The results of the policy drivers factor analysis revealed the
presence of significant coefficients above 0.3 in the correlation matrix. The Kaiser–Meyer–Oklin value was 0.75 while the Barlett’s Test of Sphericity was statistically significant which
provided statistical support for the factorability of the correlation matrix. Principal component revealed the presence of five factors explaining 63.6% of the variance and a
rotated Promax solution revealed the presence of a five factor structure with significant factor loading of above 0.5. These findings indicate five interrelated underlying factors for
policy drivers designated SEA Existing, SEA Benefits, SEA
Barriers, SEA Enablers, SEA Behaviour. The interpretation of
these five policy constructs was consistent with the hypothesized
SEA policy factors of SEA benefits, SEA Barriers, SEA
Enablers and SEA (Table 2). A GLM multivariate analysis was
conducted to determine if there was a statistically significant
difference between age, gender and sector groups. The five
policy constructs of SEA policy factors of SEA benefits, SEA
Barriers, SEA Enablers and SEA Behaviour were used as dependent variables while the independent variables were age, gender and sector groups. Generally, assumptions of
normality, multivariate outliers and homogeneity of variance–covariance matrices were met. Statistical analysis indicates that there are no significant difference for all
dependent variables for age, gender and sector groups as pvalue of all the test is >0.05.