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INFLUENCE OF TEXTUAL HEDGING AND FRAMING VARIATIONS ON DECISION MAKING CHOICES PERTAINING TO THE CLIMATE CHANGE ISSUE
DISSERTATION
Presented in Partial Fulfillment of the Requirements for
the Degree Doctor of Philosophy in the Graduate
Dissertation Committee:
School of The Ohio State University
By
Jeffrey R. Corney, M.A., M.S.
*****
The Ohio State University 2001
Approved by Dr. Rosanne W. Fortner, Adviser
Dr. Gary W. Mullins ~w.~ Adviser
Dr. Tomas M. Koontz School ofNatural Resources
ABSTRACT
Better methods of communication that enhance, not hinder, the efficient and
equitable transfer of information from expert sources to decision makers and the general
citizenry are being sought by those who must make decisions regarding complex
environmental issues such as climate change.
The effects of variations in two textual components, hedging and framing, on
subjects' choices during an environmental decision making situation were investigated.
Subjects were provided with information in a text passage that conveyed both the benefits
and detriments associated with a decision to either support or not support large-scale
climate change reductions in the U.S. Measures of subjects' attitudes, beliefs and
decision intentions were derived from the Theory of Reasoned Action. Other measures,
' derived from risk communication studies, included trust in the credibility of information,
clarity of information, and prioritization of information use during decision making.
Subjects who participated were undergraduate students enrolled at The Ohio State
University. The 160 participants were randomly assigned one of either 16 treatments or a
control. Subjects received the same factual information regarding the climate change
issue. However, each experimental treatment represented a different combination of
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textual manipulations, with information either hedged or not hedged and presented in
either a positive or negative frame.
Variations of hedging did not result in any significant findings, though a key trend
was observed. This trend suggests that a subject's trust in the credibility of information
presented may decrease when the benefits of not supporting climate change reductions
are not hedged. Variations in how climate change information was framed, however,
yielded some highly significant differences (p :5 0.001). The results suggest that negative
framing of either side of an issue, when the other is framed positively, influences the
priority a subject places on the importance of information, favoring the negatively framed
component.
This evidence suggests that if the goal of communicating information during an
environmental decision making situation, such as climate change, is to balance the
presentation of information and optimize cognitive and affective processing, then
hedging all statements and using negative framing for both sides of an issue is prudent.
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ACKNOWLEDGMENTS
I would like to express my sincere gratitude and thanks to my adviser, Dr.
Rosanne W. Fortner, for her care and support throughout my studies here at Ohio State.
Dr. Fortner always took good care of me with both material and emotional support.
I extend the same gratitude and thanks to Dr. Gary W. Mullins for supporting and
advocating both my academic and professional endeavors. Dr. Mullins always kept one
eye out for my future interests and put his trust in me to start building the foundation for
success while here at Ohio State.
I would like to thank Dr. Tomas M. Koontz for serving on my doctoral
dissertation committee and providing excellent advice for starting my career in academe.
I would also like to thank Dr. Robert J. Gates for his assistance as adviser for my M.S.
degree and for serving on my doctoral candidacy committee. Dr. Gates sharpened my
understanding and interest in the ways that human dimensions and natural science can be
interwoven to better understand ecological systems and their management. I would like
to thank Dr. Emmalou Norland for serving on my doctoral candidacy committee and for
providing research methods courses that pointed me in the right direction for developing
my own research.
IV
I would like to thank my family for their ever-present love and support. I would
like to thank my good friends and colleagues, Brad Welch and Sunita Hilton, for taking
this journey together with me. Their companionship, advice and willingness to laugh and
play when the day was done have been invaluable. Finally, I would like to thank my best
friend and fellow adventurer, Sarah Buchmann, for her love, support and unyielding faith
in my ability to do my best.
v
! i • "
VITA
October 5, 1965 Born - Dearborn, Michigan U.S.A.
1987 B.S. Biology, Hope College
1995 M.A. English, Colorado State University
2001 M.S. Natural Resources, The Ohio State University
PUBLICATIONS
Corney, J.R. (2001). Risk communication and wildland fire. In G.W. Mullins (Ed.) Communicator's Guide to Wild/and Fire (pp. 95-98). Columbus, OH: The Ohio State University, School ofNatural Resources.
Fortner, R.W., J.R. Corney, J.-Y. Lee & S. Romanello (2000). Developing a measure of public understanding of climate change and willingness to act when science is uncertain. In D. Scott et al. (Eds.) Climate ·change Communication: Proceedings of an International Conference (pp.E321-27). Waterloo, ON: University of Waterloo. Hull, PQ: Environment Canada.
Fortner, R.W., J.-Y. Lee, J.R. Corney, S. Romanello, J. Bonnell, B. Luthy, C. Figuerido & N. Ntsiko (2000). Public understanding of climate change: Certainty and willingness to act. Environmental Education Research 6(2): 127-141.
FIELDS OF STUDY
Major Field: Natural Resources
Environmental Communication, Education and Interpretation; Ecosystem Management and Environmental Decision Making
VI
TABLE OF CONTENTS
Abstract. ....................................................................................................................... ii
Acknowledgments ....................................................................................................... iv
Vita ........................................................................................................................ vi
List of Tables .............................................................................................................. ix
Chapters:
1. Introduction ............................................................................................................. 1
Climate Change Issue ....................................................................................... 1 Need for the Study ............................... -............................................................. 6 Problem Statement and Goal.. ........................................................................... 8 Definition of Terms ........................................................................................ 10 Research Questions ......................................................................................... 12 General Hypotheses ........................................................................................ 18 Limitations and Assumptions .......................................................................... 20
2. Literature Review .................................................................................................. 22
Environmental Decision Making ..................................................................... 22 Discourse and Linguistic Analysis .................................................................. 24 Risk Communication ...................................................................................... 33 Theory of Reasoned Action ............................................................................ 40
3. Instrumentation & Methodology ............................................................................ 43
Design ............................................................................................................ 43
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Subject Selection ............................................................................................ 44 Procedure ....................................................................................................... 48 Salient Beliefs ................................................................................................ 49 Text Material .................................................................................................. 54 Expert Panel ................................................................................................... 62 Field and Pilot Tests ....................................................................................... 71 Dependent Variable Measures ........................................................................ 73 Data Analysis ................................................................................................. 77
4. Results & Preliminary Discussion .......................................................................... 79
Correlations Among Variables ........................................................................ 79 Correlations With Covariate ........................................................................... 82 Analysis of Main Effects: Univariate ............................................................. 85
Comparison of Direct Responses ............................................................ 87 Comparison of Differential Responses ................................................. 104
Analysis of Main Effects: Multivariate ........................................................ 109 Analysis of Interactions ................................................................................ 112
5. Interpretation & Conclusions ............................................................................... 114
Interpretation of Hypotheses Tests ................................................................ 114 Summary and Implications ........................................................................... 124 Recommendations for Future Study .............................................................. 126
Appendices .............................................................................................................. 132
Appendix A: Climate Change Issue Questionnaire ........................................ 132 Appendix B: Alternate Versions of Experimental Text ................................. 148
List of References .................................................................................................... 157
Vlll
LIST OF TABLES
3 .1 Demographic profile of subjects expressed as modal response ........................ .46
3.2 Subjects' prior background measures ............................................................... 47
3.3 Top five most common responses to each of the salient,belief and salient referent questions pertaining to the climate change issue ....................... 53
3.4 Combination of various experimental treatment levels for each of the two main variables ..................................................................................... 61
3 .5 Number of words for each experimental text passage ....................................... 61
3.6 Unrotated component loadings on the first factor for expert panel responses ......................................................................................................... 66
3.7 Rotated component loadings on the first factor for expert panel responses ......................................................................................................... 67
3.8 Item analysis of Detriment statements, based on an internal consistency method using Item-total correlations and Cronbach's Alpha, for expert panel responses .................................................................... 69
3. 9 Item analysis of Benefit statements, based on an internal consistency method using Item-total correlations and Cronbach's Alpha, for expert panel responses .................................................................... 70
3 .10 Reliability of instrument, based on an internal consistency method using Cronbach's Alpha, for both the pilot and control groups ......................... 72
4.1 Correlations of related dependent variables with each other, for multivariate analysis of variance ...................................................................... 81
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4.2 Correlations of each of the dependent variables with potential covariate, Prior Background ............................................................................. 84
4.3 Main effects and interactions for Clarity oflnformation ................................... 92
4.4 Main effects and interactions for Trust in the Credibility of Information ...................................................................................................... 93
4.5 Main effects and interactions for Decision Intention ........................................ 94
4.6 Main effects and interactions for Overall Attitude ............................................ 95
4.7 Main effects and interactions for Subjective Norm ........................................... 96
4.8 Main effects and interactions for Belief in Climate Change .............................. 97
4.9 Main effects and interactions for Belief in Outcome (Detriments) .................... 98
4.10 Main effects and interactions for Belief in Outcome (Benefits) ........................ 99
4.11 Main effects and interactions for Outcome Evaluation (Detriments) ............... 100
4.12 Main effects and interactions for Outcome Evaluation (Benefits) ................... 101
4.13 Main effects and interactions for Prioritization of Information Use (Detriments) .................................................................................................. 102
4.14 Main effects and interactions for Prioritization of Information Use (Benefits) ....................................................................................................... 103
4.15 Main•.effects and interactions for difference between responses to Detriments versus Benefits for Belief in Outcome .......................................... 106
4.16 Main effects and interactions for difference between responses to Detriments versus Benefits for Outcome Evaluation ...................................... 107
4.17 Main effects and interactions for difference between responses to Benefits versus Detriments for Prioritization of Information Use ................... 108
4.18 Main effect of hedging on each set of correlated variables, using multivariate analysis of variance technique .................................................... 110
4.19 Main effect of framing on each set of correlated variables, using multivariate analysis of variance technique .................................................... 111
x
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CHAPTER 1
INTRODUCTION
"Most of what we learn about the world is filtered through communication. Even when we learn things directly, we perceive and interpret that experience through attitudes influenced by the words of others."
- C. Bazerman, The Informed Writer
Climate Change Issue
Global climate change research has become an international priority as evidence
mounts that increases in atmospheric carbon dioxide are caused in large part by
anthropogenic emissions. Nevertheless, the actual effects of increased C02, particularly
in terms of climatic changes, are still heavily debated. As evident in the Second
Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC)
(Kirschbaum & Fischlin, 1996; Melillo et al., 1996) and the synthesis report for the U.S.
Global Change Research Program (NAST, 2000), changes in our ecological and
sociological systems are inevitable.
1
At a conference held in 1997, in Kyoto, Japan, the Parties to the United Nations
Framework Convention on Climate Change (UNFCCC) agreed to an international treaty
to reduce greenhouse gas emissions, known as the "Kyoto Protocol" (UNFCCC, 1997).
Its aim is to stabilize greenhouse gas concentrations by reducing emissions to or below
1990 levels. Currently, 84 nations are signatories of the Protocol, including all developed
nations (UNFCCC, 2001). In order to come into effect 55 countries need to ratify the
treaty, and of those nations at least 55% of them need to be developed nations. To date,
33 countries have ratified (UNFCCC, 2001). In 1998 the United States signed the treaty.
However, in order for the treaty to become binding in the U.S., it must be ratified by the
Senate, which is pending in the United States as of the time of this study (U.S. State
Department, 2001).
The Kyoto Protocol calls for developed nations to collectively reduce their
present emissions of six key greenhouse gases, including C02, by 5% by the year 2012
(UNFCCC, 1997). The U.S. contribution is a reduction of current emissions levels by
7%. Industrial nations must reduce emissions before lesser developed countries such as
China and India. This condition of the treaty has raised some issues regarding effects on
industry competitiveness in the global economy (U.S. State Department, 2001). Others
believe this is not that large a concern, as on average energy constitutes 2.2% of total
costs to U.S. industry, energy prices already vary significantly across countries, and
roughly two-thirds of all emissions affected are not in manufacturing sectors (U.S. State
Department, 2001 ).
2
Follow-up sessions of the Conference of the Parties have occurred since Kyoto
and subsequent proposals have been added to the Protocol to help refine the treaty
(UNFCCC, 2001; U.S. State Department, 2001). Emissions targets are now to be reached
over a five-year budget period, namely 2008-2012. Activities that absorb carbon (i.e.
carbon sinks), such as planting trees and restoring degraded soils, can be used toward
obtaining emission targets. Emissions trading is included, allowing countries to purchase
emissions permits from countries that have already met their targets and have surplus
permits. Developed countries will also be able to gain emissions reduction credit through
emission-reducing project activities in developing countries (UNFCCC, 2001; U.S. State
Department, 2001).
The most difficult and disputed aspects of climate change projections involve
effects on specific regions of the world. Most climatic models are not yet sophisticated
enough to allow for both high temporal and spatial resolution. General global climate
' change models are becoming more and more accepted, but individual country and
i
j regional ecosystems are harder to model (Kirschbaum & Fischlin, 1996; Melillo et al,
1996).
Individual country and regional systems, however, are usually the most relevant
to policymakers and the public. National economies are highly dependent on regional
ecosystems. Water resources are an obvious example, affecting agriculture and urban
development. National economies also rely on continued availability of both renewable
and non-renewable natural resources for energy, construction products, ecosystem
diversity, recreation and aesthetics.
3
Several computer simulation models, collectively called General Circulation
Models (GCMs), have been designed and refined over the past few years to help
scientists better assimilate data and predict possible outcomes of increased atmospheric
carbon dioxide. Basically, these models run computer simulations of atmospheric
conditions that attempt to synthesize complex physical processes linking the atmosphere
with global circulation and hydrological cycles. The standard comparison that these
models run is between an atmosphere with 1 x C02, representing current conditions, and
a doubling of carbon dioxide (2 x C02), representing projections of anthropogenically
produced carbon dioxide buildup over the course of the next 50 to 100 years. Two
commonly used steady-state models that have been used include the Goddard Institute for
Space Studies (GISS) and the Geophysical Fluid Dynamics Lab (GFDL) models
(Kirschbaum & Fischlin, 1996; Melillo et al., 1996).
Two new models that incorporate more dynamic or transient atmospheric and
oceanic elements such as cloud formation, ocean currents, and aerosol effects, have been
applied to global and regional climate change projections (NAST, 2000 a & b ). These
models allow for a more realistic, gradual buildup of C02 gas in the simulated
atmosphere versus the steady-state models' use of instant C02 doubling. These two
models, called the Canadian Model (CGCMl) and the Hadley Model (HadCM2) have
been used recently by the National Assessment Synthesis Team (2000) to predict
potential climate futures for the entire United States under elevated C02.
The National Assessment Synthesis Team has just published the results of these
simulations as applied to the United States (NAST, 2000 a & b ). This project has been an
ongoing national-level assessment of climate change impacts conducted as part of a
federal mandate assigned to the U.S. Global Change Research Program by request of the
President's Science Advisor. This project has been an enormous collaboration of
scientists, industry representatives, government agencies, and universities working on
collecting, interpreting and synthesizing the most current, accurate and relevant data
available on climate change impacts in the United States. The result is the recently
published 150-page Overview and 800-page Foundation report, Climate Change Impacts
on the United States: The Potential Consequences of Climate Variability and Change
(NAST, 2000). Climate change projections described in these documents were used as
the factual foundation for the experimental text material created for this study.
The United States, along with all other nations, is facing a critical environmental
decision making situation with regard to the climate change issue. The technical
knowledge acquired toward understanding the potential effects of climate change on
ecological and sociological systems is growing but currently remains incomplete and
wrought with unknowns and uncertainty (NAST, 2000 a & b ). A decision to either
support or not support reductions in human-induced climate change mechanisms, such as
greenhouse gas emissions and deforestation, will require large-scale involvement and
commitment on the part of the policy makers and citizenry of each country despite these
uncertainties (NAST, 2000 a & b). Because of its global scale, long-term effects and
uncertain scientific projections, the climate change issue is perhaps the most challenging
environmental decision making situation that we face at this time.
5
Need for the Study
Effective management of environmental issues such as climate change requires
efficient decision making on the part of scientists, elected officials, resource managers,
and citizens in general (Francis, 1993; Slocombe, 1993; Grumbine, 1994 & 1997;
Christensen et al., 1996; Lackey, 1998; Yaffee, 1999). Most, if not all, environmental
issues involve some degree of consideration and eventual trade-offs between potential
benefits and detriments that may result from a particular decision. Ideally, these factors
are equitably considered and effectively synthesized toward prudent decision making that
balances benefits and detriments in a sustainable manner (Francis, 1993; Slocombe,
1993; Grumbine, 1994 & 1997; Christensen et al., 1996; Lackey, 1998; Yaffee, 1999).
During the evaluation of potential benefits and detriments, decision makers
require a high volume and efficient flow of information from technical sources, generated
mostly by researchers studying the ecological and sociological aspects of an
environmental issue (Rycroft et al., 1987). Most, if not all, of this technical information
is presented as text, which generally refers to the content of both printed and oral
communication. For simplicity, "text" will refer specifically to printed scientific
discourse in this study. Obtaining and reading most relevant material, including research
articles, environmental impact statements (EISs ), public surveys, etc., is arduous but
attainable given appropriate access and enough time (Rycroft et al., 1987). Cognitively
and affectively processing and thoroughly understanding all of the various viewpoints
that comprise an issue is another matter, posing a daunting, if not impossible, task
regardless ofresource availability (Payne et al., 1993). Accounting for inherent
6
uncertainty in ecological and sociological projections on both sides of an issue, and ever
shifting societal priorities, adds to the complexity facing decision makers. Nevertheless,
decisions and policies must be made and implemented despite these challenges, requiring
those involved to constantly acquire more knowledge and adapt to new conditions as
more viewpoints enter the process, providing claims and refuting others.
In order to obtain lasting support for decisions multiple audiences and their
viewpoints must be addressed, potential benefits and detriments of an issue must be
communicated and assessed, and a consensus building process used to focus inherent
conflict toward productive solutions (Stem, 1991; Lee, 1993 & 1999; Shindler & Cheek,
1999). Environmental decision making situations rely, to a large degree, on equitable
access to and treatment of all relevant information from all viewpoints (Holling, 1978;
Walters & Holling, 1990; Stem, 1991; Lee, 1993 & 1999; Lessard, 1998).
Decision makers' attitudes and beliefs usually form as they assess and interpret a
wide array of complex technical information (Rycroft et al., 1987). Clarity and fair
portrayal of information is critical during this process, as messages that either hinder
cognitive and·affective processing or compromise trust in the material can either be
misinterpreted or ignored (Rycroft et al., 1987). Communication mechanisms, both
textual and contextual, should convey messages in a clear and credible manner, easing
cognitive processing of information and, in tum, fostering equitable perception and
subsequent treatment of various viewpoints during the decision making process (Halliday
& Hasan, 1976; Brown & Yule, 1983; Stem, 1991; Payne et al., 1993). Furthermore,
well applied communication strategies can facilitate constructive discussion and debate
among stakeholders; a condition requisite for effective decision making in a democratic
system of policy formation (Habermas, 1984 & 1987; Plough & Krimsky, 1987; Dietz et
al., 1989; Stem, 1991; Webler, 1992).
Scientists and other professionals who deal with environmental issues, such as
climate change, on a regular basis appear to be recognizing the importance of good
communication in decision making. Better methods of communication that enhance, not
hinder, the equitable transfer of information from expert sources to decision makers and
the general citizenry are being sought by those who must study and manage complex
ecological and sociological environmental systems (Walters & Holling, 1990; Lee, 1993
& 1999; Grumbine, 1994 & 1997; Christensen et al., 1996; Lackey, 1998; Yaffee, 1999).
Problem Statement and Goal
Optimizing the transfer of information by reducing perceived uncertainty and
encouraging effective and equitable use of all information, requires identifying and
correcting textual and contextual communication practices that interfere with this
process. Conversely, mechanisms that enhance efficiency and equity and minimize
uncertainty should be used more often in communication pertaining to natural resource
issues.
Common to many communication and cognitive theories is the notion that textual
mechanisms do influence a reader's cognitive and affective processing of material, thus
have the potential to interfere with effective and equitable treatment of information
(Halliday & Hasan, 1976; Ajzen & Fishbein, 1980; Brown & Yule, 1983; Habermas,
1984 & 1987; Rycroft et al., 1987; Stem, 1991; Payne et al., 1993). It is also clear from a
number of studies in risk communication that contextual components, such as the source
of technical information and the tone in which it is conveyed, play key roles in how
readers perceive the information (e.g. Sandman et al., 1993; Gray, 1997).
To date, only a limited number of studies have reported examining message
components with an emphasis on how specific textual variations, believed to affect
readers' cognitive and affective processing of text, influence readers' decision making
processes (Ajzen & Fishbein, 1980; Tversky & Kahneman, 1981; Vande Kopple &
Crismore, 1990; Gigerenzer & Hoffrage, 1995). None have studied these responses in
relation to effective and equitable use of information during decision making regarding
an environmental issue such as climate change.
The specific aim of this study was to isolate a set of key textual message
components, known to affect clarity and credibility, and measure the effects of variations
in those message components on decision makers' cognitive and affective processing of
information. In particular, measurement was made for how subjects process different
sides of the climate change issue, namely detriments versus benefits and how they
differentially apply that information during decision making.
The overall goal of this study was to obtain empirical evidence to support claims
that textual variations influence cognitive and affective processing of information. This
study also attempted to validate development of a new instrument capable of measuring
potential influences of textual components on subjects' cognitive and affective processing
9
of information, and the subsequent influence on decision choices, in an environmental
decision making context.
Once developed, the instrument has the potential to be used in subsequent studies
to measure those effects in a diverse array of specific populations of decision makers; e.g.
federal and state land managers, elected legislators, and community action groups. The
instrument will be capable of detecting textual practices in information transfer that
enhance or detract from optimal decision making, ultimately allowing for development
and application of more effective communication practices in environmental decision
making.
Definition of Terms
Following are a few terms that are used often throughout this study to convey a
particular meaning. Though each term may have a variety of meanings depending on the
context, these definitions serve to qualify their meaning within the scope of this particular
study.
; Ecological: Physical, chemical, geological and biological aspects of a natural system and
j the dynamic interactions among those components that sustain the basic natural :functions
within a natural and human developed environment.
Sociological: Political, economic, and cultural aspects of a human social system and the
dynamic interactions among those components that sustain the basic social functions
within a natural and human developed environment.
Benefit: An outcome of a decision choice that results in a gain for either an ecological or
sociological system.
Detriment: An outcome of a decision choice that results in a loss for either an ecological
I '
or sociological system.
Environmental Decision Making: A decision making situation among involved citizens
that requires an integration of both ecological and sociological aspects of an issue, and
the potential benefits and detriments associated with the potential outcomes, during the
process of deliberating and eventually arriving at a course of action.
Text Statement: Information conveyed to an audience through printed words, presenting
a proposition regarding a factual condition or projection of a potential outcome.
Hedging: Modifying a statement by adding a term that alters the modal or epistemic
quality of the statement such that the intended meaning conveys some degree of
uncertainty. Examples of words used to hedge statements include could, might, may,
11
possibly, probably, suggest, appear, about, and roughly. Not hedging is produced by
using the definitive modal of "to be" such as is or will.
Framing: Modifying a statement by describing the outcome of a proposition in either a
positive or negative valence relative to an issue's decision alternatives such as to support
or not support a claim. Positive frames describe outcomes as either obtaining a gain or
avoiding a loss. Negative frames describe outcomes as either suffering a loss or forgoing
again.
Research Questions
Many textual features, including linguistic, framing and content components, have
been identified as intrinsic to technical discourse (Gross, 1990; Swales, 1990; Myers,
1990; Biesenbach-Lucas, 1994) and have been examined in detail in terms of their
potential effects on readers' understanding of message content. Linguistic examples
include hedging (Crismore & Vande Kopple, 1988; Myers, 1989; Vande Kopple &
Crismore, 1990; Salager-Meyer, 1994; Hyland, 1994, 1996a, 1996b), given/new
construction (Halliday & Hasan, 1976; Clark & Haviland, 1977; Brown & Yule, 1983;
Weissberg, 1984; Halliday, 1988; Allison, 1991), and third-person/passive voice (Tarone
et al., 1981; Walsh, 1982). Key framing techniques include gain versus loss (Tversky &
Kahneman, 1981; Maheswaran & Meyers-Levy, 1990; Davis, 1995), positive versus
negative goal or attribute (Levin et al., 1998), power/authority differentials (Plough &
Krimsky, 1987; Sandman et al., 1993; Gray, 1997), and scientific versus social values
12
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i I
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ii
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01 aughan & Seifert, 1992). The content of a message itself contains important factors
such as citation use (Myers, 1989), presentation of graphics (Tufte, 1983), and
presentation of statistical information (Freudenburg, 1988; Gigerenzer & Hoffrage, 1995;
Anderson, 1998).
Two of these message components were examined in detail and served as the
independent variables in this study: hedging and framing. These two were selected
because they are common to scientific and natural resources messages, and each has been
cataloged and characterized extensively in linguistic and communication studies of
technical discourse (Gross, 1990; Swales, 1990; Myers, 1990; Biesenbach-Lucas, 1994;
' Levin et al., 1998), providing methods and precedent for accurately identifying and
manipulating each as a variable. To some extent, each of these components also has been
studied in terms of psychological effects on readers. Such studies have examined
subjects' comprehension of material, in cases ofhedging (e.g. Vande Kopple &
Crismore, 1990) and decision choices, in the case of framing (e.g. Ajzen & Fishbein,
1980; Tversky & Kahneman, 1981; Levin et al., 1998).
Cognitive and affective dependent variables used in this study were derived from
well-established psychological models that pertain to information processing and
decision making strategies. Though a variety of such models exist (e.g. Fischhof~ 1991;
Shafir & Tversky, 1993; Payne et al., 1993; Epstein, 1994), the Theory of Reasoned
Action (TRA) developed by Fishbein and Ajzen (Fishbein, 1967; Fishbein & Ajzen,
1975; Ajzen & Fishbein, 1980) was used as the primary theory from which psychometric
measures of the affective domain were derived for this study.
13
The Theory of Reasoned Action (TRA) model was defined by Fishbein (1967)
and later developed into a descriptive and predictive model for analyzing human behavior
(Fishbein & Ajzen, 1975; Ajzen & Fishbein, 1980). Five separate measures from the
original model were adopted, with only minor definitional modifications, to be used as
measures of the affective domain in this study. These variables included: Behavioral
Intention (redefined here as Decision Intention), Overall Attitude, Subjective Norm,
Behavioral Belief (redefmed here as Belief in Outcome), and Outcome Evaluation.
Two other affective variables were used: Perceived Trust in the Credibility of
Information and Belief in Climate Change as an actual event. Perceived Trust was
derived from risk communication literature, stressing that credibility in the information
has a profound effect on how it is used in decision making (Kasperson et al., 1988; Chess
et al., 1992; Sandman et al., 1993).
Belief in Climate Change was used to examine if the baseline beliefs about
climate change were affected by the treatments. Prior Background, specifically a
measure of how much a subject reports knowing about the issue and how much media
and classroom exposure to the climate change issue, was also assessed as a potential
qualifier. This variable was also considered for use as a potential covariate to reduce
error variance among treatment groups if necessary to enhance power.
The cognitive domain was assessed with just two variables: Perceived Clarity of
Information and Prioritization of Information Use. Clarity of Information was a simple
look at how easy or difficult a subject perceived the readability of the text and how easy
or difficult it was to understand. Again, risk communication literature points out that
14
clarity ofreadability plays a critical role during decision making (Kasperson et al., 1988;
Chess et al., 1992; Sandman et al., 1993).
Finally, Prioritization of Information Use was used to measure how subjects
differentially rank the importance of various information components, for both detriment
and benefit sides of the issue, when considering the information while deciding which
side of the issue to chose. This is a common measure of decision making practices used
to understand how subjects process information (Payne et al., 1993).
The two message components were experimentally manipulated in a structured
text passage designed to convey information pertaining to human-induced climate
change. Subjects were asked to read an experimental text passage, conveying
information regarding the climate change issue, and answer a series of questions designed
to measure the cognitive and affective variables that are relevant to the overall decision
making process.
In total, nine different dependent variables were assessed, with sub-sets - one for
Detriments and one for Benefits - used for Belief in Outcome, Outcome Evaluation, and
Prioritization of Information Use variables.
Following are the specific research questions examined ill this study:
1) Do variations in the use of hedging within the text passage differentially affect a
subject's direct responses to the following variables (a - i)?
(a) Perceived clarity of the information presented (b) Perceived trust in the credibility of the infonnation presented (c) Belief in human-induced climate change as an actual phenomenon ( d) Decision intention regarding supporting climate change reductions
15
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( e) Overall attitude toward the outcomes of supporting climate change reductions (f) Subjective norm regarding influential referents approving of a decision to support
climate change reductions (g) Belief in the likelihood of outcomes occurring, regarding either detriments and/or
benefits associated with either supporting or not supporting climate change reductions
(h) Evaluation of outcomes, regarding either detriments and/or benefits associated with either supporting or not supporting climate change reductions
(i) Prioritization of information use during the decision making process, regarding either detriments and/ or benefits associated with the climate change issue
2) Do variations in the use of framing within the text passage differentially affect a
subject's direct responses to the following variables (a- i)?
(a) Perceived clarity of the information presented (b) Perceived trust in the credibility of the information presented (c) Belief in human-induced climate change as an actual phenomenon ( d) Decision intention regarding supporting climate change reductions ( e) Overall attitude toward the outcomes of supporting climate change reductions (f) Subjective norm regarding influential referents approving of a decision to support
climate change reductions (g) Belief in the likelihood of outcomes occurring, regarding either detriments and/or
benefits associated with either supporting or not supporting climate change reductions
(h) Evaluation of outcomes, regarding either detriments and/or benefits associated with either supporting or not supporting climate change reductions
(i) Prioritization of information use during the decision making process, regarding either detriments and/or benefits associated with the climate change issue
3) Do variations in the use of hedging within the text passage differentially affect the
difference between how subjects respond to information regarding detriments versus
benefits when responding to the following variables (a- c)?
(a) Belief in the likelihood of outcomes occurring, regarding both detriments and benefits associated with either supporting or not supporting climate change reductions
(b) Evaluation of outcomes, regarding both detriments and benefits associated with either supporting or not supporting climate change reductions
16
I '
( c) Prioritization of information use during the decision making process, regarding both detriments and benefits associated with the climate change issue
4) Do variations in the use of.framing within the text passage differentially affect the
difference between how subjects respond to information regarding detriments versus
benefits when responding to the following variables (a - c )?
(a) Belief in the likelihood of outcomes occurring, regarding both detriments and benefits associated with either supporting or not supporting climate change reductions
(b) Evaluation of outcomes, regarding both detriments and benefits associated with either supporting or not supporting climate change reductions
( c) Prioritization of information use during the decision making process, regarding both detriments and benefits associated with the climate change issue
5) Do variations in the use of hedging within the text passage differentially affect a
subject's responses to the following multivariate combinations of variables (a- d)?
(a) A combination of: Decision intention regarding supporting climate change reductions; Overall attitude toward the outcomes of supporting climate change reductions; and Subjective norm regarding influential referents approving of a decision to support climate change reductions
(b) Belief in the likelihood of outcomes occurring, regarding a combination of both detriments and benefits associated with either supporting or not supporting climate change reductions
( c) Evaluation of outcomes, regarding a combination of both detriments and benefits associated with either supporting or not supporting climate change reductions
( d) Prioritization of information use during the decision making process, regarding a combination of both detriments and benefits associated with the climate change issue
6) Do variations in the use of.framing within the text passage differentially affect a
subject's responses to the following multivariate combinations of variables (a-d)?
(a) A combination of: Decision intention regarding supporting climate change reductions; Overall attitude toward the outcomes of supporting climate change
17
I: I: I
1,
' I
reductions; and Subjective norm regarding influential referents approving of a decision to support climate change reductions
(b) Belief in the likelihood of outcomes occurring, regarding a combination of both detriments and benefits associated with either supporting or not supporting climate change reductions
( c) Evaluation of outcomes, regarding a combination of both detriments and benefits associated with either supporting or not supporting climate change reductions
( d) Prioritization of information use during the decision making process, regarding a combination of both detriments and benefits associated with the climate change issue
7) Are there interactions between or among variations in the use of hedging and framing
that contribute to effects on a subject's responses to any of the aforementioned
variables? What is/are the effect(s) of any interactions?
General Hypotheses
Hl. Hedging of both or either information component (detriments v. benefits) will result
in differential direct responses, with the hedged component(s) eliciting a response
ofrelatively greater magnitude than the not hedged component(s). When
components are differentially hedged, the direction of the response will be toward
supporting the hedged component.
H2. Framing of both or either information component (detriments v. benefits) will result
in differential direct responses, with the component(s) framed in a negative manner
eliciting a response of relatively greater magnitude than the component( s) framed in
a positive manner. When components are differentially framed, the direction of the
response will be toward supporting the negatively framed component.
18
! I
11
I I
I I
I
I
i1
H3. Differential hedging of one information component (detriments v. benefits) while
the other is not hedged will result in greater differences between responses to the i I
two components than when both components are either hedged or not hedged.
H4. Differential framing of one information component versus the other (detriments v. I
benefits), with one framed in a negative manner and the other positive, will result in ,,
I
greater differences between responses to the two components than when both are
framed either as negative or positive.
HS. Regarding multivariate combinations of variables, hedging of both or either
information component (detriments v. benefits) will result in differential direct
responses, with the hedged component( s) eliciting a response of relatively greater
magnitude than the not hedged component(s). When components are differentially
hedged, the direction of the response will be toward supporting the hedged
component.
H6. Regarding multivariate combinations of variables, framing of both or either
information component (detriments v. benefits) will result in differential direct
responses, with the component(s) framed in a negative manner eliciting a response
ofrelatively greater magnitude than the component(s) framed in a positive manner.
19
When components are differentially framed, the direction of the response will be
toward supporting the negatively framed component.
H7. Interactions between hedging and framing will occur, with the combination of
hedging and negative framing enhancing the magnitude of the response.
Limitations and Assumptions
This study was conducted against a backdrop of several potential confounding
variables inherent with most studies involving measures of human subjects' responses.
' Examples include differences in cognitive ability, prior education, age, and gender.
Efforts were made to account for these differences statistically by incorporating a
blocking technique (Gender) and covariate analysis (Prior Background) to mitigate or
measure potential influences of confounding variables.
Validity of each measure was accounted for, as best as possible, by using well
established measurement techniques and corroborating the specific questions with both
expert panel advice and pilot test item response analysis results. A key assumption was
that subjects would respond to questions based primarily on their cognitive and affective
processing of only the material presented to them during the experiment.
Sparsity of prior experimental studies of this nature rendered a priori estimates of
effect size difficult to attain. In order to optimize detection of effects a relatively
homogenous population -undergraduates enrolled in the School ofNatural Resources at
The Ohio State University- was selected for this study to minimize measurement error,
20
confounding noise and Type II error; consequently, inferential conclusions have been
compromised to some extent. Once tested and validated, this instrument can be used in
subsequent studies to examine more diverse populations.
21
CHAPTER2
LITERATURE REVIEW
Environmental Decision Making
Environmental decision making is an integrated approach to managing ecological
and human-developed sociological systems that talces into account the best known and
accepted natural and social science data and theories, as well as cultural values and
desires, and synthesizes these factors toward prudent decision making that balances
ecological, social and economic benefits in a sustainable manner (Francis, 1993;
Slocombe, 1993; Grumbine, 1994 & 1997; Christensen et al., 1996; Lackey, 1998;
Yaffee, 1999)~ The key point is that in order to obtain lasting support for management
decisions multiple stalceholders and viewpoints must be addressed, and a consensus
building process used to focus inherent conflict toward productive solutions.
Environmental decision making must be truly holistic and equitable in its inclusion of
natural and human-developed factors when decisions are being made, regardless of
whether the goal is preservation, conservation, development, or a combination of these
22
(Francis, 1993; Slocombe, 1993; Grumbine, 1994 & 1997; Christensen et al., 1996;
Lackey, 1998; Yaffee, 1999).
Communication is an active component of environmental decision making.
Communication is an approach to including multiple stakeholders in the decision making
process via two processes. First, and at its simplest, communication is a means of
conveying information - scientific, social, economic, political and value-oriented -
among various research groups and stakeholders within a system. Secondly,
communication is a manner of conducting and channeling social conflict and debate
toward a productive goal. Communication is essentially the key tool in constructing
meaning from various sources of information and for forging decisions among various
viewpoints.
Two distinct communication theory bases address the challenges and issues raised
in an environmental decision making context. Each model can be applied to explain
decision making conditions and solve communication problems. First, and at its most
fundamental level, communication can be examined from a linguistic and discourse
analysis perspective, characterizing the various textual and contextual components of a
message and measuring effect(s) on a reader's cognitive and affective processing (e.g.
Halliday & Hasan, 1976; Brown & Yule, 1983; Myers, 1989; Vande Kopple & Crismore,
1990). Second, the concept ofrisk communication, and its associated behavioral
theories, helps explain natural resources messages in an applied context, namely as
information conduits and catalysts of societal action under conditions of uncertainty and
differential framing of issues (e.g. Fischhoffet al., 1978; Ajzen & Fishbein, 1980;
23
Tversky & Kahneman, 1981; Rycroft et al., 1987; Kasperson et al., 1988; Sandman et al.,
1993; Levin et al., 1998). From a socio-political perspective, risk communication theory
helps evaluate message generation and flow in terms of equitable access and use of
information among stakeholders during the decision making process (e.g. Habermas,
1984 & 1987; Plough & Krimsky, 1987; Dietz et al., 1987; Stem, 1991; Webler, 1992).
Discourse and Linguistic Analysis
Prominent voices from the philosophical study of science point toward the
significance of rhetorical persuasion in the establishment of scientific theory. Popper
(1999) says that " ... scientific theories can be formulated linguistically, ... Thus they
become objects outside ourselves: objects open to investigation." Kuhn (1977) talks of
scientists' method of acceptance or denial being based not so much on the factual proof
in a definitive sense but rather being "about techniques of persuasion, or about argument
and counter argument in a situation in which there can be no proof." Both philosophers
would contend that scientists adopt or refute a theory, consequently shaping general
acceptance of knowledge, through consensus building within their discourse community.
Bazerman (1990) considers "the construction of scientific language is part and
parcel of the human construction of social modes of investigation and knowledge
production." He urges a closer look at the processes that shape scientific discourse and,
consequently, shape our perceptions of what we call real. Echoing Kulm, Bazerman
suggests that the "underdetermination of claims by evidence" leaves open the
interpretation of scientific knowledge to be an exercise in social construction where the
24
persuasive nature of scientific discourse plays an integral role in shaping acceptance of
theories. The study of these registers then is critical to developing a clear understanding
of what makes for effective communication of environmental issues rooted in scientific
discourse.
Professional scientific texts, such as journal articles and technical reports, are
ultimately subject to the requirements of journal editors and referees who represent the
scientific community. Consequently, scientific texts must conform to some basic
structures that distinguish them as acceptable in the community (Crookes, 1986;
Bazerman, 1988 & 1990; Gross, 1988 & 1990; Myers, 1990; Swales, 1990). Scientific
' discourse has been distinguished as a unique and important register in English. Some
researchers caution that conclusions regarding linguistic characteristics drawn for one
field of science should not be assumed to apply to all others; however, no striking
differences have been discerned (Tarone et al., 1981; Ure, 1982; Crookes, 1986;
Biesenbach-Lucas, 1994). Though each field of science certainly has its particular
language attributes, generally we can look at scientific discourse as a whole and draw
some useful conclusions.
For example, conventions in scientific discourse such as structuring information
in a given/new format, whereby the linkage between an accepted fact or theory and a
proposed new piece of information is often explicitly made; and conspicuous politeness
in the form of citation and hedging, are all key aspects of scientific discourse.
Application of these linguistic techniques clarifies meaning and facilitates access to the
scientific process to a broader audience. Both given/new contracts and the use of
25
politeness are motivated by the rhetorical nature of scientific writing. According to a
number ofresearchers, a scientist's main objective in discourse is to persuade the
scientific community and the world as a whole that the theory reported is correct and
should be accepted over all others in regards to its specific interpretation of reality
(Latour & Woolgar, 1979; Latour, 1987; Bazerman, 1988 & 1990; Gross, 1990; Myers,
1990; Swales, 1990).
Considering the presence of patterns in discourse, Halliday and Hasan (1976)
discuss cohesion in text " ... as the set of semantic resources for linking a sentence with
what has gone before." They stress the importance of sentential cohesion for the greater
understanding and interpretation of the meaning of text as a whole. Though sentences are
structurally independent, elements of one sentence are understood by reference to
elements in other sentences. Combined, these links provide coherence to the text.
Halliday (1988) points out the use of packaging complex technical information
into an explicit and digestible element within the text. The theme or "given" is typically
rendered prior to the "new" material, found in the "unmarked position". This
arrangement; " ... performs a powerful cohesive function in a text, [ and] .. .is obviously
essential to scientific discourse."
Halliday further draws a distinction between the thematic material as being
"backgrounded" and the rhemic or new material as "foregrounded" in the text. This
building of new knowledge on the shoulders of existing knowledge is a powerful tool for
scientists to construct and convey new knowledge in a logical and coherent manner.
26
More specifically, concepts are organized in hierarchical arrangements and
processes are packaged into backgrounding and foregrounding or given/new constructs.
Typically, the given or "backgrounded" material is put into "nominal groups". The verb
construction shows that a process occurs, setting up the logical relationship between the
given nominal clause and the new process.
Earlier works by Clark & Haviland (1977) and Brown and Yule (1983) also
discuss concepts of given/new, cautioning that hard and fast rules of given/new
presentation have not been found to exist. Rather, regularities of usage do show up in
text that allow linguistics to establish a greater than chance occurrence of particular
'devices that facilitate the presentation of given to new material. They generally agree
that given information is identifiable either through textual reference or as part of the
reader's general knowledge. New, on the other hand, is typically unknown information-
in regards to the text--that the writer assumes s/he is introducing to the reader for the first
time.
For all attributes of scientific discourse, Halliday (1988) stresses that they are not
merely a ritualistic method of writing, rather the acceptance of what is functional and is
most effective in helping an audience construct meaning and consequently construct
scientific knowledge.
Weissberg (1984) studied the presence and structure of topic and comment, or
given/new principles, as they are distinctly utilized in scientific text. He found them to
be regular attributes of published scientific work. Weissberg reported that test subjects
who read a series of paragraphs and then were asked to recall the information did so
27
faster and more accurately when the sentences were structured in the "unmarked
sequence" or given to new configuration.
Cohesive features such as lexical repetition, referential pronouns, conjunctions,
synonyms, and substituted phrases draw the linkages between given and new. The
explicit use of these connectors in scientific writing facilitates easier reading and
comprehension of difficult technical passages, even for a layperson, claims Weissberg
(1984). The absence or obscure presence of cohesive features requires the reader to draw
inferences or "inferential bridging" from personal background knowledge in order to
derive the full meaning of the passage. The inferences help the reader draw
"intermediary propositions" about the topic necessary in order to comprehend the
meaning of the new information as it fits into the context of the given topic. This is
frequently difficult for lay persons not as well versed in the base of knowledge and
vernacular of the particular scientific community.
Allison (1991) took yet another angle on given/new presentation, examining the
need for explicitness in scientific writing in order to make meaning clear, especially to an
audience ndt completely versed in the discipline. He discusses the attributes of the
"hypothetical-real" relationship. The contrast set up is one between information
presented as the viewpoint and conjecture of the writer- "hypothetical" -versus
information that is considered to be factual and recognized as the true model - "real''. It
is maintained that in scientific discourse, the presence of a hypothetical statement is often
followed by a contrasting statement of fact. Allison cautions that this particular ordering
is by no means the definitive. Lexical and syntactic cues are often utilized in text to
28
convey the contrast between hypothetical and real statements, thus explicitly elucidating
the meaning of a passage.
Allison's research works from two premises. One, explicit cueing of
hypothetical-real contrasts helps reduce errors in reader interpretation. Secondly, more
correct interpretations of meaning are made when the real statement precedes the
hypothetical. His fmdings indicate a definite correlation between explicitness and
effective interpretation. Though the evidence did not statistically support his second
premise, the trend was toward greater effectiveness with real to hypothetical sequencing.
Myers (1989 & 1990) asserts that the prevalent use of politeness is fostered by
'Brown and Levinson's (1987) principles regarding a Face Threatening Act (FTA). The
"model person's" goal is to maximize persuasive rhetorical effect while minimizing the
risk oflosing face with the scientific community if one's theory is found less than
acceptable. In order to persuade the science community and, in turn, the general
audience that a claim is worthy to be considered fact, the writer must maintain face.
Knowledge is really dictated by what is accepted as such by the consensus of the
discourse community.
According to Myers, there are three attributes of a discourse that determine the
degree of politeness exhibited by the writer. Among these principles are: 1) relative
social distance between individuals, particularly if considered to be large; 2) the science
community is considered more powerful than any one or group of the researchers in it;
3) every scientist must present him- or herself equally in comparison to peers and must
exhibit humility in relation to the community as a whole.
29
Reflecting Kuhnian philosophy (Kuhn, 1977), Myers points out that this claim
making and acceptance process generates a necessary tension. In most cases, a new
claim must often refute an established given theory in order to be worth reporting in the
frrst place. The refutation of existing theories is always threatening to the original
theorists and to the community as a whole who must accept the new at the expense of the
established. Often, it is the Jack of reference to a particular work that is the real threat.
Since scientific knowledge is propagated by citation in subsequent, related work; to deny
a previous claim is to silently refute it. The threat to the dogmatic community is a
weakening of their reports that may have been based upon the work that is now under
'scrutiny.
Myers (1989 & 1990) discusses hedging as one of the most prominent politeness
strategies. Hedging is simply using word choice to subtly present a more humble and
cautious delivery of a proposition. Almost always hedging is associated with the
introduction of new claims. Typically the "be" verbs are not used when presenting a new
claim. Instead "suggest" or "appear to" are examples of more appropriate transitions into
a novel bit of information. Essentially it strips away some of the authoritative punch with
which well known and accepted information is usually presented. This technique does
not necessarily reflect the hesitancy of the researchers; rather it has come to be the
" ... appropriate attitude for offering a claim to the community" (Myers, 1989: 12).
Hedging as a linguistic factor may very well have its roots in more philosophic
circles. Lakoff ( 1972) pointed out that information conveyed through language is neither
true nor false, but falls along a continuum of certainty. Hedges, then, are language
30
,.
devices that reflect this inherent uncertainty in a more precise manner by intentionally
making claims a bit more "fuzzy" than definitive when, in fact, a researcher really
doesn't know for certain that the information is accurate in terms of inference to a
population.
An example of a detailed analysis of scientific text to describe hedging
characteristics can be found in a study of medical discourse conducted by Salager-Meyer
(1994). A taxonomy of particular hedging techniques was developed in order to refine
the analysis and better categorize potential intent underlying specific hedges. For
example, modal verbs such as "could," "might," or "may" and lexical verbs such as
"seem" or "appear," epistemic verbs such as "suggest" or "speculate," and adverbs such
as "possibly," "probably," "potentially," and "likely" are all often used as a form of
plausibility shield. Adverbs such as "roughly," "approximately," and "about" act as
approximators when scientific precision is less than optimal. Personal expressions that
reflect doubt such as "I believe," or "it is our view that ... " and intensifiers such as
''unexpectedly," or "surprisingly" are common ways to linguistically insert the author's
caution when purporting a novel claim.
Salager-Meyer applied these categories to the analysis of a corpus of articles from
five leading medical journals. Results demonstrated that there is a marked increase in
most types of hedges in the Discussion sections of a typical medical article. This is
consistent with an earlier study conducted by Skelton (1988) that examined technical
articles in both science and humanities discourse communities. Hedging is a pattern
common to any scientific field when relatively new material is introduced or conjecture is
31
being put forth, as is typically the purpose for the Discussion section. Salager-Meyer
(1994) found that the densely hedged Discussion section of most technical articles
averaged 13% of the total number of words were hedges.
Further detailed analyses of hedging in scientific discourse conducted by Hyland
(1994, 1996a & 1996b) corroborates earlier studies, finding 84% of hedging occurring in
the Results and Discussion sections. Hyland worked with a corpus of biological articles
from prominent technical journals. Among many descriptive results of this study a most
interesting note is that hedges are found at a rate of slightly more than one word per every
fifty in any given scientific article, averaging one hedging comment for every two to
three sentences.
Hyland (1996) describes hedging as mostly a lexical phenomenon, with 79% of
cases involving the use of a specific hedging verb, adjective or adverb. He does go on to
categorize other forms of hedges at a broader discourse level, accounting for 15% of all
hedges. Discourse-based hedges would include entire statements that may address
tentativeness in a particular result due to experimental conditions, limitations of a method
or theory, or an outright admission ofa lack of knowledge about an aspect of the study.
Above all else, Hyland stresses that hedging strategies are "central to science," as they
"[play] a critical role in both the social ratification of knowledge and the system of
professional reward and recognition that emanate from publication" (Hyland, l 996a:
278).
The potential effects of hedging on reader comprehension and interpretation of a
scientific text has been examined by Crismore and Vande Kopple (1988) and Vande
32
Kopple & Crismore (1990). Their studies involved setting up an experimental situation
whereby a group of ninth grade subjects were given a structured text to read about a
biological concept. Treatment groups were given versions with various degrees of
hedging, while controls were given texts with no hedging whatsoever. Subjects were
then given a standardized comprehension test to measure retention of information
presented in the text. Results indicated a positive relationship between Jrnowledge
retention and presence of hedging movements, particularly when found in the second half
of the text. Vande Kopple and Crismore (1990) speculate that this effect of hedging on
comprehension may be linked to the amount of cognitive processing involved when
students perceive that information presented to them may not be completely certain.
They believe that students pay closer attention, hence devote more processing time, to
speculative material than when it is presented as definitive, indisputable fact. They
further explain that non-hedging may encourage students to read dismissively, while
hedging promotes an evaluative reading and active interpretation of the text.
Risk Communication
Risk communication is the most practical communication theory and research
base to apply to environmental decision making. In essence, natural resources issues are
all a form of risk management, whereby potential threats have been identified that may
harm either the environment or human development or both. Risk assessment,
management and communication techniques have relevance and utility in such a context.
33
Traditionally, risk communication has been applied in the health industry, and
often in regards to extreme ventures such as nuclear power or chemical toxins (e.g.
Covello, 1989; Slavic et al., 1991; Chess et al., 1992; Weinstein & Sandman, 1992). The
health field has used risk communication mostly to inform, educate and motivate
individuals to act in order to reduce risks of disease and injury . More issue-oriented uses
of risk communication have targeted large-scale transfer of information from technical
sources to lay-people on a community to national level. A survey of environmental
health and safety professionals conducted by Rycroft et al. (1987) indicates that decision
makers put a lot of credence on good risk communication, as understanding the
knowledge base of an issue is considered critical when both formulating and evaluating
policies. The survey also demonstrated that highly technical material can impede
understanding the details of an issue, and is often ignored by decision makers, who rely
instead on secondary reports on potential concerns and risks.
Risk communication is well grounded in cognitive and social psychological
theories that act as a foundation for the application of specific communication techniques.
For example, Starr (1969) then Fischhoff et al. (1978), and later Slavic (1987), took a '
look at how people perceived risk in their lives. They developed and later improved on
methods for surveying people's attitudes toward "risky" events. From this they began to
see patterns of perception and misperception that could help inform those designing risk-
oriented communication messages.
Starr (1969) discovered a benefit to risk trade-off that the public is generally
willing to accept. He found that people were considerably (roughly 1000-fold) more
34
willing to accept a risk that they take on voluntarily than one that is imposed on them.
Fischhoff et al. (1978) expanded this surveying technique and found that risk perceptions
were not formulated along such simple benefit to risk ratios as Starr suggested. Instead,
they found a number of interrelated factors that could be distilled down to two key
dimensions or continua: 1) new technology with delayed effects and involuntary
implementation versus old technology with immediate results and voluntary
implementation; 2) high certainty of fatalities involved if something goes wrong versus
uncertain that results would be fatal. Slovic (1987) further refined these studies of risk
perception. He emphasized the importance of public inclusion in the communication and \ I ~ <
) 1 ' ~ • l '
' management process, as events that are voluntary are perceived as more controllable and
worth acceptable risk if the benefits are well understood and truly desired by the public
instead of just handed down to them by experts as necessary technology.
Communication of risk information, or any material for that matter, necessarily
requires contextual framing of the issue. A certain reference, angle, or point-of-view is
unavoidable whenever ideas are committed to words. Consequently, framing has been a
key area of study for many risk communication experts in order to discover how best to
present information to involve the public and elicit particular results or behavior changes.
.. Framing of risk issues is rooted in social norms and beliefs. Interpretation ofrisks
often happens at the community level and will be filtered through the values and cultural
biases of people who are interacting and sharing information and opinions (Kasperson et
al., 1988). Disagreement over the components of an issue often are a result of differing
' r beliefs, reflected by the framing, as much as disputes over facts. Vaughan and Seifert
35
·~
t
( 1992) point out three key dimensions of framing that appear to heighten conflict. These
include which population is considered "at risk," economic or scientific versus social
values and equity concerns, and potential gains versus losses.
Tversky and Kahneman (1981) conducted a classic study on how people's
cognitive processes functioned differently when deciding on actions that involved
information formatted as a potential "loss" versus a "gain." They set up a simple
experiment whereby a risk scenario was presented that involved a set prediction ofloss of
human life. One version depicted the scenario with emphasis on the number oflives that
could be lost while the other emphasized how many would be saved. While both
scenarios involved exactly the same number of deaths, subjects made risk-averse choices
when information was presented from the gain frame and made risk-prone choices when
faced with perceived losses. Prospect theory arose out of this to describe the manner in
which estimates of probable outcomes of an event are often skewed by perceptions and
beliefs that lean subjects toward inconsistent choices, particularly when presented with
extreme risk gains or losses.
Maheswaren and Meyers-Levy (1990) added to prospect theory by discovering
that messages framed negatively had a greater persuasive effect if subjects perceived that
they were highly involved in the consequences of the stated risk; with the converse being
true for low involvement. They further found that high involvement was associated with
more detailed evaluative processing of the information presented, and that negatively
framed messages tended to carry more weight during the decision process. Davis (1995)
36
conducted a similar study and noted that messages framed such that negative outcomes
are associated with a person's inaction were the most persuasive.
' Sandman et al. (1993) conducted a series of experiments designed to manipulate
various frames and content components such as agency responsiveness versus not
responsive to public concerns, seriousness of the risky event, amount of technical
information provided, and agency tone. They found that community outrage regarding an
issue was significantly influenced by how the agency involved handled the •
communication. Non-responsiveness and a distrustful tone contributed greatly to a
negative perception of the risk situation. Seriousness and amount of technical
information appeared to have relatively little effect on public outrage.
Levin, Schneider and Gaeth (1998) categorized framing techniques into one of
three main types or paradigms of framing: risky choice, attribute, and goal oriented. All
three categories can further classify the specific manners of framing into either positive
or negative valences in terms of how the outcome of a decision choice is viewed. For
example, risky choice framing refers to statements that couch a decision along a spectrum
of risk prone {negative) versus risk averse (positive) outcome options. Attribute framing
uses modifiers to describe the outcome by either stimulating a negative association or a
positive one.
According to Levin, Schneider and Gaeth (1998), goal framing uses positive or
negative valences to enhance the persuasive effect of a statement in order to invoke a
particular decision response. Unlike the other two types of framing, goal framing is used
when the same outcome or decision is desired regardless of whether a positive or
37
negative valence is used. It is actually more a matter of which valence is considered most
effective in invoking the goal. For example, a positive valence in goal framing would
point out what may be gained ifthe action is taken. Conversely, a negative valence in
goal framing would attempt to promote a particular action by pointing out what may be
Jost ifthe action is not taken. Goal framing is common to environmental issues and lends
itself well to the climate change issue. Consequently, this was the type of framing style
used for this study.
• Reflecting a more socio-political perspective, Plough and Krimsky (1987) defme
risk communication as the transfer of information from a technologically elite community
to the policy makers and general public. They point out that the divide or tension
between these communities is often what leads to miscommunication and, more
importantly, mistrust in the decision making process. They call for an equitable weighing
of both technical and social value components of information when crafting messages.
Conflict is inherent in most issues, with the distribution and use of natural
resources being some of the most contentious. Dietz et al. (1989) point out that debate
over natural resources is often a battle over the relative value of the available resources.
Shifts in values can result in a corresponding shift in the power base that's in control.
This can ultimately change the way a resource is managed and distributed in society.
Stern (1991) emphasizes that risk communication should be far more than simply
a transfer of information from the expert community to the lay community. Rather,
communication is a socio-political tool that can be used to inform and to engage a
community in the process of democratic decision making. Stern (1991) also notes that
38
science is an enterprise that has inherent uncertainty. Consequently, communication
about science will necessarily contain bias. Risk communication is not about developing
an unbiased message; rather, it is about optimizing the educational, equitable, and
efficient manner of conveying information during a decision making process.
Though not explicitly stated by the aforementioned authors, discussing risk
communication in the context of community participation and social equity falls under
the more general theory of communicative action. This particular theory was pioneered
by Jurgen Habermas (1984 & 1987) (Webler, 1992) as a critical communication tool for
examining not just the text and mechanics of social discourse, but the manner in which
the communication process is being conducted. Habermas envisions an ideal
communicative situation for developing understanding and reaching decisions regarding
a given issue. This entails mutual trust, fair access to information, and equitable voice in
the communicative process.
Habermas (1984 & 1987) (Webler, 1992) views power structures such as
' government and large interest groups and the inaccessibility of technical material both as
threats to an equitable and fair democratic process of communication and to community-
based decision making. He calls for competent, active participation in communication
and issue resolution by representatives from all relevant stakeholders in an issue. This,
he notes, is the only way to ensure an unbiased and equitable agreement on how issues ;
such as resource use should be managed.
39
Theory of Reasoned Action
The Theory of Reasoned Action (TRA) model was defined by Fishbein (1967)
and later developed into a descriptive and predictive model for analyzing human behavior
by Fishbein & Ajzen (1975) (Ajzen & Fishbein, 1980). Seven separate measures
comprise the model: Behavioral Intention, Attitude, Subjective Norm, Behavioral Belief,
Outcome Evaluation, Normative Belief, and Motivation to Comply. The overall aim of
the model is to provide both a descriptive and predictive measure of a human subject's
behavioral intention regarding a particular action. The model assumes that the actual
behavior may not ever be observed, but that a valid measure of intention serves as a good
proxy measure of actual behavior.
Though it sometimes may suffice to simply measure intention, TRA allows for a
more detailed description of what may influence a person's choice. The model uses a
hierarchy of variables, with each tier serving as components for the next higher tier. In
other words, Actual Behavior is considered to be directly linked to Behavioral Intention.
Behavioral Intention is thought to be influenced by a linear combination of Attitude and
Subjective Norm. Each of these two variables is further sub-divided into two more
variables.
The Attitude component is based on the following constitutional definition
(Fishbein & Ajzen, 1975; Ajzen & Fishbein, 1980), paraphrased here as: Attitude toward
an object is comprised of the sum of all beliefs about the object. Belief is further defined
as a combination of both strength of belief that the object being considered is associated
(likely or unlikely) with another object or concept (e.g. Long-tenn ecosystem stability is
40
dependent on preserving biodiversity), and the subject's evaluation (good or bad) of the
objects that comprise the association (e.g. Ecosystem stability should be a key land
management priority). In order to assess Attitude both strength and evaluation of belief
toward a specific concept should be measured.
The Subjective Norm component is defined as the product of a subject's
Normative Belief and the Motivation to act on that belief (Fishbein & Ajzen, 1975; Ajzen
& Fishbein, 1980). The Normative Belief is the subject's perception that people deemed
important or influential believe that a particular behavior is appropriate. Motivation is
the subject's desire to comply with the normative belief, often a measure of the strength
of influence between the subject and the referent(s) who are associated with the belief.
As with the Attitude component, two types of questions are typically required to
complete the Subjective Norm measure. First, subjects are prompted to think of various
individuals or groups who might influence their choices, acting as referents for the
subjective normative questions. Questions geared toward normative responses will ask
subjects to rate the strength and directionality of what they perceive their referent(s)
believe is appropriate in a particular situation (e.g. According to [referent] ecosystem
stability should be a key land management priority). Motivation to Comply with this
belief will be measured by associated questions that ask subjects to rate the strength of
their referents' influence on the subject's response to each normative question.
Behavioral Intention can be a single measure in accordance with the TRA model
(Ajzen & Fishbein, 1980). The proportional influence of Attitude versus Subjective
Norm on Behavioral Intention can be estimated, as well as reflected as weights. These
41
weights can be derived by handling the TRA model as a regression equation, whereby
Attitude and Subjective Nonn are predictors and Behavioral Intention the criterion.
Standardized regression coefficients for the two predictors then serve as estimates of
empirical weights. These weights can then be incorporated into the TRA model as
multiplicative factors for the respective components (Attitude and Subjective Norm)
before finally adding the two to derive a Behavioral Intention measure.
42
CHAPTER3
INSTRUMENTATION & METHODOLOGY
Design
A two-factor (4 x 4) randomized complete block (RCB), post-test only
experimental design (Keppel, 1991; Kirk, 1995; Hair et al., 1998) was used to measure
the effects and interactions of hedging (hedging, not hedging) and framing (positive,
negative) message components on each of nine dependent variables. Gender was used as
the blocking criterion in this study. Differential concern for environmental issues
between men and women has appeared as a trend in national surveys, with women
tending to be ~ore pro-environmental in their decision making trends than men
(NEETF/Roper, 1998). Framing has also been shown to affect men and women
differently, with women generally responding to negative frames with greater magnitude
than men (Fagley & Miller, 1990).
Nine specific dependent variables were assessed, drawn from both the cognitive
and affective psychological domains, focusing primarily on variables that constitute the
Theory of Reasoned Action (TRA) (Fishbein, 1967; Fishbein & Ajzen, 1975; Ajzen &
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Fishbein, 1980). Three of these variables were further sub-divided into two measures
each, one for Detriments and one for Benefits associated with the climate change issue.
Subject responses for questions pertaining to each variable were measured on a seven
point semantic differential scale. Each measure was an average of constituent item
scores, consisting ofresponses to at least two questions per variable, and several with ten
questions per variable. Data derived from these scales were considered interval data in
accordance with relevant arguments that the advantages of robust statistical analyses
available for interval-level data outweigh the potential and rather minimal distortion of
data that may result in psychometric studies (Bohrnstedt & Borgatta, 1981 ).
For this experimental design an a priori estimated sample size of 160 (80 males
and 80 females) was calculated as necessary to attain a statistical power level of
approximately 0.80 with a= 0.05 to detect an estimated medium effect size fl' = 0.25
(Cohen, 1988; Kirk, 1995). Subjects were randomly assigned within each block to one of
16 experimental treatment combinations or the control, with a total of four subjects per
treatment combination within each block. Therefore, each level of the individual main
treatments (hedging and framing) and the control was comprised of sixteen subjects for
each block, or 32 subjects total for each main treatment group and the control.
Subject Selection
The target population was undergraduate students currently attending The Ohio
State University (OSU) who are U.S. citizens and had declared a major area of study in
the School of Natural Resources. According to Spring, 2001 enrollment data 234
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students total were registered as Natural Resources majors, comprised of 115 female and
119 male students. This population was selected because it represents a cohort of
students ofrelatively homogenous age and educational background, who are presumably
interested in becoming natural resources decision makers. Students registered for courses
during the study period were accessible via the OSU Registrar Office web-based records
for student information and course rosters, providing the sample frame. Selection and
frame errors were adequately reduced by using the OSU Registrar information available
to the participating professors, assuming that records were accurate.
The study was conducted within the context of three courses in the School of
Natural Resources at The Ohio State University that are required of most Natural
Resource undergraduates: one large freshman/sophomore level course ( 192 enrolled) and
two smaller junior/senior level courses (25 and 32 enrolled respectively). Though
conducted in classroom settings, participation in this study was completely voluntary. A
monetary gift of $5 was given as an incentive for participation. Prospective subjects
were informed that there was no obligation to participate, and that participation or
abstention would not affect their grade in the course in any way. In all three classrooms
the professor vacated the room following the announcement and remained absent
throughout the questionnaire session. Subjects were also informed that results of this
study would be reported in terms of group responses. Individuals would not be identified
or associated with the responses in any way. The record of participation was kept strictly
confidential between myself and the University's administrative assistant in charge of
processing reimbursements associated with grant funding for this project.
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A total of 168 students participated in the study. Eight cases were omitted from
the final analysis: two because of incomplete questionnaires, one because the respondent
was not a U.S. citizen, and five because they were demographic outliers, as their reported
age was considerably older (2:: 3 SD) than the average age of the rest of the sample. The
remaining 160 cases, representing 80 female and 80 male responses, were used for the
final analysis. This sample size represented 68% of all students registered as Natural
Resource majors during the time of this study.
Five specific questions targeted basic personal information such as ethnicity, age,
class rank, grade performance (G.P.A.), and country/State of residence. Comparisons
among treatment groups and the control were conducted [see Table 3 .l]. Results
revealed no significant difference among treatment groups or control along any of the
five demographic measures for either male or female respondents. This implies that the
desired homogenous sample was acquired.
FEMALES MALES
Age 21 22 Ethnicity White/Caucasian White/Caucasian Class Rank Senior Senior Grade-point Average 3.00-4.00 2.00-2.99 State of Residence Ohio Ohio
Females: N = 80; Males: N = 80. Categories indicate modal response.
Table 3.1: Demographic profile of subjects expressed as modal response.
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Prior to reading the experimental passage, subjects were asked a series of three
questions addressing their prior knowledge and exposure to the climate change issue from
both media sources and college courses. These questions were later averaged into a
single scale to assess a subject's Prior Background regarding the climate change issue.
Comparisons were made among treatment groups to confirm homogeneity of
Prior Background among the subjects [see Table 3.2]. No significant differences were
found among the treatment groups for Prior Background. Nevertheless, this variable was
later used as a covariate during construction of the factorial models during final analysis.
Prior Background
FEMALES
(2) "Knowledgeable & Informed"
MALES
(2) "Knowledgeable & Informed"
Females: N = 80; Males: N = 80. Categories indicate modal response. Modes based on 5-point scale: 0 =Not at all knowledgeable or informed, 1 = Slightly knowledgeable & informed, 2 = Knowledgeable & informed, 3 = Very knowledgeable & informed, 4 = Extremely knowledgeable & informed.
Table 3.2: Subjects' prior background measures.
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Procedure
The study was conducted in mid-May, 2001, approximately two-thirds into the I: '
Spring Quarter session for OSU students. Two of the three intact classroom sessions I
were conducted on the same day; the third took place three days later. Experimental
sessions were conducted in a classroom monitored by myself.
Questionnaires for females and males were differentiated by different colored
covers and were placed in two separate stacks, with each treatment and the control I'
sequentially placed in each stack. Female and male participants were assigned a
questionnaire from the top of their respective stack as they came forward to collect a
' questionnaire. This procedure effectively randomized assignment of treatment levels and
the control within each block.
Subjects were instructed to read the instructions provided, read the text passage
carefully and respond to the subsequent questions as honestly as possible. Instructions
included two examples of what a question/statement might look like and how to mark a
response on the scale that best represents their response. It was also expressed that "this
is not a test of your knowledge or comprehension of the issue; there are no 'correct'
answers." Subjects were informed that they may refer to the text passage as needed;
however, to reduce question and order effects, they were also asked not to change
responses once marked on the questionnaire. No time limit was set; though it took
approximately 30 minutes on average for a participant to complete a questionnaire.
The questionnaire totaled sixteen 8.5 in. x 11 in. pages double-sided [see
Appendix A]. Times New Roman typeface in 12-point font was used for the
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experimental text passage and the questions. One page of the questionnaire was devoted
to "General Instructions," and two pages for the experimental text passage, which had 16
variations [see Appendix BJ. The remaining pages contained 63 questions pertaining to
the dependent variables of interest. Another six questions targeted basic personal
information such as gender (in case ofa mix up with handling of the color-coded
questionnaire), ethnicity, age, class rank, grade performance (G.P.A.), and country/State
of residence.
Salient Beliefs
Ajzen and Fishbein (1980) recommend that researchers begin the process of
building a questionnaire designed to measure variables in the Theory of Reasoned Action
by first establishing a baseline of the study population's salient beliefs on the topic of
interest. The target population for this portion of the instrument development was all
undergraduate students at The Ohio State University, Columbus campus, who are U.S.
citizens and registered for school-year 2000/2001 in either the College of Arts and
Sciences (ASC) or the College of Food, Agricultural, and Environmental Sciences
(FAES), which includes the School of Natural Resources. This represented a total target
population of approximately 10, 700 students.
Though the experiment would be conducted on a narrower subset of this
population - School of Natural Resources students only- soliciting responses from this
broader population of similar students increased the range and diversity of salient beliefs
reported. This also provided a more generalizeable set of responses to build upon, and
49
helped insure that a more robust range of possible beliefs was represented. Both
considerations are important when calibrating the instrument to capture topics of salient
interest to all of the subjects in the final experiment.
A stratified random sample frame of 150 students (75 female and 75 male) was
selected from the University's current 200012001 Student Directory using a random
number generation technique to first select a starting page, followed by a number to
determine how many pages to turn between each selection. Once a page was selected the
first selection criterion was with which of the two target Colleges the student was
affiliated. Once a viable candidate was found the name and email address were recorded
and the search moved to the next randomly selected page. Equal numbers of female and
male candidates were selected by alternating which sex was selected during each page
search. Selecting equal numbers of each sex was conducted to reflect the nearly 50150
distribution of males and females enrolled at Ohio State. Secondly, it was prudent to
solicit salient responses equally from both sexes, in keeping with the same reasoning
behind using gender as a blocking criterion. In order to more accurately reflect the 10:1
distribution of students enrolled in the College of Arts and Sciences (ASC) and the
College of Food, Agriculture, and Environmental Sciences (FAES) respectively, ten
candidates in ASC were selected for every one FAES candidate.
Following Dillman's (2000) procedures for internet surveys, candidates
comprising the sample frame of 150 students were sent a participation notification via
email one week prior to having the questionnaire actually sent. The questionnaire was
sent as part of an email message with instructions to answer within the context of the
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email message and send back via "reply." Candidates were given one week to reply.
Two subsequent email solicitations were sent to non-respondents within two days after
each reply deadline lapsed. The final number of viable responses was 55, representing
37% of the sample frame. A comparison of early (first solicitation) versus late (third
solicitation) responses did not reveal any obvious qualitative or quantitative differences.
This sample inferentially represents the target population within an approximate ±12%
sample error at the 95% confidence level (Salant & Dillman, 1994).
Students participating in the salient belief questionnaire were asked:
1) What do you believe are the advantages of supporting an overall, nationwide decision that would favor reducing industrial and automobile greenhouse gas emissions within this country?
2) What do you believe are the disadvantages of supporting an overall, nationwide decision that would favor reducing industrial and automobile greenhouse gas emissions within this country?
3) Are there any organizations or individual people who would approve of you supporting an overall, nationwide decision that would favor reducing industrial and automobile greenhouse gas emissions within this country?
4) Are there any organizations or individual people who would disapprove of you supporting an overall, nationwide decision that would favor reducing industrial and automobile greenhouse gas emissions within this country?
Responses were compiled into a single transcript for each question. Each of the
four transcripts was analyzed by myself, starting with a qualitative content analysis to
ascertain recurrent themes. Next, a more careful reading of each response was conducted
by myself to judge which theme each statement most closely reflected. This required a
somewhat subjective judgment in that a common theme was often represented with
51
slightly different wording or phrasing by different respondents, but meant essentially the
same thing. Finally, each occurrence of a particular theme was counted for a final tally of
how frequently each theme was represented within the sample.
The most commonly stated themes for each of the four questions were recorded
[see Table 3.3]. These were considered representative of the main salient beliefs,
regarding outcomes of a decision to support large-scale human-induced climate change I
reduction and referents important to the respondents who would approve or disapprove of
such a decision.
These salient beliefs were used to help refine the search for specific information
regarding climate change that would be used to construct the experimental text passage
and subsequent questions. Where possible, topics chosen for inclusion in the instrument
were related to one of the five key salient outcomes.
There appear to be no substantial findings or hypotheses indicating that subjective
norm would be affected by variations in the textual components used as factors in this
study. Consequently, the salient referents were not as critical during the search for
relevant information in the climate change literature. However, one could consider that
these salient referents are most likely those who subjects were referring to when
responding to questions in the final instrument pertaining to the subjective norm variable.
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Salient Outcomes -Advantages 1) Decrease air pollution (23) 2) Decrease land and water pollution (15) 3) Protect the environment (14) 4) Protect human health (13) 5) Promote sustainable energy sources ( 10)
Salient Outcomes - Disadvantages 1) Higher costs for goods and services (16) 2) Economic slowdown (9) 3) Increased cost for energy and transportation (6) 4) Personal lifestyle inconvenience (5) 5) Loss of some jobs (4)
Salient Referents -Approve 1) Environmental groups (19) 2) Environmentally concerned citizens (13) 3) Scientists (8) 4) Government agencies (7) 5) Alternative energy industries ( 4)
Salient Referents - Disapprove 1) Industry in general (22) 2) Automobile manufacturers (20) 3) Coal, oil and gas companies (13) 4) Economically concerned citizens (8) 5) Conservative politicians (7)
N = 55. Numbers in parentheses represent frequency ofresponse.
Table 3.3: Top five most common responses to each of the salient belief and salient referent questions pertaining to the climate change issue.
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Text Material
The climate change issue was chosen for this study because it represents a current
and relevant natural resources problem that will require a considerable amount of
decision making to occur on the part of scientists, legislators, land managers, interest
groups and the public in general. It is also a large-scale issue in the United States and
abroad that impacts every human being to some extent, yet remains geographically
diffuse in that it cannot be pinpointed as a problem only for the West or the Great Lakes
or any particular city. Selecting an issue such as this was meant to reduce the potential
psychological effects of geographic proximity to the issue and personal investment
biases.
Climate change impacts are also wrought with scientific uncertainty and
imprecision of both measurement and predictions, which makes it a relatively contentious
issue. There are strong viewpoints regarding the detriments and benefits of either
supporting or not supporting climate change reductions.
The primary source of information for the experimental text and questions was the
technical report: Climate Change Impacts on the United States: The Potential
Consequences of Climate Variability and Change published in December, 2000, for the
U.S. Congress by the National Assessment Synthesis Team of the U.S. Global Change
Research Program (NAST, 2000 a & b ).
The experimental text passage was constructed from this source in order to
convey current and relevant information regarding climate change in a manner typical of
technical reports that natural resource decision makers may encounter. The experimental
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passage started with a brief, 183-word introduction to the climate change issue designed
to orient the participating subjects to the topic and provide background information. This
text was adapted directly from the Climate Change Impacts on the United States report
(NAST, 2000), which is considered public domain material not subject to copyright law
infringement. This section related "factual" information about the accumulation of
greenhouse gases in the atmosphere and the latest projections for the physical aspects of
climate impacts, namely temperature, precipitation and sea level projections. All
treatment levels and the control group were given this portion of introductory text to read.
Fallowing is the introductory text as stated in the questionnaire:
Humans are affecting some of the key factors that govern climate by changing the composition of the atmosphere and by modifying the land surface. Rising atmospheric concentrations of carbon dioxide (C02) and other greenhouse gases are increasing Earth's natural greenhouse warming effect. This increase has resulted from the burning of coal, oil, and natural gas, and the clearing and burning of forests around the world. If the current rate of human-produced emissions is maintained, atmospheric C02 concentration will continue to rise, reaching between two and three times its pre-industrial level by the year 2100.
Long-term observations confirm that our climate is now changing at a rapid rate. With continued growth in atmospheric greenhouse gas concentrations, average temperature in the U.S. will rise in the next 100 years. There will also be more precipitation overall, with more of it coming in heavy downpours. In spite of this, some areas will get drier as increased evaporation due to higher temperatures outpaces increased precipitation. The warming is causing permafrost to thaw, and is melting sea ice, snow cover, and mountain glaciers, and sea level is rising.
Information for the manipulated part of the passage was carefully selected from
the report by first identifying topics that reflected the salient beliefs of the target
population. Secondly, topics were chosen that clearly portrayed a detriment to either
55
ecological or sociological systems if rapid, human-induced climate change is not
mitigated, as well as portraying the converse as a benefit to either system if the rate of
climate change is allowed to continue.
Equivalent-weighted propositional statements regarding both detriments and
benefits of climate change impacts constituted the experimental text. Ten statements
referred to detriments and ten to benefits, with five topics referring to ecological and five
to sociological impacts for each of the two viewpoints. Care was taken to match
statements regarding a particular detriment with one regarding a benefit that referred to
the same general topic. For example, one statement of a detriment: "Demands for air
conditioning might increase, possibly increasing the cost of energy during the summer,"
was matched by a statement regarding a benefit of allowing climate change to continue:
"Winter heating needs might decrease, possibly reducing the seasonal cost of energy."
Following is a list of the twenty topics used in the final instrument. Same
numbered items between detriments and benefits indicate a "matched" set of similar
topics that serve as the converse of each other:
Detriments Ecological:
1) Shoreline erosion and coastal wetland losses. 2) The rate ofloss of biodiversity, and plant and animal species'
adaptability to rapid changes. 3) Sustainability of delicate and isolated ecosystems such as alpine
meadows, mangroves, and tropical mountain forests. 4) Forest susceptibility to pests and fire, and damage to forest
ecosystem productivity. 5) Fish habitat disturbance and survival of cold-water fish species.
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Sociological: 6) Demands for air conditioning and the cost of energy during the
summer. 7) Water availability for irrigation, and irrigation management
complications. 8) Water- and animal-borne related diseases, and incidents of human
illness and death. 9) The risk of flash floods and soil erosion, and agricultural
productivity. 10) Economic impacts on the U.S. gross domestic product (GDP).
Benefits Ecological:
1) The number of inland, non-tidal wetlands, and the area where floodplain wetlands can form.
2) Migrating birds extent of flying south, timing of seasonal nesting, and the odds of young birds surviving winters.
3) Tree growth and forest expansion, and the coverage and range of forest ecosystems.
4) Plant productivity throughout various ecosystems. 5) Over-winter mortality of many species of wildlife, cold stress, and
seasonal availability of food for wildlife.
Sociological: 6) Winter heating needs and the cost of energy. 7) Crops developing at faster rates and agricultural use of irrigation
water. 8) Incidents of cold-related human illnesses and deaths. 9) The food supply from agricultural productivity and prices for food
products. 10) Economic impacts on the U.S. gross domestic product (GDP).
Statements were extracted from the Climate Change Impacts on the United States
(NAST, 2000 a & b) source documents as close to verbatim as possible, again this
material is considered public domain not subject to copyright law infringement. Minor
alterations in word choice and order were necessary to standardize sentence length and
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allow for the textual manipulations to be added or omitted and still retain coherent,
parallel meaning.
A readability index, based on a system developed by Edward Fry that compares
number of syllables to number of sentences within a passage of text, was used to assess
the reading complexity throughout the text (Hopkins, 1998). The text consistently
registered at a college readability level. Sentence structure, length, language register,
voice, tone, person, and modifiers that were not part of the treatment manipulations were
kept as constant as possible throughout the text.
Subjects were informed in the instructions section that "all information presented
in the passage is based on a consensus of rigorously studied, well accepted sources."
However, sources of the content of the text were not revealed to the subjects, so that trust
in the origin of the information would not be a potential influence on responses.
The text was adapted for the experimental conditions by simultaneously
manipulating two message components [see Appendix BJ:
Hedging was altered by 1) adding terms to modify the modal and epistemic
quality of statements (e.g. Water availability for irrigation might decrease, potentially
complicating irrigation management.), or 2) not hedging was produced by using the
definitive modal will, and omitting any adverbial hedges (e.g. Water availability for
irrigation will decrease, complicating irrigation management).
Matched sets of detriments and benefits were structured with the same hedging
terms. Each statement, when hedged, contained two hedging terms. One term was a
modal verb such as might, may or could and the other was an adverb such as possibly,
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probably or potentially, or an adverbial "rounder" such as about or roughly. The
introduction to each set of ten statements was also hedged for those treatment levels, also
using two hedging terms. In this case, one term was the lexical verb suggest and the
other the adverb likely.
Each set of ten statements, with the corresponding introduction, contained
approximately 235 words [see Table 3.5]. Twenty-two hedging terms were used per 235
word passage, representing a hedging frequency of about one out of ten words. This
corresponds closely to descriptive studies of scientific texts, reporting average hedging
frequencies for the Discussion sections of typical biomedical case reports and research
papers at 10% to 13% (Salager-Meyer, 1994).
Framing was altered by 1) describing the outcome of each propositional statement
in a positive valence relative to each of the two decision alternatives (support vs. not
support climate change reductions) (e.g. Disturbance of fish habitat will be avoided,
preventing threats to the survival of some cold-water fish species), or 2) describing the
outcome of each propositional statement in a negative valence relative to each of the two
decision alternatives (e.g. Disturbance of fish habitat will occur, threatening the survival
of some cold-water fish species).
Using the typology of goal framing described in detail by Levin, Schneider and
Gaeth (1998), positive and negative valences were each represented by two different
manners of framing. One manner is considered a "strong" frame, using obtain gain and
suffer loss formats to represent positive and negative valences respectively. The other
59
manner is considered a "weak" frame relative to the first, using avoid loss and forgo gain
formats to represent positive and negative valences respectively.
The following two Tables [3.4 & 3.5] outline what constitutes each treatment
level in relation to how the two sides of the issue (detriments and benefits) are presented,
and the number of words contained in the experimental text passages for each treatment
level.
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DETRIMENTS BENEFITS
Hedging: 1) Both Hedged Hedged Hedged 2) Both Not Hedged Not Hedged Not Hedged 3) Mix(A) Hedged Not Hedged 4) Mix(B) Not Hedged Hedged 5) Control n/a n/a
Framing: 1) Strong Mix Suffer Loss Obtain Gain 2) Weak.Mix Avoid Loss Forgo Gain 3) Both Positive Avoid Loss Obtain Gain 4) Both Negative Suffer Loss Forgo Gain 5) Control n/a n/a
Table 3.4: Combination of various experimental treatment levels for each of the two main variables.
Hedged Not Hedged
Detriments: Suffer Loss 227 218 Avoid Loss 243 234
Benefits: Obtain Gain 226 217 Forgo Gain 258 249
Table 3.5: Number of words for each experimental text passage.
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~
Expert Panel
Twelve faculty members from the School of Natural Resources at The Ohio State
University were selected to serve as a panel of experts to help validate the specific
climate change topics selected for the instrument. This panel consisted of six professors
whose research focuses on the social science component of natural resource issues, and
six who focus on the natural science component. Three of the twelve panelists were
female. Most panelists are full professors who have been working in their respective
fields for a decade or more. Three panelists were relatively new faculty members, having
started their academic careers within two years prior to the study.
Each expert panelist received a questionnaire containing each of the twenty
climate change topics, specifically the ten detriment and ten benefit statements drawn
directly from the Climate Change Impacts on the United States report (NAST, 2000 a &
b ). Each topic was presented as a pair of two versions of the same statement. One
version was negatively framed and the other positively framed. This format was used to
help mitigate potential bias in response caused by differential framing. All versions were
hedged to hold that variable constant for this portion of the study.
Panelists were asked to read each topic pair and rate how important they judge the
topic represented in the statements to be when deciding whether to support or not support
a reduction in the rate of human-induced climate change. Panelists were informed that
"this is not meant to be a measure of your personal preference or attitude toward one side
of the issue or the other; rather, it is an objective (as possible) measure of how important
62
each proposition is to the overall issue." Panelists were also asked to "rate each
statement pair on its own merits, not relative to other pairs."
Responses were along a five-point, Likert-type scale: Not at all important,
Slightly important, Important, Very important, Extremely important. Panelists were also
asked to make qualitative comments regarding the accuracy and clarity of the content
presented in each statement, as well as the validity of the frame shift within each pair of
statements. Comments and criticisms from panelists were taken into account during
development of the field test version of the questionnaire.
Statement pairs that appeared grossly out ofline with the other statements were
either omitted or altered dramatically, based on panelists' qualitative comments, to
reduce whatever extraneous factor was affecting the response. For example, an early
' iteration of the human health topic used a version of the benefit side that read more or
less the same as the final version: Incidents of cold-related human illnesses and deaths
could be reduced in some areas of the country that may experience less severe winter
conditions (obtain gain version only). Meanwhile, the original detriment-oriented
alternate human health topic read: Diseases that are water and animal borne may
intensify in the summer, which could increase incidents of human illness and death in
some areas experiencing more heat waves (suffer loss version only).
The initial factor analysis interpretation indicated that the benefit alternate fit
relatively well with other sociological benefit topics. But, the detriment alternate loaded
high on a very different factor than any other topic in its category. Upon closer
examination of the panelists' written comments and a few one-on-one discussions
63
revealed that the detriment version was most likely introducing a confounding concept
that was affecting the way the panelists were responding. In this particular case it was
clear that the addition of" ... areas experiencing more heat waves" was adding an outcome
of climate change that conceptually was being perceived as above and beyond the
potential increase in water and animal borne diseases. Panelists were consistently
considering this particular topic as "extremely important," because it not only referred to
human diseases but also the rather large-scale health and economic impacts associated
with heat waves. Obviously, this additional concept skews the intended equity both
among related topics and between alternate detriment/benefit topic pairs. Consequently,
the heat wave reference was omitted from the next iteration, and follow-up analysis
during the pilot test indicated that the textual correction had more or less solved this
problem.
A factor analysis was conducted on the results to ascertain similarities in
responses in order to validate that statements pertaining to either detriments or benefits
are judged as similar enough in importance to eventually be averaged to measure a single
variable [see Tables 3.6 & 3.7]. The unrotated principal components analysis resulted in
50.4% of the variance explained by the first factor (Eigenvalue= 10.1), suggesting a
strong unidirectionality inherent in the choice of topics. All loadings were 2: 0.50, with
half of them;::: 0.70. A Varimax rotated principal components analysis revealed roughly
two discrete factors, one for detriments and one for benefits. Most loadings for each
were;::: 0.40 per factor, with two exceptions for each component. Though the sample size
for this factor analysis was small (N = 12), the high number of factor loadings 2: 0.40, at
64
least four loadings among the total that were 2: 0.60, and communalities that were very
high 2: 0.80, all helped validate the use of this analysis under these circumstances (Hair et
al., 1998).
65
I
Factor 1 2 3
Eigenvalue 10.1 2.8 1.9
Percent of Variance 50.4 14.3 9.6
Unrotated Component Matrix:
FACTOR! Detriments
1) 0.90 2) 0.71 3) 0.76 4) 0.69 5) 0.61 6) 0.73 7) 0.26 8) 0.66 9) 0.73
10) 0.68 Benefits
1) 0.78 2) 0.53 3) 0.89 4) 0.78 5) 0.88 6) 0.82
'7) 0.54 8) 0.66 9) 0.52
10) 0.76
Cum. Percent 50.4 64.6 74.3
N = 12. Principal component analysis (unrotated). Bolded loadings represent highest value among factors.
Table 3.6: Unrotated component loadings on the first factor for expert panel responses.
66
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I I
I
Factor I 2 3
Eigenvalue 10.1 2.8 1.9
Rotated Component Matrix:
FACTOR 1 Detriments
1) 0.66 2) 0.73 3) 0.88 4) 0.86 5) 0.82 6) 0.81 7) 0.55 8) 0.17 9) 0.55
10) 0.24 Benefits
1) 0.45 2) 0.04 3) 0.45 4) 0.41 5) 0.10 6) 0.23 7) 0.08 8) 0.46 9) 0.07
10) 0.18
Percent of Variance 50.4 14.3 9.6
FACTOR2
0.47 0.09 0.23 0.13 0.27 0.01 0.08 0.07 0.03 0.82
0.56 0.14 0.45 0.25 0.72 0.74 0.41 0.72 0.93 0.38
Cum. Percent 50.4 64.6 74.3
N = 12. Principal components analysis (Varimax rotation). Bolded loadings represent highest value among factors.
Table 3.7: Rotated component loadings on the first factor for expert panel responses.
67
Internal consistency was also assessed separately for the ten detriment and ten
benefit topics using item analysis with item-total correlations and Cronbach's alpha.
First, the median response for each item was calculated, and only items with a median 2:
3, representing "Important" to "Extremely important," were considered valid. Those with
medians below 3 were tagged for closer examination. One detriment topic fell below this
criterion, regarding the U.S. economy; meanwhile, two benefit topics fell below,
regarding U.S. economy and energy costs. Second, only topics with item-total
correlations 2: 0.50 were considered valid, and any below this were also tagged for closer
examination [see Tables 3.8 & 3.9]. For detriment topics, two items fell below, U.S.
economy and energy costs; however, none fell below this criterion for benefit topics.
Review of the panelists' qualitative comments and a few one-on-one discussions revealed
that the item outliers may have been due in part to those particular topics -the U.S.
economy and energy costs - being considered more as incentives or disincentives
affecting the reader's overall decision intention, rather than simply being viewed as an
outcome of that decision. Consequently, those particular items were reworded to help
redirect the implied meaning.
Finally, Cronbach's alpha for each set was calculated and taken into consideration
[see Tables 3.8 & 3.9]. Alpha forthe set often topics regarding detriments was 0.90, and
alpha for the set of benefits was 0.92; both very strong indicators that each set often
topics were measuring the same trait.
68
DETRIMENTS
Ecological: Biodiversity Ecosystems Productivity Wetlands Wildlife
Sociological: Agriculture Energy Health Economy Irrigation
Cronbach's Alpha= 0.90
N=12.
Mean
4.0 3.8 3.5 4.1 3.3
4.0 3.3 4.2 2.9 4.0
SD
0.8 1.1 0.8 0.8 0.8
0.7 0.9 0.6 1.1 1.0
Item-Total Correlation
0.83 0.72 0.86 0.79 0.75
0.69 0.25 0.68 0.44 0.72
Means based on a 5-point scale: 1 =Not important at all, 2 = Slightly important, 3 =Important, 4 =Very important, 5 =Extremely important.
Table 3.8: Item analysis of Detriment statements, based on an internal consistency method using Item-total correlations and Cronbach's Alpha, for expert panel responses.
69
BENEFITS
Ecological: Biodiversity Ecosystems Productivity Wetlands Wildlife
Sociological: Agriculture Energy Health Economy Irrigation
Cronbach's Alpha= 0.92
N= 12.
Mean
2.6 3.4 3.2 2.8 2.9
2.8 2.8 2.8 2.3 3.3
SD
0.8 0.8 1.2 0.9 0.8
1.1 0.9 1.0 1.2 0.9
Item-Total Correlation
0.72 0.54 0.78 0.69 0.83
0.86 0.58 0.83 0.55 0.80
Means based on a 5-point scale: I =Not important at all, 2 = Slightly important, 3 =Important, 4 =Very important, 5 =Extremely important.
Table 3.9: Item analysis of Benefit statements, based on an internal consistency method using Item-total correlations and Cronbach's Alpha, for expert panel responses.
70
Field and Pilot Tests
An early draft of the questionnaire was field tested with a panel of eight graduate
students currently enrolled in the School of Natural Resources who are familiar with
social science methodologies applied to natural resource issues, including the Theory of
Reasoned Action. Each field test participant received a $20 gift for participating in the
session and handing in a written critique of the instrument. Comments and criticisms
from this session were used to provide a sense of the face validity of the questionnaire
and to refine instructions, experimental passage, questions and scales prior to pilot testing
the instrument.
A pilot study was conducted with 32 subjects, consisting of22 female and 10
male respondents. Participants in the pilot test were volunteers solicited from a sample
frame that consisted of all graduate students, who are U.S. residents, currently enrolled in
the School of Natural Resources at The Ohio State University. This sample frame
represented 80 eligible individuals, of which 40% participated in the study. Each
participant received a $10 gift for completing a pilot questionnaire.
Using the pilot test results, internal reliability was calculated using Cronbach's
alpha for each set of items designed to constitute a single variable [see Table 3.10].
Gross anomalies found in any of the items were corrected before the actual experiment.
The reliability of the final experimental version of the questionnaire was also
observed as a follow-up, again using Cronbach's Alpha [see Table 3.10]. The control
group results, from 32 cases, was used as a sample, since reliabilities would not be
differentially affected by the treatments.
71
I
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I
I I
I I
I
I
Prior Background Clarity of Information Belief in Climate Change
Decision Intention Subjective Norm Overall Attitude Belief in Outcome:
Detriments Benefits
Evaluation of Outcome: Detriments Benefits
Prioritization of Use: Detriments Benefits
Number of items
3 2 5
2 4 2
10 10
10 10
10 10
Pilot Group: N = 32; Control Group: N = 32.
Pilot Test Control Group Cronbach's Alpha Cronbach's Alpha
0.60 0.69 0.61 0.69 0.80 0.83
0.93 0.77 0.86 0.83 0.41 0.56
0.73 0.68 0.51 0.56
0.44 0.54 0.43 0.65
0.85 0.81 0.78 0.78
Table 3 .10: Reliability of instrument, based on an internal consistency method using Cronbach's Alpha, for both the pilot and control groups.
72
I I I
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I
Dependent Variable Measures
The affective domain was assessed using primarily fue Theory of Reasoned
Action (TRA) model defined by Fishbein (1967) and later developed into a descriptive
and predictive model for analyzing human behavior (Fishbein & Ajzen, 1975; Ajzen &
Fishbein, 1980). This involved five separate measures: Decision Intention, Overall
Attitude, Subjective Norm, Belief in Outcome, and Outcome Evaluation.
For each TRA measure subjects were asked to rate their direction and magnitude
of agreement to a series of statements. One of two versions of a seven-point, bipolar
semantic differential scale commonly used for TRA studies (Ajzen & Fishbein, 1980)
were used for each question.
Decision Intention, Subjective Norm and Belief in Outcome variables were
measured using fue scale:
UNLIKELY___ ___ _ __ Extremely Quite Slightly Neither/Nor Slightly Quite
-,,--_,...·LIKELY Extremely
Overall, Attitude and Outcome Evaluation variables were measured using:
BAD _______ _ _ ___ GOOD Extremely Quite Slightly Neither/Nor Slightly Quite Extremely
Responses from bofu of fuese scales were coded from left to right using fuis series
of integral numbers to correspond with each descriptive component of fue scale:
-3, -2, -1, 0, 1, 2, 3. This system allowed bofu magnitude and direction of response to be
assessed for each variable.
73
(
I I I I I
I
I
I
I
I
Decision Intention was comprised of two items, asking subjects to respond on the
likelihood scale in terms of their intention to either support or not support climate change
reductions. The "not support" version was later reverse coded to allow an average
measure that was unidirectional.
Overall Attitude was measured on the evaluative scale, with subjects being asked
how they felt about the outcome, in terms of impacts on the U.S., of a decision to either
support or not support climate change reductions. Again, the latter was reverse coded.
Subjective Norm used the likelihood scale to measure how much subjects
believed their important referents would approve of their decision to either support or not
support reductions. Four items were used for this variable, with one addressing referents
as individuals and the other as groups or organizations that the subject considers
influential. The "not support" versions were later reverse coded.
Belief in Outcome used the likelihood scale to measure to what extent subjects
believed the outcomes of a decision to either support or not support reductions would
actually occur ifthe decision were implemented on a large-scale in the U.S. Two sets of
questions, one.under a "support" scenario and the other under a "not support" scenario
were presented, with equal representation of each side of the issue in both scenarios.
Responses to the two sides were combined respectively to derive two sub-sets of this
variable that were actually used during the final analysis: Belief in Outcome of
Detriments and Belief in Outcome of Benefits.
Outcome Evaluation was also broken down into its constituent sub-sets: Outcome
Evaluation of Detriments and Outcome Evaluation of Benefits. This measure used the
74
I I
I
I
I
I I
I
evaluative scale to measure subjects beliefs about how "good" or how "bad" each
' outcome would be for the U.S. if a decision to either support or not support climate
change reductions were implemented.
For both the Belief in Outcome and Outcome Evaluation items it was particularly
difficult to word the question statements in a manner that would not add an extraneous
influence on the subjects' responses. Hedging was held constant by presenting all of the
statement items in the not hedged format. Framing, on the other hand, was not as easy to
mitigate, as it is linguistically impossible to "not frame" an issue. In order to average out
the unavoidable influence of framing, now outside the treatments, each of the four
possible frames was used evenly throughout the Belief and Evaluation question sets.
Frames were alternated within sub-sets (detriments and benefits) and between the two
sub-sets. Further, the frames used for one specific item in one set of questions was
switched to its alternate form for the other variable, so that each of the 20 statements
were presented in both of their possible frames throughout the questionnaire. In order to
control for question order effect statements regarding detriments and benefits were also
alternated throughout each question set.
Two other affective dependent variables were measured that are not components
of TRA, but were considered relevant to this study: Belief in Climate Change and Trust
in the Credibility of Information.
For Belief in Climate Change, subjects were asked to respond to five statements
designed to assess their belief about the actual occurrence of climate change. Each
statement addressed a specific physical aspect of the climate change phenomenon such as
75
temperature increasing, precipitation becoming more abundant, human-induced changes
in atmospheric greenhouse gas concentrations, increased evaporation of soil moisture,
and increased overall rate of climate change. Subjects were asked to rate how strongly
they agree or disagree with what was implied by each statement on a five-point Likert
scale. Again, all five questions were averaged into a single scale to measure a subject's
general belief in climate change actually occurring.
Perceived Trust in the Credibility of Information, as well as the cognitive domain
variable Clarity of Information, were measured using seven-point, bipolar semantic
differential scales similar to the ones used for TRA variables. The endpoints for each of
the three items that constitute these two variables were modified to better reflect the
questions: Difficult to trust as credible - Easy to trust as credible, Difficult to read - Easy
to read, Difficult to understand - Easy to understand. Again, these scales were coded
from -3 to 3. Two items were averaged to derive the Clarity of Information variable,
while Trust in the Credibility of Information was measured by a single item.
Finally, subjects' Prioritization of Information Use during decision making was
assessed. This measure gave some insight into the cognitive domain of a subject's
psychological response to textual information. This section of the questionnaire was
unique in that subjects were asked to consider the issue of climate change, as presented in
the experimental text passage, as if they were being asked to make a decision whether to
support or not support a large-scale effort to reduce the rate of human-induced climate
change in the U.S. They were then asked to rank each of the climate change topics in the
order of their relative importance (i.e. 1, 2, 3, ... 10) in terms of priority during the decision
76
process. Two parallel sets often statements each were presented, representing a mix of
both detriments and benefits. Subjects were asked to rate each on a scale from (1) "most
important" to (10) "least important," and not use the same number more than once per
set. Response sets for the two sides of the issue were combined respectively to create the
two sub-set variables: Prioritization of Detriments and Prioritization of Benefits.
Data Analysis
Data were analyzed using SPSS 10.0 statistical software (SPSS, 1999 a & b). All
measures of the dependent variables were derived from a carefully constructed semantic
'differential scale that has been utilized and validated as an interval-level scale in prior
psychometric studies (e.g. Ajzen & Fishbein, 1980).
All central tendencies (other than demographic information) were analyzed and
reported as means. Missing data was minimal in this study. The SPSS statistical package
accounted for any cases of missing data using an extrapolation procedure.
As discussed earlier, nine different categories of dependent variables were
examined. Three of the nine categories of dependent variable (Prioritization of
Information Use, Outcome Evaluation, and Belief in Outcome) were further sub-divided
into descriptive variables for each of two sub-categories: Detriments (DET) and Benefits
(BEN). Second, difference scores were calculated between these two paired sub
categories.
The first step in the analysis was to determine and confirm any relevant linear
relationships among dependent variables and their corresponding sub-sets. Correlations
77
were examined in order to establish statistically significant associations among sets and
sub-sets of variables that were considered to be theoretically associated with each other.
Associated variables and sub-sets of variables were subsequently analyzed using
multivariate techniques in addition to univariate analysis.
Second, correlations were calculated to ascertain significant relationships between
the potential covariate, Prior Background, and each of the dependent variables. In cases
where Prior Background was showing a significant association with a variable, or set of
variables in a multivariate situation, then it was subsequently included as a covariate in
the factorial analyses.
Finally, a univariate (ANOVA) , and in some cases a multivariate (MANOVA),
analysis of variance or covariance technique was used to ascertain main and interaction
effects, in accordance with the goals and assumptions of each particular analysis (Keppel,
1991; Kirk, 1995; Hair et al., 1998).
Significance was determined for all of the analyses at a 95% confidence interval
(a = 0.05). Post-hoc analyses of main effects were conducted, when relevant to
determine specific main effects, using a Bonferroni multiple comparison technique.
When an interaction between the two factors was significant, a study of the simple
effects and comparisons was conducted to help ascertain the nature of the interaction and
its potential influence on the main effect results.
A report and preliminary discussion of the results of this study is presented in
Chapter 4. Interpretation of these results, conclusions and implications of this study
follow in Chapter 5.
78
I
CHAPTER4
RESULTS & PRELIMINARY DISCUSSION
Correlations Among Variables
Five of the nine dependent variables assessed in this study were derived from the
Theory of Reasoned Action (TRA) (Ajzen & Fishbein, 1980). As illustrated in Chapter
2, the variables that constitute this model of affective behavior have been well studied
and carefully chosen because of their high correlations with each other. It also makes
theoretical sense that sub-sets of these variables, namely the Detriments and Benefits,
would be highly correlated with each other.
Univariate analysis of variance involving any one of these variables would
certainly be appropriate alone, and would render a viable result to answer a simple
question regarding the effect of that particular variable. However, when a strong linear
relationship is known to exist between or among dependent variables then it is prudent to
acknowledge this relationship in the analyses and account for it through a multivariate
technique, in this case a MANOV A, in order to ascertain main effects on the linear
combination ofrelated variables.
79
Though prior studies of most of these variables suggest a relationship, and other
combinations make reasonable sense, it was still important to establish these associations
statistically. Bivariate correlations, using a Pearson correlation coefficient, were
conducted between sets of variables considered a priori to have strong associations with
each other. Because most of these relationships are known to be unidirectional, one-
tailed tests were conducted to increase sensitivity of the analyses. The confidence level
was set at 95% (a = 0.05).
This analysis revealed five sets of significantly correlated variables and sub-sets
of variables that were later analyzed together using multivariate analysis of variance
procedures [see Table 4.1]. Following are the five sets, each analyzed using a MANOVA
procedure:
I.) Clarity of Information Trust in Credibility of Information
2.) Decision Intention Overall Attitude Subjective Norm
3.) Belief in Outcome (Sub-sets): a.) Belief in Detriments b.) Belief in Benefits
4.) Outcome Evaluation (Sub-sets): a.) Evaluation of Detriments b.) Evaluation of Benefits
5.) Prioritization oflnformation Use (Sub-sets): a.) Prioritization ofDetriments b.) Prioritization of Benefits
80
I
Variables
Set A: Clarity of Information Trust in Credibility
SetB: Decision Intention Overall Attitude
Decision Intention Subjective Norm
Overall Attitude Subjective Norm
Set C: Belief in Outcome:
Detriments Benefits
SetD: Outcome Evaluation:
Detriments Benefits
SetE: Prioritization of Information Use:
Detriments Benefits
r
0.40
0.54
0.57
0.39
0.15
0.50
0.90
N = 160. Pearson correlation coefficient (r).
Sig. (p)
0.001 ***
0.001 ***
0.001 ***
0.001 ***
0.032*
0.001 ***
0.001 ***
Significance determined at a= 0.05, based a one-tailed test. *Significant at p :S 0.05 level.
**Significant at p :S 0.01 level. ***Significant atp :S 0.001 level.
Table 4.1: Correlations of related dependent variables with each other, for multivariate analysis of variance.
81
Correlations with Covariate
Prior Background regarding any issue is considered an important concomitant
variable in many studies of psychological response, particularly when a decision response
is being solicited (e.g. Ajzen & Fishbein, 1980; Payne et al., 1993). It stands to reason
that a person's cognitive and affective responses can be influenced by prior experience
and knowledge regarding an issue. Accounting for Prior Background as a covariate in
the analyses of variance could help refine error variance, in turn increasing the power of
the test to discern differences (Keppel, 1991; Kirk, 1995; Hair et al., 1998). This measure
was included in the questionnaire as a series of three questions posed just before the
experimental text passage was presented. One question asked for a self-assessment of
how knowledgeable the subject considered him or herself regarding the climate change
issue. The other two questions asked for an assessment of how informed the subject was
about the issue from media sources and classroom experiences respectively. Subjects
were asked to respond to these three before reading any further in the questionnaire. This
format hopefully insured a high degree of independence between this particular measure
and the experimental manipulations.
A bivariate correlation, using the Pearson correlation coefficient, was conducted
to ascertain the relationship of the Prior Background variable with each of the dependent
variables and their sub-sets. A reasonable assumption was made that any potential
relationships would be unidirectional, so one-tailed analyses were conducted. The
significance level was set at 95% confidence (a = 0.05).
82
Tue following results were used, in conjunction with theoretical reasoning, to
determine whether Prior Background should be added to the subsequent analyses of
variance as a covariate. Five of the nine dependent variables showed a significant
correlation with Prior Background: Belief in Climate Change, Trust in Credibility of
Information, Decision Intention, Overall Attitude, and Belief in Outcome (Detriments);
plus Clarity of Information showed a very close trend toward significance. Though none
of the significant correlations demonstrated strong magnitudes, ranging from r = 0.14 to
0.23, they were all significant at the p :':'. 0.05 level [see Table 4.2]. Prior Background was
used as a covariate in analyses of variance associated with these variables in order to help
reduce error variance and increase power.
83
Variables
Clarity of Information Trust in Credibility Decision Intention Overall Attitude Subjective Norm Belief in Climate Change Belief in Outcome:
Detriments Benefits
Outcome Evaluation: Detriments Benefits
0.16
0.09
Prioritization of Information Use: Detriments 0.01 Benefits
r
0.12 0.18 0.14 0.23 0.09 0.20
0.019* 0.10
0.121 0.06
0.431 - 0.01
N = 160. Pearson correlation coefficient (r).
Sig. (p)
0.071 0.019* 0.045* 0.002** 0.141 0.005**
0.116
0.224
0.439
Significance determined at a= 0.05, based a one-tailed test. *Significant at p :S 0.05 level.
**Significant at p :S 0.01 level.
Table 4.2: Correlations of each of the dependent variables with potential covariate, Prior Background.
84
Analysis of Main Effects: Univariate
Differences among subjects' responses to each dependent variable, and their sub
sets, were analyzed using ANOV A or the corresponding covariance procedure.
Univariate results of these analyses were used to determine main effects of the two
experimental factors after error variance from the covariate, when applicable, and gender
differences were accounted for in the factorial model.
A Levene's Test of equality of error variance was used to determine homogeneity
by testing the null hypothesis that the error variance of the dependent variable was equal
across groups. Most variables upheld the null hypothesis for this test. Those that
rejected the null did, however, all have F-values that were :S 3.00, suggesting that
heterogeneity of variance among groups was probably not severe enough to warrant
concern (Keppel, 1991 ).
Statistical power was a major concern during the design and execution of this
experiment, as very few studies have been conducted with this combination of variables.
A priori effect size was, consequently, difficult to ascertain. The target power level was
::". 0.80 to detect an effect size of approximately f* ::". 0.25. In order to increase power and,
to some extent, control for Type II error, the blocking technique with Gender and the use
of Prior Background as a covariate were used to help reduce error variance.
Significant differences among particular treatment means were determined using
a Bonferronipost-hoc, multiple comparison technique at the corresponding confidence
level for significance. When the covariate was included as part of the factorial model,
comparisons were conducted on adjusted means.
85
Research questions posed in this study were broken down into two basic
approaches to analyzing the data. The first series of questions asked if there were
differences in the magnitude and direction of responses to each of the dependent
variables among the various treatments. In other words, multiple comparisons were made
among mean response to a single variable such as Outcome Evaluation of Detriments,
where one treatment may provoke a stronger positive response to this variable than
another. These analyses were considered comparisons of direct responses to each
variable.
The other set ofresearch questions asked if there were differences among the
differential responses to variable sub-sets such as Belief in Outcome Detriments versus
Benefits. For example, one treatment may evoke a strong positive response to both
Belief in Outcome Detriments and Benefits, resulting in a small difference score
(Benefits - Detriments). Meanwhile, another treatment may evoke a strong positive
response to Belief in Outcome Detriments but a negative response to Benefits. The
resulting differential score would be much greater than in the first case. A multiple
comparison of difference scores for Detriments versus Benefits revealed differences
among treatment effects on the differential responses to these components. These
analyses were considered comparisons of differential responses between Detriments
(DET) and Benefits (BEN).
86
Comparison of Direct Responses:
Clarity of Information revealed no significant difference in responses among
hedging treatments, but did show a trend (F = 2.40,p = 0.071) among the framing
treatments [see Table 4.3]. Multiple comparison of means revealed this trend as a
difference between the Control group response (Mean = 2.1) and both the Suffer Loss
DET-Forgo Gain BEN (Mean= 1.1) and Avoid Loss DET-Forgo Gain BEN (Mean=
1.3). This indicates that the Control group, who of course did not have an experimental
passage to read, considered the introductory paragraph as "quite" easy to read and
understand. Subjects appear to have considered the two framing treatments that included
the Forgo Gain frame to be only "slightly" easy to read and understand. This result was
consistent with qualitative comments made during the development of the questionnaire
regarding the relative difficulty ofreading a passage framed as Forgo Gain.
Trust in Credibility of Information showed no significant difference among
framing treatments, but did show a trend toward significance among hedging treatments
(F = 2.28,p = 0.083) [see Table 4.4]. Multiple comparisons showed the differences to be
between two sets of treatments 1) the Control (Mean= 0.9) and Hedge DET-Not Hedge
BEN (Mean= - 0.1), and2) NotHedgeDET & BEN (Mean= 0.8) andHedgeDET-Not
Hedge BEN (Mean= - 0.1). This result reveals an interesting pattern whereby treatments
with Not Hedge BEN indicate a significantly lower level of trust in the information
presented in the text passage compared to the Control and when both DET and BEN are
Not Hedged. Given the environmentally conscientious nature of the population sampled
this may be an indicator that information perceived as not environmentally friendly,
87
namely promoting the benefits of not slowing the rate of human-induced climate change,
may be taken by this population as Jess credible if it is presented in a definitive, non-
hedged manner.
The three upper tier variables that constitute the Theory of Reasoned Action:
Decision Intention, Overall Attitude, and Subjective Norm did not reveal any significant
results among treatments of either factor [see Tables 4.5, 4.6, & 4. 7]. However, one trend
did emerge. For Decision Intention it appears Not Hedging DET & BEN (Mean= 2.0)
versus Hedging DET & BEN (Mean= 1.3) was trending toward significance (F = 2.22,
p = 0.090). Since this result is only between the treatments that did not distinguish
' between Detriments and Benefits, it is a hard to interpret what this trend may indicate at
this time, with Not Hedging eliciting a slightly stronger "likely" to support climate
change reductions response than Hedging.
Belief in Climate Change did not appear to be strongly affected by either factor,
as no significant differences were found among treatments of either hedging or framing
[see Table 4.8]. But, one trend toward significance did emerge regarding framing (F =
2.25,p = 0.085). Avoid Loss DET-Forgo Gain BEN (Mean= 1.1) appeared to elicit a
slightly more positive "agree" response than Suffer Loss DET-Forgo Gain BEN (Mean=
0.8).
Belief in Outcome and Evaluation of Outcome sub-sets ofrespective variables
(Detriments and Benefits) showed no significant differences among treatments for either
hedging or framing [see Tables 4.9 - 4.12]. The Theory of Reasoned Action describes
these two variables as constituting the Overall Attitude variable (Ajzen & Fishbein,
88
;, II
I
1980). This study revealed no significant differences among responses for that higher
order variable in this model, so it stands to reason that its constituent components would
reflect this as well.
The most compelling results involved the sub-sets of the Prioritization of
Information Use variable. Significant differences among responses under treatments
were shown for both Prioritization of Detriments and Prioritization of Benefits for
framing and a strong trend toward significance for hedging [see Tables 4.13 & 4.14].
Subjects were asked to rank the importance of each statement of topical
information regarding a detrimental or beneficial component of the climate change issue.
'The scale was from one (1) for "most important" to ten (10) "least important," so average
rankings resulted in lower numbers representing a perceived higher priority than higher
numbers.
A strong trend toward significance indicates that Prioritization of Detriments
(F = 2.47,p = 0.065) and Prioritization of Benefits (F = 2.38,p = 0.073) were affected by
hedging. Multiple comparisons revealed that for Prioritization of Detriments Hedge
DET-Not Hedge BEN elicited an average ranking of the Detriment information
components that was slightly toward "more important" (Mean= 4.8) than Not Hedge
DET-Hedge BEN (Mean= 5.2). Similarly, Prioritization of Benefits showed a trend
toward Not Hedge DET-Hedge BEN eliciting a slightly stronger "importance" ranking
for the Benefit components (Mean= 5.8) than Hedge DET-Not Hedge BEN (Mean=
6.1 ). This trend is consistent with the hypothesis that hedging of infonnation promotes a
stronger response. Curiously, though, the treatments that were either Hedging Detriments
89
and Benefits or Not Hedging both did not appear to evoke a difference in prioritization
for either Detriments or Benefits.
The most dramatically significant difference in direct response found in this study
involved the effects of framing on Prioritization of Detriments and Benefits.
Prioritization of Detriments showed significant effects of framing (F = 6.30,p = 0.001),
and Prioritization of Benefits (F = 6.11,p = 0.001).
Multiple comparisons revealed that responses were significantly different among
nearly all of the framing treatments. For Prioritization of Detriments, ranking under
Suffer Loss DET-Obtain Gain BEN (Mean= 4.8) showed a higher average priority than
both Avoid Loss DET-Forgo Gain (Mean = 5.1) and Suffer Loss DET-F orgo Gain BEN
(Mean= 5.3). Similarly, Avoid Loss DET-Obtain Gain BEN (Mean= 4.8) elicited a
higher ranking than both Avoid Loss DET-Forgo Gain (Mean= 5.1) and Suffer Loss
DET-Forgo Gain BEN (Mean= 5.3).
For Prioritization of Benefits, the same treatments caused significantly different
responses, but the direction of priority was the reverse of the Detriment responses. Suffer
Loss DET-Obtain Gain BEN (Mean= 6.2) showed a lower average priority than both
Avoid Loss DET-Forgo Gain (Mean= 5.9) and Suffer Loss DET-Forgo Gain BEN
(Mean= 5.7). Similarly, Avoid Loss DET-Obtain Gain BEN (Mean= 6.1) elicited a
lower ranking than both Avoid Loss DET-Forgo Gain (Mean= 5.9) and Suffer Loss
DET-Forgo Gain BEN (Mean= 5. 7).
Results for both Prioritization of Detriments and Benefits were consistent with the
hypothesis that negative framing elicits a stronger response than positive. For example,
90
Prioritization of Benefits information presented in two of the treatments as Forgo Gain
(negative) consistently elicited a slightly higher prioritization of Benefits than when the
same information was presented as Obtain Gain (positive).
Information presented as Suffer Loss (negative) resulted in a higher prioritization
of Detriments than when those statements were presented as Avoid Loss (positive).
However, this result appears to have reversed when Suffer Loss was presented with
Forgo Gain for the Benefits and Avoid Loss with Obtain Gains. This may indicate that
the manner in which information about the opposing view is presented influences the
viewpoint under investigation. This influence will be examined in greater detail in the
following section on comparisons of differential responses.
Also note that a significant interaction between hedging and framing effects
occurred with both Prioritization of Detriments and Benefits. The nature of this
interaction will be discussed in a subsequent section, so interpretation of main effects
from these results should be looked at in light of this interaction.
91
Variables Mean SE
Hedging: Hedge DET & BEN 1.3 0.2 Not Hedge DET & BEN 1.6 0.2 Hedge DET-Not Hedge BEN 1.5 0.2 Not Hedge DET-Hedge BEN 1.4 0.2 Control 2.1 0.2
Framing: Suffer Loss DET-Obtain Gain BEN 1.8 0.2 Avoid Loss DET-Forgo Gain BEN 1.3 0.2 A void Loss DET-Obtain Gain BEN 1.7 0.2 Suffer Loss DET-Forgo Gain BEN 1.1 0.2 Control 2.1 0.2
Hedging * Framing
DET =Detriments resulting from rapid climate change. BEN = Benefits resulting from rapid climate change. N = 160. Main effect: n = 32. Significance determined at a= 0.05.
F df Sig. (p)
0.171 3 0.916
2.400 3 0.071
1.161 9 0.326
Means based on 7-point scale: -3 =Extremely difficult to understand, -2 =Quite difficult to understand, -1 = Slightly difficult to understand, 0 =Neither/Nor, 1 = Slightly easy to understand, 2 = Quite easy to understand, 3 = Extremely easy to understand.
Table 4.3: Main effects and interactions for Clarity of Information.
92
Variables Mean SE
Hedging: Hedge DET & BEN 0.3 0.2 Not Hedge DET & BEN 0.8 0.2 Hedge DET-Not Hedge BEN - 0.1 0.2 Not Hedge DET-Hedge BEN 0.6 0.2 Control 0.9 0.2
Framing: Suffer Loss DBI-Obtain Gain BEN 0.5 0.2 Avoid Loss DET-Forgo Gain BEN 0.3 0.2 Avoid Loss DBI -Obtain Gain BEN 0.3 0.2 Suffer Loss DET-Forgo Gain BEN 0.4 0.2 Control 0.9 0.2
Hedging* Framing
DET = Detriments resulting from rapid climate change. BEN = Benefits resulting from rapid climate change. N= 160. Main effect: n=32. Significance determined at a= 0.05.
F df Sig. (p)
2.277 3 0.083
0.122 3 0.947
1.388 9 0.200
Means based on 7-point scale: -3 =Extremely difficult to trust, -2 =Quite difficult to trust, -1 =Slightly difficult to trust, 0 =Neither/Nor, 1 =Slightly easy to trust, 2 =Quite easy to trust, 3 = Extremely easy to trust.
Table 4.4: Main effects and interactions for Trust in the Credibility of Information.
93
Variables Mean SE F df Sig. (p)
Hedging: 2.215 3 0.090 Hedge DET & BEN 1.3 0.2 Not Hedge DET & BEN 2.0 0.2 Hedge DET-Not Hedge BEN 1.7 0.2 Not Hedge DET-Hedge BEN 1.6 0.2 Control 1.9 0.2
Framing: 0.215 3 0.886 Suffer Loss DET-Obtain Gain BEN 1.8 0.2 Avoid Loss DET-Forgo Gain BEN 1.6 0.2 Avoid Loss DET-Obtain Gain BEN 1.7 0.2 Suffer Loss DET-Forgo Gain BEN 1.7 0.2 Control 1.9 0.2
Hedging* Framing 0.408 9 0.929
DET =Detriments resulting from rapid climate change. BEN = Benefits resulting from rapid climate change. N= 160. Main effect: n=32. Significance determined at a= 0.05. Means based on 7-point scale: -3 =Extremely unlikely, -2 =Quite unlikely, -1 =Slightly unlikely, 0 =Neither/Nor, 1 = Slightly likely, 2 =Quite likely, 3 =Extremely likely.
Table 4.5: Main effects and interactions for Decision Intention.
94
Variables Mean SE F df Sig. (p)
Hedging: 0.272 3 0.846 Hedge DET & BEN 1.9 0.1 Not Hedge DET & BEN 2.1 0.1 Hedge DET-Not Hedge BEN 2.0 0.1 Not Hedge DET-Hedge BEN 2.0 0.1 Control 2.0 0.1
Framing: 0.577 3 0.631 Suffer Loss DET-Obtain Gain BEN 1.9 0.2 Avoid Loss DET-Forgo Gain BEN 2.1 0.2 Avoid Loss DET-Obtain Gain BEN 2.1 0.2 Suffer Loss DET-Forgo Gain BEN 2.0 0.2 Control 2.0 0.2
Hedging * Framing 0.605 9 0.791
DET =Detriments resulting from rapid climate change. BEN = Benefits resulting from rapid climate change. N = 160. Main effect: n = 32. Significance determined at a= 0.05. Means based on 7-point scale: -3 =Extremely bad, -2 =Quite bad, -1 =Slightly bad, 0 = Neither/Nor, 1 = Slightly good, 2 =Quite good, 3 =Extremely good.
Table 4.6: Main effects and interactions for Overall Attitude.
95
I
I I I I I I I I
I I I
I I
I I I
--- - ----
Variables Mean SE F df Sig. (p)
Hedging: 0.466 3 0.707 Hedge DET & BEN 1.4 0.2 Not Hedge DET & BEN 1.4 0.2 Hedge DET-Not Hedge BEN 1.1 0.2 Not Hedge DET-Hedge BEN 1.2 0.2 Control 1.2 0.2
Framing: 0.990 3 0.400 Suffer Loss DET-Obtain Gain BEN 1.4 0.2 Avoid Loss DET-Forgo Gain BEN 1.1 0.2 Avoid Loss DET--Obtain Gain BEN 1.1 0.2 Suffer Loss DET-F orgo Gain BEN 1.4 0.2 Control 1.2 0.2
Hedging* Framing 0.989 9 0.452 .. DET = Detriments resulting from rapid climate change. BEN = Benefits resulting from rapid climate change. N= 160. Main effect: n = 32. Significance determined at a= 0.05. Means based on 7-point scale: -3 =Extremely unlikely, -2 =Quite unlikely, -1 =Slightly unlikely, 0 =Neither/Nor, 1 =Slightly likely, 2 =Quite likely, 3 =Extremely likely.
Table 4.7: Main effects and interactions for Subjective Norm.
96
Variables Mean SE F df Sig. (p)
Hedging: 0.201 3 0.895 Hedge DET & BEN 1.4 0.2 Not Hedge DET & BEN 1.4 0.2 Hedge DET-Not Hedge BEN 1.1 0.2 Not Hedge DET-Hedge BEN 1.2 0.2 Control 1.2 0.2
Framing: 2.254 3 0.085 Suffer Loss DET-Obtain Gain BEN 1.4 0.2 Avoid Loss DET-Forgo Gain BEN 1.1 0.2 Avoid Loss DET-Obtain Gain BEN 1.1 0.2 Suffer Loss DET-Forgo Gain BEN 1.4 0.2 Control 1.2 0.2
Hedging * Framing 0.421 9 0.922
DET =Detriments resulting from rapid climate change. BEN = Benefits resulting from rapid climate change. N= 160. Main effect: n=32. Significance determined at a= 0.05. Means based on 5-point scale: -2 = Strongly disagree, -1 =Disagree, 0 =Neither/Nor, 1 = Agree, 2 = Strongly agree.
Table 4.8: Main effects and interactions for Belief in Climate Change.
97
Variables Mean SE F df Sig. (p)
Hedging: 0.182 3 0.909 Hedge DET & BEN 0.9 0.1 Not Hedge DET & BEN 0.8 0.1 Hedge DET-Not Hedge BEN 0.8 0.1 Not Hedge DET-Hedge BEN 0.8 0.1 Control 0.6 0.1
Framing: 2.615 3 0.115 Suffer Loss DET-Obtain Gain BEN 0.7 0.1 Avoid Loss DET-Forgo Gain BEN 1.1 0.1 Avoid Loss DET-Obtain Gain BEN 1.0 0.1 Suffer Loss DET-Forgo Gain BEN 0.5 0.1 Control 0.6 0.1
Hedging* Framing 0.921 9 0.509
DET =Detriments resulting from rapid climate change. BEN = Benefits resulting from rapid climate change. N= 160. Main effect: n=32. Significance determined at a= 0.05. Means based on 7-point scale: -3 =Extremely unlikely, -2 =Quite unlikely, -1 =Slightly unlikely, 0 =Neither/Nor, 1 =Slightly likely, 2 =Quite likely, 3 =Extremely likely.
Table 4.9: Main effects and interactions for Belief in Outcome (Detriments).
98
Variables Mean SE F df Sig. (p)
Hedging: 0.936 3 0.426 Hedge DET & BEN - 0.1 0.1 Not Hedge DET & BEN 0.2 0.1 Hedge DET-Not Hedge BEN 0.1 0.1 Not Hedge DET-Hedge BEN 0.1 0.1 Control - 0.2 0.1
Framing: 0.401 3 0.753 Suffer Loss DET-Obtain Gain BEN 0.1 0.1 Avoid Loss DET-Forgo Gain BEN 0.1 0.1 Avoid Loss DET-Obtain Gain BEN 0.2 0.1 Suffer Loss DET-Forgo Gain BEN 0.1 0.1 Control - 0.2 0.1
Hedging * Framing 0.874 9 0.551
DET = Detriments resulting from rapid climate change. BEN = Benefits resulting from rapid climate change. N= 160. Main effect: n=32. Significance determined at a= 0.05. Means based on 7-point scale: -3 =Extremely unlikely, -2 =Quite unlikely, -1 =Slightly unlikely, 0 =Neither/Nor, 1 = Slightly likely, 2 =Quite likely, 3 =Extremely likely.
Table 4.10: Main effects and interactions for Belief in Outcome (Benefits).
99
Variables Mean SE F df Sig. (p)
Hedging: 0.339 3 0.797 Hedge DET & BEN 1.4 0.1 Not Hedge DET & BEN 1.5 0.1 Hedge DET-Not Hedge BEN 1.4 0.1 Not Hedge DET-Hedge BEN 1.5 0.1 Control 1.4 0.1
Framing: 0.314 3 0.816 Suffer Loss DET-Obtain Gain BEN 1.4 0.1 Avoid Loss DET-Forgo Gain BEN 1.5 0.1 Avoid Loss DET-Obtain Gain BEN 1.4 0.1 Suffer Loss DET-Forgo Gain BEN 1.4 0.1 Control 1.4 0.1
Hedging * Framing 1.048 9 0.406
DET =Detriments resulting from rapid climate change. BEN = Benefits resulting from rapid climate change. N= 160. Main effect: n=32. Significance determined at a= 0.05. Means based on 7-point scale: -3 =Extremely bad, -2 =Quite bad, -1 =Slightly bad, 0 = Neither/Nor, 1 = Slightly good, 2 =Quite good, 3 =Extremely good.
Table 4.11: Main effects and interactions for Outcome Evaluation (Detriments).
100
Variables Mean SE F df Sig. (p)
Hedging: 0.413 3 0.744 Hedge DET & BEN 0.8 0.1 Not Hedge DET & BEN 0.8 0.1 Hedge DET-Not Hedge BEN 0.7 0.1 Not Hedge DET-Hedge BEN 0.9 0.1 Control 1.0 0.1
Framing: 1.391 3 0.249 Suffer Loss DET-Obtain Gain BEN 0.7 0.1 Avoid Loss DET-Forgo Gain BEN 0.8 0.1 Avoid Loss DET-Obtain Gain BEN 0.8 0.1 Suffer Loss DET-Forgo Gain BEN 1.0 0.1 Control 1.0 0.1
Hedging * Framing 0.512 9 0.864
DET =Detriments resulting from rapid climate change. BEN = Benefits resulting from rapid climate change. N= 160. Main effect: n=32. Significance determined at a= 0.05. Means based on 7-point scale: -3 =Extremely bad, -2 =Quite bad, -1 =Slightly bad, 0 = Neither/Nor, 1 =Slightly good, 2 =Quite good, 3 =Extremely good.
Table 4.12: Main effects and interactions for Outcome Evaluation (Benefits).
101
Variables Mean SE
Hedging: Hedge DET & BEN 5.0 0.1 Not Hedge DET & BEN 5.0 0.1 Hedge DET-Not Hedge BEN 4.8 0.1 Not Hedge DET-Hedge BEN 5.2 0.1 Control 5.1 0.1
Framing: Suffer Loss DET-Obtain Gain BEN 4.8 0.1 Avoid Loss DET-Forgo Gain BEN 5.1 0.1 Avoid Loss DET-Obtain Gain BEN 4.8 0.1 Suffer Loss DET-Forgo Gain BEN 5.3 0.1 Control 5.1 0.1
Hedging * Framing
DET = Detriments resulting from rapid climate change. BEN = Benefits resulting from rapid climate change. N= 160. Main effect: n = 32. Significance determined at a= 0.05.
F df
2.470 3
6.300 3
3.086 9
Sig. (p)
0.065
0.001 *** a b a b a,b
0.002**
Means based on 10-point scale, ranging from 1 to 10, with 1 = Most important and 10 = Least important.
*Significant at p :S 0.05 level. **Significant at p :S 0.01 level.
***Significant atp :S 0.001 level. Means with different letter designations (a,b,c) are significantly different from each other, and means with either the same letter or no letter at all are not significantly different.
Table 4.13: Main effects and interactions for Prioritization of Information Use (Detriments).
102
Variables Mean SE
Hedging: Hedge DET & BEN 6.0 0.1 Not Hedge DET & BEN 6.0 0.1 Hedge DET-Not Hedge BEN 6.1 0.1 Not Hedge DET-Hedge BEN 5.8 0.1 Control 5.9 0.1
Framing: Suffer Loss DET-Obtain Gain BEN 6.2 0.1 Avoid Loss DET-Forgo Gain BEN 5.9 0.1 Avoid Loss DET-Obtain Gain BEN 6.1 0.1 Suffer Loss DET-Forgo Gain BEN 5.7 0.1 Control 5.9 0.1
Hedging * Framing
DET =Detriments resulting from rapid climate change. BEN = Benefits resulting from rapid climate change. N= 160. Main effect: n=32. Significance determined at a= 0.05.
F df Sig. (p)
2.380 3 0.073
6.109 3, 0.001 *** a b a b a,b
3.050 9 0.002**
Means based on 10-point scale, ranging from 1 to 10, with 1 = Most important and 10 = Least important.
*Significant at p :':'. 0.05 level. **Significant at p :':'. 0.01 level.
***Significant at p :':'. 0.001 level. Means with different letter designations (a,b,c) are significantly different from each other, and means with either the same letter or no letter at all are not significantly different.
Table 4.14: Main effects and interactions for Prioritization of Information Use (Benefits).
103
Comparisons of Dijferential Responses:
Analysis of difference scores between Detriments versus Benefits for both Belief
in Outcome and Outcome Evaluation variables showed no significant differences among
differential responses for either hedging or framing effects [see Tables 4.15 & 4.16].
Given that the results of comparisons of direct response also revealed no significant
differences, this result was not surprising.
Similar to the direct responses, differentials between responses to Prioritization of
Detriments versus Benefits resulted in a strong trend toward significance for hedging
effects (F = 2.43,p = 0.069), and significant results for framing effects (F = 5.61,
p = 0.001) [see Table 4.17].
For hedging, Hedge DET-Not Hedge BEN difference score (Mean Diff. = 1.3)
trends toward being a significantly greater differential response to prioritization between
Detriments versus Benefits than when information is presented as Not Hedge
DET-Hedge BEN (Mean Diff. = 0.6). The aforementioned results from comparisons of
direct responses indicate that the hedging effect within each sub-set, Detriments and
Benefits, is about the same magnitude. Therefore, this differential result indicates that
the hedging effect produces a greater differential response between sub-sets, favoring a
higher priority for Detriments relative to Benefits, when Detriments are hedged and
Benefits are not hedged than when the converse is used, Detriments not hedged and
Benefits hedged. This is consistent with the hypothesis that hedging evokes a stronger
response, in this case toward a higher priority for Detriments.
104
Regarding framing effects, differential responses under both Suffer Loss
DET-Obtain Gain BEN (Mean Diff. = 1.3) and Avoid Loss DET-Obtain Gain BEN
(Mean Diff. = 1.3) were significantly greater than under Avoid Loss DET-Forgo Gain
BEN (Mean Diff. = 0.8) and Suffer Loss DET-Forgo Gain BEN (Mean Diff. = 0.5), as
well as the Control (Mean Diff. = 0.8). These results were consistent with the hypothesis
that negative framing elicits a stronger response than positive framing.
As with the direct response results, a significant interaction between hedging and
framing was observed. The nature of this interaction will be examined in a subsequent
section, and interpretation of these main effects will be qualified accordingly.
105
Variables Mean SE F df Sig. (p) Difference
Hedging: 0.649 3 0.585 Hedge DET & BEN 1.0 0.2 Not Hedge DET & BEN 0.7 0.2 Hedge DET-Not Hedge BEN 0.7 0.2 Not Hedge DET-Hedge BEN 0.7 0.2 Control 0.8 0.2
Framing: 1.763 3 0.158 Suffer Loss DET-Obtain Gain BEN 0.7 0.2 Avoid Loss DET-Forgo Gain BEN 1.1 0.2 Avoid Loss DET-Obtain Gain BEN 0.8 0.2 Suffer Loss DET-Forgo Gain BEN 0.5 0.2 Control 0.8 0.2
Hedging * Framing 0.614 9 0.784
DET = Detriments resulting from rapid climate change. BEN = Benefits resulting from rapid climate change. N= 160. Main effect: n=32. Significance determined at a= 0.05. Mean difference based on responses to Detriments minus responses to Benefits.
Table 4.15: Main effects and interactions for difference between responses to Detriments versus Benefits for Belief in Outcome.
106
Variables Mean SE F df Sig. (p) Difference
Hedging: 0.728 3 0.537 Hedge DET & BEN 0.5 0.1 Not Hedge DET & BEN 0.7 0.1 Hedge DET-Not Hedge BEN 0.7 0.1 Not Hedge DET-Hedge BEN 0.6 0.1 Control 0.4 0.1
Framing: 1.934 3 0.127 Suffer Loss DET-Obtain Gain BEN 0.7 0.1 Avoid Loss DET-Forgo Gain BEN 0.7 0.1 Avoid Loss DET--Obtain Gain BEN 0.6 0.1 Suffer Loss DET-Forgo Gain BEN 0.4 0.1 Control 0.4 0.1
Hedging* Framing 0.698 9 0.710
DET =Detriments resulting from rapid climate change. BEN = Benefits resulting from rapid climate change. N= 160. Main effect: n=32. Significance determined at a= 0.05. Mean difference based on responses to Detriments minus responses to Benefits.
Table 4.16: Main effects and interactions for difference between responses to Detriments versus Benefits for Outcome Evaluation.
107
Variables Mean SE
Hedging: Hedge DET & BEN 1.0 0.2 Not Hedge DET & BEN 1.0 0.2 Hedge DET-Not Hedge BEN 1.3 0.2 Not Hedge DET-Hedge BEN 0.6 0.2 Control 0.8 0.2
Framing: Suffer Loss DET-Obtain Gain BEN 1.3 0.2 Avoid Loss DET-Forgo Gain BEN 0.8 0.2 Avoid Loss DET-Obtain Gain BEN 1.3 0.2 Suffer Loss DET-Forgo Gain BEN 0.5 0.2 Control 0.8 0.2
Hedging * Framing
DET = Detriments resulting from rapid climate change. BEN = Benefits resulting from rapid climate change. N= 160. Main effect: n=32. Significance determined at a= 0.05.
F df Sig. (p)
2.426 3 0.069
6.207 3 0.001 *** a b a b a,b
3.069 9 0.002**
Mean difference based on responses to Benefits minus responses to Detriments. *Significant at p :<; 0.05 level.
**Significant atp :<; 0.01 level. ***Significant atp :<; 0.001 level. Means with different letter designations ( a,b,c) are significantly different from each other, and means with either the same letter or no letter at all are not significantly different.
Table 4.17: Main effects and interactions for difference between responses to Benefits versus Detriments for Prioritization oflnformatiort Use.
108
Analysis of Main Effects: Multivariate
A multivariate statistic, Wilks' Lambda, was used to determine the significance of
main effects on the combined "variate" of correlated dependent variables used for each
MANOVA. This was conducted for each of the five sets of significantly correlated
variables and sub-sets listed earlier. This was particularly useful for analyzing the higher
order variables comprising the Theory of Reasoned Action model (Decision Intention,
Overall Attitude, and Subjective Norm), and the relationship among sub-sets within
Belief in Outcome, Outcome Evaluation, and Prioritization of Information Use variables
respectively.
As with the univariate analysis of variance significance was determined at the
95% confidence level (a = 0.05). A Box's M Test was used to determine the equality of
covariance matrices across groups; in all cases the null hypothesis was upheld.
Of all the analyses, only one set demonstrated a significant multivariate
relationship with one of the factors [see Tables 4.18 & 4.19]. The linear combination of
Prioritization of Detriments and Prioritization of Benefits was significantly affected by
framing (Wilk's Lambda= 0.853, F = 3.457,p = 0.003). This result is not surprising in
that univariate analyses of these components revealed highly significant results for both
direct and differential responses between the two information components. In addition,
the strong directionality of prioritization, with Detriments consistently considered "more
important" than Benefits, produces a strong negative relationship between the two
components that tends to enhance discernment of multivariate effects (Hair et al., 1998).
109
I I
I
I
I
I I
I
I I I'
Set of Variables Wilk's Lambda F Sig. (p)
Set A: Clarity of Information 0.942 1.258 0.277 Trust in Credibility
Set B: Decision Intention 0.905 1.032 0.418 Overall Attitude Subjective Norm
Set C: Belief in Outcome: 0.966 0.474 0.892
Detriments Benefits
SetD: Outcome Evaluation: 0.942 0.828 0.591
Detriments Benefits
Set E: Prioritization of Information Use: 0.930 1.540 0.166
Detriments Benefits
N= 160. Significance determined at a= 0.05.
Table 4.18: Main effect of hedging on each set of correlated variables, using multivariate analysis of variance technique.
110
Set of Variables Wilk' s Lambda F Sig. (p)
Set A: Clarity of Information 0.941 1.285 0.265 Trust in Credibility
Set B: Decision Intention 0.865 1.516 0.117 Overall Attitude Subjective Norm
Set C: Belief in Outcome: 0.908 1.343 0.214
Detriments Benefits
SetD: Outcome Evaluation: 0.939 0.885 0.539
Detriments Benefits
SetE: Prioritization oflnformation Use: 0.853 3.457 0.003**
Detriments Benefits
N= 160. Significance determined at a= 0.05.
*Significant at p :5 0.05 level. **Significant atp :5 0.01 level.
Table 4.19: Main effect of framing on each set of correlated variables, using multivariate analysis of variance technique.
111
Analysis of Interactions
Interactions between factors for each of the univariate analyses were examined.
Tue only variables that demonstrated a significant interaction between hedging and
framing were Prioritization of Detriments (F = 3.09,p = 0.002) and Prioritization of
Benefits (F = 3.05,p = 0.002) direct responses [see Tables 4.13 & 4.14], and the
differential responses calculated from the two (F = 3.07,p = 0.002) [see Table 4.17].
In order to help interpret the nature of these interactions simple effects and
comparisons were conducted using a series of one-way ANOVAs of framing effects,
holding each level of the hedging factor constant for each analysis. In addition, profile
plots, that graphically illustrated the simple comparisons, were visually examined for
signs of ordinal versus disordinal interaction trends. Generally, interactions among the
four framing treatment groups appeared ordinal, with one exemption. Examination of the
actual numbers proved more useful.
Significant differences among framing groups were found within two of the four
hedging treatments. These were when both Detriments and Benefits were Hedged
(F = 4.493,p =0.012) and when Detriments Hedged but Benefits Not Hedged
(F = 7.902,p = 0.001). Multiple comparisons revealed that for the Hedged DET & BEN
situation a significant difference existed between Suffer Loss DET-Obtain Gain BEN
(Mean Diff. = 2.1) and Avoid Loss-Obtain Gain (Mean Diff. = 0.5). This result does
suggest a disordinal interaction, as the two means should be about the same. But, the
considerably lower mean difference score was apparently not significantly different than
the other two responses, so an ordinal relationship is maintained.
112
The second case in question revealed a difference between the same two treatment
groups, but with an opposite mean difference effect. With hedging held constant as
Hedge DET-Not Hedge BEN, framing treatment Suffer Loss DET---Obtain Gain BEN
elicited a mean difference (Mean Diff. = 1.2) significantly smaller than Avoid
Loss-Obtain Gain (Mean Diff. = 2.4). This maintains an ordinal interaction, but also
suggests that not hedging Benefits tends to enhance the deprioritization of the Benefits
caused by the Obtain Gain framing effect (previously demonstrated by the analysis of
main effects).
This result appears consist with the persistent effect that not hedging Benefits
seems to have on this sample groups' response to certain questions. More importantly, it
appears that the original interpretation of main effects for Prioritization of Information
Use is not compromised by the significant interaction between hedging and framing.
Rather, the interaction may actually be demonstrating an enhancement effect between
certain hedging and framing combinations.
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CHAPTERS
INTERPRETATION & CONCLUSIONS
Intemretation ofHvootheses Tests
Results of this study were analyzed and each of the seven general hypotheses,
described in Chapter 1, was tested for each of the relevant variables. Many of the
variables, particularly the affective ones associated with the Theory of Reasoned Action,
did not produce significant results to support the hypotheses. The cognitive variable,
Prioritization ofinformation Use, however, provided the most intriguing and productive
significant results that support the stated hypotheses rather well.
Hl. Hedging of both or either information component (detriments v. benefits) will result in differential direct responses, with the hedged component(s) eliciting a response of relatively greater magnitude than the not hedged component(s). When components are differentially hedged, the direction of the response will be toward supporting the hedged component.
None of the variables demonstrated a statistically significant result regarding this
hypothesis. However, three variables, namely Trust in the Credibility of Information,
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Decision Intention and Prioritization of Information Use, demonstrated strong trends
toward significance. Generally, results of the Prioritization variable supported this
hypothesis. The other two variables, however, appear to contradict this hypothesis by
supporting the converse.
Results of Trust in the Credibility of Information under hedging suggest that not
hedging information regarding Benefits, particularly when information regarding
Detriments was Hedged, actually evokes a stronger response than when Benefits were
Hedged. The trend was a decrease in Trust when Benefits were Not Hedged relative to
when either Benefits were Hedged or when both Benefits and Detriments were Not
Hedged.
This contradiction of the expected result may have a reasonable, albeit untested,
explanation. Given that the sample of subjects who participated in this study were from a
population of people who are generally environmentally conscientious, namely natural
resources students, it is reasonable to assume that most responses would favor a decision
to protect the environment. Though environmental disposition was not directly
measured, differential responses to Detriments versus Benefits of this issue serve as a
proxy measure of this; not to mention the average "agreement" with the actual presence
of human-induced climate change observed with the Belief in Climate Change variable.
Indeed, all of the direct responses to the variables indicated a strong pro-environment
inclination, favoring concern over Detriments of climate change relative to Benefits. It is
possible that Not Hedging the Benefits component when Detriments were Hedged
accentuated the difference by drawing attention to the relative "boldness" of claims that
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not supporting climate change reductions would be somehow beneficial. This may have
elicited an unanticipated negative reaction to such perceived hyperbole regarding the side
of the issue less associated with pro-environment. Whereas, the same treatment of the
more pro-environmental Detriments side of the issue did not appear to elicit the same
negative response, as the subjects' belief in the relative "correctness" of that side of the
issue may have differentially affected the way in which not hedging influenced their
perceptions.
Another curious result, albeit only a trend, was a slightly stronger positive
response to Decision Intention when both Detriments and Benefits were Not Hedged
' versus when they both were Hedged. If Hypothesis 1 held, then one would expect the
Hedged version to elicit the stronger response. However, in light of the Trust in
Credibility result it is possible that this pro-environmental Detriments-bias effect, that
was not accounted for in the initial experimental design, may be influencing this result as
well. Again, it may be that Not Hedging the less favorable Benefits side of the issue is
having a greater influence on subjects' perception of the importance to support the other,
more pro-environment side, regardless of whether the Detriment side is Hedged or Not
Hedged. This could account for the observed trend here that although both sides of the
issue were Not Hedged this treatment nevertheless elicited a stronger pro-environment
response than when both were Hedged.
A strong trend toward significance indicated that Prioritization of Detriments and
Prioritization of Benefits were affected by hedging in accordance with Hypothesis 1.
Hedging the information regarding Detriments while Not Hedging information regarding
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Benefits did result in subjects prioritizing Detriments slightly toward "more important"
than when information regarding Detriments was Not Hedged. Similarly, Prioritization
of Benefits showed a trend toward a slightly stronger "importance" ranking for the
Benefit component when information regarding Benefits was Hedged versus when it was
Not Hedged.
In both of the aforementioned cases, the hedging status of the alternate component
was the converse of the component in question. When both components, Detriments and
Benefits, where either Hedged or Not Hedged there was not a significant difference
between responses to either component between those two treatments, or the control for
· that matter. It appears that hedging affects the magnitude ofresponse only when the two
sides of the issue are differentially treated with respect to hedging; otherwise, both sides
appear to be treated the same when it comes to prioritizing information during decision
making.
H2. Framing of both or either information component (detriments v. benefits) will result in differential direct responses, with the component(s) framed in a negative manner eliciting a response of relatively greater magnitude than the component(s) framed in a positive manner. When components are differentially framed, the direction of the response will be toward supporting the negatively framed component.
Two strong trends toward significance emerged regarding the Clarity of
Information and Belief in Climate Change variables. However, a very significant finding
emerged upon examination of the Prioritization of Information variable.
Regarding the trend observed with the Clarity of Information variable, both
framing treatments that used Forgo Gain to present the Benefits were considered
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relatively more "difficult" to read and understand than the control. Superficially this is
not very surprising, as the control group did not have a series of experimental statements
to read at all. But, the other framing option for Benefits, namely Obtain Gain, remained
statistically equivalent to the control. So, it is reasonable to conclude that the Forgo Gain
frame was indeed more difficult to process. This result actually supports qualitative
comments that were made by subjects during the instrument development and pilot
testing phase. Several participants in those studies commented that the Forgo Gain frame
was difficult or awkward to read.
Subsequent efforts were made in order to mitigate this difficulty such as adding
the phrase "do without" next to the initial inclusion of "forgo gain" as a textual element
and as part of the question statements. Sentence structure was also modified to ease
understanding of this particular frame. Nevertheless, it appears this may have been a
problem during the experiment. One speculation on this issue is that Forgo Gain is not a
commonly used frame in most circumstances in which we hear or read about issues.
Suffer Loss, A void Loss, and Obtain Gain are all much more common and, therefore,
easier for subjects to recognize and process. Even the word "forgo" is not common
diction, hence some of the difficulty trying to process its meaning in the context of the
issues presented. Short of omitting this frame, which would take away one quarter of the
possible frames available, a solution to this possible disparity in readability is not obvious
at this time.
Though a trend emerged with the Belief in Climate Change variable indicating a
slightly stronger belief in the occurrence of human-induced change under the positive
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frame for Detriments (Avoid Loss) versus the negative frame (Suffer Loss), while the
Benefit frame was negative (Forgo Gain) in both cases, the actual difference may not be
theoretically significant as both means represent the same qualitative response: "agree."
Prioritization oflnformation Use revealed the most significant evidence in
support of Hypothesis 2. Responses were significantly different among nearly all of the
framing treatments for both Detriments and Benefits.
For Prioritization of Detriments, ranking under Suffer Loss DET-Obtain Gain
BEN showed a higher average priority than both Avoid Loss DET-Forgo Gain and
Suffer Loss DET-Forgo Gain BEN. Similarly, Avoid Loss DET-Obtain Gain BEN
· elicited a higher ranking than both Avoid Loss DET-Forgo Gain and Suffer Loss
DET-Forgo Gain BEN.
For Prioritization of Benefits, the same treatments caused significantly different
responses, but the direction of priority was the reverse of the Detriment responses. Suffer
Loss DET-Obtain Gain BEN showed a lower average priority than both Avoid Loss
DET-Forgo Gain and Suffer Loss DET-Forgo Gain BEN. Similarly, Avoid Loss
DET-Obtain Gain BEN elicited a lower ranking than both Avoid Loss DET-Forgo Gain
and Suffer Loss DET-Forgo Gain BEN.
These results corroborate the Hypothesis 2 notion that negative framing elicits a
stronger response toward the issue component that was presented in the negative frame.
Suffer Loss (strong negative) appears to have influenced a higher prioritization of
Detriments over the Avoid Loss (weak positive) frame. Conversely, and probably
working in concert with the aforementioned result, Forgo Gain (weak negative) appears
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to have boasted prioritization of Benefits relative to Obtain Gain (strong positive)
presentation.
H3. Differential hedging of one information component (detriments v. benefits) while the other is not hedged will result in greater differences between responses to the two components than when both components are either hedged or not hedged.
Prioritization of Information Use was the only variable that provided a trend
toward significance in support of this hypothesis. Hedge DET-Not Hedge BEN
produced a trend toward being a significantly greater differential response to
prioritization between Detriments versus Benefits than when information was presented
as Not Hedge DET-Hedge BEN. Results from comparisons of direct responses,
mentioned earlier, indicate that the hedging effect within each sub-set, Detriments and
Benefits, is about the same magnitude. Therefore, the differential result here indicates
that the hedging effect produces a greater differential response between sides of the issue,
favoring a higher priority for Detriments relative to Benefits, when Detriments are
Hedged and Benefits are Not Hedged than when the converse is used, Detriments Not
Hedged and Benefits Hedged.
This is consistent with the Hypothesis 3 notion that hedging evokes a stronger
response, in this case toward a higher priority for Detriments. It is, however, hard to
distinguish this from the potential converse notion that Not Hedging the Benefits may
have evoked a stronger de-prioritization of those information components. The trend
toward a negative influence on Trust in Credibility of Information associated with Not
Hedging Benefits would help support this alternate explanation.
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H4. Differential framing of one information component versus the other (detriments v. benefits), with one framed in a negative manner and the other positive, will result in greater differences between responses to the two components than when both are framed either as negative or positive.
Again, Prioritization of Information Use was the only variable that revealed a
highly significant effect in support of this hypothesis. Differential responses under both
Suffer Loss DET-Obtain Gain BEN and Avoid Loss DET--Obtain Gain BEN were
significantly greater than under Avoid Loss DET-Forgo Gain BEN and Suffer Loss
DET-Forgo Gain BEN. In this case, treatments that elicited statistically equal responses
used both framing alternatives for Detriments (Suffer Loss and A void Loss).
Framing used for Benefits in statistically equal responses were the same: Obtain
Gain for one and Forgo Gain for the other. This allows for a comparison of the effects of
framing for Benefits across both possible framing categories for Detriments. It appears
that when Benefits are presented in the Forgo Gain (negative) frame the differential
prioritization between Benefits and Detriments is reduced relative to the differential when
Benefits are presented in the Obtain Gain (positive) frame. In other words, framing the
Benefits in a negative manner seems to provoke subjects to "close the gap" in their
prioritization of information regarding the Detriments and Benefits associated with the
climate change issue. This result is consistent with the Hypothesis 4 notion that negative
framing elicits a stronger response than positive framing.
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HS. Regarding multivariate combinations of variables, hedging of both or either information component (detriments v. benefits) will result in differential direct responses, with the hedged component(s) eliciting a response of relatively greater magnitude than the not hedged component(s). When components are differentially hedged, the direction of the response will be toward supporting the hedged component.
There were no statistically significant findings or obvious trends to support this
hypothesis. It is assumed that this study failed to reject the null hypothesis regarding
Hypothesis 5.
H6. Regarding multivariate combinations of variables, framing of both or either information component (detriments v. benefits) will result in differential direct responses, with the component(s) framed in a negative manner eliciting a response of relatively greater magnitude than the component(s) framed in a positive manner. When components are differentially framed, the direction of the response will be toward supporting the negatively framed component.
Only one multivariate combination of variables demonstrated a statistically
significant relationship in support of this hypothesis: Prioritization of Detriments with
Prioritization of Benefits. This result is not surprising in that univariate analysis of these
components revealed highly significant results for both direct and differential responses
between the two information components. It is also possible that the strong directionality
of prioritization, with Detriments consistently considered "more important" than
Benefits, produces a strong negative relationship between the two components that
enhances discernment of multivariate effects (Hair et al., 1998).
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H7. Interactions between hedging and framing will occur, with the combination of hedging and negative framing enhancing the magnitude of the response.
Both the direct responses for each and the difference score between Prioritization
of Detriments and Prioritization of Benefits revealed a hlghly significant interaction
between hedging and framing.
Closer examination of simple effects and comparisons provided evidence that the
nature of this interaction effect was ordinal, supporting the original interpretation of main
effects. Further, results of this secondary analysis suggests that hedging and framing>
effects in certain combinations may enhance the overall influence of the textual
manipulations on a subject's responses. For example, the interaction reveals that the
persistent de-prioritization effect, presumably caused when Benefits are Not Hedged but
Detriments are Hedged, appears to be greatly enhanced when presented in conjunction
with the strong positive frame Obtain Gain.
Though this interaction has not been discussed in previous literature, as it appears
this combination has never been tested before, it does seem reasonable that two factors
known to independently have strong psychological influences on subjects would have an
enhanced effect when presented in concert.
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Summary and Implications
Summary of Hedging Effects:
• When Benefits are Not Hedged, perceived Trust in the Credibility of information presented decreases (trend).
• When both Detriments and Benefits are Not Hedged, Decision Intention toward supporting climate change reductions increases (trend).
• When either Detriments or Benefits is Hedged, Prioritization of Information Use increases for that component (trend).
• Differential responses when both Detriments and Benefits, or just Detriments, are Hedged favors Detriments over Benefits for Prioritization of Information Use (trend).
Summary of Framing Effects:
• When use Forgo Gain frame, perceived Clarity of Information presented decreases (trend).
• When a negative frame is used for either Detriments or Benefits, Prioritization of Information Use increases for that component (p:::; 0.001).
• Differential responses indicate that the Benefit component is positively affected when presented in its negative frame (Forgo Gain) more than when Detriments is presented in its negative frame for Prioritization of Information Use (p:::; 0.001).
In general, evidence gathered in this study tends to support the notion that the use
of hedging enhances the emphasis subjects cognitively assign to the information
presented in this manner, namely priority of use during decision making. Though
untested in this study, it has been suggested that this is caused by an increase in
processing time shown to be associated with the presence of hedging (Vande Kopple &
Crismore, 1990). Time spent cognitively processing is known to positively affect the
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relative importance placed on information (Payne et al., 1993). As evidenced by the
curious reverse effect of Not Hedging information regarding the Benefits of not
supporting climate change reductions, it is important to examine the context and audience
biases as well.
This finding would suggest that the use of hedging evenly during presentation of
both sides of the issue should be used. Hedging both sides of an issue should promote
more cognitive processing, perhaps scrutiny, of the detriments and benefits inherent in
most environmental issues. It also appears to negate any obvious differential treatment of
one side of the issue or the other, which could otherwise produce an inadvertent biasing
effect if differential hedging, or even all not hedging, were employed.
Evidence from this study helps support the notion that negative framing of either
side of an issue also promotes greater emphasis placed on the negatively framed
component. Again, this may be caused, in part, by more cognitive processing time
associated with negative frames, though this conclusion is not necessarily supported by
prior literature. It may be more the case that the explanation is linked with conclusions
drawn from well known studies on the cognitive effects of negative framing on human
subjects' responses that demonstrate this pattern of greater emphasis in just about any
circumstance (e.g. Tversky & Kahneman, 1981; Maheswaran & Meyers-Levy, 1990;
Davis, 1995; Levin et al., 1998).
Research suggests that this is linked to an innate human sense for paying more
attention to potential losses, often perceived as negative, as they may be life threatening
(Tversky & Kahneman, 1981 ). In other words, people tend to pay more attention to sides
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of an issue presented to them as a possibility of losing something they already have.
Whereas, the same people pay relatively less attention, and act correspondingly less,
toward the side of an issue presented as either a potential gain or even as the loss of an
opportunity to gain something they don't already have. This study corroborates these
other studies that examine a variety of different decision making scenarios but all point
toward the primacy of negative framing as a motivator and influential frame in terms of
prioritization of information use (e.g. Tversky & Kalmeman, 1981; Maheswaran &
Meyers-Levy, 1990; Davis, 1995; Levin et al., 1998).
In summary, if the goal of a communication plan for an environmental decision
making situation is to balance the equity of the information presentation and optimize
cognitive and affective processing, then hedging all statements and using negative
framing for both sides of an issue is prudent.
Recommendations for Future Study
Using a blocking technique for Gender proved extremely useful, as gender
response differences were very strong among the variables. Prior Background was
somewhat useful as a covariate, as it did account for some error variance among some of
the variables, thus increasing power. The population under study here was rather
homogenous in this regard though. If a more diverse population were to be studied, this
variable would most likely need to be assessed in advance and also used as a block. It
would also be useful to make distinctions among various environmental dispositions
within the population by conducting an environmental inventory of some sort on each
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subject. The New Environmental Paradigm, 15-question survey by Dunlap et al. (2000),
would be a good example. This variable could, in tum, be used to establish either a block
or be used as a covariate when analyzing responses from more diverse, but inferentially
sound, populations of human subjects.
A psychological principle underlying explanations as to why hedging and framing
variations differentially affect decision making, particularly cognitive processes, has to
do with the time spent by a subject actually processing or working through the
information (Payne et al., 1993). A useful variable to examine in future studies would be
a measure of precisely how much time is spent on each experimental text, and perhaps
each question set as well. A crude measure would be self-reported time, but this often
lacks the accuracy and precision necessary to detect nuances of time differentials between
short text passages. A computerized questionnaire that automatically calculates time
spent on each component would be ideal for this measure (e.g. Payne et al., 1993).
Overall, the results of this study did help corroborate previous work on both
hedging and framing, particularly regarding effects on human subjects' cognitive domain.
Continued refinement of those questions, Prioritization of Information Use and Clarity of
Information, would enhance those results. Investigating the interactions of hedging and
framing to a greater extent on cognitive variables could also prove fruitful.
Results representing the affective domain were a bit disappointing, particularly
the dominant use of variables derived from the Theory of Reasoned Action. It may be
that extrapolation of a model designed to measure tangible personal behavioral intentions,
such as voting habits or a decision to take a particular medication or not, was not
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appropriate for a study of a much larger scale and relatively intangible topic such as
climate change. Although decision intention, attitude, outcome evaluation, and belief in
outcomes are all pertinent and relevant variables for investigating such a decision making
process, it may be that this instrument was too coarse-grained to detect potential
differences using this model. Other affective models designed to elicit responses in the
context of more intangible and abstract scenarios may prove more appropriate. An
example that comes to mind is Willingness to Pay or Willingness to Act measures that
are commonly used to provide a proxy assessment of subjects' "attitude" and general
decision intention toward a particular issue (e.g. Berk, 1995).
Another potential problem with the use of the Theory of Reasoned Action was the
nature of how each statement needed to be presented within the questionnaire, especially
for eliciting Belief in Outcome and Outcome Evaluation responses. The statements
presented matched the experimental treatment versions of each statement very closely.
The hedging variable was easily held constant for these questions by simply presenting
all response statements in the Not Hedged format. Holding framing constant, on the
other hand, was not easily attainable. Actually, it is linguistically impossible to present
issue statements without any framing effects. There are two general ways this could be
handled differently. One way would have been to present each statement in both of its
possible frames and take the average of those responses. Given that this study involved
twenty different statements regarding climate change, this method would have required
either doubling each for each major variable, which would have made for a very long
questionnaire. The other option would be a double presentation of a sub-set of the
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statements. This latter option would have been a fair alternative; however, repetition of
essentially the same statement may introduce yet another bias: a form of practice or
fatigue effect. Item analyses and reliability studies during the piloting phase of this study
indicated that issue sets (Detriments and Benefits) where better off as a set of ten each,
with equal presentation of both ecological and sociological components for each side.
The approach used in this study was to include a representation of each of the
twenty statements, but alternate the framing reference evenly between the two issue sides
and then among the variables. For example, for the Belief in Outcome variable half of
the statements about Detriments were presented in its Suffer Loss frame, while the other
half in its A void Loss frame. Meanwhile, for the Outcome Evaluation variable the same
split was used but frames were reversed for individual statements. The same technique
was used for Benefits. The aim was to present all possible statements without
overburdening the questionnaire to a point where fatigue effect and redundancy would
taJce hold of subjects, but to also nullify the unwanted effects of framing in the questions
themselves by evenly distributing the various frames throughout all of the questions.
It is quite possible that despite this effort to mitigate the extraneous effects of
framing in the questions themselves, the interaction of treatment and question format may
have been overlooked. Keeping in mind that each group only received two framing
conditions out of four that were possible in the experimental text passage, it is
conceivable that presence of the same frames, or different frames for that matter, in the
question sets may have either enhanced or diminished the treatment effect respectively.
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A very different approach to this problem would be to forgo the use of the
experimental text passages all together and use the question statements within the
questionnaire to present the various treatments. This would work only for the two TRA
variables, Belief in Outcome and Outcome Evaluation, that lend themselves to directly
stating the information inherent in the issue under investigation. A concern with this
approach would be that each treatment group would, of course, receive different
questions. Validity issues may arise as to how the linguistics, beyond just the planned
manipulations, may affect the results. A repeated measures approach may help mitigate
this problem, but could introduce the practice effect again.
Another dramatically different approach to potentially enhancing the measurable
effect of hedging and framing on the affective TRA variables would be to use a more
tangible environmental issue. For example, a controversial issue such as oil exploration
and development in the Arctic National Wildlife Refuge could provide a more immediate
and tangible set of statements on both sides of the issue for subjects to relate to and
respond to with greater conviction and discernment.
Further studies designed to validate this aspect of the instrument to a greater
extent, perhaps by trying different strategies such as those mentioned here, would help
discern whether this question framing effect is actually a problem or not. As it stands
now, it is hard to say whether such an extraneous effect had any influence on the results
or not. It may be the case that the null hypothesis is truly upheld, and variations in these
two factors simply do not have a significant effect on the affective variables associated
with the Theory of Reasoned Action.
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Regardless of what new format this questionnaire may take in a future iteration, it
would be prudent to begin establishing inferential conclusions from these results by
administering this instrument to more heterogeneous populations. Ultimately, the goal of
this study was to begin validating this instrument with the intention of examining intact
populations of environmental decision makers to test if these textual variations do indeed
affect such populations. This goal will hopefully be attained with future studies based on
what was learned during this study about the variables under investigation and aspects of
the methods that should be refined.
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APPENDIX A
CLIMATE CHANGE ISSUE QUESTIONNAIRE
Note: This is an example of one of the questionnaires used for this study. The experimental text passage on pages 4 and 5 of the questionnaire is the only section that varies among the 16 different treatment combinations. See Appendix B for those variations.
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GENERAL INSTRUCTIONS
This questionnaire is designed to measure your opinions and beliefs in response to a passage you will read about the climate change issue. Note that all information presented in the passage is based on a consensus of rigorously studied, well accepted sources. This is not a test of your knowledge or comprehension of the issue; there are no "correct" answers. So, read the passage, then read each questlon carefully and respond to the statements with your honest opinion.
You are welcome to refer to the original passage as you need while responding to questions.
You will be asked to respond to statements using slightly different scales. Please make an "X" mark in the place that best describes your opinion.
Several questions will seem to repeat statements with slight changes. Please read the instructions carefully, you will be asked to respond to these similar statements in a different manner.
For example, if you were asked to rate your opinion about the outcome implied by the statement "Allowing clothing to become wrinkled," and you believe that allowing clothing to become wrinkled is quite bad, then you would place your mark as follows:
Allowing clothing to become wrinkled is ...
BAD __ _ x ___ GOOD
Extremely Quite Slightly Neither/Nor Slightly Quite Extremely
Or, if you were asked to rate the likelihood of the outcome implied by the statement "Letting laundry pile up in your room results in wrinkled clothing'' actually occurring, and you believe that it is extremely likely that letting laundry pile up in your room will wrinkle clothing, you would place your mark as follows:
Letting laundry pile up in your room results in wrinkled clothing.
UNLIKELY=---,-- ----,,..--,-- -..,,,,....,--,-- -,,,..,~ -~- -~X,.___LIKELY 1Extremely Quite Slightly Neither/N"or Slightly Quite Extremely
You will find other scales with slightly different endpoints, but each should be treated with the same general interpretation demonstrated by the examples given above.
When selecting your ratings please remember the following points:
(1) Place your marks in the middle of the spaces, not on the boundaries:
x x _____ _ THIS NOT THIS
(2) Be sure you answer all items - please do not omit any.
(3) Never put more than one mark on a single scale.
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Once you've read the General Instructions on the previous page ..•
Answer these questions before reading the following passage on the next couple of pages.
a. How knowledgeable are you about the issue of human-induced climate change and its impacts on the U.S.?
Not at All Knowledgeable
Slightly Knowledgeable Knowledgeable
Very Knowledgeable
Extremely Knowledgeable
b. How ieformed are you on the issue of human-induced climate change and its impacts on the U.S. from reading, watching or listening to news media sources?
Not at All Informed
Slightly Informed
Informed Very Informed
Extremely Informed
c. How ieformed are you on the issue of human-induced climate change and its impacts on the U.S. from lectures, class discussions or readings in your college courses?
Not at All Informed
Slightly Informed
Informed
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Very Informed
Extremely Informed
,.
CLIMATE CHANGE INFORMATION
Carefully read the following two-page passage.
Humans are affecting some of the key factors that govern climate by changing the
composition of the atmosphere and by modifying the land smface. Rising atmospheric
concentrations of carbon dioxide (C02) and other greenhouse gases are increasing Earth's natural
greenhouse warming effect. This increase has resulted from the burning of coal, oil, and natural
gas, and the clearing and burning of forests around the world. If the current rate of human
produced emissions is maintained, atmospheric COz concentration will continue to rise, reaching
between two and three times its pre-industrial level by the year 2100.
Long-term observations confirm that our climate is now changing at a rapid rate. With
continued growth in atmospheric greenhouse gas concentrations, average temperature in the U.S.
will rise in the next 100 years. There will also be more precipitation overall, with more of it
coming in heavy downpours. In spite of this, some areas will get drier as increased evaporation
due to higher temperatures outpaces increased precipitation. The warming is causing permafrost
to thaw, and is melting sea ice, snow cover, and mountain glaciers, and sea level is rising.
Studies suggest that ifthe rate of human-induced climate change is not reduced,
allowing the climate to change rapidly over the next several decades, then the U.S. will likely
suffer some losses. For example, the U.S. might experience the following outcomes:
• Shoreline erosion might intensify, and coastal wetland losses will potentially increase.
• Disturbance of fish habitat could occur, possibly threatening the survival of some cold-water fish species.
• Delicate and isolated ecosystems such as alpine meadows, mangroves, and tropical mountain forests might decline, and some could disappear altogether.
• Forests in many regions may become more susceptible to pests and fire, with damage to forest ecosystem productivity increasing by roughly 10%.
• The rate of loss of biodiversity will probably increase, as some plant and animal species may not be able to adapt to rapid changes.
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• Water availability for irrigation might decrease, potentially complicating irrigation management.
• Demands for air conditioning might increase, possibly increasing the cost of energy during the summer.
• Diseases that are water and animal borne may intensify in the summer, which could increase incidents of human illness and death in some areas.
• The U.S. could incur an economic loss of about 1% of the gross domestic product (GDP) from climate-related damages.
• The risk of flash floods and soil erosion will probably increase, which may decrease agricultural productivity.
Studies also suggest that ifthe rate of human-induced climate change is not reduced,
allowing the climate to change rapidly over the next several decades, then the U.S. will likely
obtain some gains. For example, the U.S. might experience the following outcomes:
• The number of inland, non-tidal wetlands will potentially increase, and the area where floodplain wetlands form might expand.
• Over-winter mortality of many species of wildlife could decrease as cold stress is reduced and seasonal availability of food possibly increases.
• Tree growth and forest expansion might increase, which could increase the coverage and range of forest ecosystems.
• Plant productivity may increase by roughly I 0% throughout various ecosystems.
• Migrating birds may not fly as far south, nesting earlier in the season, which will probably improve the odds of young birds swvi ving winters.
• Crops that Will potentially develop at faster rates might reduce agricultural use of irrigation water.
• Winter heating needs might decrease, possibly reducing the seasonal cost of energy.
• Incidents of cold-related human illnesses and deaths could be reduced in some areas of the country that may experience less severe winter conditions.
• The U.S. could save about I% of the gross domestic product (GDP) that would have been spent on emissions reduction.
• The food supply from agricultural productivity may increase, probably dropping prices for food products.
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Now, answer the following three questions.
d. How difficult or easy did you find it to read the preceding passage?
Difficult to Read
Extremely Quite Slightly Neither/Nor Slightly Quite
Easy to Read
Extremely
e. How difficult or easy did you find it to understand the information presented in the passage you just read?
Difficult to Understand___ -,,.---,,--- -=,.-,-,,.-
Exrremely Quite Slightly Neither/Nor Slightly Quite
Easy to ___ Understand Extremely
f. How difficult or easy did you find it to trust the credibility of the information presented in the passage you just read?
Difficult to Trust as Credible __ _
Extremely Quite Slightly Neither/Nor Slightly Quite
137
Easy to Trust
_ __ as Credible Extremely
QUESTIONNAIRE
Following are a series of questions designed to measure your OPINIONS and BELIEFS about climate change. Read each statement carefully and give your honest response. Remember, there are no "correct" answers, just viewpoints.
A) Each statement poses a situation from the perspective of one or the other possible decision choices regarding the climate change issue (i.e. to support or not support reductions). Rate how likely or unlikely you believe that what is said in each statement would actually occur.
1. My supporting a large-scale effort to reduce the rate of human-induced climate change in the U.S. is ...
UNLIKELY:--,-..,.Extremely Quite
7'""',,--~ __,,,,...,~ -~- -=--~LIKELY Slightly Neither/Nor Slightly Quite Extremely
2. My NOT supporting a large-scale effort to reduce the rate of human-induced climate change in the U.S. is ...
UNLIKELY __ _ _ ___________ IJKELY
Extremely Quite Slightly Neither/Nor Slightly Quite Extremely
3. People who are important to me and whose opinions I respect think! should support an effort to reduce the rate of climate change.
UNLIKELY __ _ _ ________ ___ UKELY Extremely Quite Slightly Neither/Nor Slightly Quite Extremely
4. Organizations that are important to me and hold opinions I respect think! should support an effort to reduce the rate of climate change.
UNLIKELY·=--..,.Extremely Quite
,..,......,.-..,.- ---,,,,..-,-,.- -,....,.- ..,,.. __ LIKELY Slightly Neither/Nor Slightly Quite Extremely
5. People who are important to me and whose opinions I respect think I should NOT support an effort to reduce the rate of climate change.
UNLIKELY __ _
Extremely Quite
_ ___________ LIKELY
Slightly Neither/Nor Slightly Quite Extremely
6. Organizations that are important to me and hold opinions I respect think I should NOT support an effort to reduce the rate of climate change.
UNLIKELY __ _ Extremely Quite
_________ ___ LIKELY Slightly Neither/Nor Slightly Quite Extremely
138
B) Each statement poses a "what if" in terms of each of two possible decision choices regarding the climate change issue. Rate each outcome in terms of whether you believe it would be a good or a bad outcome for the U.S.
7. Ifa large-scale effort to reduce the rate of human-induced climate change in the U.S. was generally supported the future outcomes or consequences of that decision would be ...
BAD~-~- -~~- -~~~ ~~~ -=~- -~~- ~-~GOOD Extremely Quite Slightly Neither/Nor Slightly Quite Extremely
8. Ifa large-scale effort to reduce the rate of human-induced climate change in the U.S. was generally NOT supported the future outcomes or consequences of that decision would be ...
BAD ____ --=-.,.--- ____ ___ ____ ---.,--,-- -=--~GOOD
Extremely Quite Slightly Neither/Nor Slightly Quite Extremely
C) Following are statements about the status of climate change. Rate how strongly you agree or disagree with what is implied in each statement.
9. Humans are affecting climate by changing greenhouse gas concentrations in the atmosphere.
Strongly Disagree
Disagree Neither/ Nor
10. Our climate is now changing at a rapid rate.
Strongly Disagree
Disagree Neither/ Nor
Agree
Agree
11. Average temperature in the U.S. will rise in the next 100 years.
Strongly Disagree
Disagree Neither/ Nor
Agree
Strongly Agree
Strongly Agree
Strongly Agree
12. There will be more precipitation overall, with more of it coming in heavy downpours.
Strongly Disagree
Disagree Neither/ Nor
Agree Strongly Agree
13. Some areas will get drier as increased evaporation outpaces increased precipitation.
Strongly Disagree
Disagree Neither/ Nor
139
Agree Strongly Agree
D) Each statement below presents an aspect of the climate change issue that you read in the preceding passage. You have been asked to consider the issue and make a decision whether to support or not support a large-scale effort to reduce the rate of human-induced climate change in the U.S.
Now, with this task in mind, prioritize these ten topics in what you believe is the order of their relative importance (i.e. 1, 2, 3, ... 10) in terms of helping you make that decision.
Place a number from one (1) !!!11§!. important to ten (10) l£!!!!. important next to each statement NOTE: Do not use the same number more than once.
Incidents of cold-related human illnesses and deaths.
__ Forest susceptibility to pests and fire, and damage to forest ecosystem productivity.
__ Over-winter mortality of many species of wildlife, cold stress, and seasonal availability of food for wildlife.
Water availability for irrigation, and irrigation management complications.
Economic impacts on the U.S. gross domestic product (GDP).
__ The rate ofloss of biodiversity, and plant and animal species' adaptability to rapid changes.
__ Tree growth and forest expansion, and the coverage and range of forest ecosystems.
__ Demands for air conditioning and the cost of energy during the summer.
__ The food supply from agricultural productivity and prices for food products.
Shoreline erosion and coastal wetland losses.
140
E) Again, each statement below presents an aspect of the climate change issue that you read in the preceding passage. You have been asked to consider the issue and make a decision whether to support or not support a largescale effort to reduce the rate of human-induced climate change in the U.S.
Now, with this task in mind, prioritize these ten topics in what you believe is the order of their relative importance (i.e. 1, 2, 3, ... 10) in terms of helping you make that decision.
Treat these statements independently from the previous set.
Place a number from one (1) "!!l!!.§!. important to ten (10) ~important next to each statement NOTE: Do not use the same number more than once.
__ The risk of flash floods and soil erosion, and agricultural productivity.
__ Plant productivity throughout various ecosystems.
__ Sustainability of delicate and isolated ecosystems such as alpine meadows, mangroves, and tropical mountain forests.
__ Winter heating needs and the cost of energy.
__ Economic impacts on the U.S. gross domestic product (GDP).
__ The number of inland, non-tidal wetlands, and the area where floodplain wetlands can form.
__ Fish hab.itat disturbance and the survival of cold-water fish species.
__ Crops developing at faster rates and agricultural use of irrigation water.
__ Water- and animal-home related diseases, and incidents of human illness and death.
__ Migrating birds extent of flying south, timing of seasonal nesting, and the odds of young birds surviving winters.
141
F) Each statement below presents an ontcome or consequence of a general, nationwide decision to support large-scale climate change reductions. Regardless of whether you agree with this decision or not, rate how likely or unlikely you believe that what is said in each statement would actually occur if climate change reduction was generally supported.
In other words, supporting a large-scale effort to reduce the rate of humaninduced climate change in the U.S. will result in ...
14. Avoiding a decline in delicate and isolated ecosystems such as alpine meadows, mangroves, and tropical mountain forests.
UNLIKELY___ ___ ___ _ _________ LIKELY
Extremely Quite Slightly Neither/Nor Slightly Quite Extremely
15. Doing without an increase in food supply from agricultural productivity.
UNLIKELY LIKELY Extremely Quite Slightly Neither/N" or Slightly Quite Extremely
16. Avoiding an increase in coastal wetland losses.
UNLIKELY LIKELY Extremely Quite Slightly Neither/N'or Slightly Quite Extremely
17. Doing without a decrease in over-winter mortality of many species of wildlife.
UNLIKELY LIKELY Extremely Quite Slightly Neither/Nor Slightly Quite Extremely
18. Avoiding irrigation management complications.
UNLIKELY : LIKELY Extremely Quite Slightly Neither/Nor Slightly Quite Extremely
19. Doing without an increase in the coverage and range of forest ecosystems.
UNLIKELY LIKELY Extremely Quite Slightly Neither/N'or Slightly Quite Extremely
20. A voiding a decrease in agricultural productivity.
UNLIKELY LIKELY Extremely Quite Slightly Neither/N" or Slightly Quite Extremely
142
21. Doing without an increase in the number of inland, non-tidal wetlands.
UNLIKELY ______ ---Extremely Quite Slightly Neither/Nor Slightly Quite
___ LIKELY Extremely
22.Avoiding an increase in threats to the survival of some cold-water fish species.
UNLIKELY___ ___ ___ _ _________ LIKELY Extremely Quite Slightly Neither/Nor Slightly Quite Extremely
23. Doing without reductions in agricultural use of irrigation water.
UNLIKELY-~~ -~~ Extremely Quite
--,o,,..,-,,-- -~-,,.....- ~o-c---c LIKELY Slightly Neither/Nor Slightly Quite Extremely
G) Each statement below presents an outcome or consequence of a general, nationwide decision to NOT support large-scale climate change reductions. Regardless of whether you agree with this decision or not, rate how likely or unlikely you believe that what is said in each statement would actually occur if climate change reduction was generally NOT supported.
In other words, NOT supporting a large-scale effort to reduce the rate of human-induced climate change in the U.S. will result in ...
24.Allowing an economic loss of 1% of the U.S. gross domestic product (GDP).
UNLIKELY _____ _
Extremely Quite ~~- -~-- ___ LIKELY
Slightly Neither/Nor Slightly Quite Extremely
25.Allowing an improvement in the odds of young birds of migratory species surviving winters.
UNLIKELY-~~ -~-- -~~ ~~~ -~~ -~- --~LIKELY Extremely Quite Slightly Neither/Nor Slightly Quite Extremely
26.Allowing an increase in incidents of human illness and death during the summer.
UNLIKELY ___ --- ______ LIKELY
Extremely Quite Slightly Neither/Nor Slightly Quite Extremely
27. A /lowing a reduction in winter heating energy costs.
UNLIKELY---,,--- -...,,--.,..--- -=c--,-,--- .,,,-,.,.--,cc-Extremely Quite Slightly Neither/Nor Slightly
--,:-..,--- ....,~~LIKELY Quite Extreme I y
143
28. Allowing an increase in damage to forest ecosystem productivity.
UNLIKELY___ -o-:--Extremely Quite
-.,.-c- ___ LIKELY Quite Extremely Slightly Neither/Nor Slightly
29. Allowing a savings of I% of the U.S. gross domestic product (GDP).
UNLIKELY _____________________ LIKELY
Extremely Quite Slightly Neither/Nor Slightly Quite Extremely
30. Allowing an increase in the rate ofloss ofbiodivei:sity.
UNLIKELY __ _ Extremely Quite Slightly Neither/Nor Slightly
___ LIKELY -Q""m~·te:-- Extremely
31. Allowing a reduction in incidents of human illnesses and deaths in winter.
UNLIKELY___ _ __ Extremely Quite
______ LIKELY
Extremely Quite Slightly Neither/Nor Slightly
32. Allowing an increase in the cost of energy during the summer.
UNLIKELY __ ~ -~- -~- ___ -~- ______ LIKELY Extremely Quite Slightly Neither/Nor Slightly Quite Extremely
33. Allowing an increase in plant productivity throughout various ecosystems.
UNLIKELY __ _ Extremely Quite
-.,.--,- ___ LIKELY Quite Extremely Slightly Neither/Nor Slightly
The next set Qf statements may appear similar to those you just read, with slight changes. Please read the instructions carefully, you will be asked to respond to these similar statements in a different manner •••
144
H) Each statement presents an outcome of a general decision to either support or not support large-scale climate change reductions. Regardless of which decision you would make, rate each future consequence in terms of whether you believe it would be a good or a bad outcome for the U.S.
34. Allowing a reduction in agricultural use of irrigation water is ... BAD _____ _
Extremely Quite
______ GOOD Slightly Neither/Nor Slightly Quite Extremely
35.Avoiding an increase in the cost of energy during the summer is ...
BAD ..,-----.,..- --,---Extremely Quite
_______ ____ GOOD
Slightly Neither/Nor Slightly Quite Extremely
36. Allowing an increase in the coverage and range of forest ecosystems is ...
BAD GOOD Extremely Quite Slightly N either/N" or Slightly Quite Extremely
37.Allowing an increase in coastal wetland losses is ...
BAD GOOD Extremely Quite Slightly Nelthen'Nor Slightly Quite Extremely
38. Doing without saving 1 % of the U.S. gross domestic product (GDP) is ...
BAD ___ ---- ___ _ ___ GOOD Extremely Quite Slightly Neither/Nor Slightly Quite Extremely
39.Allowing a decrease in agricultural productivity is ...
BAD ---- ----Extreme! y Quite Slightly Neithen'Nor Slightly
___ _ ___ GOOD
Quite Extremely
40. Doing without an increase in plant productivity throughout various ecosystems is ...
BAD ___ ---- ---- ___ GOOD Extremely Quite Slightly Neither/Nor Slightly Quite Extremely
41.Allowing an increase in threats to the swvival of some cold-water fish species is ...
BAD ____ ---- ___ _ ___ GOOD Extreme! y Quite Slightly Neilher/Nor Slightly Quite Extremely
42. Doing without a reduction in incidents of human illnesses and deaths in winter is ...
BAD GOOD Extremely Quite Slightly Neither/Nor Slightly Quite Extremely
43.Allowing irrigation management complications is ...
BAD GOOD Extremely Quite Slightly Neitht:r/Nor Slightly Quite Extremely
145
44. Doing without an improvement in the odds of young birds of migratory species surviving winters is ...
BAD ___ _ _ ___ GOOD Extremely Quite Slightly Neither/Nor Slightly Quite Extremely
45. Allowing a decline in delicate and isolated ecosystems such as alpine meadows, mangroves, and tropical mountain forests is ...
BAD GOOD Extremely Quite Slightly Neither/Nor Slightly Quite Extremely
46. Doing without a reduction in winter heating energy costs is ...
BAD GOOD Extremely Quite Slightly Neither/Nor Slightly Quite Extremely
47. Avoiding an increase in incidents of human illness and death during the summer is ...
BAD ___ _
Extremely Quite Slightly Neither/Nor Slightly Quite
48. Allowing an increase in the number of inland, non-tidal wetlands is ...
BAD ___ _ Extremely Quite Slightly Neither/Nor Slightly Quite
49. Avoiding an increase in damage to forest ecosystem productivity is ...
BAD ___ _
Extremely Quite Slightly Neither/Nor Slightly Quite
_ ___ GOOD Extremely
~~-~GOOD Extremely
____ GOOD
Extremely
50. Allowing an increase in the food supply from agricultural productivity is ...
BAD ___ _ _ ___ GOOD Extremely Quite Slightly Neither/Nor Slightly Quite Extremely
51. Avoiding an economic loss of 1 % of U.S. gross domestic product (GDP) is ...
BAD ___ _ _ ___ GOOD Extremely Quite Slightly Neither/Nor Slightly Quite Extremely
52. Allowing a decrease in over-winter mortality of many species of wildlife is ...
BAD---- ____ GOOD Extremely Quite Slightly Neither/Nor Slightly Quite Extremely
53. Avoiding an increase in the rate ofloss of biodiversity is ...
BAD ___ _ ____ GOOD Extremely Quite Slightly Neither/Nor Slightly Quite Extremely
Just a few more questions on the back ...
146
I) Please answer the following questions as directed.
54. You are ... Female
Male
55. Your class rank ...
Freshman _Sophomore
Junior
Senior
Graduate Professional
Other
56. Yourethnicity ...
African American Asian American
_Hispanic
Native American
White/Caucasian
Other
57. Your approximate cumulative G.P .A ...
under 1.00
1.00-1.99 2.00-2.99
3.oo-4.oo don't know
58. Your age: ___ _
59. Are you a U.S. resident? ___ _
Treatment: 1
60. If you are a U.S. resident, the State you have spent the most time living in: ___ _
This is the end of the questionnaire. Thank you for your time and effort!
147
APPENDIXB
ALTERNATE VERSIONS OF EXPERIMENTAL TEXT
148
VERSION A (Hedged/Strong)
Studies suggest that ifthe rate of human-induced climate change is not reduced,
allowing the climate to change rapidly over the next several decades, then the U.S. will
likely suffer some losses. For example, the U.S. might experience the following
outcomes:
• Shoreline erosion might intensify, and coastal wetland losses will potentially increase.
• Disturbance of fish habitat could occur, possibly threatening the survival of some cold-water fish species.
• Delicate and isolated ecosystems such as alpine meadows, mangroves, and tropical mountain forests might decline, and some could disappear altogether.
• Forests in many regions may become more susceptible to pests and fire, with damage to forest ecosystem productivity increasing by roughly 10%.
• The rate ofloss of biodiversity will probably increase, as some plant and animal species may not be able to adapt to rapid changes.
• Water availability for irrigation might decrease, potentially complicating irrigation management.
• Demands for air conditioning might increase, possibly increasing the cost of energy during the summer.
• Diseases ·that are water and animal borne may intensify in the summer, which could increase incidents of human illness and death in some areas.
• The U.S. could incur an economic loss of about 1 % of the gross domestic product (GDP) from climate-related damages.
• The risk of flash floods and soil erosion will probably increase, which may decrease agricultural productivity.
149
Studies also suggest that ifthe rate of human-induced climate change !!.m!! reduced, allowing the climate to change rapidly over the next several decades, then the
U.S. will likely obtain some gains. For example, the U.S. might experience the following
outcomes:
• The number of inland, non-tidal wetlands will potentially increase, and the area where floodplain wetlands form might expand.
• Over-winter mortality of many species of wildlife could decrease as cold stress is reduced and seasonal availability of food possibly increases.
• Tree growth and forest expansion might increase, which could increase the coverage and range of forest ecosystems.
• Plant productivity may increase by roughly I 0% throughout various ecosystems.
• Migrating birds may not fly as far south, nesting earlier in the season, which will probably improve the odds of young birds surviving winters.
• Crops that will potentially develop at faster rates might reduce agricultural use of irrigation water.
• Winter heating needs might decrease, possibly reducing the seasonal cost of energy.
• Incidents of cold-related human illnesses and deaths could be reduced in some areas of the country that may experience less severe winter conditions.
• The U.S. could save about I% of the gross domestic product (GDP) that would have been spent'on emissions reduction.
• The food supply from agricultural productivity may increase, probably dropping prices for food products.
150
VERSION B (Not Hedged/Strong)
Studies claim that ifthe rate of human-induced climate change is not reduced,
allowing the climate to change rapidly over the next several decades, then the U.S. will
suffer some losses. For example, the U.S. will experience the following outcomes:
• Shoreline erosion will intensify, and coastal wetland losses will increase.
• Disturbance of fish habitat will occur, threatening the survival of some cold-water fish species.
• Delicate and isolated ecosystems such as alpine meadows, mangroves, and tropical mountain forests will decline, and some will disappear altogether.
• Forests in many regions will become more susceptible to pests and fire with damage to forest ecosystem productivity increasing by 10%.
• The rate ofloss of biodiversity will increase, as some plant and animal species will not be able to adapt to rapid changes.
• Water availability for irrigation will decrease, complicating irrigation management.
• Demands for air conditioning will increase, increasing the cost of energy during the summer.
• Diseases that are water and animal borne will intensify in the summer, which will increase incidents of human illness and death in some areas.
• The U.S. will incur an economic loss of 1 % of the gross domestic product (GDP) from climate-related damages.
• The risk of flash floods and soil erosion will increase, which will decrease agricultural productivity.
151
Studies also claim that ifthe rate of human-induced climate change is not
reduced, allowing the climate to change rapidly over the next several decades, then the
U.S. will obtain some gains. For example, the U.S. will experience the following
outcomes:
• The number of inland, non-tidal wetlands will increase, and the area where floodplain wetlands form will expand.
• Over-winter mortality of many species of wildlife will decrease as cold stress is reduced and seasonal availability of food increases.
• Tree growth and forest expansion will increase, which will increase the coverage and range of forest ecosystems.
• Plant productivity will increase by 10% throughout various ecosystems.
• Migrating birds will not fly as far south, nesting earlier in the season, which will improve the odds of young birds surviving winters.
• Crops that will develop at faster rates will reduce agricultural use of irrigation water.
• Winter heating needs will decrease, reducing the seasonal cost of energy.
• Incidents of cold-related human illnesses and deaths will be reduced in some areas of the country that will experience less severe winter conditions.
• The U.S. will save 1 % of the gross domestic product (GDP) that would have been spent on emissions reduction.
• The food supply from agricultural productivity will increase, dropping prices for food products.
152
VERSION C (Hedged/Weak)
Studies suggest that ifthe rate of human-induced climate change is reduced,
slowing changes in the climate over the next several decades, then the U.S. will likely
avoid some losses. For example, the U.S. might experience the following outcomes:
• Shoreline erosion might not intensify, and coastal wetland losses will potentially avoid an increase.
• Disturbance of fish habitat could be avoided, possibly preventing threats to the survival of some cold-water species.
• Delicate and isolated ecosystems such as alpine meadows, mangroves, and tropical mountain forests might keep from declining, and some could avoid disappearing altogether.
• Forests in many regions may avoid becoming more susceptible to pests and fire with damage to productivity remaining roughly the same.
• The rate ofloss of biodiversity will probably avoid an increase, as some plant and animal species may not have to adapt to rapid changes.
• Water availability might not decrease, potentially avoiding irrigation management complications.
• Demands for air conditioning might not increase, possibly avoiding increases in the cost of energy during the summer.
• Diseases that are water and animal borne may not intensify in the summer, which could avoid increases in incidents of human .illness and death in some areas.
• The U.S. could avoid an economic loss of about 1% of the gross domestic product (GDP) from climate-related damages.
• The risk of flash floods and soil erosion will probably not increase, which may avoid a decrease in agricultural productivity.
153
Studies also suggest that ifthe rate of human-induced climate change is reduced,
slowing changes in the climate over the next several decades, then the U.S. will likely
forgo or do without some gains. For example, the U.S. might experience the following
outcomes:
• The number of inland, non-tidal wetlands will potentially not increase, and the area where floodplain wetlands form might forgo expansion.
• Over-winter mortality of many species of wildlife could forgo a decrease as cold stress remains the same and seasonal availability of food possibly does not increase.
• Tree growth and forest expansion might not increase, which could forgo an increase in the coverage and range of forest ecosystems.
• Plant productivity may forgo an increase, remaining roughly the same throughout various ecosystems.
• Migrating birds may fly just as far south, not nesting any earlier in the season, which will probably forgo any improvement in the odds of young birds surviving winters.
• Crops that will potentially not develop at faster rates might forgo reductions in agricultural use of irrigation water.
• Winter heating needs might not decrease, possibly forgoing a reduction in the seasonal cost of energy.
• Incidents of cold-related human illnesses and deaths could forgo reductions in some areas of the country that may continue to experience severe winter conditions.
• The U.S. could forgo saving about 1 % of the gross domestic product (GDP) that will be spent on emissions reduction.
• The food supply from agricultural productivity may not increase, probably forgoing a drop in prices for food products.
154
VERSION D (Not Hedged/Weak)
Studies claim that ifthe rate of human-induced climate change is reduced,
slowing changes in the climate over the next several decades, then the U.S. will avoid
some losses. For example, the U.S. will experience the following outcomes:
• Shoreline erosion will not intensify, and coastal wetland losses will avoid an increase.
• Disturbance of fish habitat will be avoided, preventing threats to the survival of some cold-water species.
• Delicate and isolated ecosystems such as alpine meadows, mangroves, and tropical mountain forests will keep from declining, and some will avoid disappearing altogether.
• Forests in many regions will avoid becoming more susceptible to pests and fire, with damage to productivity remaining the same.
• The rate ofloss of biodiversity will avoid an increase, as some plant and animal species will not have to adapt to rapid changes.
• Water availability will not decrease, avoiding irrigation management complications.
• Demands for air conditioning will not increase, avoiding increases in the cost of energy during the summer.
• Diseases that are water and animal borne will not intensify in the summer, which will avoid increases in incidents of human illness and death in some areas.
• The U.S. will avoid an economic loss of 1 % of the gross domestic product (GDP) from climate-related damages.
• The risk of flash floods and soil erosion will not increase, which will avoid a decrease in agricultural productivity.
155
Studies also claim that ifthe rate of human-induced climate change is reduced,
slowing changes in the climate over the next several decades, then the U.S. will forgo or
do without some gains. For example, the U.S. will experience the following outcomes:
• The number of inland, non-tidal wetlands will not increase, and the area where floodplain wetlands form will forgo expansion.
• Over-winter mortality of many species of wildlife will forgo a decrease as cold stress remains the same and seasonal availability of food does not increase.
• Tree growth and forest expansion will not increase, which will forgo an increase in the coverage and range of forest ecosystems.
• Plant productivity will forgo an increase, remaining the same throughout various ecosystems.
• Migrating birds will fly just as far south, not nesting any earlier in the season, which will forgo any improvement in the odds of young birds surviving winters.
• Crops that will not develop at faster rates will forgo reductions in agricultural use of irrigation water.
• Winter heating needs will not decrease, forgoing a reduction in the seasonal cost of energy.
• fucidents of cold-related human illnesses and deaths will forgo reductions in some areas of the country that will continue to experience severe winter conditions.
• The U.S. "'.ill forgo saving 1 % of the gross domestic product (GDP) that will be spent on emissions reduction.
• The food supply from agricultural productivity will not increase, forgoing a drop in prices for food products.
156
I
II
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