The Relationship of Expected Value-Based Risky Decision Making Tasks

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

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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

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).

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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

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

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

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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 differences, and situational or contextual factors. The final section of this literature review describes measures that have been used to indicate individuals’ perceptions of risk climate.

Risky Decision Making

Consider the following options or prospects:

(i) Obtaining $100 with 100% probability, OR

(ii) Obtaining $200 or obtaining $0, as determined by a toss of a fair coin with 50:50

head-tail probability

How would one choose between (i) vs. (ii)? According to classical economic theories, preference may be expressed by selecting the option with the highest expected value (EV), defined as the product of the objective (frequently monetary or monetized) value attributable to the outcome (x) and its associated probability (p). The risk neutral individual is supposed to be indifferent between the two options above with identical EV. Subsequent theoretical developments, such as the earlier version of the prospect theory (Kahneman & Tversky, 1979), or its extension, cumulative prospect theory (Tversky & Kahneman, 1992), relaxed the requirement that preference is related directly to EV. In prospect theory, for example, preference is related to the product of a value function (v) and a decision weight term (d), which is a generalization of the probability concept (d = g(p)). The value function is assumed to be a function of gains or losses with respect to the status quo, v = f(x). This value function is assumed to have a steeper slope for losses than for gains, which expresses the idea of loss aversion, that

“losses loom larger than gains,” (Kahneman & Tversky, 1979, p. 279). The value function is also assumed to have a slope that is concave for gains and convex for losses, which corresponds to the idea that “marginal values of gains and losses diminish with magnitude” (Kahneman &

Tversky, 1979, p. 277). Not only has prospect theory been applied to risky decision making, where the outcome probabilities are known (as in (ii) above), but it has also been extended to decisions under uncertainty, where the outcome probabilities may not be known:

(iii) Obtaining $100 with 100% probability, OR

(iv) Obtaining $200 or obtaining $0, as determined by a toss of a biased coin with

unknown head-tail probability

Extant data suggest that most people think about these decisions using cognitive heuristics that do not require an excessive amount of cognitive resources. Although these

2 decision heuristics do lead to adaptive outcomes for the individual most of the time, certain conditions are known to lead to preferences that may be non-optimal in the economic sense, a pattern described as “bounded rationality” (Gigerenzer & Selten, 2001). For example, decision makers often overweight small probabilities and underweight moderate and high probabilities, and tend to depart from risk neutrality (being more risk seeking or risk averse between options with equal EV) for known combinations of probabilities and gains / losses (Kahneman &

Tversky, 1979). Besides the representation of probabilities and gains / losses, other factors such as emotions, memory, and situational factors may also combine to affect the decision making process (e.g., Stanovich & West, 2000; Schwarz, 2004; Kermer, Driver-Linn, Wilson, & Gilbert,

2006; Slovic & Peters, 2006).

Measuring Risky Decision Making with Gambling Paradigms

Risky decision making has frequently been studied using two different approaches. These approaches include laboratory-based tasks and self-report survey measures. Both approaches are generally concerned with assessing risk propensity, which is defined by Gärling, Kirchler, Lewis, and van Raaij (2009) as “a general behavioral tendency to take or avoid risk in a specific domain” (p. 4). These authors further note that although risk propensity may be a somewhat stable characteristic of individuals, the principles of learning theory dictate that it is unlikely to endure if being too risky (or too conservative) does not pay off for the decision maker over time

(Sitkin & Weingart, 1995) . However, for such learning to take place, the decision maker must attribute the outcomes of their decisions to their own behavior, and not to another person or contextual factor. Since the factors that may influence the decisions people make and whether they learn from those decisions come from life histories and may be individually specific, many recent laboratory-based studies of risky decision making have opted for greater control and

3 utilized gambling-like paradigms in place of real world scenarios. These paradigms allow for control of variables such as outcome magnitude (how positive or negative the outcome is), outcome probabilities, decision domain (gain or loss), number of choice alternatives, and knowledge of results. Another potential advantage of this approach is that a number of alternative paradigms exist, such as the Iowa Gambling Task (IGT), the Balloon Analog Risk

Task (BART), and multi-choice expected value tasks. In the brief overview provided below for these paradigms, the expected value choice tasks will receive the most attention here, as they are the main focus of the current project.

The IGT was developed by Bechara, Damasio, Damasio, & Anderson (1994) as a neuropsychological probe that would allow for comparison of decision making of patients with damage to the ventromedial prefrontal cortex to healthy controls in a laboratory setting. In IGT, participants are allowed to choose a card from one of four decks of cards on each trial.

Participants begin the task with a loan of play money. Each card is associated with a gain, with the magnitude of the gain being larger for some of the decks. Some cards from certain decks are also more likely to be associated with a penalty amount that must be paid, and the penalty amount varies between decks as well. Hence, consistent choice of cards in some decks will lead to larger overall payoffs than other decks over the long term, despite the fact that they may present penalties over the short term. Participants are allowed to choose from any of the four decks on each trial, and are instructed to try and maximize their total profits.

One disadvantage of the IGT is that there has been some debate about how performance on the task may be related to intelligence and learning. Toplak, Sorge, Benoit, West, and

Stanovich (2010) suggested in their review that a small number of studies have found a significant relationship between intelligence and IGT performance, therefore it is important to be

cautious using IGT when the primary research interest is not related to how intelligence

influences decision making.

Compared to the IGT, the BART (Lejuez et al., 2002) offers a much simpler and

engaging task in which participants are in dynamic control of inflating a virtual balloon on each

trial. Their goal is to inflate the balloon, making it as large as possible without it bursting, because a larger balloon is associated with a superior payoff. Prior to each pump, the participant is required to make a decision whether to “cash out” and collect the current point total, or to continue to pump. If the participant inflated the balloon too much (burst size was randomly selected from trial to trial and unknown to the participant), the participant lost and had to proceed

to the next trial. While the BART task allows for a dynamic and incremental look at risk taking

behavior, we are unaware of any attempts to evaluate the BART longitudinally to assess

reliability; furthermore, the BART (at least in its current form) does not readily allow for the

manipulation of expected value conditions which the participants are aware of from trial to trial.

Compared to tasks like the IGT and the BART, the multi-choice risky decision making

task offers an alternative possibility. In a multi-choice risky decision making task, participants

are often asked to make a decision between several options with different combinations of

outcome magnitude and probability, but the same expected value relative to the other options.

For example, the task utilized by Lopes and Oden (1999) asked participants to make choices

between several alternatives with equal expected value but where the outcome magnitudes were

determined by different types of probability distributions. An example of this type of task can be

found below in Figure 1.

Figure 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.

Due to their increased complexity, tasks involving more than three potential options (a certain option and at least two others) seem likely to be more cognitively demanding, similar to

IGT. As it was not our intention to assess cognitive ability, our study involved a less complex case of the multi-choice risky decision making task: the two-choice risky decision making task.

In these types of tasks, participants are asked to make a decision between an option with a certain outcome (e.g., lose $1 for sure), and an option with a probabilistic outcome (e.g., a 50% chance to lose $2). Similar to IGT and BART, the probability of attaining a given outcome as well as the

magnitude of that outcome can be varied, but in this paradigm participants are made aware of the

probabilities of success or failure. Additionally, these types of tasks may also manipulate the

decision domains (i.e., whether the participant is attempting to win or to prevent a loss)

independently. While some studies using this paradigm have provided the participants with knowledge of the outcomes of their decisions (Levin & Hart, 2003), others have not (Lauriola,

Levin, & Hart, 2007); and likewise some studies have provided participants with real monetary payoffs while others have not (e.g., Lauriola, Levin, & Hart, 2007). Importantly, studies found no differences in the pattern of decision making or preferences when consequences involved real versus hypothetical amounts while using this type of task (e.g., Wiseman & Levin, 1996).

Experimental findings from studies using these expected value tasks have generally focused on four perspectives, developmental, neuropsychological, personality and individual differences, and situational or contextual factors. The various types of findings can be found in summary form in Table 1, and each will be discussed in detail.

Developmental

One of the first studies that utilized an expected value risky decision making task with young children recruited both children and their parents as participants (Levin & Hart, 2003).

This study developed and established a task called the cups task as a viable option for studying

risky decision making in children and adults. In the original cups task, participants sat between

two sets of cups identical in number, and first made a choice to play from the probabilistic side,

or the certain side. The number of cups present on each trial was used to generate the

probabilistic option, as the desired outcome on the probabilistic side could be beneath only one

of the cups. On the certain side, a gain or loss of one, depending on the domain being tested,

could be achieved without regard to the cup selected. Two probability levels (.2 and .5) were

7 combined with outcome magnitudes of two and five dimes respectively, and the two levels of domain (gains versus losses) to generate eight distinct types of trials. Children and their parents were tested in separate sessions, and both groups saw each trial type three times. The primary dependent variable in this study was the value of the preference shift between the gain and loss domain, and this was calculated by subtracting the number of probabilistic choices in the loss domain from that in the gain domain. Theoretically, preference shift should be close to zero for a person who is risk-neutral. Even with a slight elevation in risky choices due to participants making bets with money that was given to them rather than their own money (sometimes referred to as a house money effect), the paradigm allowed Levin and colleagues to reliably observe the preference shift between the gain and loss domains. Furthermore, both children and their parents were more likely to choose the probabilistic option than the certain option when they were attempting to avoid a loss compared to when they were attempting to achieve a gain.

In a follow-up longitudinal study, Levin, Hart, Weller, and Harshman (2007) found that the decision making of the child and adult participants remained relatively stable even three years later, indicating a possibility that the tendency to make risky choices is an individual characteristic which can be detected at an early age. Perhaps more importantly, the children’s temperament scores for surgency (a temperament construct similar to extraversion and possibly related to aspects of sensation seeking) in the 2003 study positively correlated with their scores on risk taking three years later. Overall, these early studies involving the cups task and development suggest that two-choice risky decision making tasks are likely to be easily understood (even by small children) and may provide an index for an underlying construct that is stable over time.

Neuropsychological

The cups task has been computerized for use in brain lesion and imaging studies (Levin,

Weller, Shiv, & Bechara, 2007; Xue, Lu, Levin, Weller, Li, & Bechara, 2008), and collectively these studies have resulted in significant progress in the process of identifying the brain regions in adults which are believed to be critical to risky decision making processes. An important change was made to the cups task in the process as well. Instead of two levels of probability and outcome magnitude, three levels of probability (.2, .33, .5) were combined with the three levels of outcome magnitude (2, 3, 5 quarters or dollars) to create trials which were risk advantageous

(EVrisky option > EVcertain option), equal expected value (EVrisky option = EVcertain option), and risk disadvantageous (EVrisky option < EVcertain option). This modification allows for an additional manipulation check to ensure that participants are not randomly selecting their responses, because data from trials with risk advantageous expected values can be compared to the data from neutral or risk disadvantageous trials to ensure that as a sample, the participants are making more risky choices when the circumstances are favorable rather than unfavorable.

In the Weller et al. (2007) study, two groups of lesion patients were recruited, those with damage to the amygdala, and those with damage to the ventromedial prefrontal cortex.

Additionally, there was a control group of non-patients. Patients with these two sets of brain areas affected were chosen because of their relationship to emotion and decision making. In particular, the amygdala was chosen because it is “an area responsible for processing emotional responses to environmental stimuli”; and the ventromedial prefrontal cortex for its role in

“integrating cognitive and emotional information” (Weller et al., 2007, p. 958). In this study, participants completed the computerized cups task and the researchers were interested in observing how the participants’ responses might differ with respect to group (amygdala lesions,

9 ventromedial prefrontal cortex lesions, or healthy controls) and expected value of the outcome of their decisions. From their results, these authors concluded that both the amygdala and ventromedial prefrontal cortex were important for processing risky decisions, but in different ways. Their results suggested that those with ventromedial prefrontal cortex damage were insensitive to changes in the expected value of the outcome, regardless of whether the situation was framed as gain or loss (i.e., they took risks without regard to whether it was a risk disadvantageous or risk advantageous trial, and showed no preference shift like the normal controls). For the patients with amygdala damage however, it seemed that the damage led primarily to insensitivity to changes in expected value of outcome only when the situation is framed as a gain.

Further research in the neuropsychological domain using the cups task was done by Xue et al. (2008), and they were able to elaborate on the work of Weller et al. (2007) in that individual differences in risky decision making seemed to be accounted for by different activation tendencies in the medial prefrontal cortex. Unlike Weller et al., this study used healthy participants and fMRI rather than patients with brain lesions. These authors proposed two potential areas of interest: one was the dorsomedial prefrontal cortex, which they believed to be responsible for signaling risk; and the other area was the ventromedial prefrontal cortex, which they believed to be responsible for signaling outcome value. In simpler terms, Xue et al. (2008) claimed that “decision making under risk depends on the balance of two competing forces, one is the ‘fear’ or ‘anxiety’ of uncertainty, and the other is the ‘lure’ of gain” (p. 7). In summary, by providing insight regarding this dual nature of the neural systems responsible for processing risk and outcome information, the results of these two studies seem to provide further support for the importance of the interaction of emotion and cognition in decision making discussed by other

authors (e.g., Stanovich & West, 2000; Schwarz, 2004; Kermer, Driver-Linn, Wilson, & Gilbert,

2006; Slovic & Peters, 2006).

Personality and Individual Differences

Lauriola and Levin (2001) used 60 two-choice expected value trials where participants

chose between a certain option and a probabilistic option with equal expected value, and found that Italian adults high in the openness to experience factor, measured by a Short Adjective

Checklist similar to the Mini-Marker list (Saucier, 1994), showed a greater preference for risky

options (at least for gain-framed trials). Although openness and extraversion are of course

separate factors, they seem to overlap somewhat in the construct of sensation seeking (Eysenck

& Zuckerman, 1978), so it is perhaps unsurprising that these constructs were correlated with

willingness to take risks on a two-choice risky decision making task.

In another attempt to better understand the relationship between individual differences,

risky decision making, and decision making under conditions of ambiguity, Lauriola, Levin, and

Hart (2007) conducted a study which utilized several different risky decision making and

ambiguity preference tasks, and collected several different self-report individual difference

measures related to decision making. These individual difference measures included the Life

Orientation Test—Revised (Scheier, Carver, & Bridges, 1994), which measures optimism; the

Multiple Stimulus Type Tolerance for Ambiguity Test (McClain, 1993), which measures

individuals’ tolerance for novel and ambiguous circumstances; the financial risk taking items

from the Domain Specific Risk Taking scale (DOSPERT) (Weber, Blais, & Betz, 2002) which

measure individuals’ perceived likelihood of engaging in various risky financial behaviors, and

the Decision Making Style Inventory (Nygren, 2000), which measures individual tendency to be

an analytical, intuitive, or a regret-based decision maker. The results from Lauriola et al. (2007)

11 suggested that the participants’ performance on different versions of both risky decision making and ambiguity tasks were relatively well-correlated with one another, and that generally speaking, individuals who were more comfortable with ambiguity were also more risk seeking.

With respect to the individual difference variables, while the correlations between the various scales mentioned above and decision making were not always statistically significant, Lauriola et al. (2007) provided evidence in favor of decision making style as a trait-like construct, and suggested that “whatever ambiguity-avoiding and risk aversion for gains have in common, this

‘core’ factor is related to participants’ dispositional tendency to be pessimistic, regret-based decision makers and less tolerant of ambiguity” (p. 146).

Table 1

Summary of Key Findings from Representative Studies Utilizing Two-Choice Expected Value Tasks and Other Paradigms Related to Risk

Perspective Task and Objectives Findings Authors The cups task was simple enough for Created an expected value task simple children and adults to understand, and • Levin & Hart 2003 enough for both children and adults to sensitive enough to pick up individual • Levin, Hart, Weller, & Developmental understand, called the cups task. Featured differences in risk propensity. Also found Harshman, 2007 gain and loss domain, two levels of p and the preference shift, such that participants

two levels of outcome magnitude. were more likely to take risks in the loss versus gain domain.

Created a computerized version of the cups Patients with damage to prefrontal areas, • Weller, Levin, Shiv, & Bechara task, included gain and loss domain, three amygdala damage, or damage to the (2007) Neuropsychological levels of probability and three levels of insula perform very differently than • Xue, Lu, Levin, Weller, Li, & outcome magnitude. Looking at performance healthy controls, and have difficulty Bechara (2009) of lesion patients and healthy controls. adapting to changes in expected value.

Risk taking correlated positively with extraversion and openness to experience, negatively with neuroticism, positively Personality and Expected value tasks, and their correlations • Levin & Lauriola 2001 with optimism, positively with tolerance Individual Differences personality constructs • Lauriola, Levin, & Hart, 2007 for ambiguity, and negatively with the regret-based decision making style

-People take more risks in domains where • Heath & Tversky, 1991; they perceive themselves as expert Rettinger & Hastie, 2001 -Others are perceived as more risk taking • Hsee & Weber, 1997; Hsee & than self Situational and Variety of tasks from different behavioral Weber, 1999 -Cultural and societal differences in risk Contextual Factors domains • propensity and judgments of others’ risk Smith & Dickhaut, 2005; Berg, propensities Dickhaut, & McCabe, 2005; -Situation parameters can alter risk Ungemach, Stewart, & Reimers propensity 2011

Situational and Contextual Factors

The situational or contextual factors that may affect an individual’s risk propensity are important to consider here because it is these factors which perhaps have the greatest capability to qualify the findings discussed so far. It is these studies which bring doubt to the notion that there is a single value that can describe an individual’s risk propensity. For example, people tend to be more risk seeking in behavioral domains that they consider themselves expert (Heath & Tversky, 1991; Rettinger & Hastie, 2001), even when the objective probabilities of success or failure are not dependent on such experience. Further, individuals tend to predict others to be more risk seeking than themselves (Hsee & Weber, 1997), a potentially relevant finding when attempting to understand how individuals will behave in risk taking situations which depend upon inferences regarding what others would do, such as choosing whether to invest in stock.

Hsee and Weber (1999) also found that individuals were poor at predicting the risk propensity of others and provided evidence to suggest that there could be cultural or societal differences in risk taking and the perception of other’s risk propensities by looking at American and Chinese participants’ risk preferences in investment, medical, and academic decisions, as well as their perceptions of what individuals of the other nationality would do.

Other studies have examined more purely contextual factors that appear to affect risky decision making and risk judgments with very little influence of the individual’s conscious judgments. For example, studies of simulated auctions have attempted to alter the parameters by which individuals are allowed to bid or sell (e.g., open vs. closed bid auctions), it is possible to elicit risk averse responses from participants under one set of

conditions, and risk seeking responses from those same participants under another set of conditions (Smith & Dickhaut, 2005; Berg, Dickhaut, & McCabe, 2005). In another recent study, Ungemach, Stewart, and Reimers (2011) argued that individual preferences

in decision making are constructed using relative judgments from memory and contextual

information rather than being the product of a rational thought process. For example,

Ungemach et al. asked shoppers as they left the supermarket whether they would prefer a

55% chance to win £0.50 (EV = £0.275) or 15% chance to win £1.50 (EV = £0.225).

These authors noted that individuals’ preferences might shift from one option to the other

on the basis of their relative ranks, that is, participant perception of the psychological

(and not monetary) distance that separates the two possible outcomes (£0.50 or £1.50).

Ungemach et al. hypothesized that the relative ranks would be determined by individuals

assessing the subjective difference between the two objective amounts offered by the

options; and during this process they should be influenced by “the proportion of

intermediate values in the context. For example, $10 and $20 will seem more different if

the context is $13, $15, $18, and $19 than if the context is $3, $6, $25, and $30” (p. 2).

The results of Ungemach et al. (2011) provided support for their hypothesis. For shoppers

who recently purchased only items priced in the £0.50 to £1.50 range, the probability of

choosing the larger £1.50 gamble was .73. However, for shoppers who only purchased

items priced outside the range of £0.50 and £1.50, the two potential outcomes appeared to

be less distinct, and here the probability of choosing the larger magnitude risky £1.50

gamble was only .43. Importantly, Ungemach et al. found no effect of income level or

receipt total on choice behavior.

In sum, each of these studies involving situational or contextual factors

emphasizes the importance of remaining conservative when considering the prospect of

using a single number to represent an individual’s risk propensity across domains and

contexts. Any attempt to do this should be accompanied by the exploration of the relationships between such a construct and other measures of risk taking and risk perception. Fortunately, the potential relationships between risk propensity measures and contextual factors can be examined. There are several domain-specific risk taking and risk perception measures that have been developed and these will be discussed further in the next section.

Measuring Risky Decision Making with Self-Report Measures

There is research which suggests that individuals’ risky decision making tendencies may vary by behavioral domain (Weber, Blais, & Betz, 2002; Hanoch,

Johnson, & Wilke, 2006), and therefore it is important to consider several domains when assessing risk propensity. Two self-report measures which have been developed for this purpose are the Domain Specific Risk Taking scale, or DOSPERT, (Weber, Blais, &

Betz, 2002), and the Cognitive Appraisal of Risky Events scale, or CARE (Fromme,

Katz, & Rivet, 1997). The DOSPERT is a 30-item self-report scale developed to measure risk taking intentions in a variety of behavioral domains: ethical, financial (betting and investing questions), health/safety, social, and recreational. To complete the scale, participants indicate the likelihood that they would engage in the behavior if they were to find themselves in that situation. The construct validity of this scale was assessed by

Weber, Blais, and Betz (2002), who found that the social and recreational risk-behavior subscales correlated negatively with intolerance for ambiguity, and positively with

sensation seeking in all five behavior subscales. Additionally, participants self-reported

past frequency of risky behaviors from each domain were correlated with their intentions

as measured by the DOSPERT.

In an attempt to better understand college students’ risky decision making,

Fromme, Katz, and Rivet (1997) developed the Cognitive Appraisal of Risky Events

scale which consists of three separate measures, expected involvement, expected risk, and

expected benefit, for 30 different risky behaviors in six domains: illicit drug use,

aggressive/illegal behavior, risky sexual activities, heavy drinking, high risk sports, and

academic/work behaviors. Fromme et al. demonstrated with a college student sample that

the CARE showed appropriate convergent and discriminant validity, as well as predictive

validity by using CARE behavioral intention scores to predict the frequency of actual risk taking behavior.

Measuring Perceptions of Risk Climate

In addition to measuring risk propensity at the individual level in the abstract, it is

also possible to measure individuals’ self-reported perceptions of their own economic

prospects in a broader context. For example, in a recent issue of Psychological Science:

In the Public Interest dedicated to financial decision making, Gärling, Kirchler, Lewis,

and van Raaij (2009) suggested that when economic conditions at the societal level are

poor, this influences individuals’ confidence and makes them more pessimistic regarding

both the future of national economic conditions and their situation. These authors posit

that this pessimism may influence individual risk propensity, such that individuals who

are more pessimistic are less likely to take risks. Although one might question the

relationship of a construct like consumer confidence to psychological research, Gärling

and colleagues stated that “The study of consumer confidence is a type of macro

psychology, the aggregation of individual evaluations and expectations to a general

feeling of optimism or pessimism” (p. 29). Interestingly, to the best of our knowledge,

decision science researchers have not yet attempted to relate laboratory-based measures

of individual risk taking attitude to these macro level measures of consumer confidence.

Two of the most prominent indices for measuring and evaluating the confidence

of consumers nationally are the Index of Consumer Sentiment (University of Michigan,

2011) and the Consumer Confidence Index (US Conference Board, 2011). The two

measures (Table 2) are similar in some respects, but differ in others. Both indices consist

of five questions, two regarding participants’ assessments of the present economic situation, and three regarding their expectations of future economic conditions. While the

Consumer Confidence expectations questions are focused on the next six months, the

Consumer Sentiment expectations questions take a longer term approach and ask about

the next year, and include one question about the next five years.

Table 2

Two Common Measures of Consumer Confidence

Index of Consumer Sentiment Consumer Confidence Index 1 Do you think now is a good or bad time for How would you rate present general people to buy major household items? [good time business conditions in your area? to buy/uncertain, depends/bad time to buy] [good/normal/bad]

2 Would you say that you (and your family living What would you say about available there) are better off or worse off financially than jobs in your area right now? you were a year ago? [better/same/worse] [plentiful/not so many/hard to get]

3 Now turning to business conditions in the country Six months from now, do you think as a whole—do you think that in the next twelve business conditions in your area will months we’ll have good times financially or bad be [better/same/worse]. times, or what? [good times/uncertain/bad times]

4 Looking ahead, which would you say is more Six months from now, do you think likely—that in the country as a whole we’ll have there will be [more/same/fewer] jobs continuous good times during the next five years available in your area? or so or that we’ll have periods of widespread unemployment or depression, or what? [good times/uncertain/bad times]

5 Now looking ahead—do you think that a year How would you guess your total from now, you (and your family living there) will family income to be six months from be better off financially, or worse off, or just now? [higher/same/lower] about the same as now? [better/same/worse]

Note. This table is from: Ludvigson, S. C. (2004). Consumer confidence and consumer spending. Journal of Economic Perspectives, 18(2), p. 32.

Another significant difference between the two measures is with respect to the sample size. According to Bram and Ludvigson (1998), the Consumer Confidence Index is delivered by mail to a random sample of 5,000 consumers each month, with a response rate of approximately 70%; while the Index of Consumer Sentiment is administered via telephone to a random sample of 500 consumers each month, with a response rate of

about 50%. Another difference according to Huth, Eppright, and Taube (1994) is that the

Consumer Confidence Index tends to be a better predictor of economic activity, while the

Index of Consumer Sentiment tends to be a better predictor of durable goods expenditure.

However, other authors have suggested that the indices may be better thought of as

lagging, rather than leading indices of economic conditions, and that the scales need to be

improved to measure confidence more accurately (Ackman, 2001; Dominitz & Manski,

2004). Regardless of whether the indices are lagging or leading indicators of economic

conditions, whether they measure the optimism of consumers with respect to economic

conditions is not in question.

The current study

Given the severe economic recession over the past several years, there has been

much debate about whether the national economic climate has entered a period of the

“new normal,” where a high unemployment rate and low economic growth will persist

for an extended period of time and be coupled with reduced consumer confidence, lower

consumer spending, and a generalized aversion to financial risk (Galston, 2009). The

national unemployment rate has remained high at a rate near 9%-10% over the past

several years, and a recent survey by the Health Foundation of Greater Cincinnati found

that among adults polled locally,“34% suffered a pay cut in the last 12 months…[and]…

26% had experienced a reduction or elimination of health care benefits” (Ritchie, 2009).

In the monthly survey of 5,000 consumers across the country conducted by the US

Conference Board, a composite index of consumer confidence plummeted from 100-110

(normalized to the 1985 level of 100) in the 2nd quarter of 2007 (March to June), to a

record low of 28 in February 2009 in more than 40 years of data; although it partially

recovered to stay in a range between 50- 65 from May 2009 to Dec 2010 (US Conference

Board, 2010). One might wonder if such drastic changes in the risk climate, as reflected

in the associated changes in consumer confidence, might begin to affect the financial risk

taking attitudes of individuals, as revealed in tasks such as the two-choice expected value

task, particularly for individuals in large urban settings of lower average socio-economic

status. To address this question, we collected data related to consumer confidence and explored its relationship to choice patterns in a two-choice EV task (Study 1). To address the question of cross-domain consistency of two-choice EV measures of risk taking and self-report measures of risk taking in other behavioral domains, we explored the relationship between performance on laboratory based tasks (such as the cups task) and self-reported risk taking in real life. Lejuez, Aklin, Zvolensky, & Pedulla (2003) used a similar approach to examine the construct validity of the BART and demonstrated that self-reported risk taking in other life domains did correlate with risky decisions made on the BART. Further support for this approach is provided by the findings of Levin and colleagues (Lauriola & Levin, 2001; Levin, Hart, Weller, & Harshman, 2007; Lauriola,

Levin, & Hart, 2007) which showed the expected correlations between two-choice expected value tasks and other personality constructs related to risk such as trait anxiety, ambiguity aversion, optimism, extraversion, and regret-based decision making.

Examination of the results of Studies 1 and 2 suggested that the risk taking tendencies of those samples in terms of loss aversion might have changed due to the recent economic conditions. Therefore in Study 3, we sought to replicate an earlier study by Lauriola,

Levin and Hart (2007), who collected data using a two-choice EV task from a sample of college students at a large Mid-western university during a time frame when consumer

confidence scores nationally were substantially higher. We also sought to examine the

reliability of some of the results from the two-choice EV task and to determine if certain

results obtained in Study 1 -3 might be subject to the influence of instructions and

wordings (Study 4). In Study 5, we returned to substantive issues regarding risky choices

and financial decision making, and attempted to experimentally manipulate consumer

confidence in order to observe any potential effects on risk propensity as measured by our

two-choice EV task.

CHAPTER 2

Study 1

Given the unique economic conditions that many Americans have faced in the two-year period between 2009 and 2010, we considered it an opportune time to study individual risky decision making. In particular, an examination of whether recent personal circumstances or perception of the state of the national economy might influence risky decision making was of interest. The purpose of Study 1 was to explore the relationship between risky decision making (as measured by a two-choice expected value task) and consumer-based economic indicators regarding the risk climate at a personal and national level. To the best of our knowledge, no studies have yet married in the same study these two types of variables that are frequently examined in different disciplines

(i.e., choice tasks in psychology and behavioral economics and macro level measures of consumer confidence in macroeconomics respectively). The nature of this relationship might then provide some insight into the extent to which college students’ risk propensity might be related to economic indicators that are frequently sampled at a micro and a macro level. Based on the literature (e.g., Gärling, Kirchler, Lewis, & van Raaij, 2009), it

was hypothesized that a positive relationship between the number of risky choices and

consumer confidence would be observed.

Method

Participants

The participants (N = 345) were introductory psychology students (age 18 - 24)

from the University of Cincinnati who participated for course credit. Data for this study

were collected in three waves, with Wave 1 (n = 84) in late May 2009, Wave 2 (n = 112)

in mid-September 2009, and Wave 3 (n = 149) in mid-November 2009. The corresponding US Conference Board Consumer Confidence Index at these 3 time points was reported to be 54.8, 53.4, and 50.6, respectively. Given that consumer confidence showed no dramatic shifts during these time points, the data from all 3 waves were combined in all subsequent analyses unless otherwise stated.

Measures

Participants completed our two-choice expected value task as a survey administered on a computer in a verbal format. There were two choice domains. In the gain domain, participants made choices in an attempt to win money; and in the loss domain, they made choices in an attempt not to lose money. For both domains, they chose between a certain option (i.e., “Gain $1 for sure” or “Lose $1 for sure”), and a probabilistic option on every trial. The probabilistic option involved varying levels of risk, where the amount of money and probability of winning or losing that money varied.

There were three levels of probability, 20%, 33%, and 50%; and three different amounts of money that could be won or lost, $2, $3, or $5. We defined risk advantageous trials as those trials in which the probabilistic option had the higher EV, and risk disadvantageous

trials as those trials in which the probabilistic option has the lower EV. When combined

with the two levels of domain, these manipulations created 18 different choice trials,

where six were risk advantageous (e.g., “choosing between $1 for sure and 33% chance

of winning $5”), six were risk disadvantageous (e.g., “choosing between $1 for sure and

20% chance of winning $2”), and six were equal in expected value (e.g., “choosing

between $1 for sure and 50% chance of winning $2”). These magnitude / probability

combinations were the same as those used by Xue and colleagues (2008). Prior to

beginning the survey, participants read the following description of the task:

“Imagine that someone offers you the chance to play a game for a chance to win

money. The catch is that on some rounds of the game, you will have to play to try not to

lose money. Still, the game is simple. In each round, there is only one choice to be made:

Do I choose the uncertain option? OR Do I choose the certain option?”

Following Xue et al. (2008), the number of trials that participants chose the

probabilistic option over the certain option was tallied when expected value for the two

choices was equal, and this measure was calculated separately by domain (i.e., gains vs.

losses). Risky choices on equal expected value (EQEV) trials provided information about

preference for risk when the situation neither favors risky nor conservative choices (either

option would provide equal return on average over a large number of trials). Furthermore,

the number of risky choices related to gains on EQEV trials can be compared to the

number of risky choices in the loss domain for each participant as an indication of loss aversion. In addition, the total number of risks taken on gain and loss trials was also calculated as a measure of overall preference for riskiness for each participant.

To measure consumer confidence, four items were utilized. Two items were

similar to Index of Consumer Sentiment expectations questions and asked about the long term:

1. How confident are you about the economy getting better in the next year or so

(a 1 - 10 scale, anchored by 1 not at all confident, and 10 extremely confident)?

2. Where do you think the economy is headed next year (a 1 – 9 scale, anchored by 1 it is getting a lot worse, and 9 it is getting a lot better)?

The other two items asked about economic events in the short term, similar to the

Consumer Confidence Index expectations questions:

1. Would you say the business conditions in the next six months will be: better, same, or worse?

2. Would you say that jobs in the next six months will be: plentiful, not so plentiful, or hard to get?

All macroeconomic questions in the survey were coded so that larger numbers were indicative of greater confidence in economic recovery. Following the consumer confidence items, participants were asked about their current income level (including those they lived with—e.g., parents), and the response scale ranged from 1 – 9, where 1 was less than $20,000, and 9 was more than $70,000. Following this, two items asked about recent personal financial events. The first item asked about recent positive events including: job promotion, pay raise, bought a home, and none of the above. The second item asked about recent negative events including: loss of health insurance or benefits, loss of employment, home foreclosure or threatened foreclosure, bank repossession of property, and bankruptcy. Additionally, one item asked participants to report their stress

level this year compared to previous years (on a 1 – 5 Likert scale, 1 = much less

stressed, 5 = much more stressed), and this item was included to determine its

relationship with risk taking and consumer confidence.

Procedure

A 27-item survey developed using Survey Monkey Professional

(www.SurveyMonkey.com) was used to administer the 18 randomly-ordered two-choice

expected value trials, followed by nine questions regarding age, income level, recent

positive and negative financial experiences, stress level compared to previous years, and

attitudes about the state of the economy. After consenting to participate, the participants

completed the survey in a self-paced manner independently in a computer lab setting, and

received no feedback regarding the outcomes of their decisions on the expected value

task. Items on the two-choice expected value task were presented individually, and all

other items were presented on pages where participants scrolled up or down the page and

mouse-clicked on their desired options.

Results

Patterns of Choices in the Two-Choice Expected Value Task

Table 3 shows the mean number of times that participants chose the probabilistic option for Study 1, and also the internal consistency reliability statistics (Cronbach’s alpha) for each trial type. The alpha values for equal expected value and risk disadvantageous trials in the loss domain were the lowest (α = .45 and .41, respectively),

and these values indicate low internal consistency reliability for those trial types. Risky

choices for different trial types (i.e., risk advantageous, equal expected value, and risk

disadvantageous) were significantly positively correlated within the gain and loss

domains (all p < .01). However, the total number of risky choices in the gain domain versus the loss domain were negatively correlated, r(343) = -.13, p = .02.

Table 3

Reliability and Descriptive Statistics by Trial Type for Participants (N = 345) in Study 1

Choice Type α Overall M Overall

Risk Advantageous Gain .60 2.32 (0.87)

Equal Expected Value Gain .59 1.48 (1.08)

Risk Disadvantageous Gain .60 0.69 (0.94)

Risk Advantageous Loss .75 2.03 (1.14)

Equal Expected Value Loss .45 1.62 (1.02)

Risk Disadvantageous Loss .41 0.94 (0.92)

Total Risky Gains .77 4.49 (2.33)

Total Risky Losses .72 4.58 (2.37)

Total Risky Choices .65 9.07 (3.10) Note. Means indicate the number of risky choices for that trial type. The maximum number of risky choices possible was 3 for each EV type (i.e., for rows 1 to 6), and 9 in each domain. The corresponding maximum number for rows 7-8 is 9, and for row 9 is 18. Standard deviations are in parentheses.

Figure 2 displays the proportion of risk taking for each trial type in each domain.

Collapsed across decision domain for the purpose of a manipulation check, the data

indicated that risky choices were most prevalent for RA trials (M = 4.35, SD = 1.48), of

medium frequency for EQEV trials (M = 3.10, SD = 1.40), and of lowest frequency for

RD trials (M = 1.62, SD = 1.29). The repeated-measures ANOVA was found to be significant, F(1.74, 598.62) = 494.49, p < .01 , partial η2 = .59, with the Greenhouse-

Geiser adjustment applied for violation of the sphericity assumption. The post-hoc t tests

(with a Bonferroni correction for multiple comparisons) indicated that risky choices were

significantly greater on RA trials than on EQEV trials (Mdifference = 1.26, SE = 0.07, p

<.01), and significantly greater on EQEV trials than on RD trials (Mdifference = 1.47, SE =

0.08, p < .01). A repeated measures t test also showed that participants chose the

probabilistic option more often on losses trials versus gains trials when conditions were

risk neutral (EQEV) and did not particularly favor risk averse or risk seeking behavior.

For these trials, participants took numerically more risks on loss trials than on gain trials,

but this difference failed to reach statistical significance t(344) = 1.70, p = .09. The ratio

of risky loss choices to risky gain choices overall was approximately 1:1.

0.90

0.80

0.70

0.60

0.50

0.40 0.77 Risk Preference 0.68 0.30 0.54 0.49 0.20 0.31 0.23 0.10

0.00 Gain Loss Gain Loss Gain Loss

RA EQEV RD Decision Type

Figure 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.

Economic Indicator variables

Data regarding participants’ perception of the economy, their own financial

prospects and stress level largely painted a picture that mirrored what was reported for

the nation at that time as a whole. For example, 75% of our sample reported that they

believed business conditions in the next six months would remain the same or become

worse, and 90% reported that they believed jobs in the next six months would be not so plentiful or hard to get. Before we examined the inter-relationships between variables, the four questions that asked about consumer confidence (α = .66) were summarized to form

a single consumer confidence score (where higher scores indicated greater confidence in

economic recovery) for each participant through a principal components analysis. All

four economic items loaded positively on a single component which explained 54% of the variance (Table 4), and this solution was also supported by the Kaiser criterion

(Kaiser, 1960), and the scree plot. A consumer confidence component score could not be calculated for two participants, who skipped two of the four questions about consumer confidence, but the other data from these participants were still included in these analyses.

Table 4

Component Loadings of the Four Consumer Confidence Items

Item Component 1 Loadings Jobs for the next six months .26 Business conditions for the next six months .60 Where is the economy headed next year .82 How confident are you about the economy .75 getting better in the next year or so

Table 5 displays the Pearson correlation coefficients between income level, stress

level compared to previous years, and the summary measure of consumer confidence.

Individuals who reported higher levels of stress compared to previous years also reported lower income levels, r(343) = -.19, p < .01, and weaker confidence in economic recovery, r(341) = -.19, p < .01. The correlation between income and consumer confidence also reached statistical significance, r(341) = .12, p = .05.

Table 5

Pearson Correlations for Climate Variables of Interest (N =

345)

Variable 1 2 3

1 Income -

Consumer Confidence .12 1 Component (.05) -

Stress Compared to Previous -.19 -.19 1 Years (<.01) (<.01) -

Note. The p-values for each correlation are in parentheses.

The descriptive statistics in Table 6 sorted participants by self-reported recent

personal experience type.

Table 6

Descriptive Statistics of Four Groups with Different Types of Self-Reported Personal Experiences

Stress this year Experience Income compared to previous Consumer Confidence Type n Level years Component

None 123 7.59 (2.12) 3.98 (0.88) -0.05 (0.90) Negative 69 5.78 (2.69) 4.35 (0.80) -0.18 (0.90) Positive 118 7.97 (1.92) 3.83 (0.89) 0.13 (1.10) Both 35 6.25 (2.60) 3.89 (1.08) 0.09 (1.37) Note. Standard deviations are in parentheses. For the experience types, Negative indicates that the participant self-reported at least one of the following: loss of health insurance or benefits, loss of employment, home foreclosure or threatened foreclosure, bank repossession, or bankruptcy. Positive indicates self-reported experience of at least one of the following: job promotion, pay raise, bought a home. None indicates that the participant did not self-report as experiencing any of the event types we asked about. Both indicates that the participant experienced at least one negative and one positive event. The consumer confidence component is based on standardized scores for each participant.

A one-way ANOVA was used to examine the between group differences by

experience type for the variables considered in Table 6. For income level, an ANOVA

revealed significant differences between conditions, F(3, 341) = 17.23, p < .01, partial η2

= 0.13. Follow-up Games-Howell post-hoc tests showed that those who self-reported as experiencing none of the events we asked about had significantly higher income levels than those who self-reported as experiencing both positive and negative event(s)

(Mdifference = 1.33, SE = 0.48, p < .05), and those who self-reported as experiencing negative event(s) only (Mdifference = 1.80, SE = 0.38, p < .01). Additionally, those who self-reported as experiencing positive event(s) only had significantly higher income levels than those who self-reported experiencing negative event(s) (Mdifference = 2.19, SE =

0.37, p <.01), or both negative and positive event(s) (Mdifference = 1.72, SE = 0.47, p < .01).

For self-reported stress compared to previous years, the ANOVA revealed significant differences across conditions, F(3, 341) = 5.14, p < .01, partial η2 = 0.04, and therefore the mean differences were examined using Games-Howell post-hoc tests. Those who indicated experiencing positive event(s) also indicated that they were less stressed

(Mdifference = -0.52, SE = 0.13, p < .01) than those who experienced negative event(s), and those who reported not experiencing any of the events we asked about indicated that they were less stressed than those who had experienced negative event(s) (Mdifference = -0.37,

SE = 0.12, p < .05). There were no statistically significant differences in terms of the consumer confidence component between the four groups, F(3, 339) = 1.64, p = .18, although those who had experienced a negative event did have lower average consumer confidence component scores compared to the other groups, particularly those who reported experiencing a positive event; but a Games-Howell post-hoc test indicated that this difference was not statistically significant, (Mdifference = -0.31, SE = 0.15, p = .15).

The results of these analyses painted a picture of our sample of college students generally consistent with many of our expectations, in that lower income level and having

32 experienced negative financial event(s) were associated with increased stress. In addition, increased stress was also related to lower consumer confidence.

Relationships between Economic Indicators and Risky Choices

Bivariate Pearson correlations were calculated between the economic indicators and the total number of risky choices (Table 7). For a measure of risky choice at the individual level, we used the total number of choices for the probabilistic (i.e., uncertain or risky) option across both domains and different EV levels. Since our goal in Study 1 was to observe the relationship between risky decisions and economic climate measures, risky gain and loss choices were not considered as distinct for our correlation analyses because despite the modest negative correlation between them, we argue that both domains measure individuals’ attitude toward risk and uncertainty (albeit in slightly different ways). These data showed that those who made more risky choices overall reported higher income levels, r(343) = .17, p < .01, greater confidence about the economy, r(341) = .15, p < .01, and they also reported less stress compared with previous years, r(343) = -.13, p < .05.

Table 7

Pearson Correlations Between Variables of Interest (N = 345)

Variable 1 2 3 4 1 1 Income - Consumer confidence .12 1 2 component (.05) - Stress Compared to -.19 -.19 1 3 Previous Years (<.01) (<.01) - .17 .15 -.13 1 4 Overall Risky Choices (<.01) (<.01) (.02) - Note. The two-tailed p-values for each correlation are in parentheses.

Since the economic indicator variables (income, consumer confidence, and stress compared to previous years) shared common variance, we investigated whether the relationship between total risky choices and various economic indicator variables was

purely an income effect. To explore that possibility, we computed the corresponding

partial correlations with the effects of income partialed out across participants. As can be seen in Table 8, the total number of risky choices made remained correlated with consumer confidence and self-reported stress levels respectively: r(340) = .14, p < .05, and r(340) = -.10, p = .05.

Table 8

Pearson Partial Correlations Between Variables of Interest, Controlling for Income Level (N = 345)

Variable 123 1 1 Overall Risky Choices - Consumer confidence .14 1 2 component (.01) - Compared to previous years, -.10 -.18 1 3 how stressed are you been? (.05) (<.01) -

In fact, the direction and magnitude of the coefficients for the partial correlations

remained very close to the overall correlation coefficients, providing little evidence that

income plays a primary role in mediating any relationship between these economic

indicators and risky decision making.

Finally, we wondered if the positive relationship between consumer confidence

and total risky choices was mediated partially by stress: That is, if higher consumer

confidence leads to lower stress, which in turn leads to more willingness to make risky

choices. In order to test for this possibility, we equated participants in terms of income

and self-reported stress by calculating the partial correlation between total risky choices

and consumer confidence with both income and stress controlled. The results showed that

total risky choices remained significantly correlated with consumer confidence, r(339) =

.12, p = .03.

Discussion

Several findings from Study 1 are noteworthy. First, consistent with extant data

(Weller et al., 2007; Xue et al., 2008) RA trials were most likely to elicit risky choices, followed by the risk neutral EQEV trials, and then RD trials. Second, there was modest evidence of a preference shift (loss aversion) on the equal expected value trials, where participants made numerically more risky choices regarding losses than risky choices regarding gains, despite the fact that the conditions neither particularly favored risk averse nor risk seeking behavior. The ratio of losses to gains that we obtained on our equal expected value trials (close to 1:1) differs from the findings of Lauriola et al.

(2007) who used a similar task and obtained a 2:1 ratio of risky loss choices to risky gain

choices. There are a few potential explanations for this difference in results, as there were

differences in outcome magnitudes (outcome magnitudes tend to influence aversion to

taking risks according to prospect theory), as well as differences in task instructions, and

item wording. It is also possible that the differences we noticed in Study 1 are due to the

dramatic changes in economic conditions and consumer confidence ratings nationally

between the two studies. These possibilities will be examined further in Studies 3 and 4.

Third, previous studies using expected value risky decision making tasks had not explored the relationship between their participants’ self-reported income level, stress level, consumer confidence, and their risk taking tendencies. We explored that

relationship for various economic indicator variables at both a global and a personal level. With respect to self-reported personal experiences, we found that those who

reported experiencing negative financial events within the past 18 months tended to have

lower income levels, and higher stress levels compared to those who reported

experiencing no events or at least one positive event. Furthermore, although the

difference was not statistically significant, those who reported experiencing a negative

event were the most pessimistic regarding economic recovery nationally.

The relationships between income, self-reported stress compared to previous

years, consumer confidence, and risky choices on a laboratory task were also explored here, and the results suggested that self-reported income level appears to influence risky choice decisions in a manner somewhat distinct from other constructs such as consumer confidence and stress—despite the fact that income level did correlate with those

constructs. Furthermore, the correlations between consumer confidence and risky choices

remained significant even when we controlled for income and, more importantly, change

in stress levels. This pattern of data does not lend support to the idea that higher

consumer confidence is solely a state indicator associated with elevated mood or reduced

stress, which in turn leads to optimism regarding risk taking.

We would certainly readily acknowledge the problem of restriction of range here,

given that our sample consisted solely of college undergraduates. One could argue that

those who have experienced recent financial events with serious negative consequences,

such as bankruptcy, are unlikely to be a part of our sample. In spite of this, the fact that

we obtained a significant correlation between consumer confidence and risky choices,

especially when we partialed out the effects of income and change in stress, is

noteworthy.

CHAPTER 3

Study 2

The main goal for Study 2 was to extend the scope of Study 1 by examining whether risky decision making propensities in a laboratory-based task were correlated with risk propensity in other life domains. Although this type of work has been done before using the BART task (Lejuez et al., 2003), the only study that we are aware of using an expected value risky decision making task was Lauriola, Russo, Lucidi, Violani, and Levin (2005), who noted in their review of the literature that there was evidence supporting the idea that similar personality factors might affect risk taking in laboratory- based tasks as well as in real-world health behaviors. In their study, they found more evidence to support this idea using positively and negatively framed health decision scenarios. In Study 2, we plan to extend the focus beyond health-related decisions to a wide variety of potential risk taking domains. To achieve this, we utilized two domain- specific self-report measures of risk taking, one focusing on risky behaviors likely to result in serious negative consequences: the Cognitive Appraisal of Risky Events

(CARE) developed by Fromme et al. (1997), and the Domain Specific Risk Taking scale developed by Weber, Blais, and Betz (2002; see also Blais & Weber, 2006), which focuses on more mundane kinds of risky behaviors with generally less severe consequences. For Study 2, it was hypothesized that if the two-choice expected value task provides a somewhat generalizable measure of individuals’ risk propensity, self-reported risk taking in other life domains should correlate positively with the number of risky

choices made on the task. While we would not expect two-choice expected value task

performance to correlate universally across all domains with all risk measures, risk taking

areas where college students have the most positive attitudes, opportunities, and social support (Ajzen, 1991), might be expected to correlate with risky choices on our two- choice expected value task.

Participants

The participants (N = 213) were introductory psychology students age 18 – 24 (M

= 19.36, SD = 2.03) who completed the study for course credit.

Measures

The two-choice expected value task from Study 1 was modified in Study 2 to

include a broader range of monetary outcomes. This adjustment allowed for an additional

manipulation check to ensure that participants were making their decisions with respect

to expected value and potential outcome magnitude. Thus, probability values from Study

1 (20%, 33%, and 50%) were combined with nine different monetary amounts, creating

distinct magnitude levels: quarters trials ($0.50, $0.75, $1.25), dollars trials ($2, $3, $5),

and tens of dollars trials ($20, $30, $50). For each of the trial types, the certain option

was win or lose $0.25, $1, or $10, respectively. The nine distinct monetary amounts,

three levels of probability, and two levels of domain (gain and loss) were combined to

create 54 trials. Similar to Study 1, participants were asked about their income (the

response scale was expanded in Study 2, ranging from 1 – 10, where 1 was less than

$25,000, and 10 was more than $250,000), and stress level compared to previous years

(scaled 1 – 5, where 1 was much less stressed, and 5 was much more stressed). To

38 measure intentions to engage in risky behaviors from behavioral domains with potentially very serious consequences, the Cognitive Appraisal of Risky Events (CARE) Expected

Involvement subscale (Fromme et al., 1997) was utilized. It consists of 30 items which measure participants’ expectations of being involved in risky behaviors from six different categories: illicit drug use (three items), aggressive and illegal behaviors (nine items), risky sexual activities (six items), heavy drinking (three items), high risk sports (four items), and academic and work behaviors (five items). Participants were instructed that they should indicate the likelihood that they would engage in the behavior described by each item within the next six months. Responses are scaled from 1 – 7, where 1 was not at all likely, 4 was moderately likely, and 7 was extremely likely.

To measure intentions to engage in risky behaviors from behavioral domains with potentially less severe consequences, the Domain Specific Risk Taking Scale

(DOSPERT) (Blais & Weber, 2006) was utilized. It consists of 30 items which measure individuals’ self-reported likelihood of engaging in risky activities from five domains (six items per domain): ethical, financial (betting and investing items), health/safety, social, and recreational. Participants were instructed that they should indicate the likelihood that they would engage in the activity or behavior described by each item if they were to find themselves in that situation. These items are scaled from 1 – 7, where 1 was extremely unlikely, 4 was not sure, and 7 was extremely likely.

Procedure

After participants read and signed the informed consent document, they were seated at a computer and asked to complete the 117-item survey which began with the 54 cups task items, and was followed by items regarding income level, stress compared to

previous years, and then the 30 CARE items followed by the 30 DOSPERT items.

Participants received no feedback regarding the outcomes of their decisions. Completion

of all items was self-paced.

Results

Table 9 displays the descriptive statistics and Cronbach’s alpha coefficients for

the variables in Study 2. In terms of the two-choice expected value task findings, the

alpha coefficients indicated that for each trial type and outcome magnitude, there was a

reasonable degree of internal consistency reliability. Three of the DOSPERT subscales:

ethical, health and safety, and social, had low alpha coefficients (α = .59, .57, and .57,

respectively) indicating decreased internal consistency reliability of those subscales. As

in Study 1, we calculated the correlations between different trial types within each

domain and found that for all three outcome magnitudes, risk advantageous, equal

expected value, and risk disadvantageous trials were positively correlated (all p < .01).

Further, the number of risky choices on trials of each outcome magnitude (i.e., quarters, dollars, and tens of dollars) were positively correlated within each domain (all p < .01, except for dollars risk advantageous loss and tens of dollars risk disadvantageous loss where the correlation was positive, but p = .06). The correlation between the overall number of risky choices on gain trials and the overall number of risky choices on loss trials was not significant (p > .50), but as in Study 1, the risky choices from all trials were combined for all correlation analyses since differences in risky choices due to framing effects was not our primary concern in Study 2, and we were also concerned that the responses to the relatively small number of trials for each specific type (e.g., three equal

expected value, dollars, loss trials with Cronbach’s α = .41) would be too variable to

detect correlations with other types of self-report risk measures.

Examining the means to determine the influence of outcome magnitude, risky

choices were combined across trial types so that the total number of risky choices for

quarters, dollars, and tens of dollars trials could be examined. Risky choices were most prominent when the outcome magnitudes were quarters (M = 9.77, SD = 3.83), followed by dollars (M = 9.31, SD = 3.79), and were least prominent for tens of dollars outcomes

(M = 8.22, SD = 3.22). These differences were examined for significance using a one- within repeated measures ANOVA and found to be significant, F(1.68, 355.35) = 30.80, p < .01 , partial η2 = .13, with the Greenhouse-Geiser adjustment applied for violation of

the sphericity assumption. The post-hoc repeated measures t tests with a Bonferroni

correction for multiple comparisons indicated that risky choices were significantly greater

on quarters magnitude trials than on dollars magnitude trials (Mdifference = 0.46, SE = 0.17, p < .05), and significantly greater on dollars magnitude trials than on tens of dollars magnitude trials (Mdifference = 1.09, SE = 0.19, p < .01).

Figure 3 suggests that the sample of participants from Study 2 also responded

appropriately to the expected value manipulation for all three levels of outcome

magnitude. In order to verify that risky choices would be most prevalent when the

conditions were favorable, we combined all similar EV types (i.e., creating a single group

for RA, EQEV, and RD trial types) by collapsing across outcome magnitude and domain.

The data indicated that risky choices were most prevalent when expected value was risk

advantageous (M = 12.94, SD = 3.96), of medium frequency when expected value was

risk neutral (M = 9.23, SD = 3.79), and of lowest frequency when expected value was risk

disadvantageous (M = 5.13, SD = 3.60). These differences were examined for significance using a one-within repeated measures ANOVA and found to be significant,

F(1.52, 321.91) = 525.19, p < .01 , partial η2 = .71, with the Greenhouse-Geiser

adjustment applied for violation of the sphericity assumption. The post-hoc repeated

measures t tests with a Bonferroni correction for multiple comparisons indicated that

risky choices were significantly greater on RA trials than on EQEV trials (Mdifference =

3.71, SE = 0.19, p < .01), and significantly greater on EQEV trials than on RD trials

(Mdifference = 4.10, SE = 0.22, p < .01).

Table 9

Descriptive Statistics and Cronbach’s Alpha Coefficients for Study 2

Variable Mean SD a Income 6.47 2.83 Stress Compared to Previous Years 3.96 0.93 Total Risky Gain Choices for Quarters 5.42 2.45 .79 Total Risky Loss Choices for Quarters 4.35 2.62 .80 Total Risky Gain Choices for Dollars 4.82 2.5 .80 Total Risky Loss Choices for Dollars 4.49 2.47 .77 Total Risky Gain Choices for Tens of 4.28 2.43 .79 Dollars Total Risky Loss Choices for Tens of 3.94 2.36 .75 Dollars Total Risky Gain Choices Overall 14.51 6.65 .91 Total Risky Loss Choices Overall 12.79 6.72 .90

Total Risky Choices Overall 27.30 9.58 .89 DOSPERT Ethical Scale 11.25 4.46 .59 DOSPERT Health and Safety Scale 17.86 6.78 .57 DOSPERT Betting Scale 4.01 2.46 .84 DOSPERT Investing Scale 7.04 4.03 .75 DOSPERT Recreational Scale 17.11 9.25 .85 DOSPERT Social Scale 25.34 5.98 .57 CARE Illicit Drug Use Scale 4.89 3.58 .80 CARE Aggressive and Illegal Behavior 14.34 6.58 .82 Scale CARE Risky Sex Scale 9.91 5.03 .68 CARE Heavy Drinking Scale 11.1 6.41 .93 CARE Academic and Work Behaviors 13.28 5.95 .81 Scale CARE High Risk Sports Scale 12.44 5.60 .64 Note. For the two-choice EV task, the maximum number of risky choices for each outcome magnitude type (e.g., quarters, dollars, tens of dollars) was nine in each domain. When outcome magnitudes are pooled, the maximum number of risky choices for each domain is 27. When all trials are included, the maximum number of risky choices possible is 54.

Similar to Study 1, there was little evidence of loss aversion for any of the trial

types in Study 2; and in fact, the average number of risky choices to achieve a gain outnumbered the average number of risky choices to avoid a loss numerically for equal expected value trials of all three outcome magnitudes. For tens of dollars trials, risky gain choices (M = 1.38, SD = 1.00) were numerically larger than risky loss choices (M = 1.33,

SD = 1.10), but the difference was not significant, t(212) = 0.50, p > .50. For dollars trials, risky gain choices (M = 1.58, SD = 1.09) were numerically larger than risky loss choices (M = 1.57, SD = 1.00), but the difference was not significant, t(212) = 0.05, p >

.50. For quarters trials, risky gain choices (M = 1.87, SD = 1.02) were larger than risky loss choices (M = 1.48, SD = 1.05), and the difference was significant, t(212) = 4.19, p <

.01, d = 0.28.

In terms of the CARE and DOSPERT results, it can be seen in Table 9 that the behavioral domains with the largest average self-reported intentions to engage in risky activity for each instrument are the DOSPERT social risk taking subscale, and the CARE aggressive and illegal behavior subscale. The subscales with the lowest average were the

DOSPERT betting (gambling) subscale, and the CARE illicit drug use subscale.

0.90

0.80 Quarters

Dollars 0.70

Tens of Dollars 0.60

0.50

0.40 0.81 0.78 0.77

Risk Preference Risk 0.68 0.68 0.65 0.62 0.30 0.59 0.51 0.52 0.48 0.42 0.20 0.38 0.32 0.28 0.26 0.27 0.10 0.17

0.00 RAGAIN RALOSS EQEVGAIN EQEVLOSS RDGAIN RDLOSS Trial Type

Figure 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.

Due to several positively skewed subscale distributions for both the CARE and

DOSPERT, non-parametric Spearman partial correlations (controlling for income) were utilized when analyzing the relationship between risky choices and self-reported intentions to engage in other risky behaviors. Since we only expected positive correlations between the number of risky choices on the two-choice EV task and the

CARE and DOSPERT subscales, a one-tailed analysis was used (Tables 10 – 11). Since the overall pattern of correlations between the number of risky choices on the two-

outcome risky decision making task and self-reported risk taking on the CARE and

DOSPERT subscales was similar regardless of outcome magnitude (i.e., quarters, dollars,

or tens of dollars) or domain (gain or loss choices); for simplicity, the trials from all

outcome magnitudes were collapsed into a single measure, total risky choices (M = 27.30,

SD = 9.58, α = .89).

The Spearman partial correlations between risky choices and the CARE subscales

reported in Table 10 suggested that stress compared to previous years was correlated with

intentions to engage in illicit drug use (p < .05), aggressive and illegal behavior (p < .01),

and risky sexual behavior (p < .05). Study 2 did not replicate the stress compared to

previous years and total risky choices correlation found in Study 1 (p = .48). The total

number of risky choices for each participant was correlated with self-reported intentions

to engage in aggressive/illegal behavior (p < .05), risky sexual behavior (p = .05), heavy

drinking (p < .05), academic/work behaviors (p < .01), and high risk sports (p < .01). The only subscale that did not significantly correlate with risky choices was illicit drug use (p

= .22).

Table 10

Spearman Partial Correlation Coefficients Between Self-Reported Stress, Risky Choices, and Self-Reported Intentions to Engage in Risk Taking on the CARE subscales

Variable 12345 6 78 1 Stress compared to 1 previous years - 2 Overall Risky Choices .00 1 (.48) - 3 Illicit drug use .12 .05 1 (.04) (.22) - 4 Aggressive/Illegal .19 .14 .37 1 Behavior (.00) (.02) (.00) - 5 Risky sexual behavior .12 .11 .29 .47 1 (.04) (.05) (.00) (.00) - 6 Heavy drinking .09 .16 .44 .52 .39 1 (.10) (.01) (.00) (.00) (.00) - 7 Academic/Work .09 .20 .29 .43 .32 .45 1 behaviors (.10) (.00) (.00) (.00) (.00) (.00) - 8 High risk sports .07 .21 .01 .16 .13 .10 .00 1 (.14) (.00) (.42) (.01) (.03) (.07) .49 - Note. The one-tailed p-values for each correlation are in parentheses.

The Spearman partial correlations for stress compared to previous years, overall risky choices, and the DOSPERT subscales are reported in Table 11. These correlations suggested that stress compared to previous years was unrelated to the domain specific subscales of the DOSPERT measure (all p > .40). The total number of risky choices for each participant was significantly correlated with self-reported intentions to engage in betting or gambling (p < .05), investing (p < .05), and recreational risky behavior (p <

.01). None of the other correlations between total risky choices and DOSPERT subscales reached statistical significance (all p > .13).

Table 11

Spearman Partial Correlation Coefficients Between Self-Reported Stress, Risky Choices, and Self-Reported Intentions to Engage in Risk Taking on the DOSPERT subscales

Variable 123456 78 1 Stress compared to 1 previous years - 2 Overall risky choices .00 1 (.48) - 3 Ethical .06 .07 1 (.20) (.14) - 4 Betting -.04 .12 .22 1 (.27) (.04) (.00) - 5 Investing -.02 .14 .12 .36 1 (.39) (.02) (.04) (.00) - 6 Health and Safety .05 .08 .40 .06 .04 1 (.23) (.13) (.00) (.21) (.28) - 7 Recreational -.03 .17 .12 .16 .26 .28 1 (.34) (.00) (.05) (.01) (.00) (.00) - 8 Social .05 .00 .14 .11 .22 .32 .30 1 (.21) (.45) (.02) (.06) (.00) (.00) (.00) - Note. The one-tailed p-values for each correlation are in parentheses.

However, as can be seen in Table 11, the correlations between risky choices and both the ethical and health and safety subscales do not appear to be dramatically different in magnitude from the correlations that did reach statistical significance. Furthermore, since

overall risky choices appeared to be modestly related to several different behavioral

domains measured by the CARE subscales, the goal of the final analysis was to

determine whether overall risky choices on the two-choice expected value task would be best considered as a measure of generalized intentions to engage in risky behaviors, or if these choices seemed to be more closely related to financial risk in particular. To explore these possibilities, the Spearman correlations between the CARE and DOSPERT

subscale scores and the overall number of risky choices for each participant were

subjected to a principal components analysis (PCA) with oblique rotation since it was

expected that the components might be correlated. Utilizing the Kaiser criterion (Kaiser,

1960), components with eigenvalues greater than one were kept, and this resulted in a four component solution. Since the fourth component of this solution consisted of only a single subscale (DOSPERT social risk), the analysis was completed a second time but was restricted to a three-component solution which explained approximately 53% of the variance (Table 12).

Table 12

Component Loadings for Overall Risky Choices and the DOSPERT and CARE Subscales

Variable Component 1 2 3 Overall risky choices .11 .20 .23 Ethical .61 -.10 .30 Betting .02 -.04 .77 Investing -.14 .12 .80 Health and safety .74 .24 -.09 Recreational -.04 .90 .07 Social .17 .25 .22 Illicit drug use .62 .06 -.23 Aggressive/Illegal .76 .02 .05 behavior Risky sexual behavior .67 -.06 .07 Heavy drinking .77 .16 -.13 Academic/Work .70 -.24 .08 behaviors High risk sports -.03 .86 -.01

The results of the principal components analysis yielded three relatively distinct components. Component 1 represented risky behavioral domains such as illicit drug use and unethical behavior that are more dangerous and likely to be related to social norms regarding moral behavior. Component 2 represented risky behavioral domains such as high-risk sports and social risk taking which are generally less dangerous and also unrelated to societal norms for moral behavior. Finally, component 3 represented financially-related risk taking behaviors such as high-risk investing or betting/gambling.

The data indicated that overall risky choices on our two-choice expected value task loaded highest (.23) on the component which included self-reported intentions to make risky decisions in the financial domain rather than on the other components. However, since the loading for overall risky choices was almost as high for component 2 (.20), it was empirically difficult to say whether risky choices are best understood as being more strongly related to component 2 or component 3. Since the DOSPERT scale specifically measures financial decision making (with the betting and investing subscales), and our

two-choice expected value task asks participants to make choices involving money, we

thought that it was also important to examine the relationship between the DOSPERT

subscales and overall risky choices on the EV task in a principal components analysis

with an oblique rotation which did not include the CARE items (Table 13).

Table 13

Component Loadings for Overall Risky Choices and the DOSPERT Subscales

Variable Component 12 3 Overall risky choices .53 -.09 .09 Ethical .22 -.11 .85 Betting .77 -.01 .07 Investing .67 .36 -.21 Health and safety -.14 .37 .74 Recreational .21 .68 .02 Social -.13 .82 .09

Performing the PCA without the CARE subscales presented a much clearer picture of overall risky choices on our task belonging to a financial component. This three- component solution accounted for approximately 62% of the variance, and was preferable to a two-component solution which only explained 48%. Additionally, in the two- component solution, the loading for recreational risk taking was of similar magnitude for both components, whereas with three components, there appears to be a similar separation of a financial component (Component 1) from two other components, one related to more socially acceptable forms of risk taking (Component 2), and the other related to less socially accepted forms of risk taking (Component 3).

Discussion

The primary objective of Study 2 was to determine whether preference for risky choices on a two-choice expected value task would correlate positively with self-reported intentions to engage in risky activities in other behavioral domains. To achieve this objective, we utilized two separate self-report scales, one that focused primarily on

behaviors with potentially very serious or deadly consequences (the Cognitive Appraisal

of Risky Events: Behavioral Intentions subscale), and another that focused primarily on

more commonplace types of risk taking (Domain Specific Risk Taking scale). An

additional change from Study 1 was that we manipulated the outcome magnitudes of the two-choice expected value task trials in an attempt to further demonstrate that our

participants were appropriately responsive to both expected value and outcome magnitude variations.

The analysis of the two-choice expected value task data suggested that overall participants were sensitive to changes in expected value and outcome magnitude.

Participants appeared to select the risky option most frequently when expected value was risk advantageous, followed by when expected value was risk neutral, and selected the risky option least frequently when expected value was risk disadvantageous. Participants also chose the risky option most frequently when the outcome magnitude involved quarters, followed by dollars, and selected the risky option least frequently when the outcome magnitude involved tens of dollars. Study 2 did not find evidence of loss aversion, and in fact, participants took more risks numerically on equal expected value gain trials compared to equal expected value loss trials for all three outcome magnitude levels (tens of dollars, dollars, and quarters trials). The relative absence of loss aversion found in Studies 1 and 2 was unexpected given the findings of Levin and colleagues using a similar two-choice expected value task (Levin & Hart, 2003; Levin, Hart, Weller,

& Harshman, 2007), and we explored these loss aversion findings more in Studies 3 and

The analysis of the CARE and DOSPERT results for Study 2 suggested that risky choices were indeed positively correlated with self-reported intentions to engage in other risky behaviors, at least for certain types of behaviors. In general, responses to the CARE items seemed to be more closely related to the type of risk propensity measured by our two-choice expected value task. Only one of the total risky choices--CARE subscale correlations failed to reach statistical significance, namely, intentions to engage in illicit drug use. Responses to the DOSPERT items generally seemed to share less in common with risk propensity as measured by our two-choice expected value task. However, two of the three subscales that were significantly correlated with the total number of risky choices were the betting and investing subscales, and the third was the recreational subscale, which is similar to the high risk sports subscale of the CARE scale. Two principal components analyses (one of the CARE and DOSPERT subscales and the overall number of risky choices scores, and one of DOSPERT and overall risky choices scores alone), provided further support for these findings, indicating that the overall number of risky choices seemed to share the most variance with financially-related constructs; although risk taking as measured by our two-choice EV task also shared some variance with intentions to engage in risky activities in other behavioral domains.

In terms of the limitations of Study 2, one might wonder why the correlations between the two-choice expected value task risky choices and the behavioral subscales are not larger in magnitude. However, given the research findings of Weber and colleagues (e.g., Weber, Blais, & Betz, 2002; Blais & Weber, 2006) which suggest that risk propensity is likely to be at least somewhat domain specific and sensitive to contextual factors, it is not surprising that these correlations were modest in magnitude.

Taken together, these correlations and patterns of factor loadings do appear to

suggest that risky choices on a laboratory-based risky decision making task share

something in common with making risky choices in other behavioral domains. It also

seems possible that the correlations could be attenuated due to a restricted range problem

(which would also affect the principal components analysis results, perhaps making it

more difficult to derive components). For example, it seems unlikely that in our sample

of college undergraduates we had appropriate access to individuals who regularly engage

in some of the behaviors that make up the DOSPERT and CARE subscales. Engaging in

such risky behaviors on a regular basis would likely result in incarceration or death.

Furthermore, it is possible that some of our participants were not comfortable indicating that they engaged in these types of behaviors, despite the confidentiality afforded by our computerized survey format.

It should also be noted here that Study 2 did not find support for the relationship between self-reported stress compared to previous years and risky choices that was found in Study 1. Further, self-reported stress compared to previous years did not correlate with intentions to engage in the risky behaviors measured by the DOSPERT scale. However,

our stress variable did correlate significantly with self-reported intentions to engage in illicit drug use, risky sexual behavior, and aggressive/illegal behavior; and it was correlated modestly with the other behavioral domains measured by the CARE scale as well. Despite its limitations, Study 2 is one of the first studies to examine the external validity of a two-choice expected value choice task by using multi-domain self-report scales such as CARE and DOSPERT.

CHAPTER 4

Study 3

Despite finding some modest relationships between the two-choice expected

value task and real world risk taking, we noticed that unlike some of the findings from

previous studies using expected value tasks such as Lauriola et al. (2007), which

suggested that the ratio of equal expected value gain trial risky choices to equal expected value loss trial risky choices should be close to 1:2, Studies 1 and 2 found mixed results regarding loss aversion. In particular, in Study 1 there was modest evidence to suggest loss aversion, but in Study 2, the participants were more likely to take risks to achieve gains rather than avoid losses on equal expected value trials. To ensure that our undergraduate samples did not differ in some way from the norm, and to explore the possibility that the differences we observed might be due to the current economic climate, we conducted a replication study of Lauriola et al. (2007), which collected data using a two-choice equal expected value risky decision task from summer through fall in 2006.

During this period, consumer confidence nationally was substantially higher (mean =

105, range = 100 to 107), than during the May 2009 to January 2011 period when the data for our four studies were collected (mean = 53, range = 46 to 63).

Method Participants

The participants (N = 138), were undergraduate introductory psychology students age 18 – 24 (M = 19.31, SD = 1.32) who completed the study for course credit in October

2010. The comparison data from Lauriola et al. (2007) included participants (N = 191)

who were undergraduate introductory psychology and marketing students. Their data

were collected in 2006, when consumer confidence nationally was much higher.

Measures

As a measure of risky decision making, a 32-item two-choice expected value risky decision making task previously used by Lauriola et al. (2007) was administered.

Participants were asked to make hypothetical choices between options with a certain outcome (e.g., a 100% chance to win $1) and options with uncertain outcomes but equal expected values (e.g., a 50% chance to win $2, and 50% chance to win nothing). Sixteen of the items were loss-framed such that participants chose to either lose a certain amount or take a chance at losing a larger amount but perhaps losing nothing, and the other 16 trials were gain-framed such that participants chose to either win a certain amount, or take a chance at winning a larger amount but perhaps winning nothing. Similar to the

Lauriola et al. study, feedback regarding choice outcomes was not provided.

Procedure

After participants read and signed the informed consent document, they were seated at a computer and asked to complete the risky decision making task. Consistent with Lauriola et al. (2007), participants read a short description of expected value which emphasized that for each choice they would make, the two options would have similar long-term pay-offs, so they should make their selections in a subjective way according to their personal preferences or attitudes. Following the description were three practice items, similar to those participants would respond to when completing the task. After completing the practice items, participants completed the risky decision making task.

Besides the fact that our study was conducted using web-based software, other aspects of

the task were otherwise identical to Lauriola et al. (2007).

Results

The descriptive statistics in Table 14 below show the comparison between the current data and the data collected by Lauriola et al. (2007). The data suggest that internal consistency reliability was good; all Cronbach’s alpha values were greater than .80. The ratio of gains to losses was approximately 1:2 for both studies, and the means were not significantly different for either domain between the two studies; for loss trials, t(327) =

-1.34, p = .18, and for gain trials, t(326) = -1.65, p = .10. In terms of loss aversion,

participants from Lauriola et al. made more risky loss choices than risky gain choices

t(189) = 13.34, p < .01, d = 0.96; as did our participants, t(137) = 11.98, p < .01, d = 1.02.

Table 14

Comparison of Descriptive Statistics from Lauriola et al. 2007 and Our 2010 Study Risky Cronbach's Cronbach's Gain α for Gain Risky Loss α for Loss N Choices Trials Choices Trials Lauriola et al. 190 5.71 (3.73) .82 11.35 (3.69) .82 Our 2010 Study 138 5.03 (3.66) .82 10.73 (4.04) .84 Note. The maximum number of risky choices in a domain was 16. Standard deviations are in parentheses.

Discussion

The primary goal of Study 3 was to examine if our samples of undergraduates

displayed a fundamentally different pattern of choices regarding risk compared to those

in previous research that was conducted when economic conditions and consumer

confidence ratings nationally were much more positive. To investigate this possibility, we

used an exact replication of a two-choice equal expected value risky decision making task that had been used by Lauriola et al. (2007). Compared to the mixed findings regarding loss aversion in our Studies 1 and 2, the task used in Study 3 produced strong loss aversion—almost a 2:1 ratio of average number of risky choices in loss framed trials compared to gain framed trials. Furthermore, our analysis revealed consistency between our data and those found by Lauriola et al --- the two studies produced similar means and similar effect size estimates. These results suggest that college students displayed a fundamentally similar pattern of choices regarding risk at two different time periods almost 4.5 years apart while each group was facing very different economic conditions.

Aside from the changes in economic climate, there were a few other potentially important differences between Studies 1 and 2 and the Lauriola et al. (2007) study. The first difference was that Lauriola et al. only used equal expected value trials, whereas both Study 1 and Study 2 utilized risk advantageous and risk disadvantageous trials. It is possible that simply mixing other types of trials in with the equal expected value trials was enough to disrupt the psychological phenomena responsible for the loss aversion finding.

The second difference was that Lauriola et al. (2007) provided directions prior to

the task which explicitly instructed participants in how to calculate the expected value for

each choice. One way to examine the potential influence of this variable would be to

allow a new sample of individuals to complete the task without the Lauriola et al.

instructions or practice trials.

A third difference, was that for the risky option Lauriola et al. (2007) specified

both the probability of a non-zero outcome as well as the probability of a zero outcome

(e.g., a 50 chances out of 100 to win/lose $2, AND 50 chances out of 100 to win/lose

nothing). This is in contrast to our instructions in Studies 1 and 2 (i.e., 50 chances out of

100 to win/lose $2). If this difference was responsible for the lack of a 2:1 ratio of loss to

gain risky choices, it would support the notion that on gain trials, telling people the probability and magnitude of the potential win but not explicitly stating that they might win nothing makes them more “promotion focused”; while on loss trials, telling people the probability and magnitude of the potential loss but not explicitly stating that they might lose nothing makes them more “prevention focused” (Higgins, 1998). In essence, not highlighting explicitly the information about winning / losing $0 to the participant for the risky option perhaps results in a sort of biased decision making wherein the decision maker seems to choose primarily on the basis of the information provided, and perhaps fails to consider other potentially relevant information that was not explicitly provided.

This phenomenon has been referred to by Sanbonmatsu, Kardes, Houghton, Ho, and

Posavac (2003) as omission neglect. Given the relative novelty of the task for the participants, and their non-expert status (none of our undergraduates were trained economists), this seems like a plausible explanation.

The fourth and final difference was that Lauriola et al. (2007) used a much broader range of outcome magnitudes, and the expected values of their risky option varied from dollars to thousands of dollars. The literature on loss aversion and preference shifts would generally suggest that as the magnitude of the potential outcome grows

larger, risk seeking to achieve a gain should diminish while risk seeking to avoid a loss

should increase (Kahneman & Tversky, 1979), resulting in enhanced loss aversion.

The latter three of these differences between our first two studies and the Lauriola

et al. (2007) methodology seemed most likely to be responsible for the differences in

results that were observed in Studies 1 and 2 compared to Study 3. Therefore, Study 4

examined the relative influence of task instructions, wording (specification of the

probability of a zero as well as non-zero outcome), and outcome magnitude on choice

behaviors.

CHAPTER 5

Study 4

The main goal of Study 4 was to elucidate the nature of the loss aversion findings from Study 1 and Study 2 on the one hand and those of Study 3 on the other. As discussed earlier, there were at least four factors that might account for this difference.

These were: (1) whether only trials with equal expected value were used, (2) whether task instructions included a description and example of how to calculate expected value, (3) whether on each trial the risky options explicitly stated the probability of both the zero as well as the non-zero outcome, and (4) the range of outcome magnitudes used, and whether it ranged from dollars up to tens of thousands of dollars. Study 4 was designed to explore the latter three of these differences, as these seemed most likely to account for the differences in loss aversion. In particular, we hypothesized that removing the instructions and examples as well as removing the specification of a zero outcome would result in participants exhibiting less aversion to losses, and closer to a 1:1 ratio of risky loss choices to risky gain choices. We also explored the possibility that neither expected value

instructions nor specification of the probability of a non-zero outcome were as influential

as the magnitude of the potential gains and losses in determining whether or not loss

aversion was observed.

Method Participants

The participants (N = 144), were undergraduate introductory psychology students

age 18 – 24 (M = 19.30, SD = 1.41) who completed the study for course credit.

Measures

As a measure of risky decision making, the 32-item two-choice expected value task used in Study 3 was adapted to create two between-subject conditions. In the no- instructions condition, participants received no instructions regarding expected value prior to the 32 items, but the probabilities of the non-zero AND the zero outcomes respectively were both explicitly stated on each trial (e.g., “50 chances out of 100 to win

$2, and 50 chances out of 100 to win nothing”). In the no-instructions-wording-changed condition, both the instructions and examples regarding the definitions of expected value

were omitted, and the wording on each trial followed those from Study 1 and Study 2 and

did not explicitly mention the probability of a zero outcome (e.g., “50 chances out of 100

to win $2—without explicitly stating that this option also included 50 chances out of 100

to win nothing). This procedural arrangement provided conditions most closely

mimicking those used in Study 1 and Study 2. All other aspects of the task remained

identical to the task used in Study 3, and feedback regarding choice outcomes was not

provided.

Procedure

After participants read and signed the informed consent document, they were seated at a computer and asked to complete the risky decision making task. Participants were randomly assigned to either the no-instructions or the no-instructions-and-wording- changed condition. Consistent with Lauriola et al. (2007), participants read a short

description prior to beginning which instructed them to make their selections in a subjective way according to their personal preferences or attitudes. The expected value portion of the instructions and the examples were omitted. Participants then completed the risky decision making task in a manner identical to Study 3.

Results

The descriptive statistics and Cronbach’s alpha coefficients for each condition of

Study 4 can be found in Table 15. Overall, there seems to be very little difference between the results of Study 4 and the results of Study 3, despite the changes in methodology. In particular, removing the instructions did not result in a significant change from the results obtained in Study 3, and although omitting the probability of a zero outcome from each trial seems to have raised the number of risky gain choices and lowered the number of risky loss choices, this difference did not result in a dramatic shift away from the roughly 2:1 ratio of losses to gains that was observed in Study 3. To examine the potential differences between Study 3 and the two groups of participants in

Study 4, a one-way ANOVA was utilized for each domain, and no significant differences were detected for gain choices F(2, 279) = 1.70, p = .18, or for loss choices F(2, 279) =

1.66, p = .19.

Table 15

Comparison of Descriptive Statistics from Our 2010 Study, and Our Study 4 Cronbach's Cronbach's Risky Gain α for Gain Risky Loss α for Loss N Choices Trials Choices Trials Study 3 138 5.03 (3.66) .82 10.73 (4.04) .84

Study 4 No 71 5.07 (4.01) .86 10.92 (3.53) .78 Instructions

Study 4 No 73 6.04 (4.51) .88 9.85 (3.88) .80 Instructions, and Wording Changed

Note. Standard deviations are in parentheses. The maximum number of risky choices in each domain is 16.

Because Study 3 and Study 4 used a range of outcome magnitude that encompassed those used in Study 1 and Study 2, it is possible to directly compare risky choices on the twelve trials from Study 3 and Study 4, (EV ranged from $1 to $10, and outcome magnitude ranged from $1 to $50) to those in Study 1 and Study 2. Table 16 displays the descriptive statistics and Cronbach’s alpha coefficients for these trials (six trials in the gain domain and six in the loss domain).

Table 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

Cronbach's Cronbach's Risky Gain α for Gain Risky Loss α for Loss N Choices Trials Choices Trials Study 3 138 2.92 (1.84) .68 4.03 (1.66) .63

Study 4 No Instructions 71 2.82 (1.80) .66 4.04 (1.68) .67

Study 4 No Instructions, 73 and Wording Changed 3.20 (2.09) .80 3.73 (1.72) .63

Note. Standard deviations are in parentheses. Six trials from each domain were similar in magnitude to the choices participants made in Studies 1 and 2.

These data generally suggest that when the outcome magnitudes were smaller and in a range with which participants might be more familiar from real life experiences, the ratio of risky choices for losses compared to gains was much closer to 1:1. To further illustrate this point, the means and Cronbach’s alpha coefficients for the remaining 20 trials which had larger outcome magnitudes than those found in our Studies 1 and 2 can be found in Table 17, where it can be seen that for trials with larger magnitudes than the ones we used in Studies 1 and 2, the ratio of risky loss choices to risky gain choices is closer to 2:1.

Table 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 Cronbach's a Cronbach's a Risky Gain for Gain Risky Loss for Loss N Choices Trials Choices Trials Study 3 138 2.11 (2.44) .81 6.70 (2.77) .80

Study 4 No 71 2.25 (2.72) .85 6.87 (2.67) .79 Instructions

Study 4 No 73 2.84 (2.91) .85 6.12 (2.72) .77 Instructions, and Wording Changed

Note. Standard deviations are in parentheses. Six trials from each domain that were similar in magnitude to the choices participants made in Studies 1 and 2 were excluded here.

Differences in loss aversion were examined more closely using repeated measures

t tests and the results are shown in Table 18, and Figure 4. The only condition which did

not elicit reliable loss aversion was when participants did not receive task instructions

and the probability of a zero outcome for the risky option was not explicitly given.

Furthermore, the loss aversion effect size for the no instructions condition was even larger than the effect size obtained in Study 3.

Table 18

Descriptive and Inferential Statistics Comparing Mean Number of Risky Choices for Loss Domain trials to those in Gain Domain Trials by Task Conditions

Gains Losses Condition Mean SD Mean SD df t p Cohen's d

Study 4 No 5.07 4.01 10.92 3.53 70 9.93 < . 01 1.18 instructions

Study 4 No 2.82 1.80 4.04 1.68 70 4.63 < .01 0.53 instructions similar magnitudes

Study 4 No 6.04 4.51 9.85 3.88 72 4.92 < .01 0.58 instructions and wording changed

Study 4 No 3.20 2.09 3.73 1.72 72 1.54 .13 0.18 instructions and wording changed similar magnitudes

Study 3 5.03 3.66 10.73 4.04 137 11.98 < .01 1.02

Study 3 with 2.92 1.84 4.03 1.66 137 5.58 < .01 0.48 similar magnitudes Note. The similar magnitudes distinction indicates that the statistics were calculated for only the trials with outcome magnitudes similar to those used in Studies 1 and 2. The maximum number of possible risky choices for a domain was 16, except for the similar magnitudes calculations where the maximum number possible for a domain was six.

0.8 Gain trials Loss trials 0.7

0.6

0.5

0.4

0.3

Proportion of riskyProportion of choices 0.2

0.1

0 Study 4 No Study 4 No Study 4 No Study 4 No Study 3 Study 3 with instructions instructions instructions and instructions and similar similar wording wording magnitudes magnitudes changed changed similar magnitudes Condition

Figure 4. The proportion of risky choices for gain and loss trials by condition. Error bars are standard errors.

Discussion

The primary objective for Study 4 was to discover the source of the discrepancy between the loss aversion findings in Studies 1 and 2 compared to Study 3. The variables of interest were: task instructions, wording, and the range of outcome magnitudes.

Overall, it appeared that removing the instructions and changing the task wording did not produce significant differences in risky choices for the gain or the loss framed trials. In contrast, outcome magnitude appeared to play a much more significant role in influencing the decision making process.

One common concern of behavioral economists regarding economic games or laboratory paradigms similar to the one used here was potential confusion on the part of

67 participants regarding the concept behind expected values and long-term payoffs, and that detailed explanations of the concept are necessary to ensure that participants’ revealed preferences did not stem from conceptual misunderstanding of the payoff situations. Our data from Study 3 and Study 4 suggest that provision of instructions and examples regarding the concept of expected values did not significantly alter the results, both for the range of expected values (larger) that led to loss aversion and for the range of expected values (smaller) that did not lead to loss aversion. In fact, the effect size for loss aversion under the latter condition suggested that removing the instructions might have actually increased loss aversion. When expected value instructions were removed and the wording was changed in Study 4 so that conditions were similar to Study 1 and Study 2

(except for outcome magnitude), numerically there was an increase in the number of risky gain choices and a decrease in the number of risky loss choices. Although these changes did not reach statistical significance, these findings are consistent with the notion that failure to provide the probability of a zero outcome for the risky option caused individuals to be more promotion focused in the domain of gains, and more prevention focused in the domain of losses. Specifically, not providing individuals with the information that they might gain nothing by choosing the risky option might accentuate the attractiveness of the risky gain option, leading to increased preference for it; while not providing the information that participants might lose nothing accentuates the potential threat associated with the risky loss option, leading to increased preference for the certain option. As Sanbonmatsu et al. (2003) have suggested, when important details are omitted, individuals often neglect to consider their importance.

Although removing task instructions and changing task wording did not seem to produce much of an effect, the effects of varying outcome magnitude on decision making were much more pronounced. For trials with outcome magnitudes similar to those used in

Studies 1 and 2, the ratio of risky loss choices to risky gain choices was much closer to the weak loss aversion that was observed in those studies. When outcome magnitudes were much larger than those that were utilized in Studies 1 and 2, the ratio of risky loss choices to risky gain choices was even greater than 2:1. This pattern of results is consistent with the loss aversion predictions made by authors such as Kahneman and

Tversky (1979) who suggested that as the magnitude of the potential outcome grows larger, risk seeking to achieve a gain should diminish while risk seeking to avoid a loss should increase.

However, there is an important corollary of the aforementioned finding. Consider an extreme case where the EVs used in our two-choice expected value task are changed to values greater than $1,000,000. This procedural change in effect would harmonize the choices across participants (few would choose the risky option for gains and almost everyone would choose the risky option for losses) so that the revealed preferences of the more risk-averse participants and those of the less risk-averse participants would be made more similar to one another. As a result, variance in risky choices across participants would be reduced, presenting a problem of restriction of range and making it more difficult to find significant correlations between risky choices and other variables of interest. This is one of the reasons why we chose to adopt EVs within a range that our participants would be more likely to have encountered in real life.

CHAPTER 6

Study 5

Because Study 3 results were largely comparable to those reported by Lauriola and colleagues (2007), one might conclude that a drastic difference in the economic climate might still have not affected an individual’s propensity to take financial risks, notwithstanding the various differences between the two studies (e.g., samples from two different cities, data collected by web-based survey or via paper-and-pencil format, etc.).

However, Study 1 did find that consumer confidence was significantly correlated with total risky choices on the two-outcome expected value choice task. If we were able to directly manipulate the consumer confidence construct, would we observe a corresponding change in total number of risky choices taken? Study 5 was an attempt to manipulate consumer confidence experimentally by asking participants to imagine and reflect on two different scenarios that might be expected to result in positive economic outcomes locally or negative economic outcomes locally.

Method

Participants

The participants (N = 116) were introductory psychology students age 18 – 24 (M

= 18.74, SD = 1.20) who completed the study for course credit.

Measures

The total number of risky choices on the two-choice expected value task from

Study 1 (Cronbach’s α = .68 for these 18 items in Study 5) was used as a measure of

risky decision making. To measure participants’ mood at the time of the study, the

Positive and Negative Affect Schedule (PANAS) was utilized (Watson, Clark, &

Tellegen, 1988). It consists of 10 positive mood words and 10 negative mood words, and

participants are asked to rate the extent to which they feel each mood-word at the current time. Responses are scaled 1 – 5 with anchors of 1, “Very slightly or not at all” and 5,

“Extremely.” The PANAS generates two scores, one for overall positive affect, and one for overall negative affect. As a self-report measure of financial risky decision making, the DOSPERT betting and investing subscales (identical to those used in Study 2) were used. As a measure of optimism, the 10-item Life Orientation Test-Revised (LOT-R,

Scheier, Carver, & Bridges, 1994) was utilized. This 10-item test consists of six items, three of which measure affirmation of optimism, and three which measure disaffirmation of pessimism. Four items are fillers. All items are scored so that higher scores indicate affirmation of optimism, and responses are scaled from 1 – 5 with anchors of 1, “I

disagree a lot” to 5, “I agree a lot.” Additionally, we measured income level (same

response scale as in Study 2), political party status (are you a: Republican, Democrat,

Independent, or Other), the four economic climate measures, and self-reported stress compared to previous years item from Study 1.

Procedure

After signing consent forms, all participants were randomly assigned to either the positive scenario or the negative scenario condition. About 30% of the participants completed the experiment online (n = 17 participants from the negative condition, and n

= 18 participants from the positive condition). Those who completed the survey in the computer lab (n = 41 participants from the negative condition, and n = 40 participants

from the positive condition) were seated at a computer where they completed the survey using Survey Monkey Professional. Participants were instructed that “You will be taking a two-part survey which has been combined for your convenience. Part 1 will ask you to respond to a hypothetical situation involving business conditions and political candidates. Part 2 consists of a separate survey of decision making attitudes.” Then they

read one of two brief hypothetical scenarios. Individuals in the positive condition read the

following scenario:

Imagine that the following scenario takes place one week from now:

Congress has passed a number of tax-related measures. These include eliminating ALL sales tax on purchases of all goods and services for one year to encourage spending. Due to favorable tax treatment of hiring people to work and tax credits for research and development, a large nationally known bank and a large nationally known consumer goods company have decided to move their headquarters to the Cincinnati area.

Whereas individuals in the negative condition read the following scenario:

Imagine that the following scenario takes place one week from now:

Congress has passed a number of tax-related measures. These include doubling ALL sales tax on purchases of all goods and services for one year to reduce the national debt and budget deficits. Due to unfavorable tax treatment of hiring people to work and higher taxes for corporations, a large nationally known bank and a large nationally known

72 consumer goods company have decided to move their headquarters away from the

Cincinnati area to another country.

After reading the assigned scenario, individuals in each condition were asked to type a brief summary of the details of the scenario in order to ensure that they had read it carefully. To further encourage them to think about the potential impact of the scenario, they were also asked to rate how easy, how fast, and how smart the approach they read about was. Then, they were also asked to enter up to four ways that the scenario could potentially affect them or their family members if it were to happen, whether they would vote for a candidate who supported these measures, and finally they were asked about their political party status. Next, they completed the 18 two-choice expected value task items, followed by the four economic climate items, the PANAS, the income item, the stress compared to previous years item, the DOSPERT betting and investing subscales, and finally the LOT-R.

Results

The descriptive statistics for the two experimental conditions in Study 5 can be found in Table 19. The first goal of this analysis was to determine whether the manipulation of consumer confidence was successful (i.e., did individuals in the positive condition indicate a more favorable outlook for economic conditions compared to those in the negative condition). In terms of business conditions in the next six months, 44% of the individuals in the positive condition indicated that they believed conditions would get better, compared to 25% in the negative condition. When asked about jobs in the next six months, 15% of the individuals in the positive condition indicated that jobs would be plentiful, compared to 0% in the negative condition. Similar to Study 1, the consumer

confidence items (α = .77 for the positive condition, and α = .70 for the negative

condition) were summarized to test for group differences using a principal components

analysis. All items loaded positively on a single component which explained 62% of the

variance (Table 20), and the solution was supported by the Kaiser criterion (Kaiser,

1960).

Table 19

Comparison Statistics for the Two Conditions in Study 5

Variable Positive Condition Negative Condition

Business conditions for the 2.24 (0.78) 2.06 (0.69) next six months Jobs for the next six 1.74 (0.71) 1.59 (0.50) months Where is the economy 5.88 (1.40) 5.72 (1.21) headed next year

How confident are you that 5.07 (2.11) 5.05 (1.72) the economy is getting better next year

Consumer confidence 0.09 (1.12) -0.09 (0.87) component Total risky choices 9.84 (2.69) 8.83 (3.64)

DOSPERT betting subscale 4.66 (3.50) 5.13 (3.72)

DOSPERT investing 11.07 (4.89) 10.21 (5.08) subscale

Note. For both conditions, n = 58. Scores for the consumer confidence component are standardized.

Table 20

Component Loadings for Economic Climate Items

Item Component 1 Business conditions for the next six months .786 Jobs for the next six months .593 Where is the economy headed next year .850 How confident are you that the economy is .880 getting better next year

Although the scenario manipulation affected consumer confidence component scores in the expected direction, and those who read and thought about the positive scenario were more confident in economic recovery (M = 0.09, SD = 1.12) compared to those who read and thought about the negative scenario (M = -0.09, SD = 0.87), this difference did not reach statistical significance, t(107.26) = 0.99, p = .32. However, since we noticed that the two short term economic indicator variables seemed to be influenced by the manipulation while the longer term indicator variables did not (see Table 19), we calculated z-scores for the two short term measures, added a constant (5) to each of these scores to make them all positive values, and then added them together to create a measure of short term economic outlook. The independent samples t test indicated that individuals in the positive condition were somewhat more optimistic about the short term economic conditions compared to those in the negative condition, t(113) = 1.76, p = .04, one-tailed, d = 0.33.

The second goal for this analysis was to determine whether the overall number of risky choices appeared to be affected by our manipulation. Those in the positive

condition made more risky choices (M = 9.84, SD = 2.69), than those in the negative condition (M = 8.83, SD = 3.64), and this difference was statistically significant, t(104.94) = 1.71, p = .05, 1-tailed, d = 0.32. These findings did not appear to be due to group differences in income level, optimism, positive affect, negative affect, or self- reported stress compared to previous years (all p > .34). The descriptive statistics for these variables can be found in Table 21.

Table 21

Descriptive Statistics for Variables Potentially Responsible for the Differences in Economic Outlook or Risky Choices, (N = 116)

Variable Positive Condition Negative Condition

Income Level 6.40 (2.61) - 6.78 (2.83) - Optimism 19.71 (5.36) .85 20.05 (4.43) .79

Positive Affect 25.52 (8.07) .88 26.95 (7.85) .86 Total Score Negative 14.81 (5.44) .84 15.10 (5.54) .84 Affect Total Score Stress 3.93 (1.14) - 3.84 (0.99) - Compared to Previous Years Note. For each condition, means and (standard deviations) are in the left column, and Cronbach's alpha values are in the right column.

The final goal for Study 5 was to explore the relationships between our income

variable, economic indicators, risky choices, and other constructs such as optimism,

pessimism, affect, and stress which Gärling et al. (2009) suggested could be related to

risky financial decision making (Table 22). Additionally, we examined the correlations

between the DOSPERT financial subscales (betting and investing) used in Study 2 to

76 better understand the relationship between these scales and economic indicators as well as the trait and state indicators. Since the DOSPERT items were positively skewed, non- parametric Spearman correlations were calculated. This analysis was performed using the entire sample rather than looking at correlations for each group because just as in Study

1, our interests here were related to understanding how these variables were related overall, not just in specific subgroups such as those who have recently experienced (or thought about) a positive or negative financial event.

Table 22

Spearman Correlations for Variables of Interest in Study 5, (N = 116)

123 45 678910 1 Income 1 - 2 Risky choices .05 1 overall (.30) - 3 Short term .09 .18 1 economic (.16) (.03) - outlook 4 Consumer .09 .09 .82 1 Confidence Component (.16) (.16) (.00) - 5 Optimism .16 .14 .13 .22 1 (.05) (.06) (.08) (.01) - 6 Positive affect .01 .10 .15 .29 .17 1 total (.47) (.14) (.05) (< .01) (.03) - 7 Negative affect -.05 .01 -.06 -.04 -.12 .26 1 total (.29) (.45) (.26) (.34) (.11) (< .01) - 8 Stress compared -.07 -.11 -.13 -.17 -.16 -.11 .12 1 to previous (.22) (.12) (.08) (.03) (.04) (.12) (.09) - years 9 DOSPERT .23 .21 .16 .17 .01 .19 .09 -.15 1 betting subscale (.01) (.01) (.04) (.03) (.47) (.02) (.16) (.05) - 10 DOSPERT .22 .14 .07 .11 .07 .24 -.06 -.14 .22 1 investing (.01) (.06) (.23) (.12) (.22) (.01) (.27) (.06) (.01) - subscale Note. The one-tailed p-values for each correlation are in parentheses.

The results of this analysis suggest that there were significant positive correlations between income and LOT-R optimism, and income and intentions to engage in risky financial behavior measured by the DOSPERT betting and investing subscales. Risky choices overall were significantly positively correlated with optimism regarding short- term economic recovery, and the DOSPERT betting subscale. It should be noted that the correlations between risky choices overall and optimism measured by the LOT-R as well as the correlations between risky choices overall and the DOSPERT investing subscale approached statistical significance (p = .06 in both cases). Short term economic outlook was positively correlated with positive affect scores, and the DOSPERT betting subscale.

The consumer confidence component was significantly positively correlated with optimism, positive affect, and the DOSPERT betting subscale; and negatively correlated with stress compared to previous years. Both of the DOSPERT subscales correlated positively with positive affect, and negatively with stress compared to previous years— although the correlation between DOSPERT investing and stress did not reach statistical significance (p = .06).

Discussion

The primary objective of Study 5 was to determine whether the positive relationship between consumer confidence and risky choices finding from Study 1 could still be found if an experimental design was employed. To achieve this end, we needed to manipulate consumer confidence in our sample of college undergraduates. Therefore, we devised two scenarios for participants to think about, one positive and one negative, and then randomly assigned participants to each condition. Our manipulation appeared to be successful in changing participants’ judgments of the economic climate in a short-term,

but not the economic climate in the long term. Participants in the positive condition were

significantly more optimistic about economic recovery occurring in the next six months.

In terms of the risk taking results, participants in the positive condition also made more

risky choices overall compared to those in the negative condition. These differences also

did not appear to be due to group differences in income level, optimism, positive or

negative affect, or self-reported stress compared to previous years. It therefore appears

that increased risk taking as measured by a two-choice EV task may result from increases

in consumer confidence (at least where short-term judgments of consumer confidence are

concerned). We also examined the intercorrelations of our variables to ensure that the

expected relationships between variables such as optimism and our consumer confidence

measure were present. Generally, each of these relationships appeared to be in the

expected direction.

The tentative implications of these findings are quite interesting. DeBoef and

Kellstedt (2004) noted that consumer confidence is essentially the result of consumers internalizing “the objective economy and transform[ing] it into a subjective economy” (p.

633). One influence that these authors noted as important in this internalizing process was

media coverage of economic events. In particular, these authors mention several

historical examples that help to illustrate how media coverage that presents the objective

economic conditions as overly optimistic or pessimistic can potentially lead to periods of

irrational optimism or pessimism among consumers. The manipulation utilized in Study 5

appears to have created a similar (albeit less powerful) change in consumer confidence,

where our participants’ perceptions of the external economic climate in the short term

became more optimistic or pessimistic based on what they had heard and thought about

the economy lately. Further research is needed to determine whether these changes in

confidence led to increased preference for risky choices as well.

In terms of limitations, data collection for Study 5 was completed using two

different formats due to restrictions on the number of participants that could be collected

in a single quarter from the undergraduate subject pool. Due to these restrictions, we were

unable to collect 100 participants per condition as we would have liked, and some participants had to complete the survey online as well. With the increased statistical

power that more participants would provide, we would expect to find effects similar in

direction but larger in magnitude compared to those presented here.

CHAPTER 7

General Discussion

The primary goal of this project was to examine the operational characteristics of a two-choice expected value task and its relationship to other variables related to risk perception in other life domains. Due to the unique economic conditions of late, a substantial portion of this work focused on financial risk propensity and perception. To the best of our knowledge, the work we presented here is some of the first to examine the relationship between laboratory-based risk propensity measures such as our monetary two-choice EV task, and macro-economic constructs such as consumer confidence. This work helped clarify and distinguish changes in risky choices on our two-choice EV task that may be attributable to economic conditions from those that may be primarily related to task parameters. We examined the possibility that risk propensity, as measured by a traditional laboratory-based expected value risky decision making task, would share some common variance with self-reported intentions to engage in risky activities in other

behavioral domains. Consideration of these potential relationships extended the current

literature by providing insight regarding the relationship of expected value based risky

decision making tasks to self-reported attitudes toward various kinds of risks.

Two-choice expected value tasks and economic indicators

The results of Study 1 suggested that the risks people take on laboratory-based

measures of monetary risky decision making do appear to be related to how confident

they are regarding the economic climate around them. Furthermore, this relationship

remains significant even when factors such as income level and self-reported stress level

are held constant. The ratio of risky loss choices to risky gain choices on equal expected

value trials (an indicator of loss aversion) observed in Studies 1 and 2 was much closer to

1:1 than the approximately 2:1 ratio reported by others using a similar task (Lauriola et

al., 2007)., provided that the range of outcome magnitudes used (e.g., $2 to $5) falls in a

range that they have frequently experienced in real life.

Together with Study 4, Studies 1-3 established that, consistent with prospect theory, the potential outcome magnitude, and not expected value instructions or wording, had the most influence on whether a 2:1 ratio of risky loss to risky gain choices was observed. However, there was some evidence to suggest that the wording of two-choice

EV tasks might play a role in determining the degree of loss aversion. In particular, for

the risky option it seems important to specify both the probability of a non-zero outcome

(e.g., a 50% chance to win/lose $10) and the probability of a zero outcome (e.g., a 50%

chance to win/lose nothing). As explained in Study 4, failure to specify the probability of

a zero outcome for the risky option may lead to an increase in prevention focus for loss

framed trials and an increase in promotion focus for gain framed trials—leading to an

82 increase in gain domain risk taking and a simultaneous decrease in loss domain risk taking. Failure to explicitly provide the information about the probability of a zero outcome might lead to participants over-emphasizing the importance of the presented information and neglecting to consider the implicit information available, similar to what

Sanbonmatsu et al. (2003) referred to as omission neglect.

In Study 5, we returned to the relationship of consumer confidence and risky choices measured by a two-choice EV task. Rather than attempting to observe this relationship through correlations as we had in Study 1, Study 5 employed an experimental design in which we intentionally attempted to manipulate the consumer confidence of participants by asking them to read and think about a scenario with either positive or negative local economic consequences. Although the manipulation did not influence the consumer confidence component consisting of long term indicators in a statistically significant way, it did appear to influence the short-term indicators. Statistical analysis revealed that, relative to those in the negative condition, those participants in the positive condition both made more risky choices and were more optimistic about short- term economic improvement. Importantly, these group differences did not appear to be due to participants’ income level, optimism as measured by the Life Orientation Test

(LOT-R), positive or negative affect as measured by the Positive and Negative Affect

Schedule (PANAS), or self-reported stress compared to previous years. These results imply a certain degree of domain specificity, wherein the nature of the relationship between consumer confidence and risk taking on the expected value task is a specific one rather than being mediated by general mood or optimism on the part of the participants.

Taken together, these findings raise two important questions that will be

addressed here. First, if consumer confidence influences risk taking as suggested by

Study 5, the fact that there were no apparent differences between the Lauriola replication

data (Study 3) collected in 2010 and the data from Lauriola et al. (2007), despite drastic

differences in consumer confidence nationally between these two time points seems

problematic. However, as we noted in Study 3, the use of very large outcome magnitudes

in Lauriola et al. (2007) likely had the effect of reducing the variability in individual

decision making somewhat, and this potential restriction of range would make it difficult to observe changes due to economic conditions.

Second, the fact that our manipulation in Study 5 influenced only the short term economic outlook of our participants seems peculiar. However, research on psychological distance (Trope, Liberman, & Wakslak, 2007) has suggested that as events become further removed from one’s direct experience by time, the mental representation of those events becomes more abstract and less influenced by specific contextual factors.

According to Trope et al. this occurs because the individual has less information directly available from their environment to make a judgment. Considered in this way, it seems likely that as the temporal distance of our economic indicator items increased from six months to one year, our participants’ judgments regarding economic recovery would be less influenced by our manipulation or recent life events (the information most readily accessible from the context). Further support for this notion was provided by a post-hoc

ANOVA of data from Study 1, where the pooled short term economic indicators (using the same methodology as described in Study 5) was significantly different by event type,

F(3, 341) = 2.60, p = .05, partial η2= 0.02 while the test for the pooled long term

indicators was non-significant (p > .40). The Games-Howell post-hoc test indicated that

the significant effect for experience type was driven by the difference in short term

confidence ratings between those who reported experiencing positive or negative events

(Mdifference = 0.65, SE = 0.23, p = .03).

Two-choice expected value tasks and self-report risk measures

Prior to beginning this project, we were aware of the literature that favored a view of risk propensity as being either a domain-specific construct influenced by contextual factors (e.g., Heath & Tversky, 1991; Hsee & Weber, 1997; Weber et al. 2002), or a relatively stable trait-like construct that correlates with other similar personality

constructs (e.g., Lauriola & Levin, 2001; Lauriola et al. 2007). Our work examined the

validity of two-choice expected value tasks with both, rather than one or the other, of

these views of risk propensity in mind. As Mischel and Shoda (1995) have pointed out, it

is an oversimplification to consider either the person OR the situation as determining

behavior; one must attempt to discover how aspects of personality interact with the

salient aspects of different behavioral domains. Our findings in Study 2 indicated that

although risk propensity as measured by our two-choice EV task tended to share the most

common variance with financially-related risk propensity, there may in fact be shared

variance with risk taking in other behavioral domains. These results seem to provide

support for the notion that there is a trait-like construct for risk propensity; albeit one that

likely interacts with situational factors to produce decisions in situations involving risk

and uncertainty.

Limitations

As noted in several places in this document, we readily acknowledge that our use

of college undergraduates may have led to restriction of range problems, since those who

have had very negative financial experiences are unlikely to be a part of our sample. The

same caution should be applied to Study 2, because those who regularly engage in some

of the more extreme types of risk taking measured by the CARE and DOSPERT

subscales are also unlikely to remain a part of the college undergraduate sample pool.

However, given these potential restriction of range problems, the fact that we still found

significant correlations between risky choices on the two-choice expected value task and economic climate indicators, as well as the CARE and DOSPERT subscales, is promising. These correlations provide support for the notion that two-choice EV tasks are capable of measuring risk propensity that is not only specific to a single behavioral

domain such as gambling as one might expect. Additionally, despite observing

differences in loss aversion between our early studies and the loss aversion reported in

the literature using a similar task (Lauriola et al., 2007), we examined and identified the

likely cause of this discrepancy as being related to outcome magnitudes. As noted earlier,

it is actually a potential strength of our study that we restricted the range of risky

decisions to outcome magnitudes that our participants were familiar with (rather than

asking them about gambles in the thousands or tens of thousands of dollars range).

Furthermore, participants in our studies responded appropriately to changes in expected

value, taking risks most frequently when conditions were advantageous to do so even

though they did not receive feedback regarding the outcome of their decisions. Also the

task used in Studies 1, 2, and 5 was significantly correlated with other risk-related

constructs, so the lack of strong loss aversion findings or provision of feedback regarding

the outcome of risky choices should not discount the validity of our task in measuring risk propensity.

Summary

When considered together, the results of these studies suggest that the risk propensity measured by two-choice expected value tasks could be considered a trait-like construct that is modestly related to several different types of risk including intentions to

engage in a variety of risky behaviors, and perception of economic conditions. However,

in agreement with the literature on the influence of contextual factors, risk propensity

also appears to be somewhat malleable given the results of Study 5, since when short-

term economic conditions are perceived as more favorable, risk taking appears to

increase.

Future studies of the relationship between economic indicator variables such as

consumer confidence and risk taking in a construct validity sense should likely use a

wider variety of economic indicators as well as including the actual items from the

Consumer Confidence Index, and the Index of Consumer Sentiment. It would also be

interesting to examine the construct validity of two-choice EV tasks in greater detail, for

example by utilizing a multi-trait multi-method approach (e.g., Campbell and Fiske,

1959) which would allow for the determination of both convergent and discriminant

validity properties of the task (our study focused exclusively on convergent validity).

As Sitkin and Weingart (1995) noted, “all decision makers have had to start

somewhere; thus research on first-time decision makers is indeed valid so long as the

researchers are clear about the population to which they can generalize” (p. 1588). While

87 studying our 18-24-year-old college undergraduates did provide some potentially useful insights regarding the relationships of constructs such as consumer confidence to risky choices involving money; additional considerations for future research would include expanding the sample beyond the college student population, and perhaps attempting to address similar questions to those covered here in samples which include those who have faced more severe economic hardship. Indeed, recent non-experimental research examining the effects of economic hardships nationally over time suggested that macroeconomic conditions may influence risk taking at an individual level (Malmendier &

Nagel, 2009); which in conjunction with the results obtained in our work presented here seems to be a good indication that further research in this area is needed.

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