THE EFFECT OF MESSAGE FRAMING AND PERCEIVED ACTION RISK ON
YOUNG WOMEN’S ATTITUDES TOWARD AND INTENTIONS TO GET
THE HUMAN PAPILLOMAVIRUS (HPV) VACCINE
Jessica C. Russell
B.A., California State University, Sacramento, 2006
THESIS
Submitted in partial satisfaction of
the requirements for the degree of
MASTER OF ARTS
in
COMMUNICATION STUDIES
at
CALIFORNIA STATE UNIVERSITY, SACRAMENTO
FALL
2009
© 2009
Jessica C. Russell
ALL RIGHTS RESERVED
ii
THE EFFECT OF MESSAGE FRAMING AND PERCEIVED ACTION RISK ON
YOUNG WOMEN’S ATTITUDES TOWARD AND INTENTIONS TO GET
THE HUMAN PAPILLOMAVIRUS (HPV) VACCINE
A Thesis
by
Jessica C. Russell
Approved by:
__________________________________, Committee Chair
Dr. James K. Ah Yun
__________________________________, Second Reader
Dr. Lisa L. Massi Lindsey
__________________________________, Third Reader
Dr. Carmen Stitt
__________________________________, Fourth Reader
Dr. Elaine Gale
Date:_____________________________
iii
Student: Jessica C. Russell
I certify that this student has met the requirements for format contained in the University
format manual, and that this thesis is suitable for shelving in the Library and credit is to
be awarded for the thesis.
_______________________________________________
Dr. Mark Williams, Graduate Coordinator
Department of Communication Studies
iv
__________________
Date
Abstract
of
THE EFFECT OF MESSAGE FRAMING AND PERCEIVED ACTION RISK ON
YOUNG WOMEN’S ATTITUDES TOWARD AND INTENTIONS TO GET
THE HUMAN PAPILLOMAVIRUS (HPV) VACCINE
by
Jessica C. Russell
The human papillomavirus (HPV) is the most prevalent sexually transmitted
disease (STD) in the United States. In June of 2006, a vaccine aimed to protect young
women against major strands of the HPV virus was licensed. Although the vaccine is
widely available, an insufficient number of young women have taken the necessary steps
to get the vaccine so that they might protect their health. One strategy to increase the
number of young women getting the vaccine is to engage in a persuasive message
campaign. Message framing is one persuasive strategy that health communication
scholars have found to be effective in positively changing health behavior attitudes and
intentions. The current thesis examined the potential interaction effect between message
framing (loss vs. gain) and perceived action risk (low vs. high), on attitudes towards and
behavioral intentions to get the HPV vaccine.
This study utilized a 2x2 factorial experimental design in which message framing
(loss vs. gain) and perceived action risk (low vs. high), were manipulated. In addition to
the four experimental conditions, a no message control group was utilized as a baseline
comparison. Participants consisted of 475 female students, of which 373 fulfilled the
screening requirements and were included in the main experiment.
These data indicate no interaction effect between message framing and perceived
action risk on attitudes towards and behavioral intentions to get the HPV vaccine.
Furthermore, no main effect for message framing was revealed. However, a main effect
for perceived action risk was found with each of the outcome variables. Limitations and
implications of these findings are discussed.
__________________________________, Committee Chair
Dr. James K. Ah Yun
____________________
Date
v
ACKNOWLEDGEMENTS
This thesis would not have been possible without the support of many individuals.
To Nana and Daddad, you taught me the importance and value of an education from a
young age. I am so appreciative of all your support in making these dreams come true. I
hope you can see the manifestation of your efforts from heaven. I love you both.
To Kimo, as promised, I dedicate this thesis to you, the giver of light and all that is good.
Without you there is only darkness. I am so grateful to have you in my life as a mentor
and a friend. Thank you for everything. Here is to the next step. You are not off the hook
yet.
To Lisa, thank you for being absolutely incredible. No big deal. I hope to follow in your
footsteps, or at the very least, not ruin your reputation at MSU. Let’s face it; Kimo’s
reputation is already shot out. Go green, go white.
To Carmen and Elaine, thank you for your support throughout this process and the lack of
“bus” attacks.
To Jarrett, you will always be hakuna matata in my eyes. Thank you for being there to
emphasize what is really important in life. While we are the same in the face, I have so
much to learn from you. You are the greatest brother and best friend I could ever ask for.
To Dad, you are an incredible human being. Your positive “Yoda” perspectives on life
are inspirational. Thank you for all your support during this process and always.
To Mom, although done in unconventional ways, you have made me the person I am
today. For this, I am grateful. Without you, I would not have the drive to have come this
far. Two degrees down, one to go!
To Ali, you are the greatest cheerleader. Thanks for providing ample amounts of sarcastic
and comedic relief. Hold the California fort down while I am gone.
vi
TABLE OF CONTENTS
Page
Acknowledgements ............................................................................................................ vi
List of Tables ...................................................................................................................... x
Chapter
1. INTRODUCTION ........................................................................................................ 1
HPV and Cervical Cancer ....................................................................................... 1
HPV Prevention ...................................................................................................... 2
HPV Vaccine Recommendations ............................................................................ 3
Key Audience.......................................................................................................... 3
Knowledge and Awareness ..................................................................................... 4
The Role of Communication ................................................................................... 5
Message Framing .................................................................................................... 5
Prospect Theory ...................................................................................................... 6
Current Study .......................................................................................................... 7
2. LITERATURE REVIEW ............................................................................................. 8
3. METHOD ................................................................................................................... 21
Induction Check .................................................................................................... 21
Participants ................................................................................................ 21
vii
Procedure .................................................................................................. 22
Independent Variables .............................................................................. 22
Measures ................................................................................................... 24
Check for Potential Confounding Variables ............................................. 24
Data Analysis ............................................................................................ 25
Main Experiment .................................................................................................. 28
Participants ................................................................................................ 28
Design ....................................................................................................... 29
Procedure .................................................................................................. 29
Instrumentation ......................................................................................... 30
Measures ................................................................................................... 30
Additional Measures of Risk .................................................................... 31
Confound Check ....................................................................................... 33
Outcome Measures.................................................................................... 34
4. RESULTS ................................................................................................................... 37
5. DISCUSSION ............................................................................................................. 41
Limitations ............................................................................................................ 45
Directions for Future Research ............................................................................. 45
viii
Appendices ........................................................................................................................ 48
Appendix A. Scenarios ..................................................................................................... 49
Appendix B. Framing Items .............................................................................................. 51
Appendix C. Perceived Action Risk Items ....................................................................... 52
Appendix D. Message Clarity Items ................................................................................. 53
Appendix E. Message Processing Effort Items................................................................. 54
Appendix F. HPV Perceived Susceptibility Items ............................................................ 55
Appendix G. Cervical Cancer Perceived Susceptibility Items ......................................... 56
Appendix H. Attitude Items .............................................................................................. 57
Appendix I. Behavioral Intention Items............................................................................ 58
References ......................................................................................................................... 63
ix
LIST OF TABLES
Page
1.
Table 1 Perceived Action Risk by Frame on Attitudes......................................... 59
2.
Table 2 Perceived Action Risk by Frame on Behavioral Intention ...................... 60
3.
Table 3 Perceived Action Risk by Frame on Perceived Susceptibility to HPV ... 61
4.
Table 4 Perceived Action Risk by Frame on Perceived Susceptibility to Cervical
Cancer ................................................................................................................... 62
x
1
Chapter 1
INTRODUCTION
The human papillomavirus (HPV) is the most prevalent sexually transmitted
disease (STD) in the United States. It is estimated that 20 million Americans are infected
and an additional 6.2 million individuals become infected annually (Weinstock, Berman,
& Cates, 2004). Spread through skin-to-skin contact during sexual activity, it is
estimated that more than 50 percent of sexually active individuals will contract HPV at
some point during their lives, though most will never be aware of it (Koutsky, 1997).
HPV and Cervical Cancer
Over 100 types of HPV exist, 40 of which infect the genital area (Koutsky &
Kiviat, 1999). Genital HPV types are categorized by risk level. The majority of these
infections has no symptoms and goes away without clinical treatment (Friedman &
Shepeard, 2006; Koutsky, 1997). However, low-risk types of infections (e.g., types 6 and
11) can cause nonmalignant or low-grade cervical cell changes and genital warts. Highrisk HPV types (e.g., 16 and 18) can cause low-grade and high-grade cervical cell
abnormalities that may serve as precursors to cervical cancer and other anogenital cancers
(Gerberding, 2004; Markowitz, Dunne, Saraiva, Lawson, Chesson, & Unger, 2007).
High-risk HPV types are present in 99.7% of cervical cancers worldwide (Walboomers et
al., 1999). Of these cervical cancer cases, 70% are caused by HPV types 16 and 18
(Bosch & Sanjose, 2003).
Cervical cancer mortality rates in the United States are in decline due to
widespread Papanicolaou (Pap) testing (Helms & Melnikow, 1999). Despite the
2
downward trend, in 2007, over 11,000 new cases of cervical cancer were diagnosed and
estimates indicate that nearly 4,000 of these women will die annually from cervical
cancer (Markowitz et al., 2007). Although, Pap testing has contributed to the decline in
the cervical cancer mortality rate, even under regular optimal use, the Pap test only
detects 90% of cervical cancers. Additionally, of women in the United States, 40% do
not engage in regular screening (Hoover, Carfioli, & Moench, 2000).
The HPV progression into cervical cancer is a slow process, at times taking over
20 years from the time of initial infection. Most HPV infections occur during the teenage
years or early twenties, whereas cervical cancer is more common among women over the
age of 35 (Hoover, Carfioli, & Moench, 2000). Cervical cancer is the most serious
outcome of HPV infection, yet is highly preventable with routine screening and followup (Gerberding, 2004).
HPV Prevention
HPV does not have a cure, only treatment for abnormalities that result from HPV,
thus preventive measures are paramount. The only way to prevent HPV is abstinence
from sexual activity. Sexually active individuals can use condoms to lower the risk of
getting HPV. However, because HPV is spread through skin to skin contact instead of
bodily fluids, any area not covered by a condom is unprotected (Koutsky, 1997; Manhart
& Koutsky, 2002). Additionally, due to the subclinical and asymptomatic nature of many
HPV types, transmission is not abated by abstaining from sexual activity during active
breakouts (Hoover, Carfioli, & Moench, 2000).
3
One strategy to reduce the spread of HPV, and thus prevent cervical cancer, is to
increase the number of individuals who get vaccinated. In June 2006, an HPV vaccine
was licensed for women between the ages of nine and 26. It is delivered in a series of
three doses over a period of six months. The vaccine protects against four major types of
HPV (6, 11, 16, and 18). Types 16 and 18 are the cause of 70% of cervical cancers and
types 6 and 11 are the cause of 90% of genital warts (Markowitz et al., 2007).
HPV Vaccine Recommendations
Due to the prevalence of HPV, health professionals recommend getting the
vaccine prior to becoming sexually active. Although the suggested age for vaccination is
between 11 and 12 years of age, catch-up vaccines are available through the age of 26.
Individuals that receive the vaccine prior to initial sexual activity are inoculated against
the four major types of HPV. Individuals that receive the vaccine after becoming
sexually active will only be protected from the types of HPV they have not yet contracted
(Markowitz et al., 2007).
Key Audience
Among those at highest risk for HPV infection are sexually active females under
the age of 25 (Ho, Bierman, Beardsley, Chang, & Burk, 1998; Koutsky, 1997; Myers,
McCrory, Nanda, Bastian, & Matchar, 2000; Trottier & Franco, 2006). Annually there is
an estimated 6.2 million new cases of HPV infection reported, of which 74% occur
among those between the ages of 15-24 (Markowitz et al., 2007). HPV is the most
common sexually transmitted disease with estimated prevalence rates ranging between
19-90% (Ho et al. 1998; Ley, Bauer, Reingold, Schiffman, Chambers, Tashiro, & Manos,
4
1991; Markowitz et al., 2007; Myers et al., 2000; Peyton, Gravitt, Hunt, Hundley, Zhao,
Apple, & Wheeler, 2001) with infection rates decreasing markedly with increasing age
(Garner, 2003). Ho et al., (1998) conducted a longitudinal study that examined
prevalence rates of HPV. Although only 26% of the participants were reported HPV
positive at the baseline, 60% were infected at some time during the three year period of
the study. This study highlights the difficulty of determining an exact prevalence rate
due to the subclinical nature of many cases of HPV, potentially causing prevalence to be
underestimated. Barriers in addressing HPV extend beyond the difficulty in identifying
prevalence rates.
Knowledge and Awareness
Despite the prevalence and complications associated with HPV, several studies
have demonstrated significant knowledge deficits about HPV (Holcomb, Baily,
Crawford, & Ruffin, 2004; Ramirez, Ramos, Clayton, Kanowitz, & Moscicki, 1997;
Waller, McCaffery, Forrest, Szarewski, Cadman, & Wardle, 2003; Yacobi, Tennant,
Ferrante, Naazneen, & Roetzheim, 1999). In one study, Ramirez et al. (1997) found that
28% of survey respondents had never heard of HPV. Among respondents that had heard
of HPV, 73% did not know that HPV could be asymptomatic, 56% were unaware of the
link between HPV and cervical cancer, and 53% did not know HPV was associated with
Pap abnormalities (Ramirez et al., 1997). Similarly, a study conducted by Yacobi et al.
(1999), found that 38% of college student respondents had never heard of HPV prior to
the study. Among those that had been diagnosed with HPV, 62% had no existing
knowledge of HPV prior to diagnosis. When Yacobi et al. assessed how knowledge of
5
HPV compared with other sexually transmitted diseases (STDs), they found that of the
seven STD’s examined, HPV was the disease that participants indicated they knew the
least about.
The Role of Communication
Cervical cancer is largely a preventable disease (Markowitz et al., 2007). With
health care screening advances and the new HPV vaccine, there is the potential to
drastically reduce the incidence of HPV, and thus greatly reduce the risk of cervical
cancer. HPV awareness is increasingly important with the availability of preventable
measures. Deficits exist when high prevalence rates are paired with low levels of HPV
understanding. Given the need to explore ways to get women vaccinated, communication
scholars should be interested in methods to increase the number of women who get the
HPV vaccine. One persuasive strategy that could be used is message framing.
Message Framing
Framing is a persuasive approach that is used to influence individuals to engage in
attitude and behavioral change. Messages can be framed in ways that emphasize either
the positive or negative outcomes of engaging or failing to engage in a given behavior
(Rothman & Salovey, 1997). The framing of health information can call attention to the
benefits of engaging in a behavior (i.e., the potential gains) or the costs of not taking
action (i.e., the potential losses). Health messages using gain-framing emphasize positive
outcomes that are possible as a result of the target behavior. In contrast, health messages
using loss-framing emphasize the detriments of not taking action (e.g. Meyerowitz &
Chaiken, 1987; Rothman & Salovey, 1997; Rothman, Bartels, Wlaschin, & Salovey,
6
2006). For example, a campaign on exercising using gain-framed messages might
emphasize the health benefits afforded by exercising such as increased energy,
heightened mobility, and a more fit physique. In contrast, a campaign on exercising
using loss-framing messages might emphasize the health losses of not exercising such as
decreased energy, loss of mobility, and a less fit physique.
Prospect Theory
Prospect theory (Kahneman & Tversky, 1979; Kahneman & Tversky, 1981)
provides a framework that addresses the conditions under which gain- or loss-frame
messages are most effective in garnering behavior change (Rothman et al., 2006).
Framing effectiveness is dependent upon the type of behavior targeted and individuals’
perceived risks of engaging in the proposed behavior (Rothman & Salovey, 1997).
Health communication research surrounding perceived risk has focused on
different types of risk. One type of risk focuses on susceptibility, or the belief that one is
vulnerable to a disease (Rimal & Real, 2003). Susceptibility concerns the risk that one
might fall prey to an illness or disease based on factors such as behavior, lifestyle, and
family history. Another form of risk commonly examined, especially with regard to
message framing research, is detection risk. This form of risk concerns recommended
disease detection behaviors such as colonoscopies, mammograms, or HIV testing. By
engaging in these behaviors, individuals face the risk of discovering they have an illness
or disease. A final type of risk is action risk, which is the risk that a behavioral change to
minimize a threat might have undesirable side effects. For example, a person might have
cancer and subsequently chooses to undergo chemotherapy treatment. In this example,
7
action risk is the degree to which a person believes that the chemotherapy will negatively
affect their health.
Current Study
Following the framing postulate of prospect theory, loss-framed messages are
preferred when advocating a detection behavior and gain-framed messages are preferred
when the target behavior is preventative in nature due to the varying levels of perceived
risk (Rothman & Salovey, 1997; Rothman et al., 2006). The current study applies gainand loss-framed messages and perceived action risk to the target behavior of HPV
vaccination. It is predicted that message framing interacts with perceived action risk to
engaging in the desired behavior to impact an individual’s attitudes and behavioral
intentions.
The next chapter provides an in-depth discussion of prospect theory, message
framing, important message outcome variables, and presents the formal research
hypotheses tested in this study.
8
Chapter 2
LITERATURE REVIEW
The theoretical foundation of message framing was drawn from prospect theory
(Kahneman & Tversky, 1979; Kahneman & Tversky, 1981). Originally used to explain
decision making under risk, prospect theory posits that individuals encode information
regarding potentially risky decisions in terms of potential gains or losses in comparison to
a neutral reference point such as current wealth or health status. According to prospect
theory, individuals react differently to messages depending on how these messages are
framed (Tversky & Kahneman, 1981). Different presentations of factually equivalent
information may change the location of the reference point, ultimately, influencing
people’s behavioral intentions, especially with regard to perceived risk (Kahneman &
Tversky, 1979). For example, consider a situation in which a person has a reference
point for their current health. They perceive their physical mobility to be at a certain
state. A loss-framed message would suggest that the individual would lose mobility in
comparison to their current reference point, whereas, a gain-framed messages would
emphasize that the individual would acquire mobility in comparison to their current
health state. It does not matter that individuals may have varying reference points, what
matters is that a loss-framed message emphasizes a detraction from a current reference
point while a gain-framed message emphasizes the ability to improve upon a reference
point.
Prospect theory proposes that individuals are loss-sensitive. Consistent with this
idea, gain-framed messages encourage individuals to be risk-aversive and loss-framed
9
messages encourage individuals to be more risk-seeking. When given a prospect of
avoiding a loss, individuals are likely to take it, even if their decision runs them the risk
of an even greater loss. Conversely, if given a prospect of a potential gain, individuals
are less likely to engage for fear of losing what they have already acquired.
In a seminal study illustrating the effects of framing, Tversky and Kahneman
(1981) presented participants with a scenario and factually equivalent choices framed in
different ways. The scenario described a situation in which 600 lives were at stake due to
a disease outbreak. Participants were asked to pick a proposed program to implement as
a response. First, participants were given the choice between an option framed in terms
of the number of lives that would be saved (i.e. “If program A is adopted, 200 people will
be saved”) or an option with less certainty regarding the number of lives saved (i.e. “If
Program B is adopted, there is 1/3 probability that 600 people will be saved, and 2/3
probability that no people will be saved”). Of the two program choices with potential
gains, the participants preferred Program A, the more certain, risk averse option, even
though the risky alternative had a potential of saving a greater number of lives. The
second group of participants was provided the same scenario and two differently
formulated proposed programs to choose from. The program options were framed in
terms of lives lost (i.e. “If Program C is adopted 400 people will die. If Program D is
adopted there is a 1/3 probability that nobody will die, and a 2/3 probability that 600
people will die”). Of the two choices with potential loses, participants were more willing
to choose Program D, the more risk-taking option to the program option with the certain
number of lives lost. Even though the loss and gain program options provided the
10
equivalent disease combatant (i.e. offered to save 200 lives and lose 400 lives vs. the
probability of either saving or losing all 600 lives), participants decisions varied based on
whether the framing emphasized lives saved or lives lost. The responses illustrated by
the study are consistent with prospect theory in that gain-framing encourages individuals
to be risk averse, while loss-framing promotes more risk-seeking behaviors.
Prospect theory has also been applied to the study of health communication. With
regard to health, individuals are faced with a variety of decisions. There are often costs
and benefits to weigh in on whether or not to engage in certain behaviors. How the
information regarding these health decisions and their potential outcomes are framed, can
impact behavioral uptake (Rothman & Salovey, 1997). Prospect theory proposes that the
framing of health information can either emphasize the benefits of taking action (i.e., the
potential gains) or the costs of not taking action (i.e., the potential losses). For example,
Umprey (2003) examined the effects of message framing on performing testicular selfexaminations (TSE). The gain-framed message explained:
By doing a monthly TSE, you will know what your normal, healthy testicle feels
like and will be able to recognize any changes. Research clearly shows that men
who do the TSE increase their chances of detecting a lump early, when it is
smaller and has not spread. Ultimately by engaging in TSE you may be
increasing your chances of surviving cancer. The earlier the detection the more
likely you will have a favorable prognosis and successful treatment. This
knowledge you will gain by doing TSE can only help you. Remember, early
detection can save your life (p. 105).
11
Conversely, the loss-framed message highlighted:
By not doing a monthly TSE, you will not know what your normal, healthy
testicle feels like and will be unaware of any changes. Research clearly shows
that men who do not perform the TSE increase their chances of discovering a
lump when it’s too late, when it is larger and may have spread to other parts of the
body. Ultimately by not engaging in TSE you may be increasing your chances of
dying from cancer. The later the detection the more likely you will have an
unfavorable prognosis and unsuccessful treatment. This lack of knowledge can
only hurt you. Remember, late detection can end your life prematurely (p. 105).
The gain-framed message emphasized a series of statements describing the health
benefits afforded by TSE, whereas, the loss-framed message emphasized the potential
costs of not engaging in TSE.
Early message framing research revealed inconsistent reporting of the benefits of
one frame over another (Rothman & Salovey, 1997). Increased target health behaviors
had been observed after both exposures to loss- and gain-framed messages. Although
some research maintains that loss-framed messages are better, other research suggests
that gain-framed messages are better. For example, Meyerowitz and Chaiken (1987)
assessed loss- and gain-framed message impact on breast self-examinations (BSE).
Participants were randomly assigned to brochures regarding BSE framed in loss, gain, or
neutral language. The gain-framed brochure emphasized the benefits of performing BSE
(i.e. increased chance of finding a tumor in the early, more treatable stages of disease)
while the loss-framed brochure focused on the costs of not performing BSE (i.e.
12
decreased chance of finding a tumor in the early, more treatable stages of disease). In a
four month follow-up, participants who read a loss-framed message manifested more
positive BSE attitudes, behaviors, and intentions in comparison to those who read a gainframed message (Meyerowitz & Chaiken, 1987).
Alternatively, research has also identified cases in which gain-framed messages
are more effective. For instance, a loss- and gain-framed message intervention with the
preventative health behavior of sunscreen application was conducted (Detweiler, Bedell,
Salovey, Pronin, & Rothman, 1999). Participants at a beach were given either a loss- or
gain-framed message concerning the use of sunscreen and then asked to report their
attitudes and behavioral intention to use sunscreen in a questionnaire. After participants
completed the questionnaire they were given a coupon to redeem for a free sample of
sunscreen later that day. Participants in the loss- and gain-framed conditions were
subsequently compared. Results revealed that participants exposed to the gain-framed
message were more likely to request and repeatedly apply sunscreen than those exposed
to the loss-framed message while at the beach (Detweiler et al., 1999).
In attempt to uncover a unifying explanation that showed consistency in the
framing research, Rothman and Salovey (1997) conducted an analysis of prospect
theory’s application to health messages. They found that the effectiveness of the loss- or
gain-frame message hinges on perceptions of risk and is dependent upon whether the
recommended behavior is prevention or detection. Detection behaviors are performed to
discover early signs of a disease so that treatment effectiveness and prognosis may be
optimized. Engaging in health detection behavior poses potential risk for unpleasant
13
outcomes (Rothman, Martino, Bedell, Detweiler, & Salovey, 1999). For example,
individuals engaging in mammogram screenings pose detection risks of identifying an
abnormality that could be cancerous. Although detection behaviors often provide longterm health benefits, the short-term potential of informing an individual that they have a
health problem categorizes them as risky (e.g. Meyerowitz & Chaiken, 1987).
Alternatively, prevention behaviors are performed to preclude the onset of a health
problem. They aid in affording the opportunity to maintain healthy lifestyles and
minimize the risks of illness and include practices such as engaging in physical exercise
and eating a balanced diet. Prevention behaviors are associated with low levels of risk,
where oftentimes the only perceived risk is not taking action (Rothman et al., 1999).
Given that health behaviors vary on their level of perceived risk, it follows that
the type of framing would differ depending upon the type of behavior promoted. For
example, it is likely that individuals have a much lower perceived risk of protection
behaviors such as putting on sunscreen, wearing seatbelts, and taking multivitamins in
comparison to detection behaviors such as breast and testicular self-examinations,
colonoscopies, and blood tests, behaviors in both detection risks and action risks might
exist. The important distinction between the functions of detection and prevention
behaviors is the level of perceived uncertainty and risk involved (Detweiler et al., 1999).
As prospect theory suggests, risk avoidance options are preferred in the domain
of gains and more risky options are preferred in the domain of losses, it follows that
prevention behaviors, those with a relatively certain outcomes and low levels of risk,
would be more persuasive when accompanied by a gain-framed message. Conversely,
14
detection behaviors, those associated with higher perceived risk or uncertainty of
outcomes, would be more persuasive when accompanied by a loss-framed message
(Rothman & Salovey, 1997; Rothman et al., 2006). Therefore, the distinct findings of the
Meyerowitz and Chaiken (1987) study and the Detweiler et al. (1999) are explained by
the different health behavior functions. Meyerowitz and Chaiken (1987) focused on
breast self-examinations (BSE). Results are consistent with Rothman and Salovey’s
(1997) addition to prospect theory in incorporating health behavior outcomes, in that BSE
is a detection (i.e. more risky) behavior and therefore, loss-framed messages were more
effective. Conversely, the Detweiler et al. (1999) study examined intentions to use
sunscreen. Given the preventative, and therefore, less risky nature of sunscreen
application, the effectiveness of the gain-frame messages was consistent with prospect
theory.
Additional research surrounding framing and detection behaviors have focused
primarily on cancer screening including mammography (Banks et al., 1995), breast selfexaminations (Meyerowitz & Chaiken, 1987), testicular self-examinations (Umphrey,
2003), and colorectal cancer screening (Myers et al., 1991), but have also included
behaviors such as blood-cholesterol screenings (Maheswaran & Myers-Levy, 1990), and
HIV testing (Apanovitch, McCarthy, & Salovey, 2003). Research concerning message
framing and preventative health include behaviors such as wearing sunscreen (Detweiler
et al., 1999; Rothman, Salovey, Antone, Keough, & Martin, 1993), engaging in physical
exercise (Jones, Sinclair, & Courneya, 2003), and safe driving practices (Millar & Millar,
2000).
15
Some health behaviors might be perceived as having both a prevention and
detection purpose (Rivers, Pizarro, Schneider, Pizarro, & Salovey, 2005; Rothman et al.,
1999). For example, Pap tests may be perceived as either preventing the possibility of
developing cervical cancer or detecting abnormal cervical cells. In this case, the
prevention and detection functions are interrelated. In order to prevent the development
of cervical cancer regular check-ups to detect any abnormal cells are emphasized. Rivers
et al. (2005) tested the effectiveness of loss- and gain-framing on a video emphasizing
different function of the Pap test. Results indicated that when the detection function of
Pap tests was highlighted, loss-framed messages were most effective, whereas when the
prevention function of Pap tests was highlighted, the gain-framed message was most
persuasive (Rivers et al., 2005). Similar results were found in another study focusing on
mouthwash and gum disease. Participants were either informed of a mouthwash that
prevents gum disease (i.e. inhibits plaque from developing) or of mouthwash which
functions to detect gum disease (i.e. indicates where plaque has accumulated and
indicates the areas of risk). Gain-framed messages were more persuasive with the
preventative mouthwash, whereas the loss-framed messages were more persuasive with
the gum disease detecting mouthwash (Rothman et al., 1999). Results of these studies
emphasize the need to consider health behavior functions when considering the message
framing. For health behaviors that may have dual purposes, either real or perceived, it is
important to emphasize one function while framing the behavior using the matched
message (Rivers et al., 2005; Rothman et al., 1999).
16
Although the categorical taxonomy that detection and prevention behaviors aided
in clarifying the mixed results of prior studies, it has limitations (O’Keefe & Jensen,
2007). Generally speaking, when applying the framing postulate of prospect theory to
health behaviors it has been hypothesized that loss-framed messages are more effective in
persuading individuals to engage in detection behaviors associated with a degree of risk
and that gain-framed messages are more effective in persuading people to engage in
prevention behaviors associated with little risk (Rothman & Salovey, 1997). Following
this line of reasoning, all preventative behaviors are similar in their levels of perceived
risks whether it is the probability of attaining an uncertain outcome or dangers associated
with engaging in the behavior. However, perceived risk has been introduced as a
subjective perception of a behavior (Rothman et al., 2006). Given the subjective nature
of perceived risk, there may be variability in how certain prevention behaviors are
construed (O’Keefe & Jensen, 2007).
To illustrate the point of variance within the prevention category, the behaviors of
dental hygiene and flu vaccination were compared. Individuals are likely to associate
dental hygiene behaviors with a certain low action risk outcome given the absence of
uncertain outcomes or dangerous side effects, thus resulting in a condition in which gainframed messages might be most effective. Conversely, a flu-shot, although still a
preventative behavior, may be perceived as having more action risk with some uncertain
outcomes, given potential side effects (O’Keefe & Jensen, 2007). Given that the flu shot
may be perceived as a risky behavior by some, a loss-framed message might be more
effective.
17
It is suggested that the degree of perceived action risk be considered at the level
of the individual given that they might vary in their perceptions of risk, specifically
concerning associated feelings of anxiety and distress regarding the target behavior
(Latimer, Salovey, & Rothman, 2007; Rothman et al., 2006). Considering the degree to
which the individual perceives action risk in engaging in the behavior, may serve to
better understand which message framing will be maximally effective. For example, with
respect to the flu-shot example, some individuals perceive getting the flu shot as a risky
behavior to engage in, for fear that the live strains of the virus in the vaccine will make
them sick. Other individuals may perceive the only action risk associated with getting a
flu shot is not getting one. The variability of individual perceptions regarding health
behaviors is likely to impact the effectiveness of the loss- and gain-framed messages
(Latimer et al., 2007; O’Keefe & Jensen, 2007).
The idea that there is perceived risk action variability associated with health based
persuasive appeals is especially important with the examination of the HPV vaccine.
Although the HPV vaccine is a preventative behavior, and therefore, traditionally
associated with low levels of action risk, variation in individual perceptions likely exists.
Typically, individuals have a degree of comfort regarding vaccination, ranging from
welcomed acceptance to outright opposition (Poland & Jacobson, 2001; Zimet, 2005;
Zimmerman et al., 2005). With the recent FDA approval of the HPV vaccine, the longterm knowledge of potential side effects may be limited (Saul & Pollack, 2007). Despite
the protection that it provides, there may be those individuals that perceive getting the
HPV vaccine as a risky behavior. Therefore, the current study tests the interaction
18
between message framing and perceived action risk of engaging in the proposed behavior
on attitudes towards, and behavioral intentions to get, the HPV vaccine.
One of the most frequently examined outcomes with regard to health related
behavior change is attitudes. The concept of attitude plays a central role in explaining
communication phenomena, specifically concerning the effects of persuasive messages
(Kim & Hunter, 1993a). Attitudes toward the behavior refer to a person’s evaluation of
performing the behavior in question (Ajzen, 1988). It is also thought that attitudes exert a
strong influence on behavior (O’Keefe, 2002). If an individual has an overall positive
evaluation they will be more likely to engage in the behavior in comparison to an
individual that has an overall negative evaluation (Ajzen, 1991). A meta-analysis of 138
studies resulted in uniformly positive correlations between relevant attitudes and behavior
(r = .79) when methodological artifacts were eliminated. Thus, suggesting that
theoretical utility of attitudes should be a fundamental condition when trying to predict
overt behavior (Kim & Hunter, 1993a). As attitudes are thought to be predictors of
behavior it should be expected that participants with more favorable attitudes towards the
HPV vaccine will be more likely to get vaccinated. Given the argument made above to
predict a differential effect of loss- and gain-framed messages, depending on whether a
target perceives the action risk of engaging the in the behavior to be low or high, the
following hypothesis is posed:
H1: Message framing and perceived action risk will interact such that the most
positive attitudes towards the HPV vaccine will be in the gain-framed condition,
when perceived action risk of engaging in the behavior is low and the loss-framed
19
condition when perceived action risk of engaging in the behavior is high and that
these two conditions will not differ from one another. Furthermore, the gainframed, high-risk condition and the loss-framed, low-risk condition, will have the
least positive attitudes towards the HPV vaccine, and these conditions will also
not differ from each other.
In addition to attitudes, behavioral intention, which is the degree to which a target
intends to engage in a behavior, is another antecedent of behavior that is commonly
examined in persuasion research. Behavioral intention depicts the motivational factors
that influence behavior and incorporates both willingness of an individual to work hard as
well as the effort they are planning to exert in order to perform a given behavior (Ajzen,
1991). A meta-analysis of studies concerning attitudes, behavioral intention, and
behaviors showed a consistent view that behavioral intention serves as a mediator in
attitude-behavior relationships (Kim & Hunter, 1993b). Thus, understanding behavioral
intentions is crucial for predicting behavior from attitudes. As behavioral intention is
thought to be a predictor of behavior it should be expected that participants with more
favorable intentions towards the HPV vaccine will be more likely to get vaccinated.
Given the argument made above to predict a differential effect of loss- and gain-framed
messages, depending on whether a target perceives the action risk of engaging in the
behavior to be low or high, the following hypothesis is posed:
H2: Message framing and perceived action risk will interact such that the most
positive behavioral intentions towards getting the HPV vaccine will be in the
gain-framed condition, when perceived action risk of engaging in the behavior is
20
low and the loss-framed condition when perceived action risk of engaging in the
behavior is high and that these two conditions will not differ from one another.
Furthermore, the gain-framed, high-risk condition and the loss-framed, low-risk
condition, will have the least positive behavioral intentions to get the HPV
vaccine, and these conditions will also not differ from each other.
This chapter reviewed existing literature surrounding decision making under risk
and message framing. Additionally, the chapter presented explanations for the potential
interaction effect between message framing and perceived action risk on the outcome
variables attitude and behavioral intention. The following chapter presents the
methodology that was utilized in the current study.
21
Chapter 3
METHOD
The current study utilized a 2x2 factorial experimental design in which message
framing (loss vs. gain) and perceived action risk (low vs. high) was induced. The
dependent variables of attitude and behavioral intention were used. Furthermore,
perceived susceptibility for both HPV and cervical cancer was measured. The current
study occurred in two phases: an induction check followed by the main experiment. The
induction check was designed to ensure that the stimuli elicited the desired responses. In
addition to ensuring the stimuli elicited the desired responses, the induction check also
tested whether or not the stimuli induced unintended effects. Thus, perceived message
clarity and message processing effort were also measured to assess if the stimuli also
impacted these outcomes. Once adequate inductions of the key variables were
established, the main experiment tested the extent to which there was an interaction
between framing and perceived action risk on attitudes and behavioral intentions.
Induction Check
Participants.
Participants included 73 female undergraduate students enrolled in a Western
state university. Participants ranged in age from 19 to 25 years old (M = 21.07, SD =
1.02). The majority of students identified themselves as juniors (49.3%), the rest
identified as seniors (46.6%), and sophomores (4.1%). The majority of the participants
identified themselves as Caucasian (53.4%), followed by Asian (34.2%), Hispanic
22
(4.1%), and other (8.2%). The respondents used in the induction check were not part of
the main experiment.
Procedure.
Respondents were notified that participation was voluntary and were informed prior to
volunteering that all provided information was confidential. Participants were assigned
randomly to one of four written scenarios in which framing (loss vs. gain) and perceived
action risk (low vs. high) had been manipulated (see Appendix A for each scenario) and
subsequently were asked to complete a semantic-differential scale measuring framing and
perceived action risk of engaging in the behavior based on the scenario. Participants
were also asked to complete semantic differential scales concerning the potential
confound variables of message clarity and effort required to process the message.
Independent variables.
Framing. Two types of framing (loss vs. gain) were manipulated across four
written stimulus scenarios. Participants exposed to the loss-framed message read:
By not getting the HPV vaccine, you have a great deal to lose. You may lose any
protection against genital warts, which have no known cure, as well as the
potential to have children. You can also lose protection to your cervix. This loss
in protection makes you more prone to getting cervical cancer and can cost you
your life. Ultimately, not getting this vaccine can lead to significant unnecessary
damages to your health.
In contrast, participants exposed to the gain-framed message read:
23
By getting this vaccine, you have a great deal to gain. You gain protection
against genital warts as well as your reproductive health. You also gain
protection to your cervix. This protection makes you less prone to getting
cervical cancer and can save you your life. Ultimately, getting this vaccine can
benefit you by preventing unnecessary damages to your health.
Perceived action risk. Perceived action risk refers to the potential negative side
effects of engaging in the target behavior. Two levels of perceived action risk (low vs.
high) were created. Participants exposed to the low action risk message read:
The HPV vaccine is low risk. The safety of the vaccine was tested in clinical
trials before it was licensed. Side effects are very minor and include redness and
itching where the shot was given. These symptoms do not last long and go away
on their own.
Whereas, participants exposed to the high action risk message read:
Although the HPV vaccine offers protection against the contraction of cervical
cancer, the HPV vaccine poses other potentially serious risks to your health. The
safety of the vaccine has been tested; however, some of the vaccine’s reported
side effects include suicidal thoughts, appendicitis, liver damage, paralysis, and in
some cases, death. No research has been done to rule out these side effects as a
risk of getting the HPV vaccine.
24
Measures.
To ensure that the scenarios induced loss- and gain-framing and low- and highperceived action risk, participants after reading one of the four scenarios completed a
series of questions.
Framing. In order to ensure that the message framing manipulation adequately
induced the intended effect, a five-item, 7-point semantic differential scale was created to
measure perceived loss- and gain-framing (see Appendix B). The scale was scored such
that higher scores represented greater perceptions of gain-framing. Based on the
message, participants were asked to indicate the number that corresponds to the word that
best describes their perception of the message’s frame. The scale included items such as
“loss/gain” and “disadvantages/advantages.”
Perceived action risk. The perceived action risk of getting the HPV vaccine was
assessed using a five-item, 7-point semantic differential scale that was scored such that
higher scores represented greater perceptions of action risk (see Appendix C). Based on
the message, participants were asked to indicate the number that corresponds to the word
that best describes their perception of the effects associated with the vaccine. The scale
included items such as “safe/risky” and “harmless/harmful.”
Check for potential confounding variables.
An effective induction should be limited to influencing only the variable that is
intended to be induced. For this study, in addition to the stimuli inducing the intended
effect (inducing message frame and perceived action risk), it might have induced
unintended effects. Specifically, these messages may differ in perceptions of how clear
25
they are as well as how much effort is required to process them. Given these potential
differences, the outcome measures of message clarity and message processing effort were
also assessed.
Message clarity. Clarity of the message was measured using a five-item, 7-point
semantic differential scale that was scored such that higher scores represented greater
perceptions of clarity (see Appendix D). Based on the message, participants were asked
to indicate the number that corresponded to the word that best described their perception
of the clarity of the message’s content. With respect to the clarity of the message’s
content, the scale included items such as “unclear/clear” and “confusing/not confusing.”
Message processing effort. Message processing effort was measured using a fiveitem, 7-point semantic differential scale that was scored such that higher scores
represented greater perceptions of effort to process the message (see Appendix E). Based
on the message, participants were asked to indicate the number that corresponds to the
word that best described their perception of the message processing effort. The scale
included items such as “easy to read/hard to read” and “not difficult to process/difficult to
process.”
Data Analysis.
Given that specific items are specified a priori to measure only one factor,
confirmatory factor analysis was performed for all scales to test both internal consistency
and parallelism (Anderson, Gerbing, & Hunter, 1987; Hunter & Gerbing, 1982; Levine,
2005). To test the measurement model, the current study examined the magnitude of the
errors between the predicted and obtained correlations, calculated the Root Mean Square
26
error for each variable for tests of internal consistency and parallelism, and assessed
reliability analysis. Reliability was assessed using Chronbach’s alpha.
A confirmatory factor analysis (CFA) was conducted on the five message framing
items to test the hypothesis that they measured a single construct. The test for internal
consistency (RMSE = .07) and parallelism (RMSE = .05) revealed small errors. A
review of the individual errors revealed they were generally small and flat. Given the
small errors in the check for internal consistency and parallelism, none of the items were
removed. A reliability check of these items indicated that the scale was highly reliable (α
= .95). Given the factor analysis and reliability findings, these items were summed to
form a composite score (M = 4.31, SD = 1.70). As predicted, participants in the gainframed condition (M = 5.12, SD = 1.36) indicated a more gain emphasized message than
participants in the loss-framed condition (M = 3.63, SD = 1.67). The differences between
the gain- and loss-framed conditions were significant and in the expected direction, t(71)
= 4.11, p <.001, r = .44.
A CFA was conducted on the five perceived action risk items to test the
hypothesis that they measured a single construct. The test for internal consistency
(RMSE = .01) and parallelism (RMSE = .06) revealed small errors. A review of the
individual errors revealed they were generally small and flat. Given the small errors in
the check for internal consistency and parallelism, none of the items were removed. A
reliability check of these items indicated that the scale was highly reliable (α = .98).
Given the factor analysis and reliability findings, these items were summed to form a
composite score (M = 2.91, SD = 1.46). As predicted, participants in the high-perceived
27
action risk condition (M = 3.87, SD = 1.16) reported higher levels of perceived action risk
than participants in the low-perceived action risk condition (M = 1.76, SD = .82). The
differences between the high and low perceived action risk conditions were in significant
and in the expected direction, t(71) = 8.77, p <.001, r = .72.
A CFA was conducted on the five message clarity items to test the hypothesis that
they measured a single construct. The test for internal consistency (RMSE = .08)
revealed generally small errors. With regard to parallelism (RMSE = .11) there were
some large errors. A review of the individual errors revealed trends of high errors with
two items. Given the errors in the parallelism check, these two items were removed (see
Appendix D). After the two items were removed, the errors for the parallelism were
adequately small (RMSE = .06), further, the errors for the test for internal consistency
became smaller as well (RMSE <.01). A reliability check of these items indicated that
the scale was highly reliable (α = .83). Given the factor analysis and reliability findings,
the remaining items were summed to form a composite score (M = 5.89, SD = 1.17). In
measuring message clarity, the current study was attempting to identify any additional
confounding variables. The induction check revealed that with regard to framing,
perceptions of clarity did not differ across conditions, t(71) = .24, p =.81. However, with
regard to action risk, perceptions of clarity did differ significantly across conditions, t(71)
= 3.49, p =.001, r = .38. These data reveal that the action risk induction impacted
perceptions of message clarity. As such, message clarity will be measured and controlled
for in the main experiment.
28
A CFA was conducted on the five message processing effort items to test the
hypothesis that they measured a single construct. The test for internal consistency
(RMSE = .01) and parallelism (RMSE = .05) revealed small errors. A review of the
individual errors revealed they were generally small and flat. Given the small errors in
the check for internal consistency and parallelism, none of the items were removed. A
reliability check of these items indicated that the scale was highly reliable (α = .96).
Given the factor analysis and reliability findings, these items were summed to form a
composite score (M = 3.27, SD = 2.31). In measuring message processing effort the
current study was attempting to identify if differences in effort required to process the
message might serve as a confounding variable. The induction check revealed that
perceptions of effort did not differ across conditions with regard to framing [t(71) = .70, p
=.49] or risk [t (71) =.72, p =.48]. Although no significant difference was found, message
processing effort is measured in the main experiment to ensure that it is not a
confounding variable in the main experiment.
Main Experiment
Participants.
Participants included 475 female students enrolled in a Western state university.
Because the questions were concerned with women who had not received the vaccine, the
current study was only interested in people who were within the ages 18-26 and who had
not previously received the vaccine. Of the 475 initial participants, 78 indicated that they
had already received the HPV vaccine and 24 were beyond the age scope for the vaccine.
By eliminating these two groups from the study, 373 useable participants remained. The
29
final group of participants included 373 female students that ranged in age from 18 to 26
years old (M = 20.84, SD = 2.64). The majority of students identified themselves as
juniors (35.2%), the rest identified as freshman (26.9%), seniors (25.3%), sophomores
(11%), and graduate students (1.6%). Participants were of varied ethnicity, the majority
identified themselves as Caucasian (47.5%), followed by Asian (18.7%), Hispanic
(16.8%), African-American (9.3%), Native American (.5%), and other (7.1%).
Design.
The study employed a 2 (message framing: loss vs. gain) x 2 (perceived action
risk: low vs. high) experimental design to test the proposed hypotheses. Additionally, a
no-message control condition was included as a baseline comparison.
Procedure.
Respondents were notified that participation was voluntary and were informed
prior to volunteering that all provided information was confidential. Participants were
assigned randomly to one of the five conditions (see Appendix A). Participants in the noexposure control condition did not receive any stimuli and instead were directed to the
questions. After reading the messages, respondents completed Likert-type and semantic
differential items measuring their attitudes, behavioral intentions, perceived action risk,
and perceived susceptibility with regard to getting the HPV vaccine as well as cervical
cancer. Finally, participants provided demographic information including age, year in
school, and race. Furthermore, participants were asked whether or not they had received
the Gardasil (HPV) vaccine. Age and vaccine uptake were utilized as screening
30
questions. Participants that were not between the ages of 18-26 or who had already
received the HPV vaccine were dropped from the study.
Instrumentation.
The semantic differential scales used in the pilot study were also used in the main
experiment. Additional scales for the outcome variables were added in the main
experiment. All of the outcome measures were comprised of seven-point, Likert-type
items on a scale ranging from one (very strongly disagree) to seven (very strongly agree),
and were scored such that higher scores indicated greater perceptions of the construct
being measured.
Measures.
Framing. A confirmatory factor analysis (CFA) was conducted on the five
message framing items to test the hypothesis that they measured a single construct. The
test for internal consistency (RMSE = .04) and parallelism (RMSE = .03) revealed small
errors. A review of the individual errors revealed they were generally small and flat.
Given the small errors in the check for internal consistency and parallelism, none of the
items were removed. A reliability check of these items indicated that the scale was
highly reliable (α = .91). Given the factor analysis and reliability findings, these items
were summed to form a composite score (M = 4.72, SD = 1.46). As predicted,
participants in the gain-framed condition (M = 5.41, SD = 1.02) indicated a more gain
emphasized message than participants in the loss-framed condition (M = 3.95, SD =
1.49). The differences between the gain- and loss-framed conditions were significant and
in the expected direction, t(296) = 9.99, p <.001, r = .50.
31
Perceived action risk. A CFA was conducted on the five perceived action risk
items to test the hypothesis that they measured a single construct. The test for internal
consistency (RMSE = .01) and parallelism (RMSE = .03) revealed small errors. A
review of the individual errors revealed they were generally small and flat. Given the
small errors in the check for internal consistency and parallelism, none of the items were
removed. A reliability check of these items indicated that the scale was highly reliable (α
= .97). Given the factor analysis and reliability findings, these items were summed to
form a composite score (M = 3.31, SD = 1.63). As predicted, participants in the highperceived action risk condition (M = 4.37, SD = 1.26) reported higher levels of action risk
than participants in the low-perceived action risk condition (M = 2.26, SD = 1.21). The
differences between the high and low perceived action risk conditions were in significant
and in the expected direction, t(298) = 14.76, p <.001, r = .65.
Additional measures of risk.
In addition to measuring the perceived action risk of engaging in the behavior, the
current study was also interested in perceptions of susceptibility to both HPV and
cervical cancer. Traditionally in the health communication literature, risk is
conceptualized as susceptibility, or the belief that one is vulnerable to a disease (Rimal &
Real, 2003). Perceptions of susceptibility are thought to be significant predictors of selfprotective behaviors (Rimal, Flora, & Schooler, 1999; Rimal & Real, 2003). As such,
beliefs individuals have regarding susceptibility are key variables in theories of health
behavior including the health belief model (Janz & Becker, 1984; Rosenstock, 1974),
extended parallel process model (Witte, 1992), theory of planned behavior (Ajzen &
32
Fishbein, 1980; Ajzen, 1991), and the subjective expected utility theory (Edwards, 1954;
Sutton, 1982). It is thought that for an individual to take action to avoid a disease they
must feel personally susceptible to it (Rosenstock, 1974). These feelings of personal
susceptibility serve as motivators for change (Chaffee & Roser, 1986; Rimal, Flora, &
Schooler, 1999). Although the current study is interested in the perceived action risk
associated with engaging in the behavior, data concerning perceptions of susceptibility to
the adverse outcomes of HPV and cervical cancer were also collected to ensure that
participants perceived themselves to be susceptible as well as to ensure that perceptions
of susceptibility did not differ across conditions. As the messages in the current study are
advocating for vaccination uptake related to two negative health outcomes, HPV and
cervical cancer, perceptions of susceptibility were measured regarding both.
Perceived susceptibility to HPV. To measure participants’ perceived
susceptibility to HPV, items were adapted from Banks et al. (1995) perceived
susceptibility measure to reflect the human papillomavirus. The adapted scale includes
items such as “It is likely I will get HPV” and “I am highly susceptible to get HPV” as
(see Appendix F). A CFA was conducted on the three HPV susceptibility items to test the
hypothesis that they measured a single construct. The test for internal consistency
(RMSE <.01) and parallelism (RMSE = .03) revealed small errors. A review of the
individual errors revealed they were generally small and flat. Given the small errors in
the check for internal consistency and parallelism, none of the items were removed. A
reliability check of these items indicated that the scale was highly reliable (α = .91).
33
Given the factor analysis and reliability findings, these items were summed to form a
composite score (M = 2.89, SD = 1.68).
Perceived susceptibility to cervical cancer. To measure perceived susceptibility
to cervical cancer, items were adapted from Banks et al. (1995) perceived susceptibility
measure to reflect cervical cancer. The adapted scale includes items such as “It is likely I
will get cervical cancer” and “I am highly susceptible to get cervical cancer” (see
Appendix G). A CFA was conducted on the three cervical cancer susceptibility items to
test the hypothesis that they measured a single construct. The test for internal
consistency (RMSE <.01) and parallelism (RMSE = .03) revealed small errors. A review
of the individual errors revealed they were generally small and flat. Given the small
errors in the check for internal consistency and parallelism, none of the items were
removed. A reliability check of these items indicated that the scale was highly reliable (α
= .86). Given the factor analysis and reliability findings, these items were summed to
form a composite score (M = 2.92, SD = 1.48).
Confound check.
Message clarity. A CFA was conducted on the three message clarity items to test
the hypothesis that they measured a single construct. The test for internal consistency
(RMSE < .01) and parallelism (RMSE = .04) revealed small errors. A review of the
individual errors revealed they were generally small and flat. A reliability check of these
items indicated that the scale was highly reliable (α = .87). Given the factor analysis and
reliability findings, these items were summed to form a composite score (M = 5.66, SD =
1.41).
34
The induction check revealed that perceptions of clarity did not differ across
conditions with regard to risk, t(298) = .70, p =.48. However, the findings revealed that
with respect to framing (Mloss = 5.52, SD = 1.47; Mgain= 5.82, SD = 1.12), perceptions of
clarity differed across conditions, t(298) = 1.97, p =.05, r = .11. As such, message clarity
will be controlled for in the testing of the proposed hypotheses.
Message processing effort. A CFA was conducted on the five message processing
effort items to test the hypothesis that they measured a single construct. The test for
internal consistency (RMSE = .03) and parallelism (RMSE = .04) revealed small errors.
A review of the individual errors revealed they were generally small and flat. A
reliability check of these items indicated that the scale was highly reliable (α = .96).
Given the factor analysis and reliability findings, these items were summed to form a
composite score (M = 3.29, SD = 2.31).
The induction check revealed that perceptions of effort did not differ across
conditions with regard to framing [t(298) = .34, p =.73] or risk [t(298) = .23, p =.82]. As
such, message processing effort will not be controlled for in the testing of the proposed
hypotheses.
Outcome measures.
Attitudes. An eight-item scale was used to measure participants’ attitudes
regarding the HPV vaccine and included items such as “Getting the HPV vaccine will
result in positive outcomes” and “I think that getting the HPV vaccine is a good idea”(see
Appendix H). A CFA was conducted on the eight attitude items to test the hypothesis
that they measured a single construct. The test for internal consistency (RMSE = .03)
35
and parallelism (RMSE = .04) revealed small errors. A review of the individual errors
revealed they were generally small and flat. Given the small errors in the check for
internal consistency and parallelism, none of the items were removed. A reliability check
of these items indicated that the scale was highly reliable (α = .97). Given the factor
analysis and reliability findings, these items were summed to form a composite score (M
= 5.03, SD = 1.50).
Behavioral intention. A five-item scale was used to measure participants’
behavioral intention to get the HPV vaccine and included items such as “I intend to get
the HPV vaccine” and “I will likely get the HPV vaccine” (see Appendix I). A CFA was
conducted on the five behavioral intention items to test the hypothesis that they measured
a single construct. The test for internal consistency (RMSE = .01) and parallelism
(RMSE = .03) revealed small errors. A review of the individual errors revealed they
were generally small and flat. Given the small errors in the check for internal consistency
and parallelism, none of the items were removed. A reliability check of these items
indicated that the scale was highly reliable (α = .95). Given the factor analysis and
reliability findings, these items were summed to form a composite score (M = 3.99, SD =
2.24).
Hypothesis testing. A priori linear contrast analyses will be utilized in testing the
above hypotheses while controlling for perceived message clarity.
This chapter explained the methodology that was utilized for this study.
Specifically, this chapter discussed the participants, procedure, induction checks, and
36
instrumentation for both the induction check and main experiment. In the next chapter,
the results of the data analyses are presented.
37
Chapter 4
RESULTS
Hypothesis one predicted message framing and perceived action risk would
interact such that the most positive attitudes towards the HPV vaccine would be in both
the gain-framed condition, when the perceived action risk of engaging in the behavior
was low, and the loss-framed condition when the perceived action risk of engaging in the
behavior was high and that these two conditions would not differ from one another. The
remaining two conditions, the gain-framed, high-risk condition and the loss-framed, lowrisk condition, would have the least positive attitudes towards and behavioral intention to
get the HPV vaccine, and these conditions would not differ from each other. To test this
hypothesis, the low-action risk, gain-framed condition and the high-action risk, lossframed condition were each given contrast coefficients of +1 and the high-action risk,
gain-framed and the low-action risk, loss-framed conditions were each given contrast
coefficients of -1. Results of this a priori contrast indicated that participants in the lowaction risk, loss-framed condition, reported similar attitudes to participants in the lowaction risk, gain-framed condition, who exhibited more positive attitudes towards the
HPV vaccine than participants in the high-action risk, loss-framed condition, whose
attitudes did not differ from participants in the high-action risk, gain-framed condition,
t(296) = .28, p = .78 (see Table 1 for descriptive statistics and confidence intervals).
An examination of the means suggested that the results were driven by a main
effect (for perceived action risk) and not an interaction effect as predicted. Thus, a post
hoc contrast analysis was conducted. The low-action risk, loss-framed condition and the
38
low-action risk, gain-framed condition were each given contrast coefficients of +1 and
the high-action risk, loss-framed and the high-action risk, gain-framed conditions were
each given contrast coefficients of -1. These data were consistent with the presence of a
main effect for perceived action risk on attitudes, t(296) = 5.35, p < .001, r = .30.
Hypothesis two predicted that message framing and perceived action risk would
interact such that the most positive behavioral intentions towards getting the HPV
vaccine would be in both the gain-framed condition, when perceived action risk of
engaging in the behavior was low and the loss-framed condition when perceived action
risk of engaging in the behavior was high and that these two conditions would not differ
from one another. The remaining two conditions, the gain-framed, high-action risk
condition and the loss-framed, low-action risk condition, would have the least positive
behavioral intentions to get the HPV vaccine, and these conditions would also not differ
from each other. To test this hypothesis, the low-action risk, gain-framed condition and
the high-action risk, loss-framed condition were each given contrast coefficients of +1
and the high-action risk, gain-framed and the low-action risk, loss-framed conditions
were each given contrast coefficients of -1. Results of this a priori contrast indicated that
participants in the low-action risk, loss-framed condition reported similar behavioral
intention to participants in the low-action risk, gain-framed condition, whose behavioral
intention also did not differ from participants in the high-action risk, loss-framed
condition, or those in the high-action risk, gain-framed condition, t(296) = .56, p = .58
(see Table 2 for descriptive statistics and confidence intervals).
39
An examination of the means suggested that the results were driven by a main
effect (for perceived action risk) and not an interaction effect as predicted. Thus, a post
hoc contrast analysis was conducted. The low-action risk, loss-framed condition and the
low-action risk, gain-framed condition were each given contrast coefficients of +1 and
the high-action risk, loss-framed and the high-action risk, gain-framed conditions were
each given contrast coefficients of -1. These data were consistent with the presence of a
main effect for perceived action risk on behavioral intention to get the HPV vaccine,
t(296) = 3.79, p < .001, r = .22.
In addition to the previously reported data, a no message control group was
included. The no message control group was collected to understand the differences
between each condition and ultimately illustrate how each of the stimuli related with
regard to a baseline comparison. With respect to attitude, the no message control
condition [M = 4.89, SD = 1.53, (4.53 < µ < 5.25)] reported similarly to those participants
in the high-action risk conditions, both of which had less favorable attitudes towards
getting the HPV vaccine in comparison to participants in the low-action risk conditions.
With respect to behavioral intention, the no message control condition [M = 3.67, SD =
1.98, (3.20 < µ < 4.14)] reported similarly to those participants in the high-action risk
conditions, both of which had less behavioral intention to get the HPV vaccine than those
participants in the low-action risk conditions.
Lastly, although the current study did not focus on the traditional
conceptualization of risk regarding susceptibility it did measure both perceptions of
susceptibility to HPV and cervical cancer. Data concerning perceptions of susceptibility
40
to the outcomes of HPV and cervical cancer were collected to ensure that participants
perceived themselves to be susceptible as well as to ensure that perceptions of
susceptibility did not differ across conditions.
Perceptions of HPV susceptibility did not differ across framing (Mloss = 2.79, SD
= 1.65; Mgain= 2.93, SD = 1.75) [t(298) = -.67, p = .50] or perceived action risk (Mlow =
3.00, SD = 1.75; Mhigh= 2.72, SD = 1.64) conditions t(298) = 1.46, p = .15. Perceptions
of susceptibility to cervical cancer also did not differ across framing (Mloss = 2.75, SD =
1.50; Mgain= 3.03, SD = 1.50) [t(297) = -1.63, p = .10] or perceived action risk (Mlow =
2.95, SD = 1.61; Mhigh= 2.84, SD = 1.40) conditions t(297) = .62, p = .54.
While perceptions of susceptibility did not differ across conditions, what is of
particular interest and concern is the overall means. To measure overall perceptions of
susceptibility a one-sample t-test was conducted to assess whether or not the means
differed from the midpoint of the seven-point scale. These data indicated that
participants’ perceived susceptibility to HPV (M = 2.89, SD = 1.68), t(372) = 12.80, p <
.001, r = .55 and cervical cancer (M = 2.92, SD = 1.48), t(372) = 14.00, p < .001, r = .59,
were both substantially lower than the midpoint of the scale.
This chapter discussed the results of the data analyses. The following chapter
examines theoretical and conceptual implications of the findings, limitations of the study,
and implications for future research.
41
Chapter 5
DISCUSSION
As previously discussed, cervical cancer is largely a preventable disease. With
health care screening advances and the new HPV vaccine, there is the potential to
drastically reduce the incidence of HPV, and thus greatly reduce the risk of cervical
cancer. Given the current push for HPV vaccination, there is a need to better understand
which messages are most effective at increasing vaccination uptake.
The goal of the current study was to examine the effects of message framing and
perceived action risk on attitudes and behavioral intentions to get the HPV vaccine.
Previous research suggested that perceived action risk of the target behavior would
determine which message frame (loss vs. gain) would be more effective at producing
favorable behavioral outcomes (e.g. Rothman & Salovey, 1997; Rothman et al., 1999;
Rothman et al., 2006). As such, the current study hypothesized that message framing and
perceived action risk would interact such that the most positive attitudes towards and
behavioral intention to get the HPV vaccine would be highest in both the gain-framed
condition, when perceived action risk of engaging in the behavior is low, and in the lossframed condition, when perceived action risk of engaging in the behavior is high and that
these two conditions would not differ from one another. The remaining two conditions,
the gain-framed, high-action risk condition and the loss-framed, low-action risk
condition, would have the least positive attitudes towards and behavioral intention to get
the HPV vaccine, and these conditions would not differ from each other.
42
Results were not consistent with the predicted interactions between message
framing and perceived action risk. These data show that message framing is not as
important a predictor of attitude and behavioral intention outcomes as is perceived action
risk. In the main, the presence of a main effect for perceived action risk, no main effect
for message framing, and no interaction effect best explain the data for the attitudes and
behavioral intention outcomes studied here. Although prior research indicated a possible
interaction between perceived action risk and message framing, the current findings posit
that individuals are more apt to favor a behavior that has low risks in comparison to a
high-risk counterpart.
These findings contribute to the growing body of framing research in two
important ways. First, the data indicate that when perceived action risk is low, more
favorable attitudes and behavioral intentions exist despite the framing utilized. Second,
participants presented with high-action risk information regarding the HPV vaccine
report similar attitudes and behavioral intentions to those provided with no information.
This is of interest given one of the major barriers regarding HPV awareness and
vaccination is lack of knowledge about the virus (Holcomb et al., 2004; Ramirez et al.,
1997; Waller et al., 2003; Yacobi et al., 1999). The current findings suggest that it is not
just information that is important, but how the risk information is presented. If
information is to contribute to increased vaccination uptake, it should be presented in a
way that emphasizes the low-action risk nature of the target behavior.
Unique to this study is the conceptualization of perceived risk, which may explain
the divergent results between this study and prior research. Traditionally, risk has been
43
conceptualized as susceptibility or belief of vulnerability to a disease (Rimal & Real,
2003). High-perceived risk, then, is associated with a high likelihood of getting a
disease, and low perceptions of risk are associated with low levels of disease
vulnerability. The current study, however, was interested in a different conceptualization
of perceived risk; the perceived action risk or the perceptions of risk or danger associated
with engaging in the target behavior.
Oftentimes, the public maintains a general consensus regarding the perceived
action risk or safety of a health behavior. However, some behaviors (e.g., vaccinations)
produce conflicting views. Especially important with regard to health behaviors that
contain some uncertainty, such as getting the HPV vaccine, is addressing the perceived
action risk variability involved with the target behavior. Getting the HPV vaccine is a
preventative behavior and thus, might be associated with low levels of perceived action
risk (Rothman et al., 1999). However, debate surrounding the risk of the vaccine’s
potential side effects exists (e.g., Saul & Pollack, 2007). Therefore, examining risk, not
only in the traditional sense by looking at susceptibility, but also considering the action
risk associated with engaging in a health behavior, is paramount to understanding the
decision to vaccinate.
Although the current study did not focus on the traditional conceptualization of
risk regarding susceptibility it did measure both perceptions of susceptibility to HPV and
cervical cancer. Perceptions of susceptibility to both HPV and cervical cancer did not
differ across conditions. However, as indicated by the data, participants did not perceive
to be at health risk for either of the two illnesses.
44
These findings have direct implications for both the current study and cervical
health education efforts. First, the purpose of the current study was to begin examining
persuasive messages aimed to impact attitudes and behavioral intentions to get the HPV
vaccine. However, in order for an individual to take action to prevent a health disease,
they need to believe that they are personally susceptible to it (Rosenstock, 1974). As
such, studying the manipulation of message framing and perceptions of risk in the
behavior can only be effective to the point that participants perceive that they are
susceptible to the disease that the target behavior aids in preventing, in this case HPV
and/or cervical cancer.
Second, the low levels of perceived susceptibility further highlight and reinforce
prior research surrounding HPV. Evidenced by past research, significant knowledge
deficits surrounding HPV exist (Holcomb et al., 2004; Ramirez et al., 1997; Waller et al.,
2003; Yacobi et al., 1999). Despite perceptions of low susceptibility, HPV is the most
prevalent sexually transmitted disease (STD) in the United States, with an estimated 20
million Americans infected and an additional 6.2 million individuals infected annually
(Weinstock, Berman, & Cates, 2004). One potential reason for the discrepancy between
high prevalence rates and low levels of susceptibility is the existing knowledge deficit.
Until individuals are made aware of HPV, its link to cervical cancer, and the substantial
prevalence rates, the knowledge deficit will likely continue to serve as a barrier to health
messages designed to impact HPV vaccination uptake.
45
Limitations
As with any study, the results of the current study are constrained by a few
methodological limitations. First, some participants were likely to have a preconceived
notion of the HPV vaccine’s safety prior to participating in the study. As participants
were partitioned equally between the high and low action risk conditions, these
preconceived notions could potentially curve the data toward the norm. This limitation
also extends to vaccination in general as some participants may have negative views
about the concept of vaccination, regardless of the illness (Poland & Jacobson, 2001;
Zimmerman et. al., 2005). Strong beliefs, whether for or against the vaccination, could
have skewed the data. However, as participants were assigned randomly to conditions, it
is assumed that these beliefs are relatively evenly distributed across conditions and are
thus, canceled out.
Another limitation lies in the age range of the participant audience. Although the
HPV vaccine is targeted to females between the ages of 9 and 26, the current study
limited the participants to female college students between the ages of 18 and 26. This
limits the generalizability of the findings as the target individuals between the ages of 9
and 17 are not represented. Furthermore, the examined college student population may
differ in their views of the HPV vaccine in comparison to those not enrolled in higher
education.
Directions for Future Research
The current study adds to the message framing literature and provides a better
understanding of the theoretical framework of prospect theory when risk lies in the target
46
behavior. Although this study provides a good starting point for measuring this type of
action risk and the impact that it may have on the outcomes of a message, scholars should
continue to explore this factor. There are a number of ways that future research can
utilize the current study’s conceptualization of action risk and further investigate how this
aspect of risk impacts message understanding and decision making with regard to health
behavior uptake.
First, future research should continue to examine the current study’s
conceptualization of risk. The action risk focus adds to the framing literature and
provides a better understanding of decision making when the perceived risk is the
uncertainty of the target behavior’s outcomes (i.e. side effects). It may also prove useful
to also manipulate susceptibility to see if the findings continue to deviate away from prior
framing research. Although the current study explored perceived action risk in one
context, there are many facets of this reconceptualization that have yet to be investigated.
Future research should also examine the phenomenon of perceived action risk in a
variety of health contexts. There are health behaviors that are promoted despite their
potentially harmful side effects. The process of deciding whether or not to engage in a
behavior with side effects results in the weighing of costs and benefits. Investigating the
framing of these potential side effects and the perceived action risk of a target health
behavior could aid understanding of the impact and outcomes of persuasive health
campaigns. As the current study was focused solely on the promotion of the HPV
vaccine, research in a variety of contexts may further validate the importance of the
perceived action risk measure.
47
Additionally, future research should continue to examine the topic of HPV. As
the number one sexually transmitted disease in the United States, there is a great deal to
be done through communicative and educational efforts to promote the prevention of
HPV and its progression to cervical cancer. Yet, despite the prevalence of the virus, at
the time of the writing of this thesis, HPV research is virtually non-existent in the
communication literature.
48
APPENDICES
49
APPENDIX A
Scenarios
Loss-frame, low-action risk
The human papillomavirus (HPV) is the most common sexually transmitted disease in the
United States. Although most types of HPV go away on their own without any health
problems, high-risk strands are found in 99.7% cases of cervical cancer. There is an HPV
vaccine that protects against 4 dominant high-risk strands of HPV that is available for
females between the ages of 9-26.
By not getting the HPV vaccine, you have a great deal to lose. You may lose any
protection against genital warts, which have no known cure, as well as the potential to
have children. You can also lose protection to your cervix. This loss in protection makes
you more prone to getting cervical cancer and can cost you your life. Ultimately, not
getting this vaccine can lead to significant unnecessary damages to your health.
The HPV vaccine is low risk. The safety of the vaccine was tested in clinical trials before
it was licensed. Side effects are very minor and include redness and itching where the
shot was given. These symptoms do not last long and go away on their own.
Loss-frame, high-action risk
The human papillomavirus (HPV) is the most common sexually transmitted disease in the
United States. Although most types of HPV go away on their own without any health
problems, high-risk strands are found in 99.7% cases of cervical cancer. There is an HPV
vaccine that protects against 4 dominant high-risk strands of HPV that is available for
females between the ages of 9-26.
By not getting the HPV vaccine, you have a great deal to lose. You may lose any
protection against genital warts, which have no known cure, as well as the potential to
have children. You can also lose protection to your cervix. This loss in protection makes
you more prone to getting cervical cancer and can cost you your life. Ultimately, not
getting this vaccine can lead to significant unnecessary damages to your health.
Although the HPV vaccine offers protection against the contraction of cervical cancer,
the HPV vaccine poses other potentially serious risks to your health. The safety of the
vaccine has been tested; however, some of the vaccine’s reported side effects include
suicidal thoughts, appendicitis, liver damage, paralysis, and in some cases death. No
research has been done to rule out the side effects as a risk of getting the HPV vaccine.
50
Gain-frame, low-action risk
The human papillomavirus (HPV) is the most common sexually transmitted disease in the
United States. Although most types of HPV go away on their own without any health
problems, high-risk strands are found in 99.7% cases of cervical cancer. There is an HPV
vaccine that protects against 4 dominant high-risk strands of HPV that is available for
females between the ages of 9-26.
By getting this vaccine, you have a great deal to gain. You gain protection against genital
warts as well as your reproductive health. You also gain protection to your cervix. This
protection makes you less prone to getting cervical cancer and can save you your life.
Ultimately, getting this vaccine can benefit you by preventing unnecessary damages to
your health.
The HPV vaccine is low risk. The safety of the vaccine was tested in clinical trials before
it was licensed. Side effects are very minor and include redness and itching where the
shot was given. These symptoms do not last long and go away on their own.
Gain-frame, high-action risk
The human papillomavirus (HPV) is the most common sexually transmitted disease in the
United States. Although most types of HPV go away on their own without any health
problems, high-risk strands are found in 99.7% cases of cervical cancer. There is an HPV
vaccine that protects against 4 dominant high-risk strands of HPV that is available for
females between the ages of 9-26.
By getting this vaccine, you have a great deal to gain. You gain protection against genital
warts as well as your reproductive health. You also gain protection to your cervix. This
protection makes you less prone to getting cervical cancer and can save you your life.
Ultimately, getting this vaccine can benefit you by preventing unnecessary damages to
your health.
Although the HPV vaccine offers protection against the contraction of cervical cancer,
the HPV vaccine poses other potentially serious risks to your health. The safety of the
vaccine has been tested; however, some of the vaccine’s reported side effects include
suicidal thoughts, appendicitis, liver damage, paralysis, and in some cases death. No
research has been done to rule out the side effects as a risk of getting the HPV vaccine.
51
APPENDIX B
Framing Items
The message emphasized the:
Negative/positive
Bad/good
Loss/gain
Disadvantages/advantages
Costs/benefits
52
APPENDIX C
Perceived Action Risk Items
How would you describe the vaccine?
Safe/risky
Harmless/harmful
Not dangerous/dangerous
Not threatening/threatening
Not damaging/damaging
53
APPENDIX D
Message Clarity Items
The content of the message was:
Unclear/clear*
Understandable/Not understandable*
Incomprehensible/comprehensible
Confusing/Not confusing
Not apparent/apparent
*Notes deleted item
54
APPENDIX E
Message Processing Effort Items
The content of the message was:
Easy to read/hard to read
Not difficult to process/difficult to process
Not challenging to read/challenging to read
Easy to understand/tough to understand
Simple to process/complex to process
55
APPENDIX F
HPV Perceived Susceptibility Items
It is likely that I will get HPV.
It is possible I will contract HPV.
I am highly susceptible to get HPV.
56
APPENDIX G
Cervical Cancer Perceived Susceptibility Items
It is likely that I will get cervical cancer.
It is possible I will contract cervical cancer.
I am highly susceptible to get cervical cancer.
57
APPENDIX H
Attitude Items
I think that getting the HPV vaccine is valuable.
It is wise to get the HPV vaccine.
Getting the HPV vaccine will result in positive outcomes for me.
Getting the HPV vaccine is beneficial.
It is important for me to get the HPV vaccine.
Getting the HPV vaccine is a good idea.
The HPV vaccine is useful.
The HPV vaccine is worthwhile for me to get.
58
APPENDIX I
Behavioral Intention Items
I plan to get the HPV vaccine.
I will likely get the HPV vaccine.
My aim is to get the HPV vaccine.
I am likely to get the HPV vaccine.
I intend to get the HPV vaccine.
59
Table 1
Descriptive Statistics for Perceived Action Risk by Frame on Attitudes
High
Risk
Low
Risk
Total
Loss
M = 4.62, SD = 1.46
P(4.28 ≤  ≤ 4.96) = .95
M = 5.57, SD = 1.28
P(5.27 ≤  ≤ 5.87) = .95
M = 5.09, SD = 1.45
P(4.86 ≤  ≤ 5.33) = .95
Gain
M = 4.62, SD = 1.43
P(4.30 ≤  ≤ 4.94) = .95
M = 5.44, SD = 1.53
P(5.10 ≤  ≤ 5.78) = .95
M = 5.03, SD = 1.53
P(4.80 ≤  ≤ 5.25) = .95
Total
M = 4.63, SD = 1.44
P(4.40 ≤  ≤ 4.86) = .95
M = 5.49, SD = 1.42
P(5.26 ≤  ≤ 5.72) = .95
60
Table 2
Descriptive Statistics for Perceived Action Risk by Frame on Behavioral Intention
High
Risk
Low
Risk
Total
Loss
M = 3.41, SD = 2.09
P(2.92 ≤  ≤ 3.91) = .95
M = 4.59, SD = 1.96
P(4.13 ≤  ≤ 5.05) = .95
M = 4.00, SD = 2.10
P(3.63 ≤  ≤ 4.37) = .95
Gain
M = 3.71, SD = 2.14
P(3.23 ≤  ≤ 4.20) = .95
M = 4.53, SD = 2.68
P(3.93 ≤  ≤ 5.13) = .95
M = 4.13, SD = 2.45
P(3.77 ≤  ≤ 4.47) = .95
Total
M = 3.59, SD = 2.11
P(3.22 ≤  ≤ 3.94) = .95
M = 4.54, SD = 2.37
P(4.18 ≤  ≤ 4.90) = .95
61
Table 3
Descriptive Statistics for Perceived Action Risk by Frame on Perceived Susceptibility to
HPV
High
Risk
Low
Risk
Total
Loss
M = 2.76, SD = 1.64
P(2.37 ≤  ≤ 3.16) = .95
M = 2.82, SD = 1.66
P(2.43 ≤  ≤ 3.22) = .95
M = 2.79, SD = 1.65
P(2.51 ≤  ≤ 3.07) = .95
Gain
M = 2.68, SD = 1.65
P(2.30 ≤  ≤ 3.06) = .95
M = 3.17, SD = 1.82
P(2.79 ≤  ≤ 3.54) = .95
M = 2.93, SD = 1.75
P(2.65 ≤  ≤ 3.19) = .95
Total
M = 2.72, SD = 1.64
P(2.45 ≤  ≤ 2.99) = .95
M = 2.99, SD = 1.75
P(2.72 ≤  ≤ 3.27)= .95
62
Table 4
Descriptive Statistics for Perceived Action Risk by Frame on Perceived Susceptibility to
Cervical Cancer
High
Risk
Low
Risk
Total
Loss
M = 2.83, SD = 1.45
P(2.49 ≤  ≤ 3.18) = .95
M = 2.66, SD = 1.55
P(2.31 ≤  ≤ 3.01) = .95
M = 2.75, SD = 1.50
P(2.50 ≤  ≤ 3.00) = .95
Gain
M = 2.85, SD = 1.36
P(2.52 ≤  ≤ 3.19) = .95
M = 3.20, SD = 1.62
P(2.88 ≤  ≤ 3.53) = .95
M = 3.03, SD = 1.50
P(2.79 ≤  ≤ 3.26) = .95
Total
M = 2.84, SD = 1.40
P(2.60 ≤  ≤ 3.09) = .95
M = 2.95, SD = 1.60
P(2.69 ≤  ≤ 3.17) = .95
63
REFERENCES
Ajzen, I. (1988). Attitudes, personality, and behavior. Chicago: The Dorsey Press.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human
Decision Processes, 50, 179-211.
Ajzen, I. & Fishbein, M. (1980). Understanding attitudes and predicting behavior.
Englewood Cliffs, NJ: Prentice Hall.
Anderson, J. C., Gerbing, D. W., & Hunter, J. E. (1987). On the assessment of
unidimensional measurement: Internal and external consistency, and overall
consistency criteria. Journal of Marketing Research, 24, 432-437.
Apanovitch, A. M., McCarthy, D., & Salovey, P. (2003). Using message framing to
motivate HIV testing among low-income, ethnic minority women. Health
Psychology, 22, 60-67.
Banks, S. M., Salovey, P., Greener, S., Rothman, A. J., Moyer, A., Beauvais, J., & Epel,
E. (1995). The effects of message framing on mammography utilization. Health
Psychology, 14, 178-184.
Bosch, F. X., & Sanjose, S. (2003). Human papillomavirus and cervical cancer-burden
and assessment of causality. Journal of the National Cancer Institute
Monographs, 31, 3-13.
Caffee, S. H., & Roser, C. (1986). Involvement and the consistency of knowledge,
attitudes, and behaviors. Communication Research, 23, 373-399.
64
Detweiler, J. B., Bedell, B. T., Salovey, P., Pronin, E., & Rothman, A. J. (1999). Message
framing and sunscreen use: Gain framed messages motivate beach goers. Health
Psychology, 18, 189-196.
Edwards, W. (1954). The theory of decision making. Psychological Bulletin, 51, 380417.
Friedman, A. L., & Shepeard, H. (2006). Exploring the knowledge, attitudes, beliefs, and
communication preferences of the general public regarding HPV: Findings from
CDC focus group research and implications for practice. Health Education and
Behavior, 34, 471-485.
Garner, E. I. (2003). Cervical cancer: Disparities in screening, treatment, and survival.
Cancer Epidemiology, Biomarkers & Prevention, 12, 242s-247s.
Gerberding, J. L. (2004). Report to congress: Prevention of genital human
papillomavirus infection. Atlanta, GA: Centers for Disease Control and
Prevention, Department of Health and Human Services.
Helms, L. J. & Melnikow, J. (1999). Determining costs of health care services for cost
effectiveness analysis: The case of cervical cancer prevention and treatment.
Medical Care, 37, 652-661.
Ho, G. Y. F., Bierman, R., Beardsley, L., Chang, C. J., & Burk, R. D. (1998). Natural
history of cervicovaginal papillomavirus infection in young women. New England
Journal of Medicine, 338, 423-428.
65
Holcomb, B., Baily, J. M., Crawford, K., & Ruffin, M. T. (2004). Adults’ knowledge and
behaviors related to human papillomavirus infection. Journal of the American
Board of Family Medicine, 17, 26-31.
Hoover, D. R., Carfioli, B., & Moench, E. (2000). Attitudes of adolescent/young adult
women toward human papillomavirus vaccination and clinical trials. Health Care
for Women International, 21, 375-391.
Hunter, J. E., & Gerbing, D. W. (1982). Unidimensional measurement, second order
factor analysis, and causal models. Research in Organizational Behavior, 4, 267320.
Janz, N. K., & Becker, M. H. (1984). The health belief model: A decade later. Health
Education and Behavior, 11, 1-47.
Jones, L. W., Sinclair, R. C., & Courneya, K. S. (2003). The effects of source credibility
and message framing on exercise intentions, behaviors, and attitudes: An
integration of the elaboration likelihood model and prospect theory. Journal of
Applied Social Psychology, 33, 179-196.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decisions under
risk. Econometrica, 47, 263-291.
Kim, M., & Hunter, J. E. (1993a). Attitude-behavior relations: A meta-analysis of
attitudinal relevance and topic. Journal of Communication, 43, 101-142.
Kim, M., & Hunter, J. E. (1993b). Relationships among attitudes, behavioral intentions,
and behavior: A meta-analysis of past research, part 2. Communication Research,
20, 331-364.
66
Koutsky, L. (1997). Epidemiology of genital human papillomavirus infection. American
Journal of Medicine, 102, 3-8.
Koutsky, L. A., & Kiviat, N. B. (1999). Biology of genital human papillomaviruses. In K.
K. Holmes, P. F. Sparling, P. Mardh, S. M. Lemon, W. E. Stamm, P. Piot, & J. M.
Wasserheit (3rd Eds.), Sexually Transmitted Diseases (pp. 335-359). New York:
McGraw-Hill.
Latimer, A. E., Salovey, P., & Rothman, A. J. (2007). The effectiveness of gain-framed
messages for encouraging disease prevention behavior: Is all hope lost? Journal
of Health Communication, 12, 645-649.
Levine, T. R. (2005). Confirmatory factor analysis and scale validation in
communication research. Communication Research Reports, 22, 335-338.
Ley, C., Bauer, H. M., Reingold, A., Schiffman, M. H., Chambers, J. C., Tashiro, C. J., &
Manos, M. M. (1991). Determinants of genital human papillomavirus infection in
young women. Journal of the National Cancer Institute, 83, 997-1003.
Maheswaran, D., & Myers-Levy, J. (1990). The influence of message framing and issue
involvement. Journal of Marketing Research, 27, 361-367.
Manhart, L. E., & Koutsky, L. A. (2002). Do condoms prevent genital HPV infections,
external warts or cervical neoplasia? A meta-analysis. Sexually Transmitted
Diseases, 29, 725-735.
Markowitz, L. E., Dunne, E. F., Saraiya, M., Lawson, H. W., Chesson, H., & Unger, E.
R. (2007). Quadrivalent human papillomavirus vaccine: Recommendations of the
advisory committee on immunization practices (ACIP). Morbidity and Mortality
67
Weekly Report: Department of Health and Human Services Centers for Disease
Control and Prevention.
Meyerowitz, B. E., & Chaiken, S. (1987). The effect of message framing on breast selfexamination attitudes, intentions, and behavior. Journal of Personality and Social
Psychology, 52, 500-510.
Millar, M. G., & Millar, K. U. (2000). Promoting safe driving behaviors: The influence of
framing and issue involvement. Journal of Applied Social Psychology, 30, 853866.
Myers, E. R., McCrory, D. C., Nanda, K., Bastian, L. & Matchar, D. B. (2000).
Mathematical model for the natural history of human papillomavirus infection and
cervical carcinogenesis. American Journal of Epidemiology, 151, 1158-1171.
O’Keefe, D. J. (2002). Persuasion: Theory and research. Thousand Oaks, CA: Sage
Publications, Inc.
O’Keefe, D. J., & Jensen, J. D. (2007). The relative persuasiveness of gain-framed lossframed messages for encouraging disease prevention behaviors: A meta-analytic
review. Journal of Health Communication, 12, 623-644.
Peyton, C. L., Gravitt, P. E., Hunt, W. C., Hundley, R. S., Zhao, M., Apple, R. J., &
Wheeler, C. M. (2001). Determinants of genital human papillomavirus detection
in a U.S. population. The Journal of Infectious Diseases, 183, 1554-1564.
Poland, G. A., & Jacobson, R. M. (2001). Understanding those who do not understand: A
brief review of the anti-vaccine movement. Vaccine, 19, 2440-2445.
68
Ramirez, J. E., Ramos, D. M., Clayton, L., Kanowitz, S., & Moscicki, A. B. (1997).
Genital human papillomavirus infections: Knowledge, perception of risk, and
actual risk in a nonclinic population of young women. Journal of Women’s
Health, 6, 113-121.
Rimal, R. N., Flora, J. A., & Schooler, C. (1999). Achieving improvements in overall
health orientation: Effects of campaign exposure, information seeking, and health
media use. Communication Research, 26, 322-348.
Rimal, R. N., & Real, R. (2003). Perceived risk and efficacy beliefs as motivators of
change. Human Communication Research, 29, 370-399.
Rivers, S. E., Pizarro, D. A., Schneider, T. R., Pizarro, J., Salovey, P. (2005). Message
framing and Pap test utilization among women attending a community health
clinic. Journal of Health Psychology, 10, 65-77.
Rosenstock, I. M. (1974). The health belief model: Origins and correlates. Health
Education Monographs, 2, 336-353.
Rothman, A. J., Bartels, R. D., Wlaschin, J., Salovey, P. (2006). The strategic use of
gain- and loss-framed messages to promote healthy behavior: How theory can
inform practice. Journal of Communication, 56, S202-S220.
Rothman, A. J., Martino, S. C., Bedell, B. T., Detweiler, J. B., Salovey, P. (1999). The
systematic influence of gain- and loss-framed messages on interest in and use of
different types of health behavior. Personality and Social Psychology Bulletin, 25,
1355-1369.
69
Rothman, A. J., & Salovey, P. (1997). Shaping perceptions to motivate healthy behavior:
The role of message framing. Psychological Bulletin, 121, 3-19.
Rothman, A. J., Salovey, P., Antone, C., Keough, K., & Martin, C. D. (1993). The
influence of message framing on intentions to perform health behaviors. Journal
of Experimental Social Psychology, 29, 408-433.
Saul, S. & Pollack, A. (2007). Furor on rush to require cervical cancer vaccine. The New
York Times. Retrieved June 3, 2009 from http://www.nytimes.com/2007/02/17/
health/17vaccine.html
Sutton, S. R. (1982). Fear arousing communications: A critical examination of theory and
research. In J. R. Eiser (Eds.), Social Psychology and Behavioral Medicine (pp.
303-338). New York: Wiley.
Trottier, H., & Franco, E. L. (2006). The epidemiology of genital human papillomavirus
infection. Vaccine, 24SI, 4-15.
Tversky, A., & Kahneman, D. (1981). The framing of decisions and the rationality of
choice. Science, 221, 453-458.
Umphrey, L. R. (2003). The effects of message framing and message processing on
testicular self-examination attitudes and perceived susceptibility. Communication
Research Reports, 20, 97-105.
Walboomers, J. M., Jacobs, M. V., Manos, M. M., Bosch, F. X., Kummer, J. A., Shah, K.
V., Snijders, P. J., Peto, J., Meijer, C. J., & Munoz, N. (1999). Human
papillomavirus is a necessary cause of invasive cervical cancer worldwide.
Journal of Pathology, 189, 12-19.
70
Waller, J., McCaffery, K., Forrest, S., Szarewski, A., Cadman, L., Wardle, J. (2003).
Awareness of human papillomavirus among women attending a well woman
clinic. Sexually Transmitted Infections, 79, 320-322.
Weinstock, H., Berman, S., & Cates, C. J. (2004). Sexually transmitted diseases among
American youth: Incidence and prevalence estimates, 2000. Perspectives on
Sexual and Reproductive Health, 36, 6-10.
Witte, K. (1992). Putting the fear back into fear appeals: The extended parallel process
model. Communication Monographs, 59, 329-349.
Yacobi, E., Tennant, C., Ferrante, J., Naazneen, P., & Roetzheim, R. (1999). University
students’ knowledge and awareness of HPV. Preventative Medicine, 28, 535-541.
Zimet, G. D. (2005). Improving adolescent health: Focus on HPV vaccine acceptance.
Journal of Adolescent Health, 37, S17-S23.
Zimmerman, R. K., Wolfe, R. M., Fox, D. E., Fox, J. R., Nowalk, M. P., Troy, J. A., &
Sharp, L. K. (2005). Vaccine criticism on the World Wide Web. Journal of
Medical Internet Research, 7, e17.