Hypothesis 3: There will be a negative association between perception of risk for depression and optimistic bias measured as a global unidimensional personality construct.
This hypothesis was supported. Pearson’s correlation revealed that a significant albeit weak negative relationship existed between perception of risk (measured on the Perception of Illness Risk) and optimistic bias (measured on the Life Events Questionnaire) (r=-.22, n=105, p<.05). While this relationship was weak, it did support the notion that as perception of risk increased, optimistic bias, or the tendency to believe oneself to be less vulnerable, decreased. When this relationship was explored further there was no evidence that perception of risk for depression actually predicted an optimistic bias. Perception of risk accounted for only 5.7% of the variance in optimistic bias when measured as a global unidimensional personality construct.
Hypothesis 4: Optimistic bias as a series of semi-independent constructs will predict perceived risk of depression
For each of the illnesses, standard multiple regression analyses were carried out in which perceived risk was the dependent variable, and the five factors postulated by Weinstein to predict perception of risk were the independent variables (perceived seriousness, perceived control, stereotyping of a sufferer, perceived commonness of
the disease, knowledge of a sufferer). In addition, knowledge and attitudes were also factored into the analysis in line with Moore and Rosenthal’s methodology (1996).
The significant correlations include, perceived seriousness (r=.21, n=105, p=.03), perceived control (r=-.29, n=105, p<05), perceived commonness, (r=.29, n=105, p<.05), knowledge of a sufferer (r=.34, n=105, p<.001) and knowledge of the illness (r=.37, n=105, p<.001).
The beta weights for the standard regression equations, with their respective F and R square values are shown in Table 9.
Table 9 Regression weights for prediction of perceived risk
Total N = 105 HIV/AIDS Diabetes Depression Breast Cancer Perceived Seriousness .09 .30** .08 .09 Perceived Control -.26* -.04 -.22* -.24* Stereotyping of sufferers .05 -.05 .11 .30 Perceived Commonness .12 -.02 -.06 .07 Knowledge of a sufferer .07 .96 .16 .07 Knowledge of illness .19 .13 .25* .30** Negative attitudes .03 .02 .05 .07 F 1.28 2.18* 4.97*** 3.45** Adj R2 .04 .07 .21 .14 Note: p* < .05; p** < .01; p*** < .001
As shown in Table 9, Perceived risk was significantly predicted by the Weinstein (1980) and the Moore and Rosenthal (1996) factors combined, for 3 out of the 4 illnesses. With the exception of HIV/AIDS F(7,97)=1.28, p=.125, all the other
regression equations were statistically significant. The relationship in the sample between perception of risk and the predictor variables for diabetes was significant (F(7,97)=2.18, p<.05). The relationship in the sample between perception of risk and the predictor variables for depression was also significant (F(7,97)=4.97, p<.001). The relationship in the sample between perception of risk and the predictor variables for breast cancer was significant (F(7,97)=3.45, p<.01). The percentage of variance accounted by the model for each illness ranged from 4% for HIV/AIDS to 21% for Depression.
Perceived seriousness was a significant predictor for perception of risk in Diabetes, but not for the other three illnesses. High seriousness in this case was associated with high perceived risk for Diabetes.
Perceived control significantly predicted perception of risk in three of the illnesses, HIV/AIDS, Depression and Breast Cancer. The associations revealed that generally the more control participants felt they had, the less at risk they felt they were. Stereotyping of sufferers, and perceived commonness did not predict perceived risk in any of the illnesses.
Those who knew a person with the illness/disease were not significantly more likely to view themselves as at risk of Depression, HIV/AIDS, Diabetes or Breast Cancer. However, knowledge of the illness was a significant predictor of perceived risk in Depression and Breast Cancer. This result suggested that the more participants reported to know about Depression and Breast Cancer the more vulnerable to the illness they perceived themselves to be.
As shown above in Table 9 the model of optimistic bias as a series of semi- independent illness based constructs was demonstrated to predict perception of risk for depression. Therefore this hypothesis was supported. It appeared that perceived control and knowledge of the illness were the most significant predictors when considering the variables as semi-independent constructs. The model accounted for twenty-one percent of the variance for perception of risk of depression. When considering the amount of variance accounted for by this model, perceived risk of depression accounted for more than any other illness. This finding was significant at the p<.001 level compared to the p<.01 and p<,05 levels for Breast Cancer and Diabetes respectively.
Hypothesis 5: Optimistic bias as a global unidimensional construct will predict perceived risk of depression.
Table 10 displays the hierarchical regression summary as described below. This analysis involved examining the contribution of the global personality construct of optimistic bias and was entered as the first step in the hierarchy. The second step involved the addition of the illness specific (state) factors described by Weinstein (1980) and Moore and Rosenthal (1996). By adding the second step the strength of each model could be tested.
This hypothesis was tested using hierarchical regression to determine to contribution of semi-independent variables (state) and the contribution of the more global unidimensional (personality) construct. When using the global measure of optimistic bias the model was revealed to significantly predict perception of risk F(3,101)=4.09,
p<.01. A comparison of models revealed that the model examined by Moore and Rosenthal (1996) using the semi-independent factors postulated by Weinstein, acted as a significant predictor for perceived risk F(10,94)=5.01, p<.001. The variance accounted for by the first optimistic bias global personality model was 8.2%, whilst the variance accounted for by the second optimistic bias illness specific state factors model was 27.8%. When examining the contribution made by both models, the state and personality based contributions accounted for 36% of the variance for perceived risk. Therefore this hypothesis that optimistic bias as a global construct would predict perception of risk for depression was also supported.
Table 10 Hierarchical regression weights for prediction of perceived risk
Total N = 105 ß (Std) t Sig Optimistic Bias -.445 -.716 .047* Pessimistic Bias .085 .168 .867 Perceived Seriousness .068 .724 .471 Perceived Control -.209 -2.34 .022* Stereotyping of sufferers .131 1.51 .135 Perceived Commonness .091 .889 .376 Knowledge of a sufferer .104 1.05 .298 Knowledge of illness .246 2.25 .027* Negative attitudes -.063 -.665 .508 Model 1 Model 2 Note: p*<.05
As expected the perceived seriousness and knowledge of the illness maintained their predictive capacity. In this analysis optimistic bias significantly predicted perception of risk of depression.