Regression analysis by gender

Splitting the sample into genders meant a female sample size of 416, and a male sample size of 229.

3.3.5.1.1. Everyday RTB.

The results of the multiple regression analysis for everyday risk taking are shown inTable 39.

Variables included in the analysis were age, qualification, children, income, population, anxiety level, and depression level.

Table 39

Multiple regression: Analysis of everyday risk taking by gender, using forward selection

Sample Variables included β t p r2total Change in r2 total Male Age -.614 -9.636 .000 26.9% 26.9% Income .255 3.436 .001 30.3% 3.4% Qualification -.141 -2.309 .022 32.2% 1.9% Children .143 2.038 .043 33.7% 1.5% Female Age -.456 -8.232 .000 18.3% 18.3% Qualification -.111 -2.003 .046 19.5% 1.2%

Age was by far the most important predictor variable for everyday risk taking in both genders, with older people more risk averse. Aside from age only income for the male sample added more than 2% to the explained variability in everyday RTB scores for the multiples regression equation, with lower income predicting people being risk averse. These variables explained more of the variance in scores for males than females.

3.3.5.1.2. Risks to belongings.

The results of the multiple regression equation for risks to belongings are shown Table 40. Age was the most important predictor variable for risks to belongings scores for both males and females, with older people being more risk averse. For males a higher level of depression, lower income and having dependent children all predicted people being risk averse. For females risk aversion was significantly associated with having lower income, and being from smaller urban areas. These variables explained significantly more of the variance in scores for males than females.

3.3.5.1.3. Unknown risks.

The results of the multiple regression equation for unknown risks are shown in Table 41. Age was by far the most important predictor variable for unknown risks, older people being less likely to take these risks. Aside from age only lower income and living in smaller urban areas explained risk aversion for unknown risks. These variables explained significantly more of the variance in scores for males than females.

Table 40

Multiple regression: Analysis of risks to belongings by gender, using forward selection

Sample Variables included β t p r2total Change in r2total Male Age -.366 -5.136 .000 6.6% 6.6% Depression Level -.213 -3.116 .002 11.7% 5.1% Income .282 3.410 .001 15.1% 3.4% Children .156 1.986 .048 16.9% 1.8% Female Age -.320 -4.907 .000 4.3% 4.3% Income .191 2.847 .005 7.5% 3.2% Population -.136 -2.360 .019 9.2% 1.7% Anxiety Level -.134 -2.300 .022 10.9% 1.7%

3.3.5.1.4. Risks involving personal danger.

The results of the multiple regression equation for risks involving personal danger are shown in Table 42. Age was by far the most important predictor variable for risks involving personal danger, older people being less likely to take these risks. Aside from age only lower income (for both samples), and males having dependent children explained risk aversion for risks involving personal danger. These variables explained significantly more of the variance in scores for males than females.

Table 41

Multiple regression: Analysis of unknown risks by gender, using forward selection

Sample Variables included β t p r2total Change in r2total Male Age -.556 -8.674 .000 26.0% 26.0% Income .172 2.689 .008 28.6% 2.6% Population .141 2.321 .021 30.6% 2.0% Female Age -.350 -6.259 .000 12.2% 12.2%

Table 42

Multiple regression: Analysis of risks involving personal danger risk by gender, using forward selection

Sample Variables included β t p r2total Change in r2total

Male Age -.572 -8.813 .000 26.1% 26.1% Income .226 2.997 .003 27.7% 1.6% Children .171 2.383 .018 29.8% 2.1% Female Age -.494 -8.103 .000 18.2% 18.2% Income .140 2.292 .023 19.7% 1.5%

3.3.5.1.5. Risks to others.

The results of the multiple regression equation for risks to others are shown in Table 43. Age was by far the most important predictor variable for risks to others, older people being less likely to take these risks. Aside from age only males with lower income and males from smaller urban areas predicted risk aversion for risks to others. These variables explained significantly more of the variance in scores for males than females, but a lower percentage overall than for other domains of RTB.

3.3.5.1.6. Social risks.

The results of the multiple regression equation for social risks are shown in Table 44. Age was by far the most important variable in explaining variance in social risk taking scores, older people being less likely to take these risks. Aside from age only males with from smaller urban areas, and males with no tertiary qualification, predicted risk aversion to social risk taking. These variables explained significantly more of the variance in scores for males than females. Table 43

Multiple regression: Analysis of risks to others by gender, using forward selection

Sample Variables included β t p r2total Change in r2total

Male Age -.314 -4.369 .000 6.9% 6.9%

Income .192 2.676 .008 10.1% 3.2%

Population .170 2.489 .014 13.0% 2.9%

Table 44

Multiple regression: Analysis of social risk taking by gender, using forward selection

Sample Variables included β t p r2total Change in r2total

Male Age -.531 -8.820 .000 26.3% 26.3%

Population .198 3.279 .001 31.6% 5.3%

Qualification -.174 -2.850 .005 34.4% 2.8%

Female Age -.420 -7.753 .000 17.6% 17.6%

3.3.5.1.7. Health risks.

The results of the multiple regression equation for health risks are shown in Table 45. Age was by far the most important variable in explaining variance in health risk taking scores, older people being less likely to take these risks. Aside from age, only males with higher depression level, and males with no tertiary qualification predicted risk aversion for health risks, both variables adding less than 3% to the explained variability of the multiple regression equation. Similar amount of total variance in these scores were explained by the models for males and females.