1. The value-added model: Multilevel Model

2. Currently available information for value-added measurement End of P6 AAI End of S5 HKCEE End of S7 HKALE S1-S5 S6-S7

3. Control Variables in Multilevel Model Gender Academic ability Average academic ability All Girls All Boys Number of different schools attended (for S1-S5 cohort only) Change of schools at S3-S4 (for S1-S5 cohort only)

4. Student exam score = Student predicted score + School residual + School differential effect + Student residual measure of school effectiveness Compute School Value-added using Multilevel Model

5. y ij = b 00 + b 1 x 1ij + … b n x nij + (u 0j + u 1j x 1ij + e ij ) Student exam score Student predicted score School residual school differential effect student residual Compute School Value-added using Multilevel Model

6.  Multilevel model provides the most statistically valid way of measuring school value-added estimate  Multilevel model takes into account complex interrelationships among the factors affecting student performance Validity of Value-added

7. Average reliabilities across subjects for different values of number of candidates in 1999 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 020406080100120 Number of Candidates Reliability Chinese Biology Reliability of Value-added

8.  Value-added will not be provided for subjects with less than 10 students taking the examination in the school  Value-added may not be provided for subjects with the low level of precision by the value- added model, for example, EPA, Social Studies, Human Biology, Commerce, Visual Arts, Music. To ensure reliable value-added

9. To ensure reliable value-added estimates  Confidence intervals are reported for all value-added estimates, indicating the degree of certainty surrounding each estimate

10. Limitation of value-added  It only reflects the academic performance of students.  It doesn't provide the absolute performance of students.  It doesn't provide the reasons behind the value- added performance.