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The Effects of Magnitude and Likelihood on Information Avoidance

Permanent Link: http://ufdc.ufl.edu/UFE0042009/00001

Material Information

Title: The Effects of Magnitude and Likelihood on Information Avoidance
Physical Description: 1 online resource (71 p.)
Language: english
Creator: Miller, Wendi
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: control, decision, ease, information, likelihood, magnitude
Psychology -- Dissertations, Academic -- UF
Genre: Psychology thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: A number of factors may influence a person s decision to seek vs. avoid information. The goal this dissertation was to explore how two factors, magnitude (severity of medical condition) and likelihood (probability of having medical condition), influence decisions to seek vs. avoid diagnostic testing. The author hypothesized that the inclusion of two additional variables, ease of test access and control over developing the condition, were necessary to fully understand the relationships between magnitude, likelihood, and information avoidance. In two experiments, the author examined the extent to which magnitude, likelihood, ease (Study 1), and control (Study 2) predicted the decision to seek vs. avoid diagnostic testing for TAA deficiency (a fictitious medical condition). In Study 1, the author hypothesized that participants would display the most avoidance when magnitude was low, likelihood was low, and ease was low and the least avoidance when magnitude was low, likelihood was low, and ease was high. Results did not support the Study 1 hypotheses. Instead, results revealed two significant main effects. Participants (N = 177) were significantly more likely to avoid diagnostic testing when 1) likelihood was low rather than high and, 2) ease was high rather than low. In Study 2, the author hypothesized that participants would display the most avoidance when magnitude was high, likelihood was high, and control was low and the least avoidance when magnitude was high, likelihood was high, and control was high. Results did not support the Study 2 hypothesis. Results revealed two significant main effects. Participants (N = 179) were significantly more likely to avoid diagnostic testing when 1) likelihood was low rather than high and, 2) magnitude was low rather than high. Taken together, these findings reveal that likelihood and ease predict avoidance, such that low likelihood and low ease corresponded with greater avoidance. However, the relationship between magnitude and avoidance was inconsistent, such that low magnitude corresponded with greater avoidance in Study 2 but did not predict avoidance in Study 1. Further, control did not predict avoidance. Future research is needed to understand the role of magnitude and control in avoidance decisions.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Wendi Miller.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Shepperd, James A.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2012-08-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042009:00001

Permanent Link: http://ufdc.ufl.edu/UFE0042009/00001

Material Information

Title: The Effects of Magnitude and Likelihood on Information Avoidance
Physical Description: 1 online resource (71 p.)
Language: english
Creator: Miller, Wendi
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: control, decision, ease, information, likelihood, magnitude
Psychology -- Dissertations, Academic -- UF
Genre: Psychology thesis, Ph.D.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: A number of factors may influence a person s decision to seek vs. avoid information. The goal this dissertation was to explore how two factors, magnitude (severity of medical condition) and likelihood (probability of having medical condition), influence decisions to seek vs. avoid diagnostic testing. The author hypothesized that the inclusion of two additional variables, ease of test access and control over developing the condition, were necessary to fully understand the relationships between magnitude, likelihood, and information avoidance. In two experiments, the author examined the extent to which magnitude, likelihood, ease (Study 1), and control (Study 2) predicted the decision to seek vs. avoid diagnostic testing for TAA deficiency (a fictitious medical condition). In Study 1, the author hypothesized that participants would display the most avoidance when magnitude was low, likelihood was low, and ease was low and the least avoidance when magnitude was low, likelihood was low, and ease was high. Results did not support the Study 1 hypotheses. Instead, results revealed two significant main effects. Participants (N = 177) were significantly more likely to avoid diagnostic testing when 1) likelihood was low rather than high and, 2) ease was high rather than low. In Study 2, the author hypothesized that participants would display the most avoidance when magnitude was high, likelihood was high, and control was low and the least avoidance when magnitude was high, likelihood was high, and control was high. Results did not support the Study 2 hypothesis. Results revealed two significant main effects. Participants (N = 179) were significantly more likely to avoid diagnostic testing when 1) likelihood was low rather than high and, 2) magnitude was low rather than high. Taken together, these findings reveal that likelihood and ease predict avoidance, such that low likelihood and low ease corresponded with greater avoidance. However, the relationship between magnitude and avoidance was inconsistent, such that low magnitude corresponded with greater avoidance in Study 2 but did not predict avoidance in Study 1. Further, control did not predict avoidance. Future research is needed to understand the role of magnitude and control in avoidance decisions.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Wendi Miller.
Thesis: Thesis (Ph.D.)--University of Florida, 2010.
Local: Adviser: Shepperd, James A.
Electronic Access: RESTRICTED TO UF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE UNTIL 2012-08-31

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042009:00001


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1 THE EFFECTS OF MAGNITUDE AND LIKELIHOOD ON INFORMATION AVOIDANCE By WENDI ANN M ILLER A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE O F DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2010

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2 2010 Wendi A. Miller

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3 To my husband, Andy

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4 ACKNOWLEDGMENTS I thank James Shepperd for his guidance and support. In addition, I would like to thank Kate Sweeny, Cathy Cottrell, Michael We igold, and Jesse Dallery for their invaluable comments and suggestions.

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5 TABLE OF CONTENTS page ACKNOWLEDGMENTS ................................ ................................ ................................ .. 4 LIST OF TABLES ................................ ................................ ................................ ............ 7 LIST OF FIGURES ................................ ................................ ................................ .......... 8 ABSTRACT ................................ ................................ ................................ ..................... 9 CHAPTER 1 INTRODUCTION ................................ ................................ ................................ .... 11 Magnitude and Likelihood ................................ ................................ ....................... 11 What is Information Avoidance? ................................ ................................ ............. 11 Exploring Magnitude and Likelihood ................................ ................................ ....... 12 Magnitude ................................ ................................ ................................ ......... 13 Information Avoidance and Severity ................................ ................................ 14 Likelihood ................................ ................................ ................................ ......... 15 Information Avoidance and Likelihood ................................ .............................. 16 Why are the Effects of Magnitude and Likelihood on Behavior Inconsistent? ......... 17 Overview and Hypotheses ................................ ................................ ...................... 21 2 STUDY 1 ................................ ................................ ................................ ................. 23 Overview ................................ ................................ ................................ ................. 23 Method ................................ ................................ ................................ .................... 23 Participants ................................ ................................ ................................ ....... 23 Materials ................................ ................................ ................................ ........... 23 Uncertainty orientation ................................ ................................ ............... 23 Dispositional optimism ................................ ................................ ............... 24 Procedure ................................ ................................ ................................ ......... 25 Results ................................ ................................ ................................ .................... 27 Manipulation Checks ................................ ................................ ........................ 27 Treating Information Avoidance Outcome as a Continuous Variable ............... 27 Testing Covariates ................................ ................................ ........................... 28 Hypothesis Testing ................................ ................................ ........................... 28 Omnibus ANOVA Test ................................ ................................ ...................... 29 Discussion ................................ ................................ ................................ .............. 30 3 STUDY 2 ................................ ................................ ................................ ................. 34 Overview ................................ ................................ ................................ ................. 34 Method ................................ ................................ ................................ .................... 34 Participants and Materials ................................ ................................ ................ 34

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6 Procedure ................................ ................................ ................................ ......... 34 Results ................................ ................................ ................................ .................... 35 Manipulation Checks ................................ ................................ ........................ 35 Treating Information Avoidance Outcome as a Continuous Variable ............... 36 Testing Covariates ................................ ................................ ........................... 36 Hyp othesis Testing ................................ ................................ ........................... 36 Omnibus ANOVA Test ................................ ................................ ...................... 37 Exploring the Motivation for Information Avoidance and Seeking ..................... 38 Exploring Treatability and Magnitude as Predictors of Information Avoidance ................................ ................................ ................................ ..... 39 Discussion ................................ ................................ ................................ .............. 40 4 GENERAL DI SCUSSION ................................ ................................ ....................... 49 Overview ................................ ................................ ................................ ................. 49 Ease ................................ ................................ ................................ ................. 49 Control ................................ ................................ ................................ .............. 50 Magnitude ................................ ................................ ................................ ......... 52 Likelihood ................................ ................................ ................................ ......... 54 Individual Difference Measures ................................ ................................ ........ 55 Revisiting the Information Avoidance Construct ................................ ............... 56 Implications ................................ ................................ ................................ ...... 57 Summary ................................ ................................ ................................ .......... 59 APPENDIX A CONSENT FORM ................................ ................................ ................................ ... 60 B DEMOGRAPHIC QUESTIONNAIRE ................................ ................................ ...... 61 C UNCERTAINTY ORIENTATION ................................ ................................ ............. 62 D DISPOSITIONAL OPTIMISM ................................ ................................ .................. 63 E LOW LIKELIHOOD CONDITION ................................ ................................ ............ 64 F HIGH LIKELIHOOD CONDITION ................................ ................................ ........... 65 G FINAL QUESTIONNAIRE ................................ ................................ ....................... 66 LIST OF REFERENCES ................................ ................................ ............................... 67 BIOGRAPHICAL SKETCH ................................ ................................ ............................ 71

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7 LIST OF TABLES Table page 2 1 Number of Excluded Participants by Condition in Study 1 ................................ .. 30 2 2 Follow up Tests to Study Re sults for Study 1. ................................ .................... 31 2 3 Cell Mean Comparisons Between Low Magnitude, Likelihood, and Ease Condition with All Other Conditions. ................................ ................................ ... 32 2 4 Cell Mean Comparisons Between the Low Magnitude, Low Likelihood, and High Ease Condition with All Other Conditions. ................................ .................. 32 3 1 Number of Excluded Participants by Condition in Study 2. ................................ 41 3 2 Follow up Tests to Study Results for Study 2. ................................ .................... 42 3 3 Cell Mean Comparisons Between the High Magnitude, High Likelihood, and Low Control Condition with All Other Conditions. ................................ ............... 43 3 4 Cell Mean Comparisons Between the High Magnitude, High Likelihood, and High Control Condition with All Other Conditions. ................................ .............. 43 3 5 Multiple Mediation Examining Relationship Between Magnitude and Information Avoidance. ................................ ................................ ....................... 43 3 6 Multiple Mediation Examining Relationship Between Likelihood and Informatio n Avoidance. ................................ ................................ ....................... 44

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8 LIST OF FIGURES Figure page 2 1 Frequency of TAA Deficiency Testing Options in Study 1. ................................ 32 2 2 Distribution of Residuals for the Full Factorial Model (Study 1). ......................... 33 3 1 Frequency of TAA Deficiency Testing Options in Study 2 ................................ .. 44 3 2 Distribution of Residuals for the Full Factorial Model (Study 2). ......................... 45 3 3 Behavior Change as a Partial Mediator of the Relationship between Magnitude and Information Avoidance. ................................ .............................. 46 3 4 Behavior Change as a Partial Mediator of the Relationship between Likelihood and Information Avoidance.. ................................ .............................. 47 3 5 Treatability and M agnitude as Predictors of Information Avoidance. .................. 48

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9 Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy THE EFFECTS OF MAGNITUDE AND LIKELIHOOD ON INFORMATION AVOIDANCE By Wendi A. Miller August 2010 Chair: James Shepperd Major: Psychology The goal this dis sertation was to explore how two factors, magnitude (severity of medical condition) and likelihood (probability of having medical condition) influen ce decisions to seek vs. avoid diagnostic testing. The author hypothesized that the inclusion of two additio nal variables, ease of test access and control over developing the condition, were necessary to fully understand the relationships between magnitude, likelihood, and information avoidance. In two experiments, the author examined the extent to which magnitu de, likelihood ease (Study 1), and control (Study 2) predicted the decision to seek vs. avoid diagnostic testing for TAA deficiency (a fictitious medical condition). In Study 1, the author hypothesized that participants would display the most avoidance wh en magnitude was low, likelihood was low, and ease was low and the least avoidance when magnitude was low, likelihood was low, and ease was high. Results did n ot support the Study 1 hypothese s. Instead, results revealed two significant main effects. Partic ipants ( N = 177) were significantly more likely to avoid diagnostic testing when 1) likelihood was low rather than high and 2) ease was high rather than low. In Study 2, the author hypothesized that participants would display the most

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10 avoidance w hen magnit ude was high, likelihood was high, and control was low and the least avoidance when magnitude was high, likelihood was high, and control was high. Results did not support the Study 2 hypothesis. Results revealed two significant main effects. Participants ( N = 179) were significantly more likely to avoid diagnostic testing when 1) likelihood was low rather than high and, 2) magnitude was low rather than high. Taken t ogether, these findings reveal that likelihood and ease predict av oidance such that low likelihood and low ease corresponded with greater avoidance. However, the relationship between magnitude and avoidance was inconsistent, such that low magnitude corresponded with greater avoidance in Study 2 but did not predict avoidance in Study 1. Further, control did not predict avoidance. Future research is needed to understand the role of magnitude and control in avoidance decisions.

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11 CHAPTER 1 INTRODUCTION Magnitude and Likelihood Imagine a woman at her local pharmacy. S he notices the machine that provides free blood pressure readings, but feels unsure about whether she should take the test. On the one hand, she believes that she should take the test because it is free and quick. Further, she realizes that hypertension is a serious medical condition. On the other hand, she believes that she should avoid the test because her blood pressure was high at her doctor and she worries the news mi ght be bad. Will she choose to get the screening? The proposed example examines the decision to seek vs. avoid potentially avoid information. Two factors that are par ticularly relevant are the magnitude of the consequences of the information (i.e., magnitude) and the likelihood that the consequences will be experienced (i.e., likelihood). What is Information Avoidance? o prevent or delay the acquisition Melnyk, Malone, & Shepperd, 2010 ). Information avoidance may be temporary or permanent. Delaying reading a potentially unpleasant email reflects temporary informat ion avoidance; never reading that the email reflects permanent avoidance. The information need not be unpleasant. People may avoid pleasant information such as the sex of an unborn child (Shipp et al., 2004). Information avoidance bears some similarity to selective exposure. Selective exposure, also called the congeniality bias, is the tendency for people to seek information consistent with their beliefs, attitudes, and past decisions, and avoid

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12 inconsistent information (Hart et al., 2008). Thus, consist ency is the underlying motive behind selective exposure. Similar to information avoidance, research on selective exposure addresses why people seek or avoid information and what factors influence their decision. However, research on selective exposure is narrower because it primarily focuses on what people do when they must choose between two types of information information consistent or inconsistent with their attitudes, beliefs and prior decisions (Mills, Aronson, & Robinson, 1959). Information avoida nce is broader in that it looks at the decision to seek vs. avoid information, rather than at the preference for consonant information over inconsonant information. Exploring Magnitude and Likelihood A variety of factors could influence the decision to se ek vs. avoid information. Two factors featured prominently in prior research on decision making are severity and willingness to take risks (e.g., gambling; Harris, Jenkins, & Glaser, 2006), comply with hazard warnings (Wogalter, Young, Brelsford, & Barlow, 1999), and engage in preventative health behaviors (Janz & Becker, 1984; Milne, Sheeran, Orbell, 2000; Floyd, Prentice Dunn, & Rogers, 2000). In each of these domains people are relying on perceived severity (comparable to our variable of magnitude) and likelihood in deciding a course of action when an outcome is unknown. Thus, severity and likelihood likely play an important role in the decision to seek vs. avoid information. Research on predictors of information avoidance is limited. The most pertinent research comes from the research on models of how people make decisions in health domains (e.g., health belief model, protection motivation theory). These health models are re levant because severity and likelihood play a central role and because, the proposed research, similar to the health models, addresses decision making in health

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13 domains. Importantly, these health models are primarily interested in intentions to engage in p reventative behavior rather than the decision to seek vs. avoids information. Nevertheless, the decision to seek vs. avoid information, similar to the decision to pursue a particular behavior (i.e., behavior intentions) reflects a complex decision that is influenced by multiple factors, involves weighing considerations of costs and benefit of a decision, and may entail taking actions the person would rather not take. Magnitude Magnitude refers to the magnitude of the implications or consequences of inform ation. For example, a student deciding whether to look at an exam grade online might consider whether the grade is for small quiz vs. a final exam. The consequences of earning a low grade on a small quiz are smaller than are the consequences for earning a are not synonymous. Severity is relevant to only negative outcomes whereas magnitude is relevant to both positive and negative information. For example, in both the h ealth belief model and protection motivation theory, severity refers to the seriousness of a disease. Because the goal of the current investigation is to examine the predictors of the decision to seek or avoid diagnostic medical testing (i.e., potentially negative information), the meanings of magnitude and severity are somewhat similar in this study. For ease of presentation, I will use the term severity in discussing past research and magnitude when discussing my research. Severity is featured prominent ly in both the health belief model and protection motivation theory. Both theories predict that perceptions of greater severity are associated with greater intentions to engage in preventative behavior. However, research examining the effects of severity w ithin the context of the health models reveals mixed results. Meta analyses find that severity is related to intentions to engage in

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14 preventative behavior in some studies, but not in others (Janz & Becker, 1984; Milne, Sheeran, Orbell, 2000; Floyd, Prentic e Dunn, & Rogers, 2000). For example, one study found that greater perceptions of the seriousness of Hepatitis B were associated with a greater likelihood of completing the Hepatitis B shot regimen (de Wit, Vet, Schutten, & van Steengergen, 2005). However other research finds that severity is not influential in the decision to engage in preventative health behavior. For example, the perceived severity of having cervical cancer did not predict whether women received a cervical cancer screening (Allahverdip our & Emami, 2008). Information Avoidance and Severity Results regarding the effects of severity on information avoidance are also mixed. Some studies find that greater perceptions of severity are associated with less information avoidance. For example, o learning about the detrimental effects of florescent lighting on academic performance in which the effects were portrayed as severe ( exposure to florescent lighting would lower their grade by one letter grade per semester) or not severe (exposure lower their grade by one half of one point in a course). Participants were more likely to seek information about the hazards of florescent lighting in the high severity condition than in the low severity condition (Neuwir th, Dunwoody, & Griffin, 2000). Similar results emerged in a TAA (Dawson, Savitsky, & Dunning, 2006). Participants in the mild severity condition were told that TAA h as no unpleasant symptoms, and those in the high severity were told that TAA is very serious and puts them at elevated risk for pancreatic disorders. Participants were more likely to seek testing for the enzyme deficiency in the high severity condition tha n in the mild severity condition. Thus, in both studies higher severity was associated with greater information seeking (i.e., less avoidance).

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15 However, other studies find that higher severity is associated with greater information avoidance. For example, participants facing the decision to seek or avoid testing for genetic hair loss in which the consequences were severe (i.e., dramatic decrease in rate of new hair production and noticeable hair loss beginning in the late 20s) or not severe (i.e., slight a nd unnoticeable decrease in new hair production), were more likely to avoid genetic testing when the consequences of the condition was severe than when it was not severe (Dawson, Savitsky, & Dunning, 2006). In another study, researchers examined reasons to people at 50% risk for the condition. Compared with participants who sought testing, participants who avoided testing indicated significantly greater consequences of learning of a positive test result. Specifically, avoiders anticipated more difficulties in families and an overall lower quality of life (van der Steenstraten, Tibben, Roos, van de Kamp, & Niermeijer, 1994). Likelihood Likelihood, also called susceptibility or vulnerability, refers to the probability that the consequences of the information will be experienced. Similar to severity, likelihood is featured prominently in both the health belief model and protection motivation theory. According to both theories, greater perceptions of likeliho od are associated with greater intentions to engage in preventative behaviors. In other words, people are more likely to intend to, and engage in, preventative behavior to the extent that they feel vulnerable to the relevant health threat. Also similar to the results regarding the effects of severity, the results on the relationship between likelihood and engagement in preventative behaviors is mixed. Meta analyses find that likelihood is related to intentions to engage in preventative behavior in some stu dies, but not in others (Janz & Becker, 1984; Milne, Sheeran,

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16 Orbell, 2000; Floyd, Prentice Dunn, & Rogers, 2000). In some cases, greater perception of likelihood is associated with greater engagement in preventative behaviors. For example, gay men who ind icated that they were more at risk of contracting Hepatitis B were more likely to complete a Hepatitis B shot regimen (de Wit, Vet, Schutten, & van Steengergen, 2005). However, sometimes greater perception of likelihood is associated with less engagement i n risk behavior. For example, mothers who perceived that their children were more likely to get sick were less likely to bring their children in for routine, preventative doctor visits (Becker, Nathanson, Drachman, & Kirscht, 1977). Finally, sometimes like lihood fails to predict engagement in preventative behaviors. For example, perceived vulnerability to osteoporosis in one study did not predict intentions to consume calcium or engage in weight baring exercise (Schmiege, Aiken, Sander, & Gerend, 2007). In formation Avoidance and Likelihood Although few studies have examined the effects of likelihood perceptions on information avoidance, the evidence suggests that lower perceptions of likelihood are associated with greater information avoidance. In one study researchers examined HD ( Babul et al., 1993). People at higher risk for HD (based on previous testing) were more interested in seeking the results of a new genetic test for HD compared with people at less risk for HD and to those unaware of their level of risk. In other words, participants were more likely to decline genetic screening if their risk was low or they were unaware of their risk. Similarly, researchers fo und that as the number of first degree relatives with breast cancer increases, so too does likelihood of seeking testing for the BRCA1 gene ( Lerman et al., 1996). Again, participants who perceived that they were less at risk for developing the disease were more likely to decline testing.

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17 Why are the Ef fects of Magnitude and Likelihood on Behavior Inconsistent? The effect of magnitude and likelihood on health behavior are weak at best and inconsistent at worst (Floyd et al., 2000; Janz & Becker, 1984; Milne et al., 2000). Researchers have proposed severa l explanations for the weak and inconsistent relationships between magnitude, likelihood, and health behavior. First, traditional measures of likelihood may be problematic. Likelihood is often measured as the chance that one will contract a disease (Rose nstock, 1966). Some researchers have argued that this traditional measurement is not effective and instead suggest a conditional measure of likelihood (see Ronis, 1992). In other words, participants are asked the likelihood that they will contract a diseas e if they engage or do not engage in preventative behavior. Consistent with his prediction, studies have often achieved greater success in predicting intentions using statements of conditional t may contribute to AIDS, including your own past and present behavior, what would you say are your chances of statement is more effective than traditional measures of li kelihood, this example of a Second, magnitude and likelihood are often confounded in the minds of participants. Although most combinations of magnitude and likelihood are not problematic, the combination of high magnitude and high likelihood can be problematic. For example, people have difficulty imagining a disease that is both severe and common. Ins tead, people appear to have a heuristic that severe diseases are rare, and researchers find that participants rate a disease as more severe when it is described as rare than when it is described as common (Jemmott, Ditto, & Croyle, 1986). Thus,

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18 participant s within the same level of magnitude may interpret the severity information differently depending on their perceived likelihood of having the condition. Third, traditional measures of severity may be problematic. Severity is a multidimensional construct a nd some operations of severity may be more effective than others (Milne, et al., 2000). For example, human immunodeficiency virus (HIV) is more deadly and thus an objectively more serious type of a sexually transmitted disease than i s c hlamydia. Thus, one way to manipulate severity is to select diseases that vary in severity. Another potential way to manipulate severity is through the description of the treatment for the condition. Again, the researcher could describe treatment for HIV as requiring a much g reater change in behavior (e.g., long term drug t herapy) than the treatment for c hlamydia (e.g., brief course of antibiotics). Severity can also vary in terms of onset of the disease (near vs. distant), the speed of onset (gradual vs. sudden), and the visi bility of symptoms (low vs. high; Smith Klohn & Rogers, 1991). Next, weak associations between magnitude and likelihood with intentions to engage in preventative behaviors may occur because researchers have omitted important individual difference variable s. Two individual difference variables that may play an important role in information avoidance are uncertainty orientation and dispositional optimism People vary in the extent to which they like to learn new things about themselves and their environment (Sorrentino & Short, 1986). Uncertainty oriented people like to learn new things about themselves and their environment and seek to gain an accurate view of both. Certainty oriented people prefer to seek information that maintains their current view of th emselves and their environment. Researchers find that uncertainty motivation moderates the impact of perceived threat (a variable that combined

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19 disease; Brouwers & Sorrentino, 1993). Uncertainty oriented participants were more likely to seek testing for the fictitious disease when threat was high and the test was described as highly diagnostic. Although the test would likely reveal unpleasant information, uncertain ty oriented participants sought the information because they prefer to have an accurate view of themselves and their environment. Certainty oriented participants were more likely to seek testing when threat was high or diagnosticity was high, but not when both were high. If only one of the variables (threat or diagnosticity) is high, certainty oriented participants can maintain their view of themselves and their environment because: a) seeking a test result is not threatening if they are at high risk, but t he test itself is not very diagnostic; and b) seeking a test result is not very threatening if the test is diagnostic but they are at a low risk for the disease. People also vary in the extent to which they believe good things will happen in the future. Dispositional optimists believe that good things will happen and negative events will be scarce (Scheier & Carver, 1985). On the other hand, dispositional pessimists believe bad things will happen and good things will be scarce. Dispositional optimists are more likely (than dispositional pessimists) to believe that unknown information will be positive (Geers, 2000; Scheier & Carver, 1985). How will optimism influence the decision to seek or avoid information? One study that examined willingness to be tested for the hereditary breast cancer found that women were more likely to avoid testing if they were dispositionally optimistic than if they were dispositionally pessimistic (Biesecker et al., 2000). The authors proposed two possible explanation s for the effe ct. First, dispositional optimists may overestimate the probability that they did not inherit breast cancer or they may underestimate the chance that they will develop breast cancer if they did inherit the breast cancer gene. This explanation is consistent tendency to think good things will happen to them. Second, dispositional optimists are

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20 more likely to take risks (Norem & Cantor, 1986). Avoiding potentially important health information represents a type of risky beh avior. Finally, difficulties in finding effects of magnitude and likelihood may occur because important situational variable s were not examined in previous studies. Two likely variables are ease of information attainment and perceived control. Ease of inf ormation attainment simply refers to the ease with which one can acquire information. Ease in the current investigation is akin to the variable cost as it is defined in both the health belief monetary and non monetary costs (e.g., time). Further, both theories predict that greater perceptions of cost are associated with less intention to engage in preventative behavior. Meta analyses of the PMT confirm this prediction and fin d that cost is the variable most strongly associated with behavioral outcomes (i.e., relative to the other PMT variables; Milne et al., 2000). However, ease is often not tested when researchers explore potential interactions among the protection motivation theory variables (Block & Keller, 1998; Neuwirth, Dunwoody, & Griffin, 2000). Thus, testing the role of ease in conjunction with magnitude and likelihood is needed to understand the effects of magnitude and likelihood on behavior. A second potential situa tional predictor is perceived control. According to protection motivation theorists, perceived control is divided in two components: response efficacy (i.e., the belief that adaptive behavior will produce the desired outcome) and self efficacy (i.e., the b elief that one has the skills to engage in the adaptive behavior; Rogers, 1983). Prior studies have shown that magnitude, likelihood, and self efficacy interact (Block & Keller, 1998), and that magnitude, likelihood, and response efficacy interact (Neuwirt h, Dunwoody, & Griffin, 2000) in predicting intentions to engage in health behaviors. In both studies, intentions to engage in health behavior were greatest when magnitude,

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21 likelihood, and control were all high. Thus, previous research examining intentions to engage in preventative behaviors suggests that magnitude, likelihood, and control will likely interact to predict information avoidance. Consistent with this prediction, researchers have shown that participants are more likely to seek testing regarding a medical condition when they believed the condition is severe and treatable, but are more likely to avoid testing when they believed the disease is severe and un treatable (Dawson, Savitsky, & Dunning, 2006). Thus, participants sought information when the disease was severe, but only when treatment was available. In summary, effects of magnitude and likelihood on preventative behavior were likely mixed in prior studies because of problems with measurement of these variables, i.e., likelihood was not mad e conditional on behavior, magnitude and likelihood were confounded in the minds of participants, and the selected dimension of magnitude was less effective. In addition, potentially important individual difference variables (i.e., dispositional optimism a nd uncertainty orientation) and situational moderators (i.e., perceived ease and perceived control) were typically omitted in previous research. Although information avoidance and intentions to engage in preventative behaviors are conceptually distinct dep endent variables, accounting for the limitations of research examining intentions to engage in preventative behavior will likely improve the study of information avoidance. The current investigation focus ed on the inclusion of situational moderators to imp rove the predictive ability of magnitude and likelihood. However, I address ed the aforementioned limitations in the design of the studies as well. Overview and Hypotheses I examine d the effects of magnitude and likelihood on information avoidance in the c ontext of screening for TAA deficiency (a fictitious medical condition). Participants learn ed about TAA deficiency and receive d the opportunity to receive diagnostic testing

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22 for TAA deficiency. In both studies, I manipulate d magnitude by describing TAA as either serious (causes discomfort and long term health consequences) or not serious (little discomfort and no long term consequences). Further in both studies I manipulated likelihood. Based on a saliva test, the experimenter told participants that it is either unlikely that they are TAA deficient (approximately 5% likelihood) or likely (approximately 60% likelihood). I explore d the ease of information attainment as a potential moderator in Study 1 and perceived control as a potential moderator in Study 2. I hypothesized the following: Hypothesis 1: I predicted that participants would display the most information avoidance (i.e., decline diagnostic testing for TAA deficiency) when magnitude was low, likelihood was low, and ease was low. Further, I predicted that participants would display the least information avoidance (i.e., g reater seeking) when magnitude was low, likelihood was low, and ease wa s high. I considered a voidance scores in the remaining cells exploratory and expected them to fall between these two extreme values. Hypothesis 2: I predict ed that participants would display the most inform ation avoidance when magnitude was high, likelihood was high, and control wa s low. Further, I predicted that participants would display the least information avo idance (i.e., greater se eking) when magnitude was high, likelihood was high, and control was high. I considered a voidance scores in the remaining cells exploratory and expected them to fall between these two extreme values.

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23 CHAPTER 2 STUDY 1 Overview Stud y 1 examine d the effects of magnitude, likelihood, and ease on people's decision to avoid vs. seek information about whether they were TAA deficient I predict ed participants w ould be most likely to display information avoidance (i.e., decline TAA testing ) when magnitude wa s low, likelihood wa s low, and ease wa s low. Conversely, I predict ed that participants w ould be least likely to display information avoidance when magnitude wa s low, likelihood wa s low, and ease wa s high. Method Participants Participants (97 women, 80 men) were undergraduate students recruited through the research pool managed by the psychology department. Participants were mostly freshman (freshman = 125, sophomores = 29, juniors = 15, seniors = 8) and Caucasian (Caucasian = 101, Hispani c = 28, African American = 25, Asian American = 14, and Other = 9). Prior to analysis, data from 11 participants were excluded because they did not find the study procedure believable. The number of excluded participants by c ondition is presented in Table 2 1 Materials Participants completed an informed consent form (see Appendix A) and demographic questionnaire (see Appendix B). Uncertainty orientation Uncertainty orientation refers to the extent to which people like to learn new things about themselves and their environment (Sorrentino & Short, 1986). People scoring high on the measure (i.e., uncertainty oriented people) are information seekers. Uncertainty oriented people seek information about themselves and their environment with the goal

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24 of accuracy. People scoring low on the measure (i.e., certainty oriented people) tend to avoid new information unless it confirms an already existing belief about themselves or their environment. Thus, certainty oriented people are drive n by the goal of consistency. T he original measure of uncertainty orientation was projective, using a variation on the Thematic Apperception Test in which participants receive minimal initial information (e.g., rite a story (Brouwers & Sorrentino, 1993; Sorrentino & Short, 1986). Researchers then code the stories for the amount of uncertainty orientation Because this type of projective measure is time consuming to administer and difficult to score reliably, I me asure d uncertainty orientation with a 7 item self report measure Sample items ed to items on a scale from 1= strongly disagree to 7 = strongly agre e (Appendix C). Dispositional optimism Dispositional optimism reflects a generalized tendency to ha ve positive expectations about the future (Scheier & Carver, 1985). I measured dispositional optimism with the LOT R (Scheier, Carver, & Bridges, 1994). The LOT R consists of four filler items, three positively worded items, and three negatively worded ite ms (Appendix D) The negatively worded items were reverse coded and added to the positively worded items to create a single index. Participants indicated their responses on a five point scale ranging from 1 = strongly disagree to 5 = strongly agree R esear ch reveals that the scale is reliable

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25 Procedure The experimenter ran participants individually. On arrival to the study, the experimenter gree t ed participants and explain ed that th ey would complete a study examining perceptions of a TAA deficiency brochure that the Student Health Center wa s considering for adoption. Participants then complete d the demographic questionnaire and measures of uncertainty orientation and dispositional optim ism while the experimenter waited outside of the room. On completion of these q uestionnaires, part icipants retrieve d the experimenter who then provide d participants with information about TAA deficiency In the high magnitude condition the experimenter explained that being TAA deficient leads to disturbances in digestion and insulin release that ca n be very severe and uncomfortable. Further, the experimenter told participants that being TAA deficient puts people at risk for severe pancreatic disorders in adult life. In the low magnitude condition, the experimenter explained that any disturbances in digestion and insulin release that may result from being TAA deficient would be mild, if detected. Further, being TAA deficient would not lead to any long term negative consequences. To bolster the cover story, the experimenter distribute d the TAA brochu re and participants evaluate d it. I created two versions of the TAA brochure: one for participants in the high magnitude condition and one for participants in the low magnitude condition. The magnitude information presented in the brochures was consistent with the magnitude information the experimenter verbally reported. Thus, exposure to the magnitude manipulation occur red in both the verbal instructions and in the brochure. Following the evaluation of the brochure, the experimenter reminded participants t hat they would have the opportunity to be tested for TAA deficiency. The experimenter explained that the only definitive test of TAA deficiency was a blood test (fingerprick).

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26 However, because some participants are uncomfortable with blood tests, the exper imenter explained that all participants would complete a saliva test prior to making the decision to undergo the blood test. The saliva test would give participants a rough indication of their risk or likelihood of being TAA deficient. Again, the experimen ter emphasized that the only definitive TAA test was the blood test. The experimenter began the saliva test procedure by asking participants to rinse their mouth out with mouthwash to ostensibly remove any food residual that may influence the saliva test. Next, the experimenter handed participants a pH strip and asked them to place it on their tongue until asked to remove it (5 seconds). Most pH strips turned a light green color. The experimenter then compared the pH strip to a Risk Assessment Chart I cre ated two Risk Assessment Charts so that light green color on the pH strip indicated approximately 5% risk in the low risk condition and approximately 60% in the high risk condition (see Appendices E and F ). The experimenter then inform ed participants that they had four options r egarding testing for TAA deficiency : (1) get tested immediately, (2) sign up immediately for an appointment to be tested in the next week, (3 ) wait and take a contact number that they could call to set up an ap pointment at a later d ate, or (4 ) decline testing. In the high ease condition, the experimenter told participants that their supervisor, who was working next door, would come to the lab and perform the test immediately. In the low ease condition, the experimenter told participa nts that they must walk to Shands Hospital to take the TAA test In all conditions, t he experimenter assured participants that they would receive credit for completing the study regardless of testing decision. The experimenter left the room while particip ants made their testing decision on a q uestionnaire. After making their testing decision participants were debriefed.

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27 Results Manipulation Checks The manipulations of the magnitude and likelihood variables were successful. Participants were significantly more likely in the high magnitude condition ( M = 6.0, SD = 1.5) than in the low magnitude condition ( M = 4.1, SD = 1.9) to believe that TAA deficiency is a serious medical condition, F (1, 174) = 59.37, p < .001, 2 = .25. Similarly, participants were significantly more likely in the high likelihood condition ( M = 5.9, SD = 1.6) than in the low likelihood condition ( M = 1.7, SD = .8) to believe that they were at a high risk of being TAA deficient F (1, 175) = 449.24 p 2 = .72. The ease manipulation was not successful. Participants in the high ease condition ( M = 7.3, SD = 1.4) and low ease condition ( M = 6.9, SD = 1.8) reported F (1, 175) = 2.05, p = .15, 2 = .01. The failure of the ease manipulation check item to detect a difference in conditions is likely due to the item wording rather than due to a failure on the part of participants to detect the ease manipulation. I su spect that participants responded to the ease manipulation check question based on their evaluation of the finger prick information, which was the same in both the low and high ease conditions, rather than to whether they could be tested on the premises vs at Shands Hospital. Treating Information Avoidance Outcome as a Continuous Variable The four decisions were analyzed on a continuum with lower scores indicating greater avoidance (see Figure 2 1 for frequencies). I examined normality and homogeneity of variance assumptions to determine whether analysis of variance (ANOVA) tests were appropriate. Results from the full factorial model indicated that the model residuals deviated from normality (see Figure 2 2) and that the homogeneity of variance assumptio n was F (7,166) = 11.35, p < .001. To ensure

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28 accuracy in the study results, I conducted comparable statistical tests that do not assume normality (i.e., Mann Whitney test; Mann & Whitney, 1947) and homogeneity of variance (i.e., Welch tes t; Welch, 1947) as follow up tests to all study results when information avoidance was the outcome. In each case, the conclusions regarding the rejection of the null hypothesis obtained from the alternative statistical tests and from the ANOVA tests were i dentical. For ease of presentation, results from the ANOVAs appear in the text. Results from the comparable statistic tests appear in Table 2 2 Testing Covariates Prior research suggests that two individual difference variables, uncertainty orientation a nd dispositional optimism, may predict decisions to seek or avoid information (Biesecker et al., 2000; Brouwers & Sorrentino, 1993). As such, I entered uncertainty orientation ( = .86, M = 36.8, SD = 6.0) and dispositional optimism ( = .83, M = 22.2, SD = 3.9), along with demographic variables (gender, class rank, and dichotomized ethnicity) in a regression model to predict information avoidance. None of these potential covariates predicted avoidance, p Hypothesis Testing The primary hypotheses o f Study 1 were as follows: 1) I predicted that participants would be most likely to display information avoidance (i.e., decline TAA deficiency screening) when magnitude was low, likelihood was low, and ease was low ; and 2) I predict ed that participants wo uld be least likely to display informa tion avoidance when magnitude was low, likelihood was low, and ease was high. To test the first hypothesis I conducted a series of planned contrasts that compared participants in the low magnitude, low likelihood, and low ease condition with participants in all other conditions. Inconsistent with predictions, the level of avoidance in the low magnitude, low likelihood, and low ease condition was not significantly greater than the level of

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29 avoidance in all other conditi ons. As evident in Table 2 3, participants in the low magnitude, low likelihood, and low ease condition ( M = 3.8, SD = .7) displayed significantly greater avoidance than did participants in all other conditions ( p except one. The single exception was participants in the high magnitude, low likelihood, and low ease condition ( M = 3.8, SD = .7), t (39) = .18, ns Otherwise, regardless of whether participants believed that TAA deficiency was or was not seri ous, participants displayed the greatest avoidance when they were unlikely to have the condition and the test was difficult to obtain. To test hypothesis 2 I conducted a series of planned contrasts that compared participants in the low magnitude, low like lihood, and high ease condition with participants in all other conditions. Hypothesis 2 was not supported. I predicted that the least avoidance would be observed in the low magnitude, low likelihood, and high ease condition. However, as evident in Table 2 4, avoidance was significantly lower in the high magnitude, high likelihood, and high ease condition ( M = 1.6, SD = 1.1) than in the low magnitude, low likelihood, and high ease condition ( M = 2.5, SD = 1.5, t (49) = 2.42, p < .05. Participants were least likely to avoid the TAA test when they believed it was likely that they had a serious condition and it was easy to get the diagnostic test. Omnibus ANOVA Test I also tested the full factorial 2 (magnitude: low vs. high) x 2 (likelihood: low vs. high) x 2 (ease: low vs. high) ANOVA for exploratory purposes. All interactions and the main effect of magnitude were nonsignificant, p s > .07. Only the main effects of likelihood and ease were significant. Participants in the low likelihood condition ( M = 3.2, SD = 1.3) were more likely than participants in the high likelihood condition ( M = 2.5, SD = 1.3) to avoid TAA deficiency testing, F (1,166) = 15.22, p 2 = .08. Similarly, participants in the low ease condition ( M = 3.3, SD = 1.1) were more likely than

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30 participants in the high ease condition ( M = 2.4, SD = 1.4) to avoid TAA deficiency testing, F (1,166) = 26.62, p 2 = .14. Discussion The Study 1 focal hypotheses were not supported. Participants did not display significantly more avoidance when TAA deficiency was not serious, not likely and testing to difficult to access. Further, participants did not display significantly more seeking whe n TAA deficiency was not serious, not likely, and testing was easy to access. The pattern of means observed in Study 1 is perhaps best understood in terms of the significant main effects. Avoidance is greatest when 1) TAA risk is low than when it is hi gh and 2) diagnostic testing is difficult to obtain than when it is easy to obtain. The hand, participants acted as wise consumers of health information. High risk part icipants were more likely than low risk participants to agree to be tested. Both testing decisions are rather reasonable. On the other hand, participants perhaps did not act as wise health consumers in that they allowed a relatively minor variation in ease of test access influence their decision to undergo diagnostic testing. Table 2 1. Number of Excluded Participants by Condition in Study 1 Ease Low High Magnitude Magnitude Likelihood Low High Low High Low 2 1 High 4 1 3 No te: All participants were excluded prior to data analysis.

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31 Table 2 2 Follow up Tests to Study Results for Study 1 F or t Statistic Welch F or t Statistic Mann Whitney U Z Statistic (Normality and Equal Variance Assumed) (Equal Variance not As sumed) (Normality not Assumed) Low MLE vs. High M and Low LE .18 .18 .39 Low ML and High E 3.46* 3.51* 3.00* Low ME and High L 2.84* 3.07* 3.09* High ML and Low E 3.87** 3.79* 3.95* High ME and Lo w L 3.19* 3.08* 2.79* Low M and High LE 3.44* 3.39* 3.37* High MLE 7.65** 8.39** 5.25** Low ML and High E vs. High M LE 2.42* 2.23* 2.23* Low MLE 3.46* 3.51* 3.00* High M and High LE 3.42* 3.42* 2.84* Low ME and High L 1.02 1.00 .62 High ML and Low E .34 .34 .03 High ME and Low L .18 .18 .18 Low M and High LE .24 .24 .00 Main Effect L 15.22** 15.80** 4.40** Main Effect E 26.62** 28.84** 4.64** p < .05, ** p < .001 Notes: M = Ma gnitude, L = Likelihood, and E = Ease.

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32 Table 2 3. Cell Mean Comparisons Between Low Magnitude, Likelihood, and Ease Condition with All Other Conditions. Ease Low High Magnitude Magnitude Likelihood Low High Low High Low 3.8 (.7) a 3.8 (.7) a 2.5 (1.5) b 2.6 (1.5) b High 2.9 (1.2) b 2.7 (1.1) b 2.6 (1.3) b 1.6 (1.1) b Note: Significant means differences are denoted by differing superscripts. Higher numbers indicate greater avoidance. Table 2 4. Cell Mean Comparisons Between the Low Ma gnitude, Low Likelihood, and High Ease Condition with All Other Conditions. Ease Low High Magnitude Magnitude Likelihood Low High Low High Low 3.8 (.7) b 3.8 (.7) b 2.5 (1.5) a 2.6 (1.5) a High 2.9 (1.2) a 2.7 (1.1) a 2.6 (1.3) b 1.6 (1.1) b Note: Significant means differences are denoted by differing superscripts. Higher numbers indicate greater avoidance. Figure 2 1. Frequency of TAA Deficiency Testing Options in Study 1 (Option 1 = Get tested; Option 2 = Make appointment to tested; Option 3 = Maybe make appointment at later date; and Option 4 = Decline testing). 60 3 29 82 0 10 20 30 40 50 60 70 80 90 1 2 3 4 Frequency Testing Options

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33 Figure 2 2. Distribution of Residuals for the Full Factorial Model (Study 1).

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34 CHAPTER 3 STUDY 2 Overview Study 2 examined the effects of magnitude, likelihood, and con trol on people's decision to avoid vs. seek information about whether they were TAA deficient. I predicted that the greatest levels of avoidance of TAA testing would occur when magnitude was high, likelihood was high, and control was low. Further, I predic ted that the lowest levels of avoidance (i.e., greater seeking) would occur when magnitude was high, likelihood was high, and control was high. Method Participants and Materials Participants (96 women and 83 men) were undergraduate students recruited thr ough the research pool managed by the psychology department. As in Study 1, participants were mostly freshman (freshman = 93, sophomores = 47, juniors = 28, seniors = 10 and missing = 1) and Caucasian (Caucasian = 107, Hispanic = 22, African American = 24, Asian American = 13, and Other = 13). Prior to analysis, data from 19 participants were excluded because they did not find the study procedure believable. The number of excluded participants by conditions is presented in Table 3 1 Study materials were id entical to the materials used in Study 1 with one exception. An additional questionnaire that measured other potential predictors of information avoidance was added to S tudy 2 for exploratory purposes (see Appendix G ). Procedure Similar t o Study 1, the ex perimenter explain ed to participants that they would complete a study examining perceptions of a TAA deficiency brochure that the Student Health Center wa s considering for adoption. After participants complete d the initial questionnaires (i.e., demographic s measure and individual difference m easures), the

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35 experimenter provide d information about TAA deficiency. The information include d the manipulation of magnitude (identical to the magnitude manipulation in Study 1) and a manipulation of control. In the high control c ondition, the experimenter explain ed that people can influence the amount of TAA they have through changes in their daily health habits. Thus, participants have a great deal of control over the amount of TAA they possess. In the low control c ondition, the experimenter explain ed that changing health habits has little to no influence on TAA levels. Thus, participants have very little control over the amount of TAA they possess Again, participants evaluate d a TAA brochure that contained gener a l information about TAA and the magnitude manipulation. Participants also underwent the saliva test and likelihood manipulation. After delivering the likelihood manipulation, the experimenter offer ed pa rticipants an opportunity to be tested for TAA defici ency (ease wa participants completed a final questionnaire (exploratory measure). Finally, all participants were debriefed and thanked for their participation Results Manipulation Checks Results indic ated that the manipulations of all independent variables were successful. Participants were significantly more likely in the high magnitude condition (M = 6.1, SD = 1.6) than in the low magnitude condition (M = 3.6, SD = 1.7) to believe that TAA deficiency Participants were significantly more likely in the high likelihood condition (M = 6.1, SD = 1.7) than in the low likelihood condition (M = 1.7, SD = .9) to believe that they were at a

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36 participants were significantly more likely in the high control condition (M = 6.4, SD = 2.2) than in the low control condition (M = 4.2, SD = 2.1) to perceive that they ha d control Treating Information Avoidance Outcome as a Continuous Variable The frequencies of the information avoidance response options are presented in Figure 3 1 Again I examined the normali ty and homogeneity of variance assumptions in the full factorial model to determine if ANOVA tests were appropriate. Results indicated that the model residuals slightly devia ted from normality (see Figure 3 2 ) and the homogeneity of variance assumption was not met, F (7,1 71 ) = 14.63, p < .001. When available, I conducted comparable statistical tests that do not assume normality and homogeneity of variance as follow up tests to all study results. In all cases, the conclusions reached from the compara ble tests were identical to those reached with the typical ANOVA tests (see Table 3 2 ). Testing Covariates .83, M = 37.3, SD M = 22.2, SD = 3.8) were tested as potential predictors of information avoidance. Only one covariate, d ichotomized marginally predicted avoidance, b = .39, t (173) = 1.83, p = .07. Non Caucasian partic ipants were more likely than Caucasian participants avoid information. Because none of the covariates significantly predicted avoidance, none were entered as covariates in subsequent models. Hypothesis Testing The primary hypotheses of Study 2 were as fol lows: 1) I predicted that the greatest level of avoidance of TAA testing would occur when magnitude wa s high, likeliho od was high, and control was low; and 2) I predicted that the lowest level of avoida nce (i.e.,

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37 greater seeking) would occur when magnitude was high, likelihood was high, and control wa s high. To test the first hypothesis I conducted a series of planned contrasts that compared participants in the high magnitude, high likelihood, and low control condition with participants in all other conditi ons. Contrary to prediction, the highest level of avoidance was not found in the high magnitude, high likelihood, and low control condition. Significantly higher levels of avoidance were found in four other conditions (see Tab le 3 3 ). To test the second p rimary hypothesis, I compared participants in the high magnitude, high likelihood, and high control conditions to participants in all other conditions. Inconsistent with predictions, the lowest level of avoidance was not observed in the high magnitude, hig h likelihood, hig h control condition (see Table 3 4 ). Participants in the high magnitude, high likelihood, and high control condition ( M = 1.5, SD = 1.0) did not significantly differ from participants in the high magnitude, high likelihood, and low control condition ( M = 1.6, SD = 1.0; t (44) = .57, ns ). When participants consider whether to take a diagnostic test for a serious condition that they are likely to have, the extent to which participants perceive that they can control their TAA levels had no in fluence on their testing decision. Omnibus ANOVA Test I also tested the full factorial 2 (magnitude: low vs. high) x 2 (likelihood: low vs. high) x 2 (control: low vs. high) ANOVA for exploratory purposes. All interactions and the main effect of control were not significant, p magnitude and likelihood were significant. Participants displayed greater avoidance in the low magnitude condition ( M = 2.9, SD = 1.3) than in the high magnitude condition ( M = 2.1, SD = 1. 4), F (1,171) = 18.95, p 2 = .10. Similarly, participants displayed greater avoidance in the low likelihood condition ( M = 3.2, SD = 1.2) than in the high likelihood condition ( M = 1.8, SD = 1.2), F (1,171) = 63.81, p 2 = .27.

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38 Exploring the Motivation for Informa tion Avoidance and Seeking Researchers believe people may choose to avoid information for three reasons: a) the information may produce an unpleasant emotional experience; b) the information may challenge a held cherished belief (e.g., that one is healthy ); and c) learning the information may require a change in behavior (Sweeny, Melnyk, Malone & Shepperd, 2010). For example, participants may avoid learning whether they are TAA deficient because they think it will upset them, challenge their view as health y young people, or compel them to change their health habits. I included items assessing these three potential motivations in Study 2 for exploratory purposes. I tested the three motivations as potential mediators of the significant main effect relationshi ps between magnitude and likelihood with information avoidance. I conducted a multiple mediation analysis (with a SPSS script developed by Preacher & Hayes, 2009) wherein I allowed all three motives to simultaneously mediate the main effect relationships. A multiple mediation analysis is preferable over separate mediation analyses because the indirect effects estimates produced by the program reflect the unique contributions of each mediator controlling for the other mediators. A second advantage of using the multiple mediation script is that it employs a bootstrapping technique to test for mediation. Bootstrapping is a resampling method that entails taking many samples from the data set (e.g., 1000) with replacement, and calculating the desired parameter e stimates in each of these samples. The bootstrapping program then calculates the mean values for the desired parameters over the many randomly selected samples. Thus, the bootstrap estimates of the indirect effect(s) are more reliable than are estimates ob tained by the standard Sobel test. I present the results from the multiple mediation analysis exploring the relationship between magnitude and information avoidance in Table 3 5 The only significant indirect

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39 effect pertained to the motive to avoid a chan motivate seeking rather than avoidance. As evident in the Figure 3 3 participants in the high magnitude condition, compared to participa nts in the low magnitude condition, indicated that the possibility that they would have to change their behavior as a result of their TAA test results was a greater influence on their testing decision, b = 1.11, t (177) = 2.99, p < .01. Further, the influen ce of the possible behavior change was associated with greater information seeking, b = .26, t (177) = 5.14, p < .001 Thus, just as participants may avoid information to avoid an unwanted behavior change it is also possible, as in the current study, tha t participants may seek information because they want to make a behavior change if they perceive the change as needed. Because the direct effect of magnitude on information avoidance was significant with the mediators in the model, b = .55, t (177) = 2.92 p < .01 the behavior change motive was a partial mediator. The pattern of results in the multiple mediation analyses examining the relationship between likelihood and inf ormation avoidance (see Table 3 6 ) was identical to the pattern in the magnitude m ultiple mediation. Again, only the motive involving a change in behavior on the testing decision was greater among participants in the high likelihood condition than in t he low likelihood condition b = 1.71, t (177) = 4.76, p < .001 and corresponded to greater seeking behavior, b = .23, t (177) = 4.79, p < .001 (see Figure 3 4 ). Finally, the behavior change motive partially mediated the relationship between likelihood and information avoidance, b = 1. 13 t (177) = 6 20 p < .001 Exploring Treatability and Magnitude as Predictors of Information Avoidance There are multiple ways to operationalize control. In the current study, I examined control over developing a medi cal condition. Previous researchers have examined

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40 control over the management of the condition (i.e., treatability). Dawson, Savitsky & Dunning (2006) examined the effects of treatability and seriousness of a medical condition on willingness to undergo dia gnostic testing. Results indicated that participants were most likely to avoid diagnostic testing when they perceived that the condition was severe and untreatable. In an attempt to replicate this prior study, treatability was measured in the current stud y and allowed to inter act with magnitude (see Figure 3 5 ). Although the treatability by magnitude interaction was not significant, b = .15, t (175) = 1.52, p = .13, the simple effect tests revealed that treatability was unrelated to avoidance when magnitud e was high ( b = .04, t (176) = .49, ns ) and marginally related to avoidance when magnitude was low ( b = .11, t (176) = 1.80, p = .07). In short, the treatability by magnitude interaction effect pattern found in prior research was not replicated in the cu rrent study. In prior research, the greatest level of avoidance occurred when severity was high and treatability was low, whereas the greatest level of avoidance in the current study occurred when both severity and treatability were low. Discussion The S tudy 2 focal hypotheses were not supported. Participants did not display significantly more avoidance when TAA deficiency was serious, likely and uncontrollable. Further, participants did not display significantly more seeking when TAA deficiency was serio us, likely, and controllable. As was true in Study 1, the study results are perhaps best explained by the significant main effects. Participants avoided diagnostic testing for TAA deficiency when the experimenter explained that TAA deficiency was not ser ious and that participants were unlikely to experience the condition. Thus, participants that were the least likely to need diagnostic testing were also the participants least likely to seek diagnostic testing.

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41 Further, exploratory analyses revealed that deficiency test may require a change in their health behavior partially explained how perceptions of likely and magnitude influence testing decisions. Table 3 1. Number of Excluded Participants by Condition in St udy 2. Control Low High Magnitude Magnitude Likelihood Low High Low High Low 2 1 3 High 2 2 5 4 Note: All participants were excluded prior to data analysis.

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42 Table 3 2. Follow up Tests to Study Results for Study 2. F or t S tatistic Welch F or t Statistic Mann Whitney U Z Statistic (Normality and Equal Variance Assumed) (Equal Variance not Assumed) (Normality not Assumed) High ML and Low C vs. Low MLC 8.57** 8.64** 5.38** High M and Low LC 2.59* 2.59* 2.23* Low MC and High L 1.91 1.88 1.65 Low ML and High C 7.70** 7.75** 5.03** High MC and Low L 3.53* 3.45* 3.01* Low M and High LC .79 .77 .49 High MLC .57 .57 .94 High MLC vs. Low M LC 9.14** 9.06** 5.41** High M and Low LC 2.96* 3.01* 2.75* Low MC and High L 2.32* 2.32* 2.27* High ML and Low C .57 .57 .94 Low ML and High C 8.25** 8.19** 5.06** High MC and Low L 3.89** 3.86** 3.38* Low M and High LC 1.25 1.24 1.24 Omnibus Tests Main Effect M 18.95** 16.15** 3.74** Main Effect L 63.81** 58.78** 6.66** p < .05, ** p < .001 Notes: M = Magnitude, L = Likelihood, and C = Control

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43 Table 3 3. Cell Mean Comparisons Between the High Magnitude, High Likelihood, and Low Control Condition with All Other Conditions. Control Low High Magnitude Magnitude Likelihood Low High Low High Low 3.8 (.7) b 2.6 ( 1.5 ) b 3.6 ( .7 ) b 2. 9 (1. 4 ) b High 2. 3 (1. 4 ) a 1.6 (1. 0 ) a 1.9 (1.3) a 1. 5 (1. 0 ) a Note: Significant m eans differences are denoted by differing superscripts. Higher numbers indicate greater avoidance. Table 3 4. Cell Mean Comparisons Between the High Magnitude, High Likelihood, and High Control Condition with All Other Conditions. Control Low High Magnitude Magnitude Likelihood Low High Low High Low 3.8 (.7) b 2.6 ( 1.5 ) b 3.6 ( .7 ) b 2. 9 (1. 4 ) b High 2. 3 (1. 4 ) b 1.6 (1. 0 ) a 1.9 (1.3) a 1. 5 (1. 0 ) a Note: Significant means differences are denoted by differing superscripts. Higher numbers indi cate greater avoidance. Table 3 5. Multiple Mediation Examining Relationship Between Magnitude and Information Avoidance. Normal Theory Bootstrap (J = 1000) Bias Corrected and Accelerated Indirect Effects b SE p M SD 95% CI Lower Upper Negative Emotions .03 .03 ns .03 .04 .03 .15 Cherished Beliefs .01 .04 ns .01 .04 .06 .16 Change Behavior .29 .11 < .05 .29 .11 .54 .09

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44 Table 3 6. Multiple Mediation Examining Relationship Between Likelihood and Information Avoidance. Normal Theory Bootstrap (J = 1000) Bias Corrected and Accelerated Indirect Effects b SE p M SD 95% CI Lower Upper Negative Emotions .07 .05 ns .08 .06 .00 .24 Cherished Beliefs .04 .07 ns .04 .09 .09 .27 Change Behavior .39 .12 < .05 .39 .10 .62 .20 Figure 3 1. Frequency of TAA Deficiency Testing Options in Study 2 (Option 1 = Get tested; Option 2 = Make appointment to tested; Option 3 = Maybe make appointment at later date; and Option 4 = Decline testing). 78 4 21 76 0 10 20 30 40 50 60 70 80 90 1 2 3 4 Frequency Testing Options

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45 Figure 3 2 Distribution of Residuals for the Full Factorial Model (Study 2).

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46 Figure 3 3 Behavior Change as a Partial Mediator of the Relationship between Magnitude and Information Avoidance. p < .05. Magnit ude Avoidance b = .81* Avoidance Magnitude Change Behavior b = 1.11* b = .26* b = .55*

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47 Figure 3 4. Behavior Change as a Partial Mediator of the Relationship between Likelihood and Information Avoidance. p < .05. Likelihood Avoidance b = 1.40* Avoidance Likelihood Change Behavior b = 1.71* b = .23* b = 1.13*

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48 Figure 3 5. Treatability and Magnitude as Predictors of Information Avoidance.

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49 CHAPTER 4 GENERAL DISCUSSION Overview The goal this dissertation was to explore how perceptions of the magnitude of a medical condition and the likelihood of having that condition influence decisions to seek or avoid diagnostic testing. I hypothesized that the inclusion of two additional var iables, ease of test access (Study 1) and control over developing the condition (Study 2), were necessary to fully understand the relationships between magnitude, likelihood, and information avoidance. My hypotheses were not supported. I summarized results relating to each of the independent variables below. Ease Ea se of info rmation attainment refers to the ease wit h which one can acquire diagnostic testing In Study 1, I examined the roles of ease, magnitude, and likelihood in predicting information avo idance. I proposed that the inclusion of ease was necessary to understand how magnitude and likelihood relate to information avoidance when both magnitude and likelihood are low. The Study 1 results did not support my hypotheses. Although avoidance was hi gh in the low magnitude, low likelihood, and low ease condition, as expected, it was equally high in the high magnitude, low likelihood, and low ease condition. When both likelihood and ease were low, level of magnitude did not influence decisions to avoid diagnostic testing. Also contrary to predictions, the least amount of avoidance occurred in the high magnitude, high likelihood, and high ease condition, rather than in the low magnitude, low likelihood, and high ease condition. Even though participants i n the high magnitude, high likelihood, and high ease condition are likely to hear bad news, they are

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50 significantly more likely to seek testing than are participants in the low magnitude, low likelihood, and high ease condition who are likely to receive goo d news if they undergo testing. Additional analyses revealed a significant main effect of ease on information avoidance. Participants were more likely to undergo testing when testing was easy versus when it was difficult. Consistent with previous results, the effect of ease on health behavior outcomes was stronger than the other variables considered (see Milne et al., 2000). Although ease of attainment is perhaps less psychologically interesting than other variables of interest (e.g., likelihood), research ers should take the effects of ease in to account when trying to understand or explain health behavior. The results of Study 1, along with previous investigations, suggest that people consider the ease of test access when deciding to undergo diagnostic tes ting, regardless of the severity of the disease or their likelihood of having the disease. Control I defined control mined the effects of control, magnitude, and likelihood on information avoidance. I believed that the inclusion of control was necessary to understand how magnitude and likelihood related to information avoidance when both magnitude and likelihood were hig h. Again, my hypotheses were not supported. The observed pattern of means suggest that control did not influence participant decisions. Participants in the high magnitude, high likelihood, and high control condition did not differ from participants in the high magnitude, high likelihood, and low control condition in their TAA deficiency testing decision. Further, the main effect of control was not significant.

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51 Why did control fail to influence testing decisions? Recall that control in Study 2 was defined as the extent to which participants could control the development of TAA deficiency through their health behaviors. By the time participants arrived at the study and learned about TAA deficiency, the window of time in which they could potentially control their health behavior to minimize their TAA deficiency risk had already passed. There was little participants could do to change their outcome between learning about TAA deficiency and making their testing decisions. Thus, this operation of control (i.e., control over development of the condition) was perhaps a poor way to define control when the outcome measure was testing decision. In prior investigations of decisions to engage in diagnostic testing, researchers operationalized control as the extent to w hich a medical condition was treatable. Researchers found that participants were more likely to seek testing when they believed a medical condition was severe and treatable, but more likely to avoid testing when they believed the condition was severe and u ntreatable (Dawson et al., 2006). Thus, treatability may be a better operation of control when testing decision is the outcome of interest. For exploratory purposes, I attempted to replicate the treatability by magnitude interaction found in previous rese arch (i.e., Dawson et al., 2006). However, the results were inconsistent with past research. In Study 2, I found that treatability was unrelated to avoidance when magnitude was high and marginally related to avoidance when magnitude was low. Avoidance was greatest when both treatability and magnitude were low.

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52 The inconsistency between my research and the prior research may do due to methodological differences. For example, treatability was measured in Study 2 yet manipulated in the prior research. Regard less, researchers should continue to evaluate role of treatability in testing decisions in future research. In addition, researchers should not ignore the potential role of control, as defined as control over developing the condition, in information avoid ance. Modifications to the study paradigm may allow researchers to explore this link. For example, instead of using testing decision as the outcome, researchers could use risk for developing the condition as the outcome. Researchers that want to study test ing decisions as the outcome could consider increasing the time delay between the delivery of the control manipulation and the measurement of the testing decision. An increased time delay between the manipulation and the outcome measurement should increase the likelihood that control will predict the outcome. Magnitude Magnitude (i.e., the severity of TAA deficiency) was not a consistent significant predictor of information avoidance in my studies. Magnitude did not significantly predict testing decisions in Study1 but did significantly predict testing decisions in Study 2. Participants were more likely to avoid testing in the low magnitude condition than in the high magnitude condition. Although the magnitude main effect in Study 1 did not reach conventio nal levels of significance, the direction of the means was consistent with the direction observed in Study 2. In both studies lower levels of magnitude corresponded with greater avoidance. It is unclear why magnitude significantly predicted information a voidance in Study 2 but not in Study 1? The manipulation of magnitude in both studies was identical.

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53 Thus, the difference in study results is not due to changes in the manipulation of magnitude. However, several other explanations seem possible. First, th e likelihood manipulation may have affected perceptions of magnitude. Past researchers have noted that magnitude and likelihood can become confounded in the minds of participants. Specially, participants have difficulty imagining that a disease can be both severe and common (see Jemmott, Ditto, & Croyle, 1986). Thus, perceptions of severity among participants in the high magnitude may differ depending on which likelihood condition they were assigned. Although this explanation seems plausible, additional ana lyses do not support it. Specifically, if the likelihood manipulation affected perceptions of magnitude, then participants in the high and low likelihood conditions would differ in their responses to the magnitude manipulation check item. However, likeliho od did not influence perceptions of the severity of TAA deficiency in either Study 1, F (1, 174) = .01, ns or Study 2, F (1, 177) = .02, ns Thus, the inconsistent effect of magnitude across studies was not due to a confound between likelihood and magnitude one of the studies. Second, perhaps the manipulation of magnitude was not strong enough to produce consistent effects. In the low magnitude condition, the experimenter explained that any discomfort from TAA deficiency was mild and that there were no long term negative consequences. In the high magnitude condition, the experimenter explained that TAA deficiency leads to disturbances in digestion that can be severe and long term (i.e., disturbances throughout adulthood). In both studies, participants in the high magnitude condition rated TAA deficiency as a significantly more serious condition than did participants in the low magnitude condition. However, in both studies, mean scores

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54 on the magnitude manipulation check item (a 9 point scale) hovered just bel ow the middle of the scale in the low magnitude group (Study 1: M = 4.1, Study 2: M = 3.6) and just above in the high magnitude group (Study 1: M = 6.0, Study 2: M = 6.1). Thus, a stronger manipulation that portrayed TAA deficiency as even less serious in the low magnitude condition and even more serious in the high magnitude condition may produce consistent effects of magnitude. Finally, it is possible that the choice of magnitude operation, rather than the manipulation strength, is responsible for the in consistent magnitude effects. Researchers have argued that inconsistencies in the effects of magnitude in prior investigations were due to the use of less effective operations of severity ( Milne, et al., 2000). In the current studies, magnitude varied in t erms of the severity of symptoms (low vs. high) and the duration of the symptoms (brief vs. long term). Other potential operations of severity include variations in the onset of the disease (near vs. distant), the speed of onset (gradual vs. sudden), and t he visibility of symptoms (low vs. high; see Smith Klohn & Rogers, 1991). Future researchers may want to consider alternative operations of the magnitude variable. Likelihood Likelihood consistently predicted information avoidance in both studies. Avoidan ce was greater in the low likelihood condition than in the high likelihood condition. The few prior studies that have examined the effects of likelihood on information avoidance also found that lower perceptions of likelihood corresponded with greater info rmation avoidance (e.g., Babul et al., 1993 Ler man et al., 1996 ). Thus, a consistent link between low risk and avoidance is beginning to emerge in the information avoidance literature.

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55 Individual Difference Measures I examined two potential individual difference predictors of information avoidance: di spositional optimism and uncertainty orientation. Neither significantly predicted information avoidance. It is unclear why dispositional optimism failed to predict avoidance. In prior research, greater optimism corresponded with greater avoidance (Biesecke r et al., 2000). However, researchers (i.e., Biesecker et al., 2000) listed a R) in their sample. Such a low alpha makes it difficult to draw conclusions regarding relationships betwee n optimism and any other variables. Thus, it is difficult to determine why I found no relationship between optimism (with an adequate alpha) and avoidance in the current studies. It is perhaps not surprising that uncertainty orientation failed to predict information avoidance. In prior research, uncertainty orientation was used as a moderator rather than a standalone covariate. Researchers found that uncertainty orientation interacted with threat (a variable that combined both magnitude and likelihood) and diagnosticity (the extent to which a test was diagnostic) to predict testing decisions (Brouwers & Sorrentino, 1993). I attempted to replicate this finding in the current studies by testing the interaction between magnitude, likelihood, and uncertainty or ientation. (Because the test for TAA deficiency was described to participants as diagnostic, I assumed that diagnosticity was held constant at high in the current studies.) In both studies, the interaction was not significant, p h is needed to understand the circumstances in which uncertainty orientation predicts avoidance. Undoubtedly, individual differences in information avoidance exist. No doubt, some people are more likely than are others to avoid information regardless of t he domain

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56 (e.g., health, relationships). My collaborators and I are currently working to develop an informational avoidance scale that attempts to measure individual differences in the tendency to avoid potentially unwanted information. Our hope is that th e scale will capture variation due to general tendencies to seek or avoid information, thus allowing for a clearer examination of the effects of situational variables (e.g., ease and control) on avoidance. Revisiting the Information Avoidance Construct In formation avoidance is defined as Malone, & Shepperd, 2008). In my studies, information avoidance was operationalized as participan Regardless of the reason for making the testing decision, higher scores on the outcome measure were considered indicative of greater avoidance. An important next step in studying informa tion avoidance is moving toward an understanding of why people avoid information. In the current studies, participants avoided diagnostic testing when it was more difficult to attain, they were at low risk for the condition, and the condition was considere d not very severe (Study 2 but not Study 1). Why did participants avoid in these circumstances? It seems likely that participants felt that the costs of undergoing a diagnostic test (e.g., extra time and potential slight pain) outweighed the benefits of kn owing whether they were TAA deficient given their circumstances. Consistent with this reasoning, in prior research finds that participants consider the costs and benefits of avoiding vs. not avoiding information when deciding whether to seek or avoid infor mation (Sweeny & Malone, 2010).

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57 Although a cost/benefit analysis framework is useful in understanding information avoidance, it is also very broad. My collaborators and I have proposed three specific reasons why people avoid information: a) the information may produce an unpleasant emotional experience; b) the information may challenge a held cherished belief (e.g., that one is healthy); and c) learning the information may require a change in behavior (Sweeny et al., 2010). For exploratory purposes, I test ed whether these three motivations mediated the links between magnitude and likelihood with testing decisions in Study 2. Only the motive involving a change in behavior was a significant partial mediator. However, behavior appeared to motivate seeking behavior rather than avoidance. Participants opted to learn if they were TAA deficient so that they could implement behavior changes. Thus, it is possible that concern over ions to seek and decisions to avoid information. Implications Research exploring predictors of information avoidance has implications for everyday health decision making. People may sometimes avoid health information that would greatly benefit them. By u nderstanding the factors that predict avoidance, researchers and health practitioners can design more effective programs and health campaigns that increase participation of those in need of services. Because magnitude did not consistently predict avoidance I only present implications regarding ease and likelihood. Ease was the strongest predictor of avoidance in Study 1. Researchers and health practitioners should consider increasing the ease with which people can obtain health

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58 information and services if they wish to increase rates of service use. Ease can be increased in numerous ways. For example, researchers found that gay and bisexual men were more willing to undergo screening for anal cancer when it was free than when it was $150 (Reed et al., 2010). In addition, past research suggests that patients are more likely to undergo a mammogram when appointments are available on the same day as the primary care visit in which the doctor recommends a mammogram than when participants must make an appointment f or a later date (Dolan et al., 1999). However, other operations of ease may be less effective. Past research indicates that the effects of transportation incentives on willingness to attend follow up visits after an abnormal pap smear were mixed (Yabroff, Kerner, & Mandelblatt 2000). Thus, additional research is needed to determine the conditions in which various operations of ease are most effective. Results from both Studies 1 and 2 suggest that people consider their risk when deciding to seek vs. avoid health information. Low risk corresponded with greater avoidance. The link between low risk and avoidance is not problematic for people who are legitimately at low risk for a given condition. Problems occur when people are at high risk for a condition but they perceive that they are at low risk. Thus, interventions should seek to increase perceptions of risk among those in the high risk population. Further, designers of interventions may want to consider providing physical evidence of risk to participants. In the current studies, participants saw physical evidence of their risk when they viewed their pH strip relative to the risk assessment chart. People may find physical evidence of their risk more persuasive than simply being told that they are at risk.

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59 S ummary People frequently face potentially unwanted information (e.g., the balance on their credit card, their current weight, whether they have high blood pressure). The goal of this dissertation was to explore how four factors, magnitude, likelihood, eas e and control, influence information avoidance decisions. Clear relationships between both likelihood and ease with avoidance emerged, such that low likelihood and low ease both corresponded with greater avoidance. However, the relationship between magnitu de and avoidance was inconsistent and control was unrelated to avoidance. Future research is needed to understand if, and under what circumstances, magnitude and control might influence information avoidance

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60 APPENDIX A CONSENT FORM I will evaluate a broch ure and answer several questionnaires in this study. These questionnaires will ask about my feelings towards different behaviors. I will receive 2 experimental credits for completing the questionnaires today. Time Required : 1 hour Risks and Benefits : I will benefit by learning about research. There are no risks. Compensation: I will receive 2 credits for participation. Confidentiality : My responses will be confidential to the extent provided by the law. I will be assigned a code number, and my responses will be stored in a computer according to the code number and not by my name. As such, my name will not be associated with my responses and will not be used in any report. Moreover, all data will be analyzed by group averages and not by ind ividual responses. Voluntary Participation & Right to Withdraw : I understand that my participation in this study is voluntary. There is no penalty for not participating. I have the right to withdraw from the study at any time without consequence. Wh om to Contact if You have Questions about the Study : James A. Shepperd, Faculty Advisor, Dept. of Psychology, University of Florida, 392 0601 x 248. Wendi Malone, Principal Investigator, Dept. of Psychology, University of Florida, 392 0601 x 261. Whom to Contact about Your Rights as a Research Participant in the Study : UFIRB Office, Box 112250, University of Florida, Gainesville, FL 32611 2250; ph. 392 0433. By signing below I acknowledge that I have read the above information and agree to participate in this study. ______________________________ ____________ Signature of Research Participant Date

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61 APPENDIX B DEMOGRAPHIC QUESTION NAIRE Please answer the following demographic questions. 1. Sex: _____Female _____Male 2. Ethnicity: a. American Indian o r Alaska Native b. Asian c. Black or African American d. Native Hawaiian or Other Pacific Islander e. Hispanic f. White, non Hispanic g. Other:_________________ (please specify) 3. Class rank: a. Freshman b. Sophomore c. Junior d. Senior e. Grad Student f. Other:__________ (please specify)

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62 AP PENDIX C UNCERTAINTY ORIENTAT ION Please read and indicate the extent to which you agree with the following items: 1. I believe it is important for me to challenge my beliefs. 1 2 3 4 5 6 7 Strongly disagree Strongly agree 2. If I do not understand some thing I find out about it. 1 2 3 4 5 6 7 Strongly disagree Strongly agree 3. I like to experiment with new ideas, even if they turn out later to be a total waste of time. 1 2 3 4 5 6 7 Strongly disagree Strongly agree 4. I enjoy spending time discovering new things. 1 2 3 4 5 6 7 Strongly disagree Strongly agree 5. I like to find out why things happen. 1 2 3 4 5 6 7 Strongly disagree Strongly agree 6. I often put myself in situations in which I could learn something new. 1 2 3 4 5 6 7 Strongly disagree Strongly agree 7. I enjoy thinking about ideas that challenge my views of the world. 1 2 3 4 5 6 7 Strongly disagree Strongly agree

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63 APPENDIX D DISPOSITIONAL OPTIMI SM Please respond to each item by writing the number th at best describes your feelings, using the following scale: 1 2 3 4 5 Strongly Disagree Disagree Neither a gree nor disagree Agree Strongly Agree _____ 1. In uncertain times, I usually expect the best. _____ 2. If something can go wrong for me, it wil l. _____ 3. I'm always optimistic about my future. _____ _____ 5. I enjoy my friends a lot. _____ _____ 7. I hardly ever expect things to go my way _____ _____ 9. I rarely count on good things to happen to me. _____ 10. Overall, I expect more good things to happen to me than bad.

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64 APPENDIX E LOW LIKELIHOOD CONDI TION Very High Risk % High Risk 40 60 % M oderate Risk 20 40 % Very Low Risk 5 % Low Risk 5 20 % Risk Assessment Chart: Thioamine Acetylase (TAA)

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65 APPENDIX F HIGH LIKELIHOOD COND ITION Very Low Risk % Low Risk 5 20 % Moderate Risk 20 40 % Very High Risk % High Risk 40 60 % Risk Assessment Char t: Thioamine Acetylase (TAA)

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66 APPENDIX G FINAL QUESTIONNAIRE Please answer the following items using the scale below: 1 2 3 4 5 6 7 8 9 Strongly Disagree Strongly Agree _______ 1. The possibility that my test resu lts will make me feel bad (e.g., sad disappointed) influenced my decision to get tested. _______ 2. The possibility that my test results will make me feel negatively about myself influenced my decision to get tested. _______ 3. The possibility that my test results will challenge my view of myself as healthy influenced my decision to get tested. _______ 4. The possibility that my test results will force me to change my daily health behavior influenced my decision to get tested. _______ 5. I feel tha t I will regret not learning if I am TAA deficient. _______ 6. I feel that there is much to be gained by learning if I am TAA deficient. _______ 7. If I am TAA deficient, I am confident that I can deal with being TAA deficient. _______ 8. I feel that I will regret learning if I am TAA deficient. _______ 9. If I am TAA deficient, I have the emotional help and support I need to deal with being TAA deficient. _______ 10. I feel that I will improve my situation in some way by learning if I am TAA deficient _______ 11. If I am TAA deficient, there are people that I know who will help me deal with being TAA deficient. _______ 12. I believe there will be negative consequences as a result of learning if I am TAA deficient.

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67 LIST OF REFERENCES Allahverdip our H., & Emami, A. ( 2008 ). Perceptions of cervical cancer threat, benefits, and barriers of papanicolaou smear screening programs for women in Iran. Women & Health, 47 23 37. Aspinwall L. G., Kemeny, M. E., Taylor, S. E., & Schneider, S. G. ( 1991 ). Ps ychosocial reduction behavior. Health Psychology, 10 432 444. Babul R., Adam, S., Kremer, B., Dufrasne, S., Wiggins, S., Huggins, M., et al. ( 1993 ). Attitudes toward direct predictive testing for the Huntington disease g ene. Relevance for other adult onset disorders. The Canadian Collaborative Group on Predictive Testing for Huntington Disease. Journal of American Medical Association, 270 2321 2325. Becker, M. H., Nathanson, C. A., Drachman, R. H., & Kirscht, J. P. ( 1977 Journal of Community Health, 3 125 135. Biesecker, B. B., Ishibe, N., Hadley, D. W., Giambarresi, T. R., Kase, R. G., Lerman, C., et al. ( 2000 ). Psychosocial factors predicting BRCA1/BRCA2 testing decisions in members of hereditary breast and ovarian cancer families. American Journal of Medical Genetics, 93 257 263. Block L. G. & Keller, P. A. ( 1998 ). Beyond protection motivation: An integrative theory of health appeals. Journ al of Applied Social Psychology, 28 1584 1608. Brouwers M. C., & Sorrentino, R.M. ( 1993 ). Uncertainty orientation and protection motivation theory: The role of individual differences in health compliance. Journal of Personality and Social Psychology, 65 102 11. Dawson, E., Savitsky, K., & Dunning, D. ( 2006 Journal of Applied Social Psychology, 36 751 768. de Wit, J. B. F., Vet, R. Schut ten, M., & van Steengergen, J. ( 2005 ). Social cognitive determinants of vaccination behavior against hepatitis B: An assessment among men who have sex with men. Preventative Medicine, 40 795 802. Dolan, N. C., McGrae McDermott, M., Morrow, M., Venta, L., & Martin, G. J. (1999). Impact of same day screening mammography availability. Archives of Internal Medicine, 159 393 398. Floyd, D. L., Prentice Dunn, S. & Rogers, R. W. ( 2000 ). A meta analysis of research on protection motivation theory. Journal of Appl ied Social Psychology, 30 407 429.

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68 Geers, A. L. (2000). Examining the relationship between specific expectations and optimism and pessimism. Representative Research in Social Psychology, 24 33 40. Harris, C. R., Jenkins, M., & Glaser, D. (2006). Gender differences in risk assessment: Why do women take fewer risks than men? Judgment and Decision Making, 1 48 63. Hart, W., Albarracin, D., Lindberg, M., Merrill, M., Brechan, I., Lee, K. H., & Eagly, A. H. (2008). Feeling validated versus being correct? a m eta analysis of selective exposure to information. Unpublished manuscript, University of Florida. Janz N. K. & Becker, M. H. ( 1984 ). The health belief model: A decade later. Health Education Quarterly, 11 1 47. Jemmott, J. B., Ditto, P. H., & Croyle, R. T. ( 1986 ). Judging health status: Effects of perceived prevalence and personal relevance. Journal of Personality and Social Psychology, 50 899 905. Lerman, C., Schwartz, M. D., Miller, S. M., Daly, M., Sands, C., & Rimer, B. K., ( 1996 ). A randomized t rial of breast cancer risk counseling: Interacting effects of counseling, educational level, and coping style. Health Psychology, 15 75 83. Mann, H. B. & Whitney, D. R. (1947). On a test of whether one of two random variables is stoch astically larger tha n the other Annals of Mathematical Statistics, 18 50 60 Mills, J., Aronson, E., & Robinson, H. ( 1959 ). Selectivity in exposure to information. Journal of Abnormal and Social Psychology, 59 250 253. Milne, S., Sheeran, P., & Orbell, S. (2000). Predicti on and intervention in health related behavior: A meta analytic review of protection motivation theory. Journal of Applied Social Psychology, 30 106 143. Neuwirth, K., Dunwoody, S., & Griffin, R. J. ( 2000 ). Protection motivation and risk communication. Risk Analysis, 20 721 734. Norem J. K. & Cantor, N. ( 1986 ). Anticipatory and post hoc cushioning strategies: Cognitive Therapy and Research, 10 347 362. Reed, A. C., Reiter, P. L., Smith, J. S., Pa lefsky, J. M., & Brewer, N.T. (2010). Gay and American Journal of Public Health, 100 1123 1129.

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69 Rogers, R. W. (198 3 ). Cognitive and physiological processes in fear appeals and attitude chang e: A revised theory of protection motivation. In B. L. Cacioppo & L. L. Petty (Eds.), Social Psychophysiology: A Sourcebook (pp. 153 176). London, UK: Guilford. Ronis, D. L. ( 1992 ). Conditional health threats: Health beliefs, decisions, and behaviors among adults. Health Psychology, 11 127 134. Rosenstock, I. M. ( 1966 ). Why people use health services. Milbank Memorial Fund Quarterly, 44 94 124. Scheier M. F. & Carver, C. S. ( 1985 ). Optimism, coping, and health: Assessment and implications of generalize d outcome expectancies. Health Psychology, 4 219 247. Schmiege, S. J., Aiken, L. S., Sander, J. L., & Gerend, M. A. ( 2007 ). Osteoporosis prevention among young women: Psychosocial models of calcium consumption and weight bearing exercise. Health Psycholog y, 26 577 587. Smith Klohn L., & Rogers, R. W. ( 1991 ). Dimensions of the severity of a health threat: The persuasive effects of visibility, time of onset, and rate of onset on young Healthy Psychology, 10 32 3 329. Shipp T. D., Shipp, D. Z., Bromley, B., Sheahan, R., Cohen, A., Lieberman, E., et al. ( 2004 unborn child? Birth: Issues in Perinatal Care, 31 272 279. Sorrentino R. M. & Short, J. C. ( 1986 ). Uncertainty orientation, motivation, and cognition. In R. M. Sorrentino & E. T. Higgins (Eds.), Handbook of motivation and cognition: Foundations of social behavior (pp. 379 403). New York, US: Guilford Press. Sweeny, K., Melnyk, D., Ma lone, W. & Shepperd, J. A. (2010 ). Information avoidance: A model of calculated ignorance. Unpublished manuscript, University of Florida. van der Steenstraten, I. M., Tibben, A., Roos, R. A. C., van de Kamp, J. J. P., & Niermeijer, M. F. ( 1994 ). Predi ctive testing for Huntington disease: Nonparticipants compared with participants in the Dutch program. American Journal of Human Genetics, 55 618 625. Welch, B. L. (1947). The generalization of "Student's" problem when several different po pulation varianc es are involved. Biometrika 34 28 35 Wogalter, M. S., Young, S. L., Brelsford, J. W., & Barlow, T. ( 1999 ). The relative contributions of injury severity and likelihood information on hazard risk judgments and warning compliance. Journal of Safety Resear ch, 30 151 162.

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70 Yabroff, K. R., Kerner, J. F., & Mandelblatt J. S. 2000 Effectiveness of interventions to improve follow up after abnormal cervical cancer screening. Preventative Medicine 31 429 439.

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71 BIOGRAPHICAL SKETCH Wendi A. M iller is a fourth year graduate student in social psychology. In 2008, she earned a Master of Science degree in psychology from the University of Florida (Gainesville, Florida). In 2006, she earned a Master of Science degree in psychology from Augusta State University (Aug usta, Georgia). She received her bachelor s degree in psychology in 2004 from William Penn University (Oskaloosa, Iowa).