REGRESSION-ANALYSIS OF MULTILEVEL DATA WITH MEASUREMENT ERROR

Multilevel data structures, such as data with individuals within groups, groups within areas, etc., arise in a variety of contexts. Regression models for such data allow for variation of the within-group regression coefficients. Since some of the explanatory variables may be observed subject to measurement error, it is desirable to define models that combine within-group correlation and measurement error. We discuss a general model formulation and a scoring algorithm for maximum likelihood estimation.