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Hierarchical Linear Modeling (HLM) is a type of regression model used frequently for education data sets.  Education data sets typically select students from a set schools and thus information about students are correlated (such that students from the same schools are similar in their traits).  With this type of data, classical methods, such as OLS regression, would not produce correct standard errors; therefore, HLM needs to be used as it takes the issue of correlated errors into consideration and provides more realistic and conservative statistical testing.  Parameter estimates, however, are not drastically different in classical methods and HLM.  If OLS tells you the US junior high school students scored 555 points on average, HLM would give you almost the same information.  However, standard errors would be larger for HLM than OLS, as HLM considers sources of errors more rigorously than OLS.  (To exaggerate a little bit, it is interesting that good statistical models are the ones that give you poorer results in terms of the size of standard errors.)

• My SAS PROC MIXED Macro library

Comparison of HLM Software and PROC MIXED

USEFUL tips

• How to replicate B&R's HLM using SAS PROC MIXED -- for people who already use HLM software and want to try it with SAS PROC MIXED

Running Bryk, Raudenbush, and Congdon's HLM (r) Using SAS -- but what if you hated a click-and-drop way of using a software.  I myself cannot stand it.  It feels for me like handwriting when you handwrite one a year before Holiday seasons.  I feel impatient when I have to click so many buttons.)

HLM Result Report Table Example (Random Coefficient Models)

 My SAS manuals SAS explanation of FIXED AND RANDOM EFFECTS

Keywords: HLM, Multilevel modeling; SAS, multilevel models, multilevel statistical models
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