Given that many problems can emerge when respondents construct a self-reported judgment, the final issue that we will address concerns the accuracy and validity of the self-report method. Although errors surely do occur, they often do not severely limit the validity of the measures. For instance, self-reports often agree with non-self-report measures of the same construct. Within the well-being domain, for instance, researchers have shown that self-reports of happiness and life satisfaction correlate moderately to strongly with such diverse methods as observer ratings, online assessments, and cognitive measures including the number of positive and negative memories that can be recalled in a short period of time (Lucas, Diener, & Suh, 1996; Pavot, Diener, Colvin, & Sandvik, 1991). Similarly, personality researchers have shown that although the accuracy of self-reports varies across individuals, contexts, and the specific trait or behavior being rated, self-reports are often very good predictors of alternative measures of the same construct (Gosling, John, Craik, & Robins, 1998; John & Robins, 1993; Spain, Eaton, & Funder, 2000).
Furthermore, even when self-reports disagree with non-self-report methods, there is often evidence that the disagreement is not due to mistakes on the part of the respondent. For instance, Nelson et al. (1983) examined the discrepancies between self-reported and doctor-rated health. When Nelson et al. asked doctors about the discrepancies, they found that in 44% of the cases, the doctors reported that the discrepancy was due to their own error. An additional 12% of discrepancies stemmed from a lack of knowledge of the patient. Other studies show that even when self-reports of health differ from non-self-report methods, the self-reports often predict important outcomes including mortality (e.g., Ganz, Lee, & Siau, 1991; McClellan, Anson, Birkeli, & Tuttle, 1991; Mossey & Shapiro, 1982; Rumsfeld et al., 1999). Thus, although errors in self-reported judgments surely occur, self-reports often demonstrate impressive accuracy, predictability, and utility in important research settings.
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