ED216C Winter 2010 Prof. Rumberger

Week 8 Class Assignment

1. Open the SPSS file nels8d.sav. Note the file is already sorted first by F1SCH_ID and then by ID, which is the required structure to create a three-level MDM file.

2. Use the DataRestructure command to create a level-1 file, nels8dL1, with three records per student and with four variables: math (BY2XMTH , F12XMTH, F22XMT), TIME (0, .5, 1), and two constants (ID, F1SCH_ID).

3. Open SPSS file nels8d.sav. Create a level-2 file, nels8dL2, with the following variables: ID, F1SCH_ID, FEMALE, ASIAN, HISPANIC, BLACK, NATIVE, F1SES, HELDBACK.

4. Create a level-3 file, nels8dL3, by aggregating the file with the break variable, F1SCH_ID, containing the following variables: MEANSES (mean of F1SES), PRIVATE, CATHOLIC.

5. Create an MDM file from the three datasets.

6. Estimate an unconditional random-coefficient model with TIME as the only level-one predictor.

7. Examine the fixed and random effects. If any of the parameters are not significant, make appropriate adjustments in the next model.

8. Calculate the percent variance in initial status and growth within and between schools.

9. Examine the reliability of the intercept (initial status) and growth parameters at level 1 and level 2. Are they sufficiently high to proceed with adding person-level predictors to the model?

10. Examine the correlation between individual initial status and growth. What does it say about the relationship?

11. Estimate an intercepts- and slopes-as-outcome model with F1SES as a predictor, grand- mean centered.

12. Examine the fixed and random effects. If any of the parameters are not significant, make appropriate adjustments and rerun the model.

13. Calculate the proportion of variance in the intercept and slope explained by this model. 14. Examine the reliability of the intercept (initial status) and growth parameters. Are they sufficiently high to proceed with adding person-level predictors to the model?

15. Add MEANSES to the level-3 model in the two intercept equations for initial status and growth.

16. Note whether the coefficients are significant and remove non-significant coefficients

17. Estimate a new student-level intercepts- and slopes-as-outcome model with additional predictors.

18. Examine the fixed and random effects. If any of the parameters are not significant, make appropriate adjustments and rerun the model.

19. Estimate school-level models based on the final student-level model.

20. Calculate the proportion of variance in the intercept and slope explained by the final model.