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Individuals rely more on dispositional information when making affective forecasts for others than for… Forrin, Noah David 2007

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INDIVIDUALS RELY MORE ON DISPOSITIONAL INFORMATION WHEN MAKING AFFECTIVE FORECASTS FOR OTHERS THAN FOR THEMSELVES by NOAH DAVID FORRIN B.A., Queen's University, 2005  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS in THE FACULTY OF GRADUATE STUDIES (Psychology)  THE UNIVERSITY OF BRITISH COLUMBIA June 2007 © Noah David Forrin, 2007  Research on affective forecasting has, thus far, focused on how individuals predict their own future emotions. Daily experience, however, suggests that people also make affective forecasts for others on a regular basis. Across five studies, we found that people making affective forecasts for others relied more on dispositional information than those who made forecasts for the self. This trend emerged for affective forecasts of both hypothetical (Studies 1-3) and real events (Studies 4 and 5), and regardless of whether the other person was a stranger (Studies 1, 2, 4, and 5) or a friend (Study 3). Further, individuals made less biased affective forecasts for others than for themselves (Study 5), perhaps due to the greater weight placed on dispositional information when making forecasts for others. These findings suggest that individuals can benefit from asking others how they will feel in the future.  n  TABLE OF CONTENTS Abstract  ii  Table of Contents  iii  List of Tables  iv  List of Figures  v  Acknowledgements  vi  Introduction  1  Study 1  8  Study 2  13  Study 3  17  Study4 Study 5  ......  20 :  24  General Discussion  29  Conclusion  35  References  36  Footnotes  40  LIST OF TABLES Table 1 Output from H L M Analysis (Study 1)  11  Table 2  Output from H L M Analysis (Study 2)  .15  Table 3  Output from H L M Analysis (Study 3).  19  iv  LIST OF FIGURES Figure 1  22  Figure 2  28  ACKNOWLEDGEMENTS I thank my advisor, Dr. Elizabeth Dunn, for her guidance and encouragement. I also greatly appreciate the efforts of the other members of my thesis committee, Dr. J. Schooler, Dr. J. Tracy, and Dr. J Biesanz. I am also very grateful for the constant support of my parents and my beautiful girlfriend Sacha.  vi  Imagine how you would feel if it rained throughout your long-awaited vacation. Visions of grey skies and soggy beaches may come to mind, and you probably think that the miserable weather would have you feeling miserable. But what if you were asked to predict how someone else would feel in the same situation: How would Gene feel if it constantly rained during his upcoming vacation? Would you arrive at the same prediction? Not necessarily. Instead of focusing on the rainy weather, you might first consider Gene's sunny disposition. Consequently, rather than picturing him sulking indoors, you might imagine him outside, singing in the rain. The above thought experiment placed two distinct types of affective forecasts side-byside: The first forecast was for the self, and the second was for someone else. Thus far, affective forecasting research has focused primarily on the former process, predicting one's own future emotions (for a review see Wilson & Gilbert, 2003). At first blush this is perhaps not surprising given how prominently affective forecasts feature in one's daily life, whether the event is minor ("How will watching tonight's game make me feel?") or momentous ("How will marrying Lucy make me feel?"). But people can also imagine the future emotions of others, such as how Gene will feel during his upcoming vacation. We propose, therefore, that the human talent of "emotional time travel" (Dunn & Laham, 2005) is not limited to one's own future emotions. That said, a fundamental difference may exist between affective forecasts made for the self and those made for others. As illustrated in the opening paragraph, we suggest that people focus on situational information (e.g., rainy weather) when making affective forecasts for the self and dispositional information (e.g., Gene's sunny disposition) when making forecasts for others. The following two sections advance this position, addressing each type of forecast in turn.  1  Affective forecasts for the self As mentioned earlier, affective forecasting research to this point has focused mainly on the forecasts individuals make for the self (Wilson & Gilbert, 2003). A familiar finding in affective forecasting research is the impact bias (Gilbert, Driver-Linn, & Wilson, 2002), the tendency to overestimate the intensity (Buehler & McFarland, 2001) and duration (Gilbert, Pinel, Wilson, Blumberg, & Wheatley, 1998) of one's future emotions. These miscalculations are common across a variety of events, positive and negative. For example, assistant professors overestimated how happy they would feel after receiving tenure (Gilbert et al., 1998), and college students overestimated how unhappy they would feel after being assigned to an unattractive dorm (Dunn, Wilson, & Gilbert, 2003). A major factor leading to the impact bias is that individuals tend to focus too narrowly on the "focal event" when making their affective forecasts, a phenomenon known as focalism (Wilson, Wheatley, Meyers, Gilbert, & Axsom, 2000). This rigid focus on a specific situation comes at the expense of considering the daily events that inevitably sway one's happiness, diminishing the impact of the focal event. Wilson and colleagues (2000) were able to reduce focalism by having college students write a prospective diary of activities that they would be engaging in following the focal event, the outcome of a campus football game. This task brought to individuals' attention other events such as upcoming exams, diverting their focus away from the focal event and thereby mitigating the impact bias. A second reason why individuals succumb to the impact bias is that they tend to underestimate their ability to adapt to various circumstances, both positive (Wilson, Centerbar, Kermer, and Gilbert, 2005; Wilson & Gilbert, 2005) and negative (Gilbert, Pinel, Wilson, Blumberg, & Wheatley, 1998). Most people predict that they would experience a permanent  2  boost in happiness after winning the lottery, but in reality their happiness would likely return to baseline, as Brickman, Coates, and Janoff-Bulman (1978) demonstrated in their classic "hedonic treadmill" research. Indeed, people deftly adapt to most circumstances (Lucas, Clark, Georgellis, & Diener, 2003), their happiness returning to a dispositional "setpoint" that is largely genetically determined (Lyubomirsky, Sheldon, & Schkade/2005). But despite a wealth of experience, people give short shrift to adaptation when they make affective forecasts for the self, which contributes to the impact bias. It should come as no surprise, then, that the impact bias can be mitigated if forecasters think about how they adapted to similar situations in the past (Ubel, Loewenstein, & Jepson, 2005). Similarly, perhaps the impact bias would be reduced if forecasters took into account their (relatively stable) dispositional happiness. But do people consider their dispositional happiness when making affective forecasts for the self? This question has yet to be examined directly, but the research discussed above indirectly suggests that little weight is placed on dispositional factors. As mentioned, affective forecasts for the self are attuned to the focal event (focalism). This narrow focus probably comes at the expense of considering not only other relevant events, but also relevant dispositional factors. In addition, people do not give ample credit the power of emotional adaptation, suggesting that they under-appreciate the stability of their dispositional happiness. We propose, then, that "disposition neglect" can be added to the list of factors contributing to the impact bias. Insofar as one's happiness will almost invariably return to its dispositional baseline following an event, it seems that less biased affective forecasts would be made if individuals placed more weight on dispositional information. Such information could be particularly useful when individuals predict how they will feel days following an event, when their happiness is likely to have returned to baseline (depending on the severity of the event).  3  In short, when individuals make affective forecasts for the self they are attuned to the focal event and downplay emotional adaptation, which suggests that they are also neglecting the influence of their dispositional happiness. On the other hand, affective forecasts made for others may be a different story. Although individuals may have to be induced into relying on dispositional factors when making affective forecasts for themselves, forecasts made for others may naturally gravitate in this direction. Affective forecasts for others As mentioned at the outset, relatively little research has been directed at the process of predicting others' future emotions. There are, however, a couple of existing theories—indeed classic ones—that explain how people perceive others' behaviour; perhaps these theories can shed some light on how individuals predict others' emotions as well. The first relevant theory is the correspondence bias (Gilbert & Malone, 1995), which states that individuals tend to make dispositional inferences regarding others' behaviour, even when their behaviour can be explained entirely by situational factors. Clearly, there is a sizable difference between making inferences regarding others' behaviour and predicting their future emotions; that said, the mechanisms underlying the correspondence bias may be relevant to how others are perceived more broadly. For example, Krull (1993) found that when observers wanted to understand an individual, they initially drew dispositional inferences and then adjusted for situational factors, but only when they were motivated and had sufficient cognitive resources. Perhaps the same process occurs when observers make affective forecasts for others. Again, it would seem that their goal is to understand the individual, not the situation, so observers may also initially focus on dispositional information (e.g., the individual's dispositional happiness) and then adjust for the situation.  4  After all, it would take additional cognitive resources to ponder the unique effect of the situation on the individual. It is comparatively easy to base one's affective forecasts on dispositional information; that is, for example, to simply assume that someone will be about as happy tomorrow as they are everyday. A second classic theory relating to the perception of others is Jones and Nisbett's (1971) actor-observer effect. The actor-observer effect states that people tend to attribute their own behaviour to situational factors and others' behaviour to dispositional factors. Perhaps the same trend holds true for affective forecasts. This appears to be the case for forecasts of the self, which are strongly influenced by one situational factor in particular: the focal event (Wilson et al., 2000). Correspondingly, given that explanations of others' behaviour are guided mainly by dispositional factors, perhaps the same can be said of predictions of others' future emotions. Again, there are obvious differences between explaining someone else's behaviour and predicting his or her emotions, but the point is that elements of the former may still prove relevant to the latter. For example, one of the explanations for the actor-observer effect is that situational factors are more salient to the actor and dispositional factors are more salient to the observer (Storms, 1973). The same could be true of affective forecasts: When making affective forecasts for the self, the target situation (i.e., focal event) may become quite salient; conversely, when making an affective forecast for someone else, the person may stand out more than the situation. There is also an informational explanation (Malle, 2006) for the actor-observer effect that states that individuals have a better understanding of how they respond to a given situation than of how other people respond to the same situation. Consequently, actors tend to rely on situational information more than observers when making attributions. Again, the same could be said of affective forecasts. When individuals predict others' future emotions they are often not  5  aware of the meaning a given situation holds for an individual; thus, they may downplay situational information, relying instead on their knowledge of the individual's disposition. The present research The aim of the present research, broadly stated, is to determine whether affective forecasts made for others are arrived at differently than forecasts for the self. We hypothesize that people making affective forecasts for others—relative to those making forecasts for the self—tend to rely more on dispositional information (Hypothesis 1), less on situational information (Hypothesis 2), and arrive at less biased forecasts (Hypothesis 3). The first and second hypotheses are based on the correspondence bias (Gilbert & Malone, 1995) and the actorobserver effect (Jones & Nisbett, 1971). Just as individuals tend to make dispositional inferences and attributions regarding others' behaviour, they may also rely on dispositional information when predicting others' emotions. In contrast, when people make affective forecasts for themselves they appear to largely neglect dispositional information, which may be a byproduct of their narrow focus on the target situation (i.e., focalism; Wilson et al., 2000). Assuming that the first hypothesis is correct, it seems likely that people are not as rigidly focused on the target situation when making affective forecasts for others; after all, they are considering dispositional information as well, which diverts their attention away from the focal event. Consequently, individuals making forecasts for others may take into account situational information less than those making forecasts for themselves, at least with respect to the target situation (Hypothesis 2). Lastly, the third hypothesis—that people's affective forecasts for others are less biased than their forecasts for themselves—follows from the previous two. If an individual's affective forecasts for others rely more on dispositional information (and less on situational information) than their forecasts for themselves, then forecasts for others may also be  6  less susceptible to focalism and, by extension, to the impact bias. Furthermore, given that happiness returns to its dispositional setpoint soon after virtually any event, forecasts that weigh dispositional information heavily would seem (intentionally or not) to better account for emotional adaptation, which would also attenuate the impact bias. Five studies were designed to test the above hypotheses. In Study 1, individuals made affective forecasts for themselves or strangers regarding several hypothetical events. In line with Hypotheses 1 and 2, we expected individuals to rely more on dispositional information—and less on situational information—when making forecasts for strangers rather than for themselves. Study 2 addressed a methodological issue with the original study, and Study 3 examined whether the same two hypotheses were supported when the forecasts made for strangers were instead made for friends. In Study 4, we extended the previous findings by having participants make forecasts regarding a real event as opposed to hypothetical events. In Study 5, lastly, we tested whether people make less biased forecasts for others than for themselves (Hypothesis 3).  7  Study 1 In Study 1, participants were assigned to either the self condition or the stranger condition. Those in the self condition predicted how they would feel while experiencing several hypothetical events, and those in the stranger condition predicted how a stranger would feel while experiencing the same events. We expected that participants making affective forecasts for strangers would rely more on dispositional ratings—but less on situational ratings—than participants making affective forecasts for themselves. For the dispositional ratings, participants either rated their general happiness (self condition) or were shown the general happiness rating of a stranger (stranger condition). For the situational ratings, participants in both conditions rated the objective positivity/negativity of each event. Method Participants Forty individuals at the University of British Columbia were approached on campus and filled out a survey in exchange for a candy bar. Age and gender were not recorded. Self condition At the beginning of the survey, participants in the self condition rated how happy/sad they were "in general these days" on scale ranging from -5 (extremely sad) to +5 (extremely happy). One-item happiness measures like this have been found to have good psychometric properties (Andrews & Robinson, 1991; Fordyce, 1988). Moreover, past affective forecasting research (e.g., Gilbert et al., 1998) has found that these simple measures are strongly correlated with more extensive measures such as the Satisfaction with Life Scale (Diener, Emmons, Larsen, & Griffin, 1985).  8  Following the dispositional happiness measure, self condition participants predicted how happy/sad they would feel while experiencing eight hypothetical events, on a scale from -5 (extremely sad) to +5 (extremely happy). Similar one-item measures have been found to possess acceptable psychometric properties (Diener et al., 1999; Fordyce, 1988) and have been used successfully in other affective forecasting studies (e.g., Dunn et al., 2003). The events were intended to be commonplace; four were positive (e.g., spending an afternoon with a close friend) and four were negative (e.g., arguing with one's parents). Lastly, participants objectively rated the positivity/negativity of each event, on a scale from -5 (extremely negative) to +5 (extremely positive)} We asked participants to objectively rate each situation in order to obtain as pure a situational measure of each event as possible; despite these instructions, we acknowledge that it would be unrealistic to expect these measures to be completely untainted by subjectivity. Stranger condition At the beginning of the survey, instead of rating their own dispositional happiness, each member of the stranger condition was instead shown the happiness rating given by a random member of the self condition. Therefore, each member of the stranger condition was yoked to a random member of the self condition—a stranger to them—by virtue of being shown his or her dispositional happiness rating. Next, individuals in the stranger condition predicted how happy/sad this individual would feel while experiencing the same eight hypothetical events (on the same scale used in the self condition). Lastly, they gave situational ratings for each event, as was the case in the self condition.  v  Results and Discussion The data were analyzed using Hierarchical Linear Modeling (HLM; Raudenbush & Bryk, 2002) and HLM6 software (Raudenbush, Bryk, Cheong, Congdon, & du Toit, 2004). H L M is  9  especially effective for repeated-measures designs in which each participant provides a series of ratings on the independent and dependent variables (Kashy & Kenny, 2000; Kenny, Kashy, & Bolger, 1998). The present study fits this description, as participants each gave situational ratings and affective forecasts for eight different hypothetical events. H L M permitted us to include our data into a powerful analysis that took into account the lack of independence between a single participant's ratings of all eight events. In our model, hypothetical event was a lower-level unit nested within participant, the upper-level unit. More specifically, affective forecasts (the dependent variable) and situational ratings were the lower-level variables, and condition (self vs. stranger) and yoked dispositional happiness ratings were the upper-level variables. The lower- and upper- level variables and all of the interactions of these variables were entered into a hierarchical linear model predicting forecasted happiness (measured at the lower-level of hypothetical event). The model, in effect, created a regression equation predicting forecasted happiness from situational ratings for each participant across hypothetical events. It then combined these equations to create an aggregate and tested whether the regression coefficients differed by condition or dispositional ratings. Table 1 shows the output of the H L M analysis. Of primary interest was the Condition x Dispositional Rating interaction, which indicated whether there was a difference between the two conditions in terms of the weight placed on dispositional information in arriving at affective forecasts. As expected, the analysis revealed that this interaction was significant, ^(35) = -4.00, p < .001. To break down this interaction we ran two more H L M analyses, one for participants from the self condition and the other for participants in the stranger condition. The model for each analysis was identical to the original model described above, with the exception of condition being removed as an upper-level variable. The analysis for stranger condition  10  Table 1. Output from HLM Analysis (Study 1) Effect  Coefficient  SE  df  t Ratio .61  Condition  .06  .10  35  Dispositional ratings  .22  .03  35  7 Q4***  S ituational ratings  .82  .03  35  30.16***  -.11  .03  35  -4.00***  .07  .03  35  2.48*  Disposition x Situation  -.01  .01  35 -  -1.39  Condition x Disposition x Situation  -.01  .01  '35  -1.47  Condition x Disposition Condition x Situation  Note. Lower-level variables: forecasted happiness and situational ratings. Upper-level variables: condition and (yoked) dispositional ratings. *p<.05. ***p<.001.  participants revealed that they placed highly significant weight on dispositional information, t(\S) = 7.21, b = .33, p < .001. The analysis for self condition participants revealed that they also placed significant weight on dispositional information, ^(17) = 3.07, b = .1 l,p < .01, though not to the same extent as stranger condition participants. Thus, participants in both conditions relied on dispositional information in making their affective forecasts, though stranger condition participants (as hypothesized) placed greater weight on this information. Returning to the original H L M analysis, in which condition was an upper-level variable, there was also a significant Condition x Situational Rating interaction, t(36) = 2.48,p < .05, indicating that there was a difference between the two conditions in terms of the weight placed on situational information. We broke down this interaction as we did for the Condition x Dispositional Rating interaction above, running two separate H L M analyses (one for each  11  condition). The analysis for self condition participants revealed that they placed highly significant weight on situational information, t(\7) = 25.45, b = .89,p < .001. The analysis for stranger condition participants also revealed that they placed significant weight on situational information, t(\%)= 17.36, b = .76, p < .001, though not to the same extent as self condition participants. Therefore, participants from both conditions relied heavily on situational information in making their affective forecasts; as hypothesized, however, individuals in the stranger condition did not place as much weight on situational information as those in the self condition. Summary of results In Study 1 we found support for our first two hypotheses: Individuals who made affective forecasts for others relied more on dispositional information (Hypothesis 1)—but less on situational information (Hypothesis 2)—than those who made forecasts for the self. However, we.did not find evidence that people making affective forecasts for the self neglected dispositional information; to the contrary, individuals in the self condition placed significant weight on dispositional information when arriving at their forecasts, just not to the same extent as those in the stranger condition. Moreover, it is possible that participants in the stranger condition relied more on dispositional information due to a "conversational norm" (Schwarz, 1999); that is, participants may have believed that they were provided with dispositional information because they were supposed to use it in arriving at their forecasts, an issue that Study 2 was designed to address.  12  Study 2 Three different versions of Study 2 were designed to address the conversational norms issue present in Study 1. All three versions included slight variations to the design of the original study, which were intended to eliminate the pressure on participants to use the dispositional happiness information. With the exception of these minor changes, the design of Study 2 was parallel to that of the original study. As before, participants in the self condition predicted how happy/sad they would feel while experiencing several hypothetical events, whereas participants in the stranger condition predicted how happy/sad a stranger (a yoked member of the self condition) would feel while experiencing the same events: Method Version 1  Members of the self condition were told that the dispositional happiness measure was randomly chosen from a pool of possible questions. Likewise, members of the stranger condition were told that the dispositional rating they were being shown was randomly chosen from multiple responses given by another participant in the study. Referring to the dispositional measure as "random" in both conditions was intended to reduce the conversational norm to rely on that measure in making one's affective forecasts. Forty individuals (55% female, mean age = 20.7 years) filled out Version 1. Version 2  Several background items were added alongside the dispositional happiness measure. These included demographic questions as well as measures of how frequently the participant had engaged in each hypothetical event, rated on a scale from 0 (never) to 10 (very often). As with the dispositional happiness measure, the addition background items were only filled out in the  13  self condition. Members of the stranger condition were instead shown the responses given by the self condition participant with whom they were paired. These measures provided additional salient—though not necessarily diagnostic—information that participants in both conditions could use to arrive at their subsequent predictions. Sixty four individuals (67% female, mean age = 21.3 years) filled out Version 2. Version 3 In both conditions of Version 3, the situational measures preceded affective forecasts, alongside the dispositional happiness measure. Unlike Study 1, however, participants in the stranger condition did not give the situational ratings themselves; instead, they were shown the situational ratings given by the member of the self condition with whom they were paired, just as they were shown his or her dispositional rating. Consequently, members of the self and stranger conditions were yoked by not only dispositional ratings, but also by situational ratings. Similar to Version 2, the situational ratings provided additional salient information that participants in both conditions could use to arrive at their subsequent affective forecasts. Forty six individuals (54% female, mean age = 22.9 years) filled out Version 3. Results and Discussion As in Study 1, we used Hierarchical Linear Modeling to establish whether affective forecasts for strangers also relied more on dispositional information than those for the self. Once again, affective forecasts (the dependent variable) and situational ratings were the lower-level variables, and condition (self vs. friend), dispositional happiness ratings were the upper-level variables, as well as the version of the design (Version 1, 2, or 3). The lower- and upper- level variables and all of the interactions of these variables were entered into a hierarchical linear model predicting forecasted happiness.  14  Table 2 shows the output of the H L M analysis. Combining the ratings from all three versions, the analysis revealed a significant Condition x Dispositional Rating interaction, /(133) = -2.44, p < .05. In general, the strength of the effect was similar across the three versions of the design. The main effect for Version was not significant, ^(133) = -.96, ns, nor was the Version x Condition x Dispositional Rating interaction, ^(133) = -.04, ns. As in Study 1, we broke down the significant Condition x Dispositional Rating interaction by running two more H L M analyses, one for participants in the self condition, the other for participants in the stranger condition. The model for each analysis was parallel to the initial model described above, with the exception of condition not being included as an upper-level variable. These analyses revealed that stranger condition participants placed significant weight on dispositional information, t(61) = 2.57, b = A0,p< .05, but self condition participants did not place significant weight on dispositional information, t(67) = -.68, b = -.02, ns. Thus, as expected, participants in Table 2. Output from HLM Analysis (Study 2) Effect  Coefficient  SE  df  t Ratio  Version  -.07  .07  133  -.96  Condition  -.03  .06  133  -.49  Dispositional ratings  .04  .03  133  1.48  Situational ratings  .88  .02  133  45.95***  -.07  .03  133  -2.44*  .02  .02  133  1.04  -.001  .03  133  -.04  .02  .02  133  1.04  Condition x Disposition Condition x Situation Version x Condition x Disposition Version x Condition x Situation *p<.05.  ***p<.001.  15  the stranger condition placed more weight on dispositional information than those in the self condition. Participants in the self condition, rather, appeared to have largely neglected dispositional information, in line with our expectations. We were also interested in whether there was a difference between the two conditions in terms of the weight placed on situational information in making affective forecasts. Contrary to expectations, the initial H L M analysis used in the present study (which included condition as an upper-level variable), did not reveal a significant Condition x Situational Rating interaction, J(133) = 1.04, ns. Thus, unlike Study 1, individuals in the stranger condition did not rely on situational information less than those in the self condition. Individuals in the self condition placed significant weight on situational information, t(67) = 41.03, b = .90, p < .001, and so did individuals in the stranger condition, t(67) = 26.85, b = .87,/? < .001. Summary of Results Study 2 addressed the "conversational norms" (Schwarz, 1999) confound present in the original study. All three versions of Study 2 were designed to ensure that participants did not feel pressured into relying on dispositional information. Consistent with our main hypothesis, we found that individuals making affective forecasts for strangers relied more on dispositional information than individuals making forecasts for themselves, who appeared to have largely neglected this information. There is another methodological issue, however, that should be addressed—namely, in real life people usually do not have access to the dispositional happiness of complete strangers, a detail that hampers the external validity of the previous results. Study 3, therefore, was designed to resolve this issue—and extend our findings—by examining affective forecasts for friends rather than strangers. Instead of being shown the dispositional happiness rating of a friend, individuals provided their own ratings of a friend's happiness.  16  Study 3 The results of the first two studies suggested that individuals making affective forecasts for strangers relied more on dispositional information than individuals making forecasts for themselves. Study 3 was designed to extend thesefindingsby examining whether the affective forecasts made for friends (friend condition) also rely more heavily on dispositional information than those made for the self (self condition). Method Participants Forty individuals at the University of British Columbia (50% female, mean age = 19.4 years) filled out a survey after being approached on campus. Self condition Participants in the self condition filled out the exact same survey as that was used in the previous two studies. They first rated their dispositional happiness, then predicted how happy/sad they would feel following the same eight hypothetical events, and lastly provided situational ratings for each event. Friend condition Instead of being shown the dispositional rating of a random member of the self condition (as in Studies 1 and 2) individuals in the friend condition rated the dispositional happiness of a friend of their choice, on the same scale used in the self condition. Therefore, unlike the first two studies, individuals in the self and friend conditions were not yoked. After rating their friend's dispositional happiness, participants in the friend condition predicted how happy/sad this friend would feel while experiencing the same eight hypothetical events. Lastly, like individuals in the self condition, they gave situational ratings for each event.  17  Results and Discussion Unlike the previous studies, participants in the self and friend conditions of Study 3 were not yoked by dispositional happiness ratings. Rather, individuals in the friend condition made their own estimates of a friend's dispositional happiness. Dispositional happiness ratings in the self condition (M= 2.30, SD = 2.03) were not significantly different from those in the friend condition (M= 2.35, SD = 1.79), /(38) = -.083, ns. In order to establish whether affective forecasts for friends also relied more on dispositional information than affective forecasts for the self, we again used Hierarchical Linear Modeling. As in the previous studies, affective forecasts (the dependent variable) and situational ratings were the lower-level variables, and condition (self vs. friend) and dispositional happiness ratings were the upper-level variables. The lowerand upper- level variables and all of the interactions of these variables were entered into a hierarchical linear model predicting forecasted happiness. Table 3 displays the output of the H L M analysis. Consistent with the previous two studies, the model revealed a significant Condition x Dispositional Rating interaction, f(36) = -3.19,/? < .01. There was also, as expected, a significant Condition x Situational Rating interaction, ^(36) = 3.20, p < .01. As in the previous studies, we ran two more H L M analyses in order to break down these interactions—one for self condition participants and the other for friend condition participants. Regarding dispositional information, friend condition participants placed significant weight on dispositional information, /(18) = 3.64, b = .39,p < .01. Self condition participants also placed significant weight on dispositional information, t(\S) = 2.12, b = .13, p < .05, though not to the same extent as participants in the friend condition. Friend condition participants also placed highly significant weight on situational information, f(18) = 9.37, b = .64, p < .001, though not to the same extent as self condition participants,  18  Table 3. Output from HLM Analysis (Study 3) Effect  Coefficient  SE  df  t Ratio .13  Condition  .01  .08  36  Dispositional ratings  .25  .04  36  6.16***  Situational ratings  .75  .04  36  19 37***  -.13  .04  36  -3.19**  Condition x Disposition Condition x Situation  • 12  .04  36  3.20**  Disposition x Situation  -.003  .02  36  -.16  Condition x Disposition x Situation  -.001  .02  36  -.08  **p<.01. ***p<.001.  ^(18) = 18.71, b = .88, p < .001. In sum, though participants in both conditions took into account both types of information in making affective forecasts, participants in the friend condition placed more weight on dispositional information—but less weight on situational information— than those in the self condition. Summary of results In Study 3 we found that people who made affective forecasts for friends relied more on dispositional information—and less on situational information—than those who made forecasts for the self. It appears, then, that affective forecasts for friends are arrived at similarly to affective forecasts for strangers (Studies 1 and 2). Thus, far, however, we have only looked at affective forecasts for hypothetical events, which limits the external validity of our findings. Study 4 investigated whether these trends also carried over to affective forecasts made regarding real events.  19  Study 4 In Study 4 we examined whether the previous findings could be replicated with real rather than hypothetical events. The event of interest was the Vancouver Canucks' performance in the first round of the 2007 National Hockey League Stanley Cup playoffs. At the time the study was run, the Canucks had just started their first round series versus the Dallas Stars. Participants in the self condition predicted how they would feel if the Canucks won or lost the series, whereas participants in the stranger condition predicted how a stranger would feel following each outcome. Based on the results of the previous studies, we expected participants in the stranger condition to rely more on dispositional information—and less on situational information—than those in the self condition. Method Participants One hundred and four individuals at the University of British Columbia (54% female, mean age = 21.8 years) filled out a survey after being approached on campus. Given that not all individuals would be familiar with hockey or interested in the outcome of this event, only individuals who indicated that they were Canucks' fans participated in the study. Self condition The design of Study 4 was similar to the previous studies. In the self condition participants were asked to rate their "happiness in general these days" (dispositional rating), once again using an 11-point Likert scale. They were also asked to rate the objective positivity/ negativity of the Canucks advancing, or not advancing, to the second round of the playoffs (situational ratings). Participants were then asked to predict how happy/sad they would feel a day after the Canucks advanced or did not advance.  20  Stranger condition Participants in the stranger condition were shown the dispositional rating and situational ratings given by a random member of the self condition. Therefore, participants in the self and stranger conditions were yoked by not only the same dispositional rating, but also by the same situational ratings. As in Study 2, this feature of the design addressed the "conversational norms" (Schwarz, 1999) confound—namely, that participants might assume that they are supposed to make use of the dispositional information because it is the only information that they are given. After being shown a self condition participant's dispositional and situational ratings, individuals in the stranger condition predicted how happy this individual would feel a day after the Canucks advanced or did not advance. Results and Discussion Like the previous hypothetical events studies, we examined the extent to which participants relied on dispositional ratings and situational ratings when predicting how they, or a stranger, would feel a day after the Vancouver Canucks advanced (or did not advance) to the second round of the playoffs. We expected that individuals making affective forecasts for strangers would place more weight on dispositional information than those making forecasts for themselves. We tested for this effect separately for both positive (advancing) and negative (not advancing) events. For each event, we performed a multiple linear regression with condition (self vs. stranger), dispositional ratings, and situational ratings all predicting forecasted happiness. All interaction terms were entered into the regression equation, of primary interest being the Condition x Dispositional Rating interaction, which indicated whether there was a difference between the two conditions in terms of the weight individuals placed on dispositional information when making affective forecasts.  21  For the positive event (advancing), this interaction was nearly significant, t(96) = -1.89, /? = -.20,/? = .062. As expected, participants in the stranger condition placed significant weight 2  on dispositional information, ^(49) = 4.89, /? = .54, p < .001, whereas self condition participants did not place significant weight on dispositional information, ^(49) = .74, p° = .08, ns. In other words, the standardized beta weight for dispositional ratings was significantly greater than zero in the stranger condition but not in the self condition (Figure 1). Together, these results support our hypothesis that individual making forecasts for others rely more on dispositional information than those making forecasts for the self. For the negative event, however, the Condition x Dispositional Rating interaction was not significant, ^(96) = -.05, p° = -.01, ns.  3  0.8 £  0.7 -  Dispositional  0.6 -  information  "S 0.5 H  Situational  H __ 0.3 H  information  to 0.4  *  0.2 *  0.1 -  = p<.0l  * = /X.001  0 Self condition  Stranger condition  Figure 1. Beta weights indicating the extent to which participants in the self condition and the stranger condition placed weight on dispositional and situational information in arriving at their forecasts (Study 4). Participants in the stranger condition weighed dispositional information more heavily in making their affective forecasts than individuals in the self condition.  22  We were also interested in the Condition x Situational Rating interaction, which indicated whether individuals in one of the conditions placed greater weight on situational information in arriving at their forecasts. Unexpectedly, the Condition x Situational Rating interaction was not significant, both for the positive event, t(96) = .38, /? = .03, ns, and the negative event, t(96) = .05, /? = .004, ns. Therefore, individuals in the stranger condition did not place less weight on situational information than those in the self condition. The range of situational ratings, however, was restricted in the present study because ratings for only one event (either positive or negative) was entered into each analysis, unlike the previous studies which included situational ratings for eight different events in the same powerful analysis. This restriction of range may explain why we were unable to replicate the previously significant Condition x Situational Rating interaction. Summary of results  ,  In sum, Study 4 largely replicated the results of the previous studies using a real event instead of hypothetical ones. Individuals making forecasts for a stranger placed more weight on dispositional information than those making forecasts for the self (though only for positive events). In Study 5, we investigated whether people's affective forecasts for others are also less biased than their forecasts for themselves (Hypothesis 3). We believe that this may be the case because placing weight on dispositional information may debias affective forecasts by diverting one's attention way from the focal event, thereby reducing focalism (a cause of the impact bias). In addition, dispositional information is important to consider given that one's happiness returns to its dispositional baseline following most events.  23  Study 5 In Study 5 we examined affective forecasts made for the self and for strangers regarding another real event, an undergraduate student's completion of his or her last exam of the year. Individuals making affective forecasts for strangers were again expected to rely more on dispositional information than those making forecasts for themselves. In addition to collecting forecasting data, we also called back participants a day after their last exam to see how happy they actually felt. We expected that individuals in the self condition would report being less happy than they had predicted, in line with the impact bias. Affective forecasts in the stranger condition, on the other hand, were not expected to exhibit the impact bias due to their stronger reliance on dispositional information. Method Participants During exam week, sixty students at the University of British Columbia (72% female, mean age = 20.5 years) were approached on campus and filled out a survey in exchange for a candy bar. We only requested participation from individuals whom this outcome concerned, namely students who had not yet written their last exam of the year. Affective forecasts The design of Study 5 was parallel to the previous studies. Using the same scales as in the previous studies, participants in the self condition rated their dispositional happiness, predicted how happy they would feel a day after finishing their last exam, and then rated the situation objectively. Participants in the stranger condition were yoked to a random member of the self condition and were shown this individual's happiness rating. Next, using the same scales  24  as in the self condition, they predicted how happy this individual would feel a day after his or her last exam, and then they also rated the situation objectively. Callbacks A day following their last exam of the year, individuals in the self condition were contacted and asked to rate how happy/sad they felt "right now on a scale from -5 (extremely sad) to +5 (extremely happy)". We were able to contact 22 of the 30 (73.3%) self condition participants. The bias and accuracy of both forecasting groups could, therefore, be determined by comparing post-event happiness ratings to the forecasted happiness ratings in the self and stranger conditions. Results and Discussion Factors contributing to the affective forecasts As in the previous studies, we expected that individuals making affective forecasts for strangers would place more weight on dispositional information than those making affective forecasts for themselves. We tested for this effect as we did in Study 4, using a multiple linear regression in which condition (self vs. stranger), dispositional ratings, and situational ratings were used to predict forecasted happiness. All possible interaction terms were also entered into the regression equation. As expected, the Condition x Dispositional Rating interaction was significant, t(5\) - -2.17, /? = -.24,p < .05. Participants in the stranger condition placed significant weight on dispositional information, t(26) = 3.30, /? = .52, p < .01, whereas self condition participants did not place significant weight on dispositional information, t(26) = 1.22, /? = .23, ns. On the other hand, the Condition x Situational Rating interaction was not significant, 7(51) = -1.16, /? = -.081, ns, nor was it even in the expected direction. As in Study 4, then, we did not find  25  evidence that individuals in the stranger condition placed less weight on situational information in making their affective forecasts than those in the self condition. Once again, this may have been due to the limited range of the situational ratings. Thus far, the results of the present study (and the previous ones) have shown that individuals making affective forecasts for others rely more on dispositional information than those making affective forecasts for themselves. What remains to be tested is whether this increased weight on dispositional information results in less biased affective forecasts. Study 5 explored this possibility by calling back participants in the self condition a day after their last exam and asking them how happy they were presently. This enabled us to compare the bias of the forecasts in each condition. Bias of the affective forecasts Of initial interest, it appears that participants in both conditions forecasted increased happiness a day after final exams. Individuals in the self condition predicted that they would feel significantly happier a day after their last exam (M= 4.32, SD = .65) than they felt "in general these days," (M= 1.09, SD = 2.05), t(2\) = 7.40,/? < .001. Individuals in the stranger condition 4  also forecasted a significant increase in happiness (M= 3.09, SD = 2.41), t(2\)•= 4.58,p < .001. Note, however, that the forecasted happiness in the self condition was significantly greater than that in the stranger condition, ^(21) = 2.61,p = .016. These predicted increases in happiness are not surprising given that individuals were in the midst of exams at the time of reporting their dispositional happiness, which may have resulted in happiness ratings that were lower than normal. Moreover, the results suggest that participants in both conditions were correct in predicting that they would be happier a day following their last exam; individuals in the self condition, who provided ratings of their happiness a day following their last exam, did in fact  26  report being much happier (M= 3.36, SD = 1.26) than their dispositional baseline (M= 1.09, SD = 2.05),7(21) = 4.25, p < .001, But even though individuals did, in fact, feel happier a day after their last exam, they still could have exaggerated their post-exam jubilance. The post-event happiness ratings enabled us to determine whether forecasted happiness ratings were biased in either the self or stranger condition. As can be seen in Figure 2, participants making affective forecasts for themselves \  showed evidence of the impact bias; their forecasted happiness (M= 4.32, SD = .65) was significantly greater than their actual happiness (M= 3.36, SD = 1.26) a day following their last exam, 7(21) = 3.13, p < .01. On the other hand, as hypothesized, the forecasted happiness of participants in the stranger condition (M= 3.09, SD = 2.41) was not significantly higher than actual happiness ratings, 7(21) = -.43, ns, suggesting that these individuals did not succumb to the impact bias. Although we had expected that individuals would make less biased affective forecasts for others than for themselves, we did not expect them to make more accurate affective forecasts for others than for themselves. We expected that, if anything, affective forecasts for the self would be more accurate because individuals predicting their own happiness have privileged selfknowledge (Epley & Dunning, 2006) regarding what makes them happy. The accuracy of 5  affective forecasts was examined by computing the absolute value of the difference between forecasted and actual happiness ratings. The mean 'absolute difference' score for the self condition (M= 1.23, 573 = 1.91) was actually slightly (though not significantly) lower than that of the stranger condition (M= 1.91, SD = 2.27), 7(21) = -1.38,/? = .18.  27  a Self Forecasted • Stranger forecasted • Actual  Rating Type Figure 2. Forecasted happiness in the self and stranger conditions as well as post-event happiness ratings given by participants in the self condition a day following their last exam (Study 5). Participants in the self condition exaggerated how happy they would feel a day after their last exam of the year, exhibiting the impact bias. Participants in the stranger condition, however, did not succumb to the impact bias.  Summary of results Study 5, like the previous studies, demonstrated that more weight is placed on dispositional information when individuals make affective forecasts for others as opposed to themselves. In addition, the actual post-exam happiness ratings revealed that.forecasts for strangers were not exaggerated, unlike forecasts for the self. Thus, is it plausible that affective forecasts for others are resistant to the impact bias due to their increased reliance on dispositional information. But even though this is an attractive possibility, it is not one that the present research tests directly. Further research is needed to explore this possibility.  28  General Discussion In the present research we examined how affective forecasts made for others compared to those made for the self. The results of the five studies largely supported the hypotheses outlined in the introduction, in particular Hypothesis 1. In all five studies, we found that individuals making affective forecasts for others relied more on dispositional information than those making forecasts for themselves. This trend emerged for affective forecasts of both hypothetical (Studies 1-3) and real events (Studies 4 and 5), and it held up regardless of whether the other person was a stranger (Studies 1, 2, 4, and 5) or a friend (Study 3). Hypothesis 2, however, did not receive as strong support. Only Study 1 and Study 3 revealed that individuals making affective forecasts for others relied less on situational information than those making forecasts for themselves. However, this effect may have been difficult to obtain in Studies 4 and 5 because the range of situational ratings in these two studies was narrow compared to the previous studies, in which ratings were given for several events of differing valences (as opposed to a single event). Lastly, Study 5 revealed that individuals made less biased affective forecasts for others than for themselves (Hypothesis 3), perhaps due to the greater weight placed on dispositional information when making forecasts for others.  6  But why do individuals strongly rely on dispositional information when making affective forecasts for others? As mentioned in the introduction to this paper, explanations for the correspondence bias (Gilbert & Malone, 1995) and the actor-observer effect (Nisbett & Jones, 1971) may also be relevant in the realm of affective forecasting. For the same reasons that individuals make dispositional inferences and attributions regarding others' behaviour, they may also rely on dispositional information when predicting others' emotions. One such explanation is that attributing others' behaviour to dispositional factors is cognitively less demanding than  29  contemplating how they will be influenced by a given situation (Malle, 2006). Similarly, when individuals make affective forecasts for others, they may take a cognitive shortcut by largely downplaying the influence of the situation on the individual. Instead, forecasters may focus mainly on dispositional information, especially when they are unaware of the meaning that a situation holds for a given individual. However, somewhat at odds with the above explanation, participants who forecasted others' happiness in the present studies tended to rely strongly on not only dispositional information, but also on. situational information. Perhaps a more accurate explanation of how people arrive at affective forecasts for someone else is that they first imagine how the event would make themselves feel, and then they adjust this self-prediction based on what they know about the given individual (in this case, his or her dispositional happiness). This explanation is based on Van Boven and colleagues' dual judgment model of emotion perspective taking (Van Boven & Loewenstein, 2003, 2005; Van Boven, Loewenstein, & Dunning, 2005). According to this model, when people predict how someone else will feel or behave in an emotional situation, they first imagine how they themselves would feel or behave in the same situation. This selfprediction is then adjusted based on an assessment of how similar one is to the individual in question. For example, Van Boven, Loewenstein, and Dunning (2005) found that individuals based their predictions of whether a classmate would dance in front of the rest of the class for $5 on whether they themselves would accept the offer. Individuals then assessed their similarity with this classmate, and adjusted their self-prediction accordingly. Resulting from a tendency to see one's self as being more susceptible to embarrassment than others, individuals predicted that others were more likely than themselves to accept the offer.  30  Similarly, in the present studies, when individuals made affective forecasts for others they may have first imagined how the situation would make themselves feel (taking into account situational information), and then adjusted this prediction by considering how similar they were to the other person (taking into account dispositional information). For example, the thought process of a member of the stranger condition in Study 4 could have been something like: "I would be very happy if the Vancouver Canucks advanced in the playoffs—therefore this individual should also be very happy. However, he rated his dispositional happiness lower than I would rate my own, so he probably would not feel as happy as I would feel." Although individuals' affective forecasts were fairly balanced in the present studies (relying on both dispositional and situational information), their forecasts for themselves tended to neglect dispositional information. In only two of the five studies (Study 1 and Study 3) did individuals in the self condition place significant weight on dispositional information in arriving at their affective forecasts. Overall, then, the present research suggests that when individuals make affective forecasts for themselves they succumb to "dispositional neglect," though perhaps not to the extent that they completely disregard dispositional information. We propose that dispositional neglect may be consequence of focalism (Wilson et al., 2000). Individuals' narrow focus on a target event may not only cause them to fail to take in account other relevant events, but also fail to take into account relevant dispositional factors. The Accuracy of Affective Forecasts for Others Although individuals made less biased affective forecasts for others than for themselves, they did not arrive at more accurate forecasts for others. In fact, individuals' forecasts for others were slightly less accurate than their forecasts for themselves (Study 5). This is not surprising given the potential obstacles that stand in the way of making accurate forecasts for others. For  31  example, individuals may be unaware of the meaning that a given situation holds for someone else. Similarly, individuals may also be unaware what other events (aside from the "focal event") are taking place in someone else's life. Another obstacle to making accurate affective forecasts for others is that one cannot rely on "immediate emotions" (e.g., Loewenstein & Lerner, 2003). When individuals make affective forecasts for the self, they have the advantage of being able to preview how they will feel by imagining the future outcome, a process that.can evoke anticipatory emotions prior to experiencing the actual event. When individuals predict someone else's emotions, on the other hand, they do not have access to these anticipatory emotions (though they could engage in a simulation by imagining themselves experiencing the event). Limitations and Future Directions There are a few limitations to the present research that warrant discussion. First, giving participants in the stranger condition dispositional information prior to making their affective forecasts—which occurred in all studies but Study 3—could have led them to believe that they were supposed to make use of this information. It is possible that forecasters in the stranger condition placed more weight on dispositional information than those in the self condition due to this potential "conversational norm" (Schwarz, 1999). But recall that participants in the self condition also gave their dispositional happiness ratings prior to making their affective forecasts—this could have similarly led them to believe that they were supposed to use this information in generating their forecasts. If anything, then, we may have inadvertently primed both  conditions to focus on dispositional information when making their forecasts, which would  have reduced the size of the effect of interest (i.e., that individuals relied more on dispositional information when making affective forecasts for others than for themselves). Furthermore, in Study 2 we addressed the above methodological issue by providing participants with both  32  dispositional and situational information prior to making their affective forecasts. Even when they had access to both types of information, individuals in the stranger condition still relied more on dispositional information. A second issue with presenting individuals in the stranger condition with the dispositional happiness information is that this lacks external validity. In the real world, individuals are not given dispositional information about strangers that they could use to predict their future happiness. While this is true, we feel that Study 3 boosts the external validity of our findings by demonstrating that participants in the friend condition—who made their own rating of a friend's dispositional happiness—also placed more weight on dispositional information than participants in the self condition in arriving at their forecasts. A third limitation revolves around the finding that individuals made less biased affective forecasts for others than for themselves (Study 5), perhaps due to the greater weight they placed on dispositional information when making forecasts for others. Taking into account dispositional information could have lead to less biased forecasts because, a day after finishing their last exam of the year, individuals may have started to emotionally adapt to the initial joy they felt, their happiness returning to dispositional levels (though individuals' happiness a day after their last exam was still greater than their dispositional happiness ratings). We could address this possibility in future research by obtaining happiness ratings immediately after the focal event. By comparing these immediate happiness ratings to the day later ratings we could determine whether individuals had started to return to their dispositional happiness setpoint. Thus, although we demonstrated that individuals' forecasts for others were less biased than their forecasts- for themselves, future research should explore the mechanism underlying this finding.  33  The present research also did not examine the bias of affective forecasts for the self vs. others when they involved one's happiness immediately following an event. It is reasonable to assume that most events have an immediate, though short-lived, influence on well being regardless of one's dispositional happiness. Therefore, forecasts of immediate happiness may be more accurate if they strongly take into account the nature of the situation. This suggests that, immediately after an event, affective forecasts for the self may be more accurate than forecasts for others, assuming that forecasts for the self place more weight on situational information (which we found some evidence for in the present studies). On the other hand, the more time that passes following an event, the less of an impact the situation has on an individual's happiness, and the more dispositional factors become relevant (as one returns to one's dispositional happiness setpoint). In short, then, we propose that when individuals forecast "immediate happiness" they should focus on situational information, but when they forecast "delayed happiness" they should focus on dispositional information. Looking at the broader picture, daily experience suggests that predicting others' future emotions is a valuable decision making tool that may help individuals navigate their social relationships. For example, deciding whether to propose to a loved one may hinge upon predictions of this individual's emotional reaction. Similarly, predictions of others' future emotions may serve as potent motivational factors. For example, imagining one's parents beaming with pride could motivate undergraduate students to work hard to be admitted into graduate school. In short, though research to this point suggests that people's decision making is strongly influenced by their expected emotions (e.g., Loewenstein & Lerner, 2007), daily experience suggests that these decisions are also influenced by people's expectations regarding others' emotions.  34  Conclusion The present research suggests, that affective forecasts for others are arrived at quite differently than affective forecasts for the self. When predicting others' future emotions it appears that individuals weigh dispositional information heavily. This could, in turn, mitigate the impact bias, given that individuals return to their happiness setpoint soon after most events. On the other hand, affective forecasts for the self seem to place relatively little weight on dispositional information: This "disposition neglect," has not been reported previously and may be another cause of the impact bias. The take home message is this: When predicting your emotional future you could perhaps benefit from focusing less on the situation and more on how happy a person you are to begin with. Failing this, you could always ask friends—or even strangers—how you will feel on that upcoming vacation. While you may be focused on the rainy whether, they may notice your sunny disposition.  35  References Andrews, F. M., & Robinson, J. P. (1991). Measures of subjective well-being. In J. P. Robinson, P. R. Shaver, & L. S. Wrightsman (Eds.), Measures of personality and social psychological attitudes, (Vol. 1, pp. 61-114). San Diego: Academic Press. Brickman, P., Coates, D., & Janoff-Bulman, R. (1978). Lottery winners and accident victims: Is happiness relative? 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E., Georgellis, Y., & Diener, E. (2003). Reexamining adaptation and the set point model of happiness: Reactions to changes in marital status. Journal of Personality and Social Psychology, 84, 527-539. Lyubomirsky, S., Sheldon, K. M., & Schkade, D. (2005). Pursuing happiness: The architecture of sustainable change. Review of General Psychology, 9, 111-131. Malle, B. F. (2006). The actor-observer asymmetry in attribution: A (surprising) meta-analysis. Psychological Bulletin, 132, 895-919. Raudenbush, S. W. & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2 ed.). Thousand Oaks, CA: Sage. nd  Raudenbush, S., Bryk, A., Cheong, Y., Congdon, R., & du Toit, M. (2004). HLM6: Hierarchical linear and nonlinear modeling. Lincolnwood, IL: Scientific Software International. Schwarz, N. (1999). Self-reports: How the questions shape the answers. American Psychologist, 54, 93-105. Storms, M . D. (1973). 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The illusion of courage in social predictions: Underestimating the impact of fear of embarrassment on other people. Organizational'Behavior and Human Decision Processes, 96, 130-141. Wilson, T. D., Centerbar, D. B., Kermer, D. A., & Gilbert, D. T. (2005). The pleasures of uncertainty: Prolonging positive moods in ways people do not anticipate. Journal of Personality and Social Psychology, 88, 5-21. Wilson, T. D., & Gilbert, D. T. (2003). Affective forecasting. In M.P. Zanna (Ed.), Advances in experimental social psychology (Vol. 35, pp. 345-^-11). San Diego, CA: Academic Press. Wilson, T. D., & Gilbert, D. T. (2005). Affective forecasting: Knowing what to want. Current Directions in.Psychological Science, 14, 131-134. Wilson, T. D., Wheatley, T., Meyers, J. M., Gilbert, D. T., & Axsom, D. (2000). Focalism: A source of durability bias in affective forecasting. Journal ofPersonality and Social Psychology, 78, 821-836.  39  Footnotes 'One participant from the self condition was removed from the subsequent analyses because he or she gave the same rating for all eight hypothetical events. This interaction became highly significant after the three-way interaction term was removed from the regression equation, 7(96) = -3.75, /? = -.29,p < .001. 3  In Studies 1 and 3 we also found that this effect was stronger for positive events than for  negative events. This may be the case because negative events more strongly draw people's attention to the situation and away from dispositional information than positive events, an instance of bad being stronger than good (Baumeister, Bratslavsky, Finkenauer, & Vohs, 2001). 4  The analyses in this section only involve the twenty-two participants in the self  condition who were contacted by phone, as well as the participants in the stranger condition with whom they were randomly paired. 5  It is quite possible for ratings to be accurate even when they are biased. For example,  forecasted happiness ratings for the self could consistently overestimate actual happiness ratings by half a point on our scale, which would make them biased but also fairly accurate. 6  Even though affective forecasts for others may be more biased than forecasts for the self,  this does not imply that forecasts for the self are in some way flawed. In fact, Wilson and Gilbert (2003) point out that the impact bias may be quite functional; for example, there may be motivational benefits when one exaggerates the impact of positive events (Lowenstein, 1987).  40  

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