<p>mixed-models:</p>

<p>"Mixed models" refers to a class of models that are variously known as: mixed-effects models, multilevel models, hierarchical linear models, [these things have about three different names per discipline, feel free to add your favorite].  This class of models was developed to account for correlation that may occur within nested data.  A classic example is the estimation of test scores of students: if test scores are correlated within classes, schools, districts, etc., mixed models allow the modeler to simultaneously estimate the differences between individual students and between the groups to which they belong (with the possibility of including covariates at all levels).</p>

<p>StatsExchangers often recommend the following resources for learning more about mixed models: Venables and Ripley (2002), Laird and Ware (1982), Baayen (2008), Raudenbush and Bryk (2001), Gelman and Hill (2006), and Singer and Willett (2003).</p>

<p>Mixed models are available in the following statistical packages: R (lme4 and nlme), SAS (PROC MIXED), MLwiN, Stata (xtreg and xtmixed), and HLM.</p>

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<p>Please edit to improve - I just wanted to get the ball rolling on this one.  Thought I would throw out the book and package recommendations as those are likely to be asked often, but I'm not sure how we could come to a consensus about what books and packages should go there.  However, maybe the upkeep of those lists could be left to whomever has the rep and the interest, with disputes hashed out here on meta.</p>
