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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.)

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