## 4 Results
### Correlations  
Table 3 gives details of the paired correlation among all four observed variables (\(n=290\)) and one transformed variable of our data. We can observe that control has the strongest correlation with outcomes followed by structure and rules. On the other hand, outcomes have the strongest correlation with our transformed variable, followed by structure, control, and rules, respectively. The strong correlation of outcome with a transformed variable is understandable, as the latter is a transformation of the former.  
It is evident from the results that control measures are most strongly correlated with outcomes, which means performance variances must be observed closely to increase the effectiveness of the BPR regime. The structure is also positively associated with outcomes, though we still found a lack of understanding regarding the structural changes (summary statistics). However, considering our binary variable (agree/disagree), most measures are moderately correlated.  
As our data are Likert scale data gathered from a five-point questionnaire-based survey, we have further calculated Spearman correlations. Spearman correlations are a nonparametric measure of rank correlation and are used to explain the strength of the relationship between two variables. Here, we use a monotonic function, i.e., when the relationship between the variables is not linear. As it is evident from our earlier explanation of the data, it will be more insightful to calculate Spearman correlations than just simple paired correlations. Table 4 gives details of Spearman correlations among our variables. It is with this result that we can look deeper and find that structure (or structural reforms) has the strongest correlation with all independent as well as the dependent variable of the outcome, i.e., structural reforms are most closely related with the outcome. From amongst the independent variables, the structure is also more correlated with our transformed variable (agree/disagree). Finally, our transformed variable's correlation has been heightened by using Spearman correlation. The general trend of correlations  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **Structure** & **Rules** & **Control** & **Outcome** & **Agree/Disagree** \\ \hline Structure & 1.0000 & & & & \\ Rules & 0.6458 & 1.0000 & & & \\ Control & 0.5985 & 0.6131 & 1.0000 & & \\ Outcome & 0.7306 & 0.6985 & 0.7563 & 1.0000 & \\ \hline Agree/Disagree & 0.5231 & 0.4748 & 0.4963 & 0.7500 & 1.0000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Correlations (paired) (Obs. = 290).  
Figure 2: Variables response plot.  
from both paired (Table 3) and Spearman (Table 4) is similar; this is because Spearman correlation is a particular case of general correlations. We shall now further strengthen our understanding of the outcomes by calculating Kendall correlations.  
Because we have a transformed variable (agree/disagree) with a binary (0, 1) rank as against the ordinal ranking (\(1,3,\ldots,5\)) of all other independent and dependent variables, it will be appropriate to further refine our results by calculating Kendall's tau correlation coefficients (Table 5). This will allow for consideration of the disparity of ranking amongst the data variables (observed and transformed). Here again, we find that the structure has the strongest correlation with all independent as well as the dependent variable of the outcome, i.e., structural reforms are most closely related to the outcome. From amongst the independent variables, the structure is also more correlated with our transformed variable (agree/disagree). The comparative results of the correlation are not very different from the Spearman results, although Kendall results have dampened down the intensity of the relationship. Again, the overall correlations trend amongst all variables, i.e., reform measures and the outcomes have not altered in general.