Optimistic Bias
William M. P. Klein

Bias at the group level. The simplest method of establishing an optimistic bias (see Appendix) is to ask a sample of individuals to estimate their risk relative to that of other members of the sample (or the population from which that sample is taken). This is called the "direct" method of elicitation. For example, a respondent might be asked to "compare your risk with that of the average person of your age and sex" on a scale that ranges from "below average" to "above average" with "average" as the midpoint. Investigators have generally used odd-numbered scales (e.g., 5-pt. or 7-pt. scales) to ensure that "average" is in the middle of the scale. If the mean response is higher or lower than this midpoint, one has demonstrated an optimistic bias (assuming that the sample is fully representative of the reference group, and that actual risk is not highly skewed). One might also ask respondents to compare others' risk to their own risk, which turns out to elicit less bias (Otten & van der Pligt, 1996 xClose
Otten, W., & van der Pligt, J. (1996). Context effects in the measurement of comparative optimism in probability judgments. Journal of Social and Clinical Psychology, 15, 80-101. ).

Another approach is to ask participants to make two judgments - an estimate of their own risk (on a likelihood scale, for example), and an estimate of the risk of the average peer (see Appendix). These ratings can then be subtracted, and if the mean difference is not zero, a bias can be said to exist. This is called the "indirect" method of measuring optimistic bias. The attractiveness of such an approach is that it is possible to assess whether a given moderator influences estimates of personal risk or the comparative target's risk. In a review of studies using the indirect method, Helweg-Larsen & Shepperd (2001) xClose
Helweg-Larsen, M., & Shepperd, J. A. (2001). Do moderators of the optimistic bias affect personal or target risk estimates? A review of the literature. Personality and Social Psychology Review, 51, 74-95. showed that negative affect influences personal risk estimates whereas positive affect influences target risk estimates, a finding that would have been obscured had comparative risk not been assessed with separate items. Finally, separate samples can be asked to make the two judgments; for example, Weinstein, Marcus, & Moser (2005) xClose
Weinstein, N. D., Marcus, S., & Moser, R. P. (2005). Smokers' unrealistic optimism about their risk. Unpublished manuscript.  asked separate groups of smokers to estimate their own or other smokers' risks of experiencing tobacco-related illnesses, and again observed an optimistic bias when assessing the difference in estimates between the two groups. Interestingly, some studies show that the magnitude of bias is greater when using the direct method (e.g., Goszsczyska & Roskan, 1989 xClose
Goszczyska, M., & Roskan, A. (1989). Self-evaluation of drivers' skill: A cross-cultural comparison. Accident Analysis and Prevention, 21, 217-224. ) yet others show the opposite pattern (e.g., Sutton, 2002 xClose
Sutton, S. (2002). Influencing optimism in smokers by giving information about the average smoker. Risk, Decision, and Policy, 7, 165-174. ).

Bias at the individual level. Although the above methods are effective when evaluating optimistic bias at the level of the group, they cannot be used to determine which members of a group are biased. A woman who believes her risk of breast cancer is below average, for example, may be quite accurate if she has no risk factors for breast cancer. In fact, this woman may be unrealistically pessimistic if her comparative risk is even more below average than she thinks it is. It is important to be able to identify which members of a sample are biased, however, in order to determine whether biases are correlated with other individual-level variables such as personality and behavior (see Appendix). Many studies attempting to link optimistic biases and related "positive illusions" with other variables such as health behavior simply define bias as a tendency to make self-serving judgments, without taking the important step of assessing the accuracy of these judgments. Consequently, although we know that optimistic beliefs are related to precautionary behaviors and ultimately to a more adaptive psychophysiological profile (e.g., Taylor, Lerner, Sherman, Sage, & McDowell, 2003 xClose
Taylor, S. E., Lerner, J. S., Sherman, D. K., Sage, R. M., & McDowell, N. K. (2003). Are self-enhancing cognitions associated with healthy or unhealthy biological profiles? Journal of Personality and Social Psychology, 85, 605-615. ), we do not have sufficient data to determine whether such beliefs are adaptive when they are illusory.

A small number of studies have attempted to use objective criteria to assess individual bias. Several of these studies use experimenter-initiated models to determine which members of the sample are at higher risk (e.g., Gerrard & Luus, 1995 xClose
Gerrard, M., & Luus, C. A. E. (1995). Judgments of vulnerability to pregnancy: The role of risk factors and individual differences. Personality and Social Psychology Bulletin, 21, 160-171. ; Wiebe & Black, 1997 xClose
Wiebe, D. J., & Black, D. (1997). Illusional beliefs in the context of risky sexual behaviors. Journal of Applied Social Psychology, 27, 1727-1749. ). Others use "risk engines" to compute a person's risk based on epidemiological models (which are built from large epidemiological data sets such as the Framingham study) and then determine how participants' estimates compare with values computed by these risk engines (e.g., Kreuter & Strecher, 1995 xClose
Kreuter, M. W., & Strecher, V. J. (1995). Changing inaccurate perceptions of health risk: Results from a randomized trial. Health Psychology, 14, 56-63. ; Radcliffe & Klein, 2002 xClose
Radcliffe, N. M., & Klein, W. M. P. (2002). Dispositional, unrealistic, and comparative optimism: Differential relations with knowledge and processing of risk information and beliefs about personal risk. Personality and Social Psychology Bulletin, 28, 836-846. ). Very few studies measure actual outcomes to determine accuracy, and such studies are needed. In one example, college students estimated their comparative risk of having unplanned sexual intercourse in the next year, and reported six months later whether such an event had occurred (Klein, Geaghan, & MacDonald, 2005 xClose
Klein, W. M. P., Geaghan, T. R., & MacDonald, T. K. (2005). Unplanned sexual activity as a consequence of alcohol use: A prospective study of risk perceptions and alcohol use among college freshmen. Unpublished manuscript. ).

Absolute vs. comparative optimistic bias. There is no reason, of course, to limit optimistic biases to comparative beliefs. The use of a comparative measure was initially based on the ease of demonstrating optimistic bias at the group level (Weinstein, 1980 xClose
Weinstein, N. D. (1980). Unrealistic optimism about future life events. Journal of Personality and Social Psychology, 39, 806-820. ). However, if a man predicts that he will not get prostate cancer and then he does, he would clearly be optimistically biased. Similarly, most HIV-seropositive individuals who do not believe they will succumb to AIDS are optimistically biased (Taylor, Kemeny, Aspinwall, Schneider, Rodriguez, & Herbert, 1992 xClose
Taylor, S. E., Kemeny, M. E., Aspinwall, L. G., Schneider, S. G., Rodriguez, R., & Herbert, M. (1992). Optimism, coping, psychological distress, and high-risk sexual behavior among men at risk for acquired immunodeficiency syndrome (AIDS). Journal of Personality and Social Psychology, 63, 460-473. ). Whether an investigator measures optimistic bias based on comparative or absolute measures should depend on the hypothesis being tested. For example, given findings that comparative risk perceptions are more predictive than absolute risk perceptions of colorectal cancer screening (Blalock, DeVellis, Sandler, & Afifi, 1990 xClose
Blalock, S. J., DeVellis, B. M., Afifi, R. A. & Sandler, R. S. (1990). Risk perceptions and participation in colorectal cancer screening. Health Psychology, 9, 792-806. ), research on screening behaviors may benefit from the use of comparative measures. Absolute and comparative risk perceptions are not redundant; each explains independent variance in worry, behavior, and other related constructs (Lipkus et al., 2000 xClose
Lipkus, I. M., Kuchibhatla, M., McBride, C. M., Bosworth, H. B., Pollak, K. I., Siegler, I. C., and Rimer, B. K. (2000). Relationships among breast cancer perceived absolute risk, comparative risk, and worries. Cancer Epidemiology, Biomarkers and Prevention, 9, 973-975. ).

Cross-sectional and prospective designs. An important methodological issue one faces when attempting to link optimistic biases with other constructs such as risk-reducing behavior is the type of design in which these constructs are measured. Assessing any type of risk perception and behavior in a cross-sectional design makes it difficult to determine whether bias influences behavior, behavior influences bias (or both), or whether a third variable (such as education or negative affectivity) influences both (Gerrard et al., 1996 xClose
Gerrard, M., Gibbons, F. X., Benthin, A. C., & Hessling, R. M. (1996). A longitudinal study of the reciprocal nature of risk behaviors and cognitions in adolescents: What you do shapes what you think, and vice versa. Health Psychology, 15, 344-354. ; Weinstein, Rothman, & Nicolich, 1998 xClose
Weinstein, N. D., Rothman, A. J., & Nicolich, M. (1998). Use of correlational data to examine the effects of risk perception on precautionary behaviors. Psychology and Health, 13, 479-501. ). The same problem applies when attempting to link biased risk perceptions with other constructs. Although there is now a growing literature using prospective designs to assess the link between risk perceptions and behavior, very few of these studies evaluate the accuracy of these risk perceptions.

Reliability. Given the difficulty of measuring optimistic biases at the level of the individual, there are few if any studies that determine the test-retest reliability of optimistically biased judgments. Moreover, because bias is usually established for single events, no data are available to determine whether bias is consistent across multiple events, so there are no published scales that measure a general form of the optimistic bias. Although some studies have collapsed comparative ratings across multiple events based on high reliability coefficients and identified the collapsed index as a generalized measure of optimistic bias (e.g., Davidson & Prkachin, 1997 xClose
Davidson, K. & Prkachin, K. (1997). Optimism and unrealistic optimism have an interacting impact on health-promoting behavior and knowledge changes. Personality and Social Psychology Bulletin, 23, 617-625. ; Taylor et al., 2003 xClose
Taylor, S. E., Lerner, J. S., Sherman, D. K., Sage, R. M., & McDowell, N. K. (2003). Are self-enhancing cognitions associated with healthy or unhealthy biological profiles? Journal of Personality and Social Psychology, 85, 605-615. ), these measures are better characterized as generalized risk beliefs rather than biased risk beliefs per se. However, it is worth noting that comparative risk judgments have been shown to be reliable over time (Shepperd, Helweg-Larsen, & Ortega, 2003 xClose
Shepperd, J. A., Helweg-Larsen, M, & Ortega, L. (2003). Are comparative risk judgments consistent across time and events? Personality and Social Psychology Bulletin, 29, 1169-1180. ), suggesting that biases in these judgment may also be reliable.