University of Cincinnati Date: 5/2/2011 I, Andrew B Brown , hereby submit this original work as part of the requirements for the degree of Master of Arts in Psychology. It is entitled: 7KH5HODWLRQVKLSRI([SHFWHG9DOXHEDVHG5LVN\'HFLVLRQ0DNLQJ7DVNV WR$WWLWXGHV7RZDUG9DULRXV.LQGVRI5LVNV Student's name: Andrew B Brown This work and its defense approved by: Committee chair: Chung-Yiu Chiu, PhD Committee member: Frank Kardes, PhD Committee member: Gerald Matthews, PhD 1434 Last Printed:5/2/2011 Document Of Defense Form The relationship of expected value-based risky decision making tasks to attitudes toward various kinds of risks A thesis submitted to the Graduate School of the University of Cincinnati in partial fulfillment of the requirements for the degree of Master of Arts In the Department of Psychology of the College of Arts and Sciences by Andrew B. Brown B.A., Kalamazoo College 2007 Committee Chair: C.-Y. Peter Chiu, Ph.D. ABSTRACT Laboratory-based risky decision making paradigms developed by researchers in behavioral economics (e.g., Kahneman & Tversky, 1979) ask participants to choose between two options with varying expected values (EV). Very few studies have explored the relationships between these expected-value tasks and risk perceptions and intentions in other life domains. Five studies were conducted to explore these relationships. Study 1 (N = 345) used a survey format to examine the relationship between a hypothetical two-choice EV risky decision making task and perceptions of financial risk climate. Findings suggested that the number of risky choices remained positively correlated with our measures of consumer confidence, even after controlling for income level and self-reported stress. Study 2 (N = 213) expanded the scope of Study 1 by examining the relationship between risky choices on a two-choice EV task and intentions to engage in risky activities in a variety of behavioral domains. Results suggested that risky choices were positively correlated with risk taking intentions from behavioral domains such as: aggressive and illegal behavior, risky sexual behavior, heavy drinking, risky academic/work behaviors, high risk sports, betting, investing, and recreational risk taking. Study 3 (N = 138) examined whether the pattern of results found in Studies 1 and 2 might be the result of a change in attitude caused by the current economic recession. No statistically significant differences between the two samples were detected, although the number of risky choices for our sample in 2010 was numerically smaller for both gain ii and loss framed trials compared to participants from 2006 collected by Lauriola, Levin, and Hart (2007). Study 4 (N = 144) was conducted to elucidate the nature of the loss aversion findings from Studies 1 and 2 (where the ratio of risky loss to risky gain choices was close to 1:1) and those of Study 3 (where the ratio of risky loss to risky gain choices was close to 2:1). Results suggested that outcome magnitude appeared to be the strongest influence on loss aversion. Study 5 (N = 116) was a pilot study conducted to determine whether consumer confidence and risk taking could be experimentally manipulated by asking participants to consider hypothetical positive or negative financial events. Results indicated that those in the positive condition reported greater consumer confidence, and also took more risks on a two-choice EV task compared to those in the negative condition. There were no statistically significant group differences in positive or negative affect, income level, optimism, or self-reported stress. Overall, these results indicate that risk propensity measured by two-choice EV tasks does appear to be related to perceptions of financial risk climate, intentions to engage in risk taking in other behavioral domains, and that it may be possible to experimentally manipulate both risk taking and perception of financial risk climate at a macro-level (e.g., consumer confidence). iii This page is intentionally left blank. iv TABLE OF CONTENTS TABLE OF CONTENTS………………………………………………………….. v LIST OF TABLES………………………………………………………………… vi LIST OF FIGURES………………………………………………………………. vii CHAPTER 1: Introduction……………………………………………………….. 1 CHAPTER 2: Study 1…………………………………………………………….. 22 CHAPTER 3: Study 2……………………………………………………………... 37 CHAPTER 4: Study 3……………………………………………………………... 55 CHAPTER 5: Study 4……………………………………………………………... 60 CHAPTER 6: Study 5……………………………………………………………... 70 CHAPTER 7: General Discussion………………………………………………… 81 REFERENCES……………………………………………………………………. 89 v LIST OF TABLES 1. Summary of key findings from representative studies utilizing two-choice expected value tasks and other paradigms related to risk………………………………………… 13 2. Two common measures of consumer confidence……………………………………… 19 3. Reliability and descriptive statistics by trial type for participants (N =345) in Study 1.. 27 4. Component loadings of the four consumer confidence items………………………….. 30 5. Pearson correlations for climate variables of interest (N = 345)……………………….. 31 6. Descriptive statistics of four groups with different types of self-reported personal experiences…………………………………………………………………………… 31 7. Pearson correlations between variables of interest (N =345)…………………………... 33 8. Pearson partial correlations between variables of interest, controlling for income level (N =345)………………………………………………………………………………. 34 9. Descriptive statistics and Cronbach’s alpha coefficients for Study 2………………….. 43 10. Spearman partial correlation coefficients between self-reported stress, risky choices, and self-reported intentions to engage in risk taking on the CARE subscales…………. 47 11. Spearman partial correlation coefficients between self-reported stress, risky choices, and self-reported intentions to engage in risk taking on the DOSPERT subscales…….. 48 12. Component loadings for overall risky choices and the DOSPERT and CARE subscales…………………………………………………………………………….... 49 13. Component loadings for overall risky choices and the DOSPERT subscales……… 51 14. Comparison of descriptive statistics from Lauriola et al. (2007) and our 2010 study…................................................................................................................... 57 15. Comparison of descriptive statistics from our 2010 study, and our Study 4……… 63 16. Comparison of descriptive statistics from Study 3 and Study 4 for trials similar in magnitude to those used in our Studies 1 and 2…………....................................... 64 17. Comparison of descriptive statistics from Study 3 and Study 4 for trials with larger EVs than those used in our Studies 1 and 2…………………………………………… 65 18. Descriptive and inferential statistics comparing mean number of risky choices for loss domain trials to those in gain domain trials by task conditions………………………. 66 19. Comparison statistics for the two conditions in Study 5……………………………… 74 20. Component loadings for economic climate items…………………………………….. 75 21. Descriptive statistics for variables potentially responsible for the differences in economic outlook or risky choices, (N = 116)……………………………………….. 76 22. Spearman correlations for variables of interest in Study 5, (N = 116)……………….. 78 vi LIST OF FIGURES 1. Example of a multi-choice expected value risky decision task. Figure taken from Lopes & Oden, (1999), The role of aspiration level in risky choice: A comparison of cumulative prospect theory and SP/A theory. Journal of Mathematical Psychology, 43, p. 293……………………………………………………………………………... 6 2. Risk preference for each trial type (RA = risk advantageous, EQEV = equal expected value, RD = risk disadvantageous). Risk preference indicates the proportion of the time participants selected the probabilistic or risky option over the certain option for that trial type. Error bars indicate standard errors………………… 29 3. Risk preference for each trial type (RA = risk advantageous, EQEV = equal expected value, RD = risk disadvantageous) and each outcome magnitude. Risk preference indicates the proportion of the time participants selected the risky option over the certain option for that trial type. Error bars are standard errors……………. 45 4. The proportion of risky choices for gain and loss trials by condition. Error bars are standard errors………………………………………………………………………... 67 vii CHAPTER 1 Introduction Researchers in the field of risky decision making have taken much interest over the years in discovering the factors affecting choice and preference (Fox & Poldrack, 2008; Weber & Johnson, 2008, 2009). Although most early research in this area focused primarily on the generation of a “list of phenomena that show[ed] deviations from the predictions of normative models,” Weber and Johnson (2009) note that research involving judgment and decision making has gradually transformed into a field interested in “developing and testing hypotheses about the psychological processes that give rise to judgments and choices and about the mental representations used by these processes” (p. 22.3). The research described in this manuscript is largely a product of this more nuanced view of decision making, and our goal was chiefly one related to discovering potential relationships between constructs involving risk and uncertainty throughout a broad realm of decision making contexts. In the pages that follow, we will begin with a review of the literature most relevant to the five studies that we conducted; which will include an introduction to risky decision making and its measurement from four primary perspectives: developmental, neuropsychological, personality and individual