Introduction to Multilevel Modeling by Kreft and de Leeuw Chapter 2: Overview of Contextual Models | SAS Textbook Examples

Page 27, Table 2.3 SAS program for for null model

proc mixed data=imm10;
  model math = / solution ;
run;

Results for null model

Covariance Parameter
      Estimates

Cov Parm     Estimate

Residual       124.00


           Fit Statistics

-2 Res Log Likelihood          1989.0
AIC (smaller is better)        1991.0
AICC (smaller is better)       1991.0
BIC (smaller is better)        1994.6


                   Solution for Fixed Effects

                         Standard
Effect       Estimate       Error      DF    t Value    Pr > |t|

Intercept     51.3000      0.6906     259      74.28      <.0001

SAS Program for "With Homework" model

proc mixed data=imm10;
  model math = homework / solution ;
run;

Results

Covariance Parameter
      Estimates

Cov Parm     Estimate

Residual      93.7318


           Fit Statistics

-2 Res Log Likelihood          1915.6
AIC (smaller is better)        1917.6
AICC (smaller is better)       1917.6
BIC (smaller is better)        1921.2


                   Solution for Fixed Effects

                         Standard
Effect       Estimate       Error      DF    t Value    Pr > |t|

Intercept     44.0739      0.9886     258      44.58      <.0001
homework       3.5719      0.3882     258       9.20      <.0001

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Table 2.4 (skipped)

 

 

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Table 2.5 equations (for model "with homework").  The "meanhomework" is the mean of homework by school, computed within MLwiN via the "data manipulation" and "multilevel operations" menu.

skipped for now.

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Page 30, Table 2.6

Results (these results have a different intercept and different standard errors)

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Table 2.7, page 31, skipped

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