Not exact matches
It offers a user - friendly single source for a comprehensive range of education data,
enabling users to both holistically assess education in a given city as well as make
valid comparisons among multiple cities.
If these metrics are to be used, it is essential that important differences in institutional character and student profile should be recognised to
enable more
valid comparisons to be made.»
According to Damasio & Koller (2015) invariance suggests a significant quality indicator for the MLQ,
enabling valid group
comparisons between genders, free from response bias.