Graphs in HLM. Model setup, Run the analysis before graphing Sector = 0 public school Sector = 1 private school.

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Presentation transcript:

Graphs in HLM

Model setup, Run the analysis before graphing Sector = 0 public school Sector = 1 private school

Graph entire model

With level 1 predictor

Here it is the graph

Add level 2 categorical variable

And the graph

Add level 2 continuous variable

What happened if we choose 25 th and 75 th percentiles?

We can choose other options, say, 25 th /50 th /75 th percentiles

The graph with 3 MEANSES levels

More complicated graph

Graph level 1 equation

The graph with first 10 groups (schools)

What if we choose n=160 schools?

With level 2 predictor: sector

Add level 2 categorical variable

Add a level 2 continuous variable

Level 1 residual box-whisker To examine distributions of level-1 residuals. Normality assumption Homogeneity of variance

Level 1 residual box-whisker

Also could add a level 2 predictor

Level-1 residual vs predicted value

Observe the pattern of residual scatter Level-1 residual vs predicted value

Add level 2 - sector

One graph per group, multiple graphs per page

Level-2 EB/OLS coefficient confidence intervals Compare the estimated empirical Bayes (EB) and OLS estimates of randomly varying level-1 coefficients (intercept and other coefficients).

Intercept with level 2 sectors

Intercept with level 2 MEANSES

Slope of SES

Graph data

Math regressed on SES (10 schools)

One graph per group, multiple graphs per page

Longitudinal data

Whole model (CB- HLM Longitudinal Example)

With gender and year (level 2)

Graph data