Select a school by filtering
Select a school by fitlering on state, school sector and type and/or type the first few characters of the school name in the search box.
Notes and caveats
- The model used student-level logistic regression to estimate each student’s probability of achieving Strong or Exceeding. The predictors were consistent with those used in the current similar-students model for school average scores.
P(Strong or exceeding) = SEA + SchoolSEA + ATSI + MATSI + PERATSI + ARIA
where
• SEA = student-level index of socio-educational advantage
• SchoolSEA = average student-level socio-educational advantage
• ATSI = Indicator of Aboriginal and Torres Strait Islander status
• MATSI = Indicator of missing Aboriginal and Torres Strait Islander status
• PERATSI = percentage of Aboriginal and Torres Strait Islander students
• ARIA = remoteness classification
- Predicted school-level percentages are calibrated to the corresponding jurisdiction-level result. This is particularly important in the Australian Capital Territory and Northern Territory, where the aggregate predicted percentage may otherwise overstate or understate the actual jurisdiction-level percentage.
- Each school’s result is classified according to the difference between its observed and predicted percentages, expressed relative to the estimated standard error of the school result. The standard error is calculated using a delete-one-student jackknife, under which the estimate is recalculated ntimes, with one student omitted on each occasion.