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Estimating HLM Models Using R: Part 2

Estimating HLM Models Using R: Part 2

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Note: For a fuller treatment, download our series of lectures Hierarchical Linear Models.

Means-as-Outcomes Model

After estimating the empty model, R&B develop a Means-as-Outcomes model in which a school-level variable, meanses, is added to the model for the intercept. This variable reflects the average student SES level in each school. Recall Equation (1):

\(
\begin{equation} \tag{1}
Y_{ij} = \beta_{0j} + e_{ij}
\end{equation}
\)

The intercept can be modeled as a grand mean γ00, plus the effect of the average SES score γ01, plus a random error u0j.

\(
\begin{equation} \tag{4}
\beta_{0j} = \gamma_{00} + \gamma_{01}(MEAN \; SES_{j}) + u_{0j}
\end{equation}
\)

Substituting (4) into (1) yields

\(
\begin{equation} \tag{5}
Y_{ij} = \gamma_{00} + \gamma_{01}(MEAN \; SES_{j}) + u_{0j} + e_{ij}
\end{equation}
\)

The following estimates the model in R:


model2<-lme(mathach ~ meanses, random = ~ 1 | id, data=hsb)

The only difference from Model 1 was that the model now includes a fixed effect for mean SES. Thus, if Model 1 were already estimated, Model 2 could be obtained with the update function:


model2<-update(model1,mathach ~ meanses)

Either way, the results can once again be viewed using summary, and the VarCorr function returns the variance components as variances.

> summary(model2)
Linear mixed-effects model fit by REML
 Data: hsb 
       AIC     BIC    logLik
  46969.28 46996.8 -23480.64

Random effects:
 Formula: ~1 | id
        (Intercept) Residual
StdDev:     1.62441 6.257562

Fixed effects: mathach ~ meanses 
                Value Std.Error   DF  t-value p-value
(Intercept) 12.649435 0.1492801 7025 84.73622       0
meanses      5.863538 0.3614580  158 16.22191       0
 Correlation: 
        (Intr)
meanses -0.004

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max 
-3.13479829 -0.75256339  0.02408657  0.76773257  2.78501439 

Number of Observations: 7185
Number of Groups: 160 



> VarCorr(model2)
id = pdLogChol(1) 
            Variance  StdDev  
(Intercept)  2.638708 1.624410
Residual    39.157082 6.257562

This corresponds to Table 4.3 in R&B.

The next step is to estimate a random coefficient model.

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