# Estimating HLM Models Using SPSS Menus: Part 3

Jeremy Albright

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HLM SPSS

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

#### Random Coefficient Model

Next, R&B present a model in which student-level SES is included instead of average SES, and they treat the slope of student SES as random. One complication is that R&B present results after group-mean centering student SES. Group-mean centering means that the average SES for each student’s school is subtracted from each student’s individual SES. To complete the group-mean centering, subtract meanses from each ses variable. Go to Transform > Compute Variable.

knitr::include_graphics("../images/hlm-spss-13.jpg", error = FALSE) In the menu that appears, create a Target Variable named grp_ses that is equal to ses minus meanses.

knitr::include_graphics("../images/hlm-spss-14.jpg", error = FALSE) Click OK. The group-centered SES variable can now be used.

The level-1 equation is the following:

$\begin{equation}Y_{ij} = \beta_{0j} + \beta_{1j}(SES)+ e_{ij} \tag{6}\end{equation}$

The intercept $$\beta_{0j}$$ can be modelled as a grand mean $$\gamma_{00}$$ plus random error, $$u_{0j}$$. Similarly the slope $$\beta_{1j}$$ can be modelled as having a grand mean $$\gamma_{10}$$ plus random error $$u_{1j}$$.

$\begin{equation}\beta_{0j} = \gamma_{00} + u_{0j} \tag{7}\end{equation}$

$\begin{equation}\beta_{1j} = \gamma_{10} + u_{1j} \tag{8}\end{equation}$

Combining (7) and (8) into (6) produces:

$\begin{equation} Y_{ij} = \gamma_{00} + \gamma_{10}(SES) + u_{0j} + u_{1j}(SES) + e_{ij} \tag{9}\end{equation}$

To estimate (9) in SPSS go to Analyze > Mixed Models > Linear. The Specify Subjects and Repeated menu appears again. As before, place schid in the Subjects box and leave Repeated blank.

knitr::include_graphics("../images/hlm-spss-15.jpg", error = FALSE) Click Continue. In the next menu one specifies the dependent and independent variables. The dependent variable is mathach, and the single covariate will be grp_ses.

knitr::include_graphics("../images/hlm-spss-16.jpg", error = FALSE) To specify the model’s fixed effects click on Fixed. In the Fixed Effects menu, bring grp_ses variable over to the Model box and make sure Include Intercept is checked.

knitr::include_graphics("../images/hlm-spss-17.jpg", error = FALSE) Click Continue, then on Random.

In the Random Effects menu, place the grouping variable schid in the Combinations box. Also, because grp_ses will have a random slope it is necessary to place it in the Model box. Next, make sure that Include Intercept is checked so that the intercept is also allowed to vary randomly. Finally, the presence of two random effects means that the dimensions of the covariance matrix G are now 2?2. The default in SPSS is to assume a variance components structure, which implies that there is no covariance between the random intercept and random slope (see the table of covariance structures in A Review of Random Effects ANOVA Models). This assumption can be loosened so that the covariances are free parameters to be estimated from the data. Specify Unstructured for the Covariance Type.

knitr::include_graphics("../images/hlm-spss-18.jpg", error = FALSE) Click Continue. Then click Statistics to specify what appears in the output. Check Parameter Estimates to get results for the fixed effects.

knitr::include_graphics("../images/hlm-spss-19.jpg", error = FALSE) Click Continue, then OK. A portion of the results is the following:

knitr::include_graphics("../images/hlm-spss-20.jpg", error = FALSE) These results correspond to Table 4.4 in R&B. See also the variance-covariance components at the bottom of their page 77.

The final model R&B present is an intercept- and slopes-as-outomes model.