Unless otherwise noted, we used IGLS estimation.
In this chapter we create and use the variables constant which is equal to the constant value of 1; GndC_verb which is equal to iq_verb centered around the grand mean; GrpMC_verb which contains the group means of GndC_verb, so it contains the group means of iq_verb centered around the grand mean.
Creating the constant variable.
Data Manipulation
Names
This opens up a window listing all the variables in the dataset with the
number of non-missing observations, missing observation, max and min for
each variable. We just need to find out the total number of observations
in the dataset but this is a nice window to have open to keep an eye on
which variables have been corrected and do they look reasonable.
Generate Vector
For the output column choose an unused variable and rename it cons by
using ctrl+n which brings up a rename window. Then enter the total
number of observations in the data set for number of copies, in this
case n=2287. Finally, enter 1 for the value since this will be a constant
variable equal to 1.
Click generate in order to execute the command
Table 4.1, p. 47.
Estimating the intercept only model using langpost as the dependent variable, schoolnr as level 2 (group level) and pupilnr as level 1 (individual level).
Model
Equations
Click on Y
Choose y: langpost, N levels: 2 - ij, level2(j): schoolnr, and
level1(i): pupilnr.
Click on the x0 variable
Choose the variable constant and select both schoolnr and pupilnr.
Click on Names, and Estimates at the bottom of the window
Click on the Start button below the file menu to execute
If the estimates do not appear automatically then click on the estimates button to see the results.
Calculating the grand mean for iq_verb and then creating the variable gndc_verb which is centered around the grand mean.
Basic Statistics Averages and Correlations Choose iq_verb and click on calculate.
Data Manipulation
Calculate
Choose an unused variable and rename it gndc_verb by using ctrl+n
Enter "gndc_verb" = "iq_verb" - 11.834 and click on calculate.
Table 4.2, p. 49.
The model includes only the predictor gndc_verb.
Add Term Click on the new term x1 Choose gndc_verb as a fixed parameter.
Click on the Start button located below the file menu and then click on estimates at the bottom of the equations window to make the estimates appear.
Generate the predicted values from the model in Table 4.2 and storing them in the variable pred.
Model
Predictions
in the field called output from prediction to choose an unused variable and rename it
pred using ctrl+N to bring up the rename window
Click on Name to see the names of the variables in the model
Click on constant and gndc_verb which will make all the parameters in the model
appear in black
Click on the level 1 error term to make it grey since we do not want to model this error term
Click on Calc to generate the predicted values and store them in the variable pred
Fig. 4.2, p. 49.
Graphing the regression lines.
Graphs Customised Graph(s) in the field called y choose pred, for x choose gndc_verb, for group choose schoolnr, and for plot type choose line Click Apply to generate the graph
Creating the variable grpmc_verb which contains the group means of gndc_verb.
Data Manipulation
Multilevel Data Manipulation
in the input column choose gndc_verb, in the output column choose an unused variable and use ctrl+N
to rename it grpmc_verb then this will be the output variable
Click Add to action list which will make the input and output choices appear in the action list
on the right hand side
Click on Execute
Table 4.4, p. 55
Model including both gndc_verb (within group effect) and grpmc_verb as fixed effects.
Model
Equations
Click on Y
Choose y: langpost, N levels: 2 - ij, level2(j): schoolnr, and
level1(i): pupilnr.
Click on the x0 variable
Choose the variable constant and select both schoolnr and pupilnr.
Add Term
Click on the x1 variable
Choose gndc_verb as a fixed parameter
Add Term
Click on the x2 variable
Choose grpmc_verb as a fixed parameter
Click on the Start button located below the file menu and then click on estimates at the bottom of the equations window to make the estimates appear.
Fig. 4.4, p. 62
The comparative posterior confidence intervals.
Model
Residuals
Settings tab
in the field labeled level choose 2:schoolnr
in the field labeled SD(comparative) change the value from 1.0 to 1.96
Click Calc
Plots tab
choose residual +/-1.96 sd x rank
Click Apply
Table 4.5, p. 64 and table 12.6, p. 180.
Click on the Start button located below the file menu and then click on estimates at the bottom of the equations window to make the estimates appear.
