Risk Perception in Community Context.
Evaluating the psychometric paradigm and its relationship
to risk amplification and reported communication channel usefulness.
A paper presented to the Science Interest Group at the
Annual Meeting of the Association for Education in Journalism and Mass
Communication
Washington, DC
August 1995
by Craig W. Trumbo
Graduate Student
Department of Agricultural Journalism
440 Henry Mall
The University of Wisconsin
Madison, WI 53706
608-262-4902 (office)
608-233-9189 (home)
[log in to unmask]
ABSTRACT
This project incorporates three sequential steps. First, the
psychometric model of risk perception is evaluated for its validity
under field conditions. Second, individuals are classified as
amplifiers
or attenuators within the social amplification of risk model.
Finally,
the characteristics of attenuators and amplifiers are explored, with
special focus on their use of communication channels. Survey data from
an on-going case study is employed in the analysis. The case study
involves a mid-western community in which a controversy exists over the
possibility of the existence of a cancer cluster caused by the
operation
of a small reactor.
Results show that the psychometric model of risk perception has validity
under the field conditions utilized in this study. Use of the
psychometric model to classify individuals as risk amplifiers or risk
attenuators produces a useful dichotomy that reveals differences
between
the two polar groups in terms of demographics, satisfaction with
institutional response to the risk, concern over individual and social
levels of risk, and the evaluation of various communication channels
as
having been useful in coming to a judgment about the risk.
A final model comparing the two groups suggests that, in this case, two
dominant forces are in play against one another: an evaluation
of
personal risk versus satisfaction with the institution managing the
risk. Subordinate to these forces are the demographically based
variables of education and years of residence in the community. This
model also illustrates that aggregate-level observations may not be
fully characteristic of underlying processes of polarization.
ACKNOWLEDGMENT
The author would like to thank the following individuals for their
insightful comments on this project: Garrett O'Keefe, Sharon
Dunwoody,
and Jack McLeod.
1. INTRODUCTION
Risk has recently grown as a topic of interest within the field of
communication, with two related problems generally of greatest
interest.
The first involves the nature and consequences of news media
representation of risk, typically with respect to technological or
environmental controversies. The second area involves the difficulties
encountered by scientists and other experts charged with informing
the
lay public about their risk from various natural or man-made
hazards.
The second area has most involved communication researchers in the
area
of risk perception: understanding how the individual perceives and
makes
judgments about risks faced in life.
This investigation has the overarching goal of advancing understanding
of how individuals come to a judgment about risk. This goal
includes a
focus on the role of communication channels within the process of
risk
judgment.
This project incorporates three sequential steps. First, the
psychometric model of risk perception is evaluated for its validity
under field conditions. Second, individuals are classified as
amplifiers
or attenuators within the social amplification of risk model.
Finally,
the characteristics of attenuators and amplifiers are explored, with
special focus on their use of communication channels. These areas of
the
literature will be addressed following a description of the case
study
being utilized in this project.
2. CONTEXT: THE CASE STUDY
Since the Manhattan Project, the U.S. Department of Energy has operated
its Ames Laboratory at Iowa State University in Ames, Iowa. The
wartime
assignment of the Ames Lab was to purify enriched uranium for use in
the
atomic bomb program. Since then the Ames Lab has continued to be
involved in ceramics and metals development, methods of non-destructive
analysis, and the development of environmental remediation
technologies.
Ames Lab is also very active in the area of technology transfer.
Over the years, various activities and accidents at the Ames Lab have
created a handful of waste sites in the Ames area. The Ames Lab
and the
citizens of Ames have recently been in conflict over three such
situations. The first involves the construction of a youth sports
complex on a site contaminated in 1953 by low-level radioactive thorium.
Despite being cleaned-up in 1988, community resistance stalled the
city's plans to build there.[1] The second issue involves the clean-up
of a
waste burial site in Ames where some 7,000 pounds of low level
radioactive and other mildly hazardous materials have been interred for
about 40 years. The DOE has recently concluded the remediation of
that
site.[2]
The third issue involves a small research reactor that the Ames Lab and
Iowa State University jointly operated from 1965 until
decommissioning
in 1981. Some residents of the Ross Road neighborhood (one-half mile
directly south from the former reactor site) believe that they are now
part of a cancer cluster caused by the reactor. One section of the
neighborhood had 13 cases. Two epidemiological studies have been done.
The first was inconclusive and the second found that cancer rates in
the
area were not significantly above normal.
To address concerns in the community, the Ames Lab has hosted four
public forums, a community workshop, and has created an information
repository on reserve at the ISU library. These issues have also
received a fair amount of attention from the local newspaper.[3]
This investigation has as its primary focus the third issue: the
perceived cancer cluster. This issue is being emphasized for two main
reasons. First, it is of an enduring nature. It predates the other
issues and will out-last them as well. Second, it is an intractable
problem: there is no evidence of a cancer cluster D and even if there
was such evidence it could not be causally associated with the former
reactor. While the cancer cluster issue cannot be termed a "hot
button"
topic, it is a issue that is widely known of in the community. The
key
aspect is that due to the general recognition of the issue most
individuals have had to come to some conclusion about it.
In more general terms, the situation faced by Ames residents is not
uncommon. Many communities face relatively small and localized
hazard
issues. Cases like Love Canal or Times Beach capture high media
attention, but are atypical of the experience Americans more commonly
have with environmental hazards. Understanding how individuals
perceive
their situation relative to this variety of hazard and how they use
information channels to form opinions is a task of some importance.
3. LITERATURE
To frame the primary questions of this investigation, three areas of
literature will be briefly examined: the psychometric model of
risk
perception, the social amplification of risk model, and communication
channel utility.
The Psychometric Model. A considerable amount of research has been done in
the area of risk perception. Dunwoody and Neuwirth prefer the
term "risk
judgment" to emphasize the active information processing inherent in
the
construct.[4] In any case, the psychometric model of risk perception
has
grown from research that asks individuals to compare a range of
hazards
based on a set of attributes, such as how well the hazard is
understood
or how many people might be affected by the hazard. The research has
consistently shown that people evaluate hazards not only on the
hazard's
objective harm (e.g., deaths per year) but also on a range of more
subjective criteria.[5]
The series of projects initiated by Fischoff and coworkers, and carried
on by Slovic and associates, have shown that two important
dimensions
can be seen to describe the perception of risk.[6] [7] One dimension
of risk
is termed dread. This is related to the scale of the risk and the
degree
to which it impacts innocent individuals. The second dimension,
termed
knowledge, involves how well a risk is understood and how observable
its
consequences are.
The model has since been widely replicated and cited. The risk space
model has been validated in various countries and researchers have
also
expanded risk into additional dimensions using a wide variety of
attributes such as the number of people affected or the voluntariness of
the risk. [8] [9] Non-human mortality and transgenerational effects
have also
been examined as risk perception factors.[10] But the basic premise
of the
two dimensional psychometric risk perception model has remained
essentially unchanged.[11]
Some salient criticisms of the model have been addressed, including
consistency among individuals in risk perception and the nature of
risk
perception within a single technological domain.[12] The model held
up in
these investigations, and also faired well in a reanalysis of the
original data carried out by Gregory and Mendelsohn, who also found that
perceived benefit plays a role in risk perception.[13]
While these studies do suggest a remarkable validity to the psychometric
model, an important weakness remains in this direction of
research:
risks are examined in the abstract. People can certainly be called upon
to evaluate a set of risks, or attributes of a single risk, even
though
they do not personally face that risk as an important aspect of
daily
life. But is it safe to assume that people perceive or judge risk in
the
same way when it involves a "live risk," a hazard or a risk
controversy
that is part of daily life?
This question is not ignored in the literature. Risk perception studies
have looked at specific risks in context. Such research has
typically
been in the form of case studies.[14] While these studies and others
have
great merit, they have not employed the psychometric risk perception
model. It would be useful to know if the psychometric model can be
used
to understand the perception of such local hazards. That is the
first
issue to be addressed in this study.
Social Amplification of Risk. In response to the acute need to find some way
to bring the diverse array of perspectives on risk, risk
perception, and
risk communication together into an integrative framework, Kasperson
and
coworkers formulated the social amplification of risk.[15]
Social amplification of risk holds that the communication and behavioral
responses of individuals, groups, and institutions operating
under a
risk event or controversy act as "amplification stations." It is the
interaction among the risk interpretations and responses of these
stations that determine the nature of the life course, or "rippling," of
the risk event or controversy.
Within the concept of the amplification station exists a linkage between
the macro level social functions of institutions or groups and
the micro
level processes that operate within and between individuals.
Kasperson
conceptualizes the micro level functioning as "individual stations
of
amplification" and the macro level analog as the "social stations of
amplification."
Social amplification is primarily a framework in which to utilize a
variety of discrete theories and methods. Renn describes how
social
amplification might integrate the strengths and weaknesses of the
social, psychological, and cultural approaches to risk.[16] The
distinctive
problems of each approach D social relevance for psychometrics,
complexity for sociology, and empirical validity for cultural theory D
may tend to cancel out and allow for an emergent perspective.
The full application of the social amplification framework is a complex
and long-term research goal. The present investigation has a
more
specific focus: to develop an understanding of how individuals can be
classified as being either amplifiers or attenuators within the
concept
of the individual station of amplification. The existence of this
dichotomy is a key micro-level prediction of the social amplification of
risk model.
Since no objectively-based definition of risk exists in this case study
it must be held that amplifiers and attenuators exist relative
to each
other by way of their perception of risk from the threat D as
opposed to
existing relative to some "true" condition. In other words, there is
no
"correct" position in this controversy. Within this framework, the
evaluation of individuals' perception of risk might be based on the
psychometric model and this evaluation may in turn be used to group
individuals as either amplifiers or attenuators.
While largely a semantic matter, placing amplifiers and attenuators
under the umbrella of amplification (in its engineering guise as to
either increase or decrease) can create unnecessary confusion. Rather,
the idea will be recast as what it essentially is: polarization.
Channel Utility. The final aspect of this study seeks to examine the
information channels people use in forming an opinion about a
perceived
risk. The focus of concern will be the relative roles of mass
communication, interpersonal communication, and other forms of informa
tion-seeking behavior.
Chaffee presents an argument that the dichotomy of mass versus
interpersonal communication has been endowed with excessive
polarity.[17]
His analysis of the literature builds the case that individuals use a
given channel based on how accessible the channel is and how likely
the
individual believes it is that the desired information will be found
in
a particular channel. In this light, it should be emphasized that
this
research does not seek to determine mass or interpersonal supremacy,
but
rather to assess the various ways in which individuals use both
channels
in coping with a specific risk situation and how these channels
relate
to other forms of information-seeking.
A handful of studies inform this question. Mazur and Hall examine how
members of a New York county evaluate the risk of radon as either
a
specific concern in the home or as a more diffuse national hazard.[18]
They
find that neither interpersonal contact with family members nor mass
media messages correlate with a specific concern about radon in the
home. However, both were strongly correlated with a more general
concern
about radon as a national hazard, with family influence considerably
stronger than mass media influence.
McCallum and coworkers compared mass media and interpersonal channels as
preferred ways of gathering information about toxic chemicals in
the
local environment. They surveyed subjects in six communities around
the
nation that were facing toxic chemical issues and found that mass
media
channels were strongly preferred as sources of such information.
Interpersonal sources were used only 12% of the time.
Following Tyler and Cook's observation that mass media impact
social-level judgments more than individual-level judgments,[19] Coleman
found that mass media are stronger than interpersonal channels in
influencing society-level risk judgments.[20] Mass media also had some
influence on personal risk judgments, an effect which interpersonal
channels did not have.
Contrasting the variety of findings in these studies goes to Chaffee's
argument: interpersonal and mass communication channels have
varying
roles in shaping people's perceptions and no broad generalization can
hold. The role of other information-seeking behavior is less clear
from
these studies. Chaffee points out that people have varying abilities
to
use other information resources, such as libraries or expert
opinion.
Dunwoody and Neuwirth also point out that people probably make
different
use of various channels during the life span of their relationship
with
a given risk. This study will attempt to address these issues,
looking
at the relative usefulness of mass communication, interpersonal
communication, and other forms of information-seeking in the process of
risk judgment.
4. RESEARCH QUESTIONS
This study is both confirmatory and exploratory in nature. It seeks to
confirm that the psychometric model describes how individuals in
this
case study evaluate the given risk. It also seeks to confirm that the
individuals engaged in this risk controversy can be productively seen
as
being polarized into amplifiers and attenuators. Finally, it seeks
to
explore the characteristics of polarization and look for important
differences between the two camps. Toward these ends, this study employs
a set of three research questions:
RQ1: Do individuals evaluate the given risk in terms of dread and
knowledge as the psychometric model predicts?
RQ2: Based on the psychometric model, can individuals be consistently
grouped as amplifiers and attenuators as polarization predicts?
RQ3: What differences are there between attenuators and amplifiers in
terms of the demographic, risk, and channel use variables
measured?
5. METHODS
A mail survey instrument was developed to achieve the goals of this
investigation. The instrument consists of three general segments.
First,
the set of original psychometric model questions were modified
slightly
to fit the specific issue at hand. While researchers have expanded
and
modified this set of questions, the model is most strongly
associated
with nine aspects of dread and five aspects of knowledge. These
questions are shown in Table 1. The second part of the instrument
consists of a set of questions seeking to ascertain what sources of
information people have found to be useful in making judgments about
this risk issue. Questions are also asked concerning satisfaction with
attention paid to this issue by local media, Ames Lab officials,
government representatives and others. Finally, a few general
demographic variables are included.
The sampling unit is the non-rental household. Subjects are drawn from
the northwest quadrant of Ames D the area defined by previous
epidemiological studies and by stories in the Ames Daily Tribune .[21] The
area is also defined by social and geographic boundaries: the city
limits to the north and west, and a large park to the south and east.
The current Polk's City Directory for Ames was consulted as a
sampling
frame. A random sample of 50% identified 223 households to
participate
in the survey. Either principal adult member of the household could
complete the questionnaire.
Mail survey procedures described by Dillman[22] were adhered to and the
survey arrived in Ames on about September 23, 1994. The survey
return
period was closed on November 1, 1994. At that time, 130
questionnaires
were received for a return rate of 58 percent.
6. DISCUSSION OF THE RESULTS
The first task is to evaluate the psychometric model and determine if it
can be used as an effective means of proceeding with the
analysis. The
14 questions shown in Table I were entered into a factor analysis.
The
rotated factor matrix was virtually uninterpretable. The results
presented one strong factor with a mix of knowledge and dread variables
and several weak factors with one or two variables each. No clear
pattern was discernible.
The further evaluate the model, the communalities of the variables were
examined to see if there were any very weakly associated
variables that
might be appropriately excluded from the analysis (overall, the KMO
statistic was adequate at .75). All variables but one had strong
communality: the dread variable FATAL describing the likelihood that any
illness from the risk would be fatal. This single weak variable was
ejected and the factor analysis was again executed. Table II presents
the results. With the exclusion of the one weak variable the rotated
matrix provides a satisfying solution that conforms well to the
prediction of the model.
One knowledge factor emerges made up of individual and scientific
knowledge about the risk, how familiar the risk is to the individual,
and how observable the consequences of the risk are to the
individual.
Dread appears to be made up of 2 components. Factor 2 might be
called
"pure dread" as it consists of elements that relate more clearly to
fear: catastrophe, transgenerational effects, an increase in the risk,
and being unable to calmly contemplate the risk. Factor 3 appears
most
closely related to the idea of personal efficacy. This factor
involves
the individual's ability to control exposure to the risk, ability to
exercise choice in accepting the risk, and personal ability to reduce
the risk. These all speak to the degree of individual agency with
respe
ct to the risk. A fourth uninterpretable factor emerged that has an
equal measure of both knowledge and dread.
Overall, the solution to the factor analysis supports the application of
the psychometric model in this field situation, satisfying the
first
research question. This is a fairly important result in itself since
the
psychometric model has been most frequently applied to the
evaluation of
individual perception of a range of risks considered in the
abstract.
This analysis suggests, at least for the specific risk examined in
this
case, that individuals may indeed evaluate risks they face through
processes that can be understood in terms of knowledge and dread.
The four factor solution approaches but does not completely satisfy the
proposition of a two-dimensional model of risk perception.
Questions
remain: how do the four factors relate to one another, are the two
dread
factors associated, and how should the fourth uninterpreted factor
be
treated? To resolve these questions, factor scores were treated in a
second-order factor analysis. The resulting two factor solution
grouped
the dread factors together and grouped the knowledge factor with the
fourth uninterpreted factor. With this result taken as evidence of
association, variables for the dimensions of dread and knowledge are
created by averaging the associated factor scores. Both variables are
approximately normal with mean of 0 and are uncorrelated.
The second research question asks if the psychometric model might be
applied to the task of separating individuals into amplifiers and
attenuators. The literature on the recent concept of social
amplification does not suggest what characteristics might indicate the
two groups. To define the groups, the variables dread and knowledge
are
plotted against each other and the scatter is divided at the mean
created by the line with slope -1 running through the origin of the
plane.
A discriminant analysis was run to confirm that this method of group
determination is in fact consistent with the nature of the
original
variables being used. The classification analysis used the full set of
13 variables to predict the polarization groups. The classification
matrix shows strong agreement that the 13 variables can identify two
groups divided along mean responses to dread and knowledge. The
analysis
correctly classifies 100% of the cases, identifying 54 amplifiers
and 40
attenuators (some cases are lost due to incomplete responses). No
significant differences were found between this group of 94 and the 36
other survey respondents.
Research question three asks what differences might exist between the
two polarization groups. Table III provides the significant
results,
which can be organized in blocks: demographics, risk perception and
behavior, satisfaction with institutional response to the issue, and
the
reported usefulness of various information channels.
Two of the demographic variables show a significant difference between
attenuators and amplifiers. Both gender and education are
significantly
related to polarization. While amplifiers are slightly more likely
to be
female, attenuators are very much more likely to be male (overall,
respondents are 47 percent female). Attenuators also have typically
completed more education, with over half having completed a graduate
degree (recall that Ames is a college town). As these results suggest,
further crosstabulation confirms that gender and education are
significantly related with males having more education. The respondent's
age is independent of group status.
Two other demographic variables approach significance and are worth
discussion. It was suspected that the presence of cancer in the
respondent's family might tend to be related to amplification. The data
show this association, but only at a weak level of significance (p =
.11). Length of residence in the area is also of interest since the
reactor was removed in 1981. The data again provide weak evidence for
this association (p = .098). Amplifiers tend to be newer to the area,
with a mean length of residence of 15 years as opposed to 20 years
for
attenuators. It may very well be a coincidence that the reactor was
removed 14 years ago. Nonetheless, amplification may be intertwined
with
a fear of the unknown, as some of these individuals have never
actually
seen the former facility. It is also likely that generational
differences may exist between the two groups with respect to
environmental values and trust in government, for example
Risk perception and behavior variables provide a number of significant
differences between the two groups. Taken together, these items
might be
roughly conceptualized as worry. Most of these differences would be
expected by virtue of the method of differentiating the groups. As
such,
they also provide additional support for the validity of the two
groups.
Amplifiers think about and talk about the cancer cluster issue more
frequently than attenuators and also feel that both they and others are
at greater risk. These differences are quite pronounced. On the
other
hand, there were no significant differences found when asking how
long
an individual had been aware of the issue or when asking what
actions an
individual might have taken because of concern over the issue.
Further,
attenuators are no different from amplifiers when it comes to
knowing
other individuals in the area who have cancer.
The set of four satisfaction variables were all predictably different.
Attenuators are uniformly more satisfied with attention paid to
the
issue by Ames Lab, by elected officials, and by the news media. They
are
also more satisfied with the results of the existing epidemiology.
The channel usefulness variables provide rather dramatic results in
terms of non-significance. Of the 10 sources of
information evaluated as having been useful in making a judgment about
personal risk, only neighbors reveal a significant difference
between
the two groups. Usefulness of family members differs between the
groups,
but only weakly (p = .076). Amplifiers rated both neighbors and
family
members as being more useful sources of information. The 8
information
sources that showed no difference between the groups were the
newspaper,
television, friends, physician, elected officials, Ames Lab
officials,
public meetings, and the library's information repository.
Ranking of the usefulness of information channels is different for the
two groups. An examination of the means of the 10 source
usefulness
variables shows that both groups rated the newspaper as the most useful
source of information. After that, the rankings diverge. Amplifiers'
top
five are newspaper, neighbors, television, friends, and family.
Attenuators' top 5 are newspaper, television, friends, neighbors and
Ames Lab officials. Spearman's rho between the two rankings of 10
items
is only .11 (not significant). This lack of association in the two
groups provides evidence that the two groups are utilizing information
sources differently.
For the next step in the analysis, all of the variables found to be
significant (at p < .1) in detecting group differences were
entered
into a discriminant analysis. Table IV presents the results. The
discriminant function produced by the 13 variables performed well,
significantly accounting for about 40 percent of variance. Correlations
between the individual discriminating variables and the discriminant
function can be interpreted as an indication of the relative strength
of
the variable's contribution to discriminating the groups. Further,
grouping variables by the sign of the correlation can be useful (note
that all variables are analyzed simultaneously and contribute to case
assignment to both groups).
Variables are sorted by sign and listed by size of the correlation. It
can be seen that evaluation of personal risk, risk to others,
frequency
of thinking and talking about the issue, and the usefulness of
neighbors
and family are grouped together. Conversely the grouping of positive
correlations include education, gender, years of residence, and what
might be taken as a set of variables indicating satisfaction with the
institutional response to the issue.
The sign of the correlation can sometimes be interpreted as indicating
which group it is more closely associated with. In this case,
amplifiers
were assigned the lower value so the set of negative correlations
are
associated with amplification. Such interpretation must be approached
cautiously. Here, it appears that a lack of the qualities indicated
by
the negatively correlated variables indicate amplification and a
lack of
the qualities indicated by the positively correlated values indicate
attenuation.
The more important results of this analysis are the manner in which the
variables group and their ability to predict group membership.
Overall,
the variables found to significantly differentiate between the
groups
were in good agreement with the creation of the groups based on the
psychometric model, with a classification rate from the discriminant
analysis of 85%.
Finally, it is important to recognize that attenuators and amplifiers do
not represent two homogeneous groups. There is a distribution of
risk
perception within each group. To recognize this while still pressing
to
examine for differences between the two groups, a continuous measure
of
risk perception was created by averaging the dread and knowledge
factors. This scale is then used as the dependent variable in
hierarchical regressions utilizing the 13 variables which show
significant differences between the groups. Variables are analyzed in
blocks representing demographics, interpersonal sources, satisfaction
with institutions, and worry. Results are presented in Table V.
For the analysis of the full sample, each block increments R2
significantly (demographics only at p = .06). The full model achieves an
adjusted R2 of .52 with 5 variables displaying significant partial
coefficients: education, usefulness of neighbors, satisfaction with Ames
Lab, and evaluation of both personal risk and other's risk.
The more interesting results come from a comparison of the two polar
groups. For the attenuators, only the block representing
satisfaction
with institutions significantly changed R2. The primary variable in
this
column is satisfaction with Ames Lab. It appears possible that an
interaction between gender and education prevents the demographic block
from achieving significance. Inclusion of an interaction term did
not
alter either the change in R2 or the adjusted R2 for any of the
blocks.
For amplifiers, the only significant block was the one involving the
worry variables, although the years of residence variable in the
demographic block is itself significant. For the worry block, evaluation
of personal risk is the one strong element.
Stepwise regressions both focus and confirm the significant predictors
of risk perception in the three groups, providing the following
models
(alpha = .05, showing standardized betas):
Full Sample:
Risk = -2.5 + .48 (Personal Risk) + .27 (Other's Risk) - .16 (Education).
Adj R2 = .54 p < .001
Attenuators:
Risk = - 0.5 - .57 (Satisfaction Ames Lab) + .36 (Education). Adj. R2
= .33 p < .001
Amplifiers:
Risk = -0.1 + .62 (Personal Risk) + .25 (Years Residence). Adj. R2
= .43 p < .001
7. CONCLUSION
The analysis presented in this paper is a preliminary investigation in
what will eventually be a larger case study. The primary mission
of this
preliminary investigation is three-fold: to gather initial general
information about the population and the controversy, to test the
usefulness of the psychometric model, and to evaluate the viability of
the polarization supposition made by the social amplification model.
The
results of the analysis to this point suggest that each of these
goals
have been met.
The secondary mission of this preliminary investigation conforms to the
overarching goal of the larger case study: to understand the
role of
communication in risk controversies and to add to the understanding of
effective risk communication. Framing these goals in terms of risk
polarization is not far from stating the problem as one of audience
analysis. Much of the work done to date in risk communication has
treated receivers as a somewhat homogeneous mass D evaluating, for
example, the effectiveness of various forms of message construction
without regard to important differences that might exist in the target
audience. It is likely that risk communication could benefit greatly
by
shifting some attention from message construction to audience
analysis.
With respect to the idea of audience analysis, an important lesson is
demonstrated by the hierarchical regression models and their
stepwise
counterparts: looking at a community's aggregate response to a risk
controversy may be misleading. Different components in the community,
at
least in this case, have significantly different orientations toward
the
risk. These differing orientations may be most clearly seen if
conceptualized and measured as polar opposites.
Utilizing such a light to examine the Ames case, it appears that two
dominant forces are in play against one another: an evaluation of
personal risk versus satisfaction with the institution managing the
risk. Conceptually, these forces may approximate the notions of trust
and outrage that have recently come to the risk perception
literature.[23]
Subordinate to these forces are the demographically based variables
of
education and years of residence in the community. Interpreting the
stepwise regression equations for these two subordinate forces further
demonstrates how aggregate characteristics may not be
representative.
While attenuators tend to have more education, within their group
greater education is associated with a greater perception of risk. And
while amplifiers tend to have been residents of the area for fewer
years, within their group those who have lived in the area longer
perceive a greater level of risk.
The instrument used in this investigation provides only a rough look at
the communication behaviors of this population. The main purpose
of this
set of questions is to point the way for the construction of a new
instrument to be applied in the near future. There are, however, two
interesting results with respect to communication from the analysis so
far.
First, there is apparently a dynamic involving mediated communication.
Some background on the news coverage is in order. Very little
attention
was paid to this issue by the television stations covering central
Iowa.
Two of the three stations are located in Des Moines, some 40 miles
away.
These stations pay only cursory attention to Ames. The third network
affiliate is located in Ames but has what is widely considered to be
the
weakest news operation. Attention from the nearest metro daily
newspaper, The Des Moines Register, has been non-existent. The only
significant coverage has been in the local newspaper, The Ames Daily
Tribune.
It is not overly surprising that respondents would rank the local
newspaper as the most useful source of information on the issue. What
is
interesting, however, is that while attenuators and amplifiers alike
rated the newspaper as the most useful source of information they
differed significantly in their satisfaction with the attention paid to
the issue by the news media overall. This result may suggest that
the
two groups are processing news information differently.
More refined measures of media use are needed to support this
proposition, but if respondents are basing their satisfaction in part on
how the local newspaper has covered the issue (as opposed to how the
other media outlets have not covered the issue) then amplifiers have
rated the newspaper as most useful but were less satisfied while
attenuators have rated the newspaper as most useful but have been more
satisfied. This suggests the possibility that the same information
from
the same source had different consequences for different audience
segments. This conclusion is a stretch for these data but not an
insupportable proposition.
The other interesting result from the communication variables is that
the two groups did not differ in their evaluation of the
usefulness of
any of the information channels except for neighbors and to a lesser
degree family members. Since amplification is associated with finding
neighbors and family members more useful as sources of information,
it
is a fair conclusion to state that concern over risk in this case is
driven by interpersonal communication to a greater degree than by
mediated communication. This finding is in agreement with much of the
research previously cited.
Finally, it is important to return to the notion of polarization, drawn
from the social amplification model, that underlies this
analysis.
Further investigation of the phenomenon of polarization might proceed
along two lines, utilizing principles of social identification and
models of information processing.
Social psychology has long recognized a process of group polarization.[24]
In the most general terms, this is the process through which a
group can
come to hold and express attitudes that are more extreme than those
held
individually by its members. There are two dominant theories, social
comparison and persuasive arguments. In social comparison, individuals
compare their own views to the perceived average view held by the
group
and then tend to shift their attitudes to the more extreme side of
the
perceived group consensus. The final outcome of a group decision
therefore tends to be polarized. In the persuasive arguments theory,
individuals construct mental lists of arguments for and against a
choice. When individuals discuss these lists in a group that tends to
hold a polarized position, the arguments that are more universally
held
by group members tend to be those that are more extreme in the
polarized
direction. These more extreme arguments then tend to dominate both
individual and group attitudes.
It is likely that some process of this nature has been in play in the
Ames case. The Ames Lab, and its strong association with the
University,
creates an in-group/out-group situation that may have served to
provide
a polarization identity for some individuals. Further, the public
forums
and coverage by the newspaper have likely provided a set of
polarized
arguments around which the groups could have formed and strengthened.
Individual polarization has also been related to cognitive consistency.
Chaiken and Yates found support for the hypothesis that
"thought-induced
attitude polarization requires the presence of a well-developed
knowledge structure."[25] They found in an experiment that individuals with
a high degree of consistency tended to polarize on an issue more
readily
than their low-consistency counterparts. This point of view
dovetails
into an information processing perspective offered by Eagly and
Chaiken,
in which individuals are hypothesized to process information in
either a
systematic or heuristic manner.[26] Griffin and Dunwoody utilize
this
perspective to build a model of information processing specifically for
risk information.[27]
Griffin and Dunwoody consider the heuristic-systematic processing model
and hypothesize on characteristics that might lead individuals
to
process risk information in one way or the other. They propose a
framework of variables that they organize in categories of demographics,
characteristics of the hazard, individual worry, how individuals
feel
that their information needs are being satisfied, and how confident
individuals feel that they are able to gather needed information. If
there is a relationship among heuristic-systematic processing,
cognitive
consistency, and polarization D then it is also likely that the
model
that Griffin and Dunwoody propose could be profitably related to
polarization.
For the case of the risk controversy in Ames, it might be hypothesized
that amplification is associated with (for example) systematic
information processing of news information intertwined with
interpersonal influence. Conversely, attenuation might be associated
with more rapid "gut level" heuristic information processing of news
information that is intertwined with pre-existing attitude structures
related to trust in the institutions involved. Further research may
approach these questions.
Table I.
Questions used for risk perception model with codewords.
Dread or knowledge questions indicated with (D) and (K).
For the following questions please give your own personal opinion about possible
risks
to yourself from the former reactor. Circle the number you think
best locates your
position on the 1 to 7 point scale. (low values are for judgments
of low risk)
Catastrophe (D) Do you think this kind of risk - that caused by small nuclear
reactors
- has the potential to cause catastrophic death and destruction?
Generations (D) Do you feel that any risk that may be posed from the former
reactor
extends to future generations?
Dread (D) Is this the kind of risk you can learn to live with and calmly
deliberate
about, or one that you constantly dread and worry about?
Changing Risk (D) Do you feel that your risk from the former reactor in Ames is
increasing, decreasing, or staying the same?
Personal Control (D) How much control do you think you personally have over
avoiding
possible risks to yourself from the former reactor?
Personally Reduce Risk (D) How easy or difficult would it be for you to reduce
any
risk you might face from the reactor?
Fairness of Risk (D) Do you think the people who may have been exposed to some
risk
from the reactor are the same people who may have benefited from
its operation?
Fatality of Risk (D) If you were to become ill from this risk, how likely is it
that
the illness would be fatal?
Personal Choice (D) Do you think you have much choice over accepting any
possible
risks from the former reactor?
Harm Delay (K) Is it more likely that any possible harm to you from the reactor
would
have occurred immediately after exposure, or that it would be
delayed over time?
Science Knows (K) How knowledgeable do you think scientists are about any
possible
risks from the former reactor?
You Know (K) How knowledgeable do you think you are about any possible risks
from the
former reactor?
Familiarity of Risk (K) Is this a new, novel kind of risk for you, or one
that's old
and familiar to you?
Observability of Exposure(K) If you were exposed to a risk from the reactor,
how aware
do you think you would be of your risk from that exposure?
Table II.
Factor Analysis of Psychometric Variables
Factor 1 Factor 2 Factor 3 Factor 4
Variable Mean SD Knowledge Pure Personal Indeterminate
Dread Efficacy
You Know (K) 4.6 2.0 .80
Science Knows (K) 3.4 2.0 .77
Observability of Exposure (K) 4.8 2.1 .56
Familiarity of Risk (K) 4.6 2.3 .51
Catastrophe (D) 2.7 1.8 .80
Generations (D) 4.4 1.9 .69
Changing Risk (D) 2.9 1.4 .61
Dread (D) 2.6 1.8 .59
Personal Control (D) 5.2 2.1 .79
Personal Choice(D) 5.1 2.0 .78
Personally Reduce Risk (D) 4.7 2.1 .62
Harm Delay (K) 5.9 1.6 .75
Fairness of Risk (D) 4.2 2.1 .73
Percentage total variance 32.9 12.1 9.9 8.3
Eigenvalues 4.3 1.6 1.3 1.1
Factors were determined with an eigenvalue cutoff of 1.0, principle components
analysis with varimax
rotation. Loadings under .5 are blanked. Four factors explain 63.2
percent of total variance. KMO
statistic = .75.
Table III.
Comparison of Amplifiers and Attenuators.
Item Attenuators Amplifiers
mean mean p
Frequency talking about issue (1 never to 6 daily) 1.9 2.3 .004
Frequency thinking about issue (1 never to 6 daily) 2.1 2.9 .002
Risk to others (1 no risk to 7 high) 2.1 4.2 < .001
Risk to self (1 no risk to 7 high) 2.1 4.3 < .001
Usefulness of neighbors in making judgment (0 low to 7 high) 1.1 2.5 .003
Usefulness of family members in making judgment (0 low to 7 high) 0.6 1.3 .076
Satisfaction with epidemiology (1 not to 7 very) 4.5 3.1 .001
Satisfaction with attention from Ames Lab (1 not to 7 very) 4.6 3.1 .001
Satisfaction with attention from elected officials (1 not to 7 very) 3.9 2.4
<.001
Satisfaction with attention from news media (1 not to 7 very) 4.5 3.5 .018
Years as resident of northwest Ames 19.5 14.8 .098
Item Attenuators Amplifiers
col % col % c2 p
Respondent's gender 4.5 .03
female 34.2 56.6
male 65.8 43.4
Highest level of education completed 18.5 <.001
high school 16.2 22.6
bachelor's 13.5 50.9
graduate 70.3 26.4
Have any members of immediate family had cancer? 2.6 .11
no 87.5 74.1
yes 12.5 25.9
Table IV.
Evaluation of significant differences between groups.
Saturated discriminant model using variables showing differences between groups
Function 1: Canonical Correlation = .64 Wilks' Lambda = .59 c2 = 37.8
p
< .001
Pooled within-groups correlations between discriminating variables and
discriminant function:
.62 Personal risk
.61 Other's risk
.37 Frequency thinking
.35 Frequency talking
.31 Usefulness of neighbors
.22 Usefulness of family
-.45 Education
-.42 Satisfaction with representatives
-.39 Satisfaction with news coverage
-.36 Satisfaction with Ames Lab
-.33 Satisfaction with epidemiology
-.22 Gender
-.18 Years as resident of NW Ames
Risk Perception in Community Context
NO. OF PREDICTED
GROUPS
ACTUAL GROUP CASES 1 2
---------------------- ------------
-----------------------------
GROUP 1 44 37 7
AMPLIFY 84.1% 15.9%
GROUP 2 34 5 29
ATTENUATE 14.7% 85.3%
PERCENT OF CASES CORRECTLY CLASSIFIED: 84.6%
Table V.
Hierarchical Regressions: Risk on Significant Group Differences
FULL SAMPLE
ATTENUATORS
AMPLIFIERS
BLOCKS
beta
R2 cha
adj. R2
beta
R2 cha
adj. R2
beta
R2 cha
adj. R2
1. DEMOGRAPHIC
.09
.06
.16
.08
.11
.04
Gender
-.15
-.30*
.05
Education
-.20*
.36**
.20
Years Residence
-.09
.17
.29*
2. INTERPERSONAL
.16***
.21***
.00
.02
.05
.05
Neighbors
.27**
-.01
.22
Family Members
.10
-.01
.10
3. INSTITUTIONS
.12**
.30***
.33**
.30**
.02
.03
Ames Lab
-.22*
-.37**
-.04
Elected Officials
-.18
.22
.01
Epidemiology
-.06
.03
.01
News
.07
-.07
-.02
4. WORRY
.21***
.52***
.08
.30*
.40***
.38**
Thinking About Risk
-.02
-.07
.14
Talking About Risk
.01
-.01
.06
Personal Risk
.42***
.04
.49***
Other's Risk
.28*
.28
.01
* p < .10 ** p < .05 *** p < .01
Betas are partial coefficients from regression on each block independently.
Dependnet variable is
risk, as an averaged score of the variables knowledge and dread.
High values on risk equal
perception of great risk.
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