Four OLS regression analyses were executed to explain the variation in ‘Y1’, gender differences in attrition rate, by the dependent variables ‘X1’ to ‘X4’ at the level of the 25 (remaining) specialties or training programmes. A Kruskal-Wallis test was done to analyse the gender differences in attrition rate by type specialty training (‘X5’), as the number of observations per subgroups (specialty) were relatively small.
Results of the first regression analysis showed the strongest predictor for X2, the proportion of males working in the profession per 01-01-2003. 55% of the variation in the difference in attrition rates is explained by this dependent variable, a significant finding (R2: .545, F(27,60), p<.000). Figure 4 depicts this result. The regression model shows that residuals were normally distributed, giving no signs for other outliers. Also, the prediction interval for the regression model equation felt within the 95% boundaries.
The negative and significant coefficient implies that the lower the percentage of males working in a speciality, the higher the difference in attrition rate between males and females that are training
for that specialty (top left hand of the fitted line plot, figure 4). Specifically, in the specialties with lower percentages of males working, males have higher drop-out rates compared to females in training. The opposite can obviously be concluded as well, as the lower right end of the fitted line plot in figure 4 implies: in specialties with more males working in that profession, the drop-out of females in training is significantly higher.
Another OLS regression analysis was done for the relationship between Y1 and the ‘X1’, the proportion of males in training. This analysis showed that 42% of the variation in the gender difference in attrition rates was explained by this predictor, as significant finding (R2: .417, F(16,01), p<.000), and depicted by Figure 5. Residuals were normally distributed, the prediction interval for this regression equation felt within the 95% boundaries. This negative and significant coefficient implies that the lower the percentages of males in training in a specialty, the higher the difference in attrition between males and females in training for that speciality (top left hand of the fitted line plot, figure 5). In contrast to the previous regression analysis result however, the specific result is that less male residents drop-out if the proportion of males is higher in a specialty. And vice versa, shown by the lower right end of the fitted line plot (figure 5): if specialties have more males in training, more females will stop their training before completion, i.e. females have significant higher attrition rates.
Third, OLS regression analysis was done to analyse the relationship between difference in male and female attrition rates and the total attrition rate for each training program (‘X3’). The result can be summarized as a relative small but significant effect (R2: .163, F(4,46), p<.046). From Figure 6, it can be derived that for specialty training programs where the overall attrition rates are higher, the difference in attrition between males and females is higher as well (top/right side of figure 6). Likewise, in specialties where the overall drop-out rates in the training program are lower, the difference in attrition between males and females in training for that speciality is lower as well (bottom/left side of figure 6).
A fourth OLS regression analysis was performed to explore the relationship between the difference in attrition between males and females in training, and the duration of the specialty training (‘X4’). In sum, it can be showed this relation is negative and significant (R2: .299, F(9,85), p<.005), see figure 7. Longer specialty training programs thus show larger differences in attrition between males and females in training.
In a final step, the differences between the specialties as such were explored (‘X5’), in particular the distinction between surgical and other specialties. This is partly related to the previous analysis as most surgical specialties also have the highest training duration. A Kruskal-Wallis test was preformed to analyse the difference in attrition between males and females in training by type of specialty. The difference between the types of specialisms were significant (H=6,66, p.0,036). The ranking results of the Kruskal-Wallis test implied that the gender differences in attrition rate was negative for ‘surgical’ specialisms (Z-value -1,98), positive for ‘auxillary’ specialisms (Z-value 2,29) and ranked in between for the ‘non-surgical’ specialisms (Z-value -0,11).