedeschi, J. (Ed.) The Social Influence
Processes.  Chicago:
                   Aldine-Atherton: 50-99.
 
              Patterson, M.  (1994).  Interaction behavior and person
                   perception: An integrative approach.  Small Group Research
25(2): 172-188.
 
              Pavitt, C., & Cappella, J.  (1979).  Coorientation accuracy in
                   interpersonal and small group discussions.  In D. Nimmo
(Ed.), Communication
                   Yearbook 3, New Brunswick, NJ: Transaction Books, 123-156.
 
              Prentice-Dunn, S. (1991).  Half-baked idea: Deindividuation and
                   the nonreactive assessment of self-awareness.  Contemporary
Social Psychology,
                   15: 16-17.
 
              QUANL.  Program documentation. n.d.
 
              Reicher, S. (1984).  Social influence in the crowd: Attitudinal
                   and behavioural effects of de-individuation in conditions of
high and low group
                   salience.  British Journal of Social Psychology 23: 341-350.
 
              Sanders, G. & Baron, R. (1977).  Is social comparison irrelevant
                   for producing choice shifts?, Journal of Experimental Social
Psychology, 13:
                   303-14.
 
              Siegal, J., Dubrovsky, V., Kiesler, S. & McGuire, T.  (1986).
                   Group processes in computer-mediated communication.
Organizational Behavior
                   and Human Decision Processes, 37: 157-187.
 
              Singer, J., Brush, C., & Lublin, S.  (1967).  Some aspects of
                   deindividuation: Identification and conformity. Journal of
Experimental Social
                   Psychology, 1: 356-378.
 
              Spears, R., Lea, M., & Lee, S.  (1990).  Deindividuation and
                   group polarization in computer-mediated communication.
British Journal of
                   Social Psychology, 29: 121-134.
 
              Stamm, K., & Pearce, W.  (1971).  Communication behavior and
                   coorientational relations.  Journal of Communication 21,
208-220.
 
              ------.  (1974).  Message locus and message content: Two studies
                   in communication behavior and coorientational relations.
Communication
                   Research, 1: 184-203.
 
              Steeves, H.  (1984).  Developing coorientation measures for small
                   groups.  Communication Monographs, 51: 185-192.
 
              Stegall, S.  (1985).  A Q-Methodological Coorientation Study of
                   College and University Public Relations Directors and
Newspaper Reporters in
                   Missouri.  Unpublished thesis, University of Missouri, 1985.
 
              Stephenson, W.  (1936).  The inverted factor technique.  British
                   Journal of Psychology, 26: 344-361.
 
              ------.  (1939).  Methodological consideration of Jung's
                   typology.  Journal of Mental Science, 85: 185-205.
 
              ------.  (1953).  The Study of Behavior: Q Technique and its
                   Methodology.  Chicago: The University of Chicago Press.
 
              ------.  (1961).  Scientific Creed-1961: Philosophical Credo.
                   The Psychological Record, 11: 1-8.
 
              Taha, L., & Calwell, B.  (1993).  Social isolation and
                   integration in electronic environments.  Behaviour and
Information Technology,
                   12: 276-283.
 
              Tajfel, H.  (1978).  Differentiation between Social Groups:
                   Studies in the Social Psychology of Intergroup Relations.
London: Academic
                   Press.
 
              Turner, J.C.  (1982).  Towards a cognitive redefinition of the
                   social group.  In Tajfel, Henri (ed.), Social Identity and
Intergroup Relations
                   , Cambridge: Cambridge University Press: 15-40.
 
              ------.  (1985).  Social categorization and group behavior.  In
                   E. J. Lawler (Ed.), Advances in Group Processes, Vol 2.
Greenwich, CT: JAI
                   Press.
 
              Tversky, A. & Kahneman, D.  (1973).  Availability: A heuristic
                   for judging frequency and probability.  Cognitive Psychology,
5: 207-232.
 
              Vinkour, A. & Burnstein, E.  (1974).  The effects of partially
                   shared persuasive arguments on group-induced shifts.: A
problem-solving
                   approach.  Journal of Personality and Social Psychology, 29:
301-15.
 
              Wicklund, R. & Gollwitzer, P.  (1987).  The fallacy of the
                   private-public self-focus distinction.  Journal of
Personality, 55: 491-523.
 
              Zimbardo, P.  (1969).  The human choice: Individuation, reason
                   and order versus deindividuation, impulse and chaos.  In W.
Arnold & D. Levine
                   (Eds.),  Nebraska Symposium on Motivation.  Lincoln
University of Nebraska
                   Press.

Back to: Top of Message | Previous Page | Main AEJMC Page

Permalink



LIST.MSU.EDU

CataList Email List Search Powered by the LISTSERV Email List Manager