Monday 5 September 2016

Assessing the Consequences of Unrealistic Optimism

This post is by James Shepperd, Gabrielle Pogge and Jennifer Howell who recently authored a paper entitled, "Assessing the Consequences of Unrealistic Optimism: Challenges and Recommendations", to be published in a special issue of Consciousness & Cognition on unrealistic optimism, guest-edited by Anneli Jefferson, Bojana Kuzmanovic and Lisa Bortolotti. In this post, James, Gabrielle and Jennifer summarise the content of their new paper.


Researchers have argued that unrealistic optimism (UO) can have both desirable and undesirable consequences. Yet, understanding the consequences of UO is a remarkably difficult. We identified eight challenges faced by researchers wanting to understand the consequences of UO.
  • Unrealistic comparative optimism might be overstated. Recent evidence suggests that measurement decisions may exaggerate both the frequency and magnitude of UO in research—UO may neither be as common or sizable as once believed. Accordingly, the potential consequences may be relatively infrequent, and minor when they occur.

  • Separating UO from positive expectations. The most common form of UO occurs when people report they are less likely than their peers to experience undesirable events. Yet studies show that people think only about themselves and little about their peers when making these judgments. Thus, what appears to be UO may actually be a positive personal expectation that may or may not be unrealistic. Researchers wishing to understand the consequences of unrealistic optimism must, therefore, disentangle realistic and unrealistic positive expectations. 

  • Measuring UO independently from its consequences. Often, the criterion for determining whether estimates are unrealistically optimistic is the same as the outcome used to assess the consequence of UO. For example, researchers wishing to examine whether students who display UO about a classroom exam subsequently perform better (or worse) on the exam might be tempted to compare the students’ exam estimate with their exam performance. If the estimate exceeds the performance, then the student displayed UO. They might then examine whether OU predicts performance on the exam. However, because the criterion used to establish UO is the same as the measure of the consequence (exam performance), it impossible to determine whether the predictions affected the outcome. Solving this issue requires careful forethought and multiple objective criterion measures. 

  • Few studies examine consequences. UO is a compelling phenomenon because of its presumed consequences. Yet few studies examine the consequences of UO. Work on UO must start including measures of potential consequences. 

  • Failing to report findings. Some studies report collecting data bearing on the consequences of UO yet do not report the results. We also suspect that some researchers neglected to report null results because describing them detracted from the narrative the investigators attempted to tell. Needed is research that attempts to empty the field’s file drawer to examine typical consequences of UO. 

  • Focusing on intentions rather than behavior. Intentions are the most common outcome examined in studies of the consequences of UO. Although intentions can be a useful surrogate outcome, needed is research that examines the relationship between UO and actual behavior. 

  • Reliance on cross-sectional designs. Most studies investigating the consequences of UO are cross-sectional surveys where participants indicate both their risk and their behavior/intentions in a single sitting. Cross-sectional surveys are open to interpretational limitations including reverse causality (i.e., outcomes may cause UO, rather than vice versa) and the third variable problem (i.e., some unmeasured variable cause both UO and its supposed consequences).

  • Non-experimental studies. The experiment is the gold standard for establishing causal relationships. Still, we know of no published study that has successfully manipulated UO and examined the consequences of such a manipulation. Researchers are thus limited in how strongly they can assert that UO causes any outcome.


We offer several recommendations for future research including:

  • Collecting longitudinal and experimental data. 

  • Examining and reporting all findings. 

  • Examining UO at the individual level by collecting data regarding individual participants’ actual risk.

For further readings on unrealistic optimism, you can look up these papers by James and colleagues: "A Primer on Unrealistic Optimism" (2013) and "Taking Stock of Unrealistic Optimism" (2015).

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