Multilevel
May 23-25, 2011
The goals of the MLM workshop are: (1)
to highlight the core theoretical and statistical issues that accompany
multilevel and hierarchical data; (2) to emphasize the unique substantive
opportunities that accompany the multilevel modeling framework; (3) to
introduce and secure an understanding of the range of different models that
fall under the multilevel modeling umbrella. The class will cover topics such
as unobserved heterogeneity; complete pooling, partial pooling and no pooling
modeling approaches to multilevel data; the simple "variance
components" model; the random intercept (aka, "random effects")
model; substantive and statistical issues surrounding fixed versus random
effects models; modeling causal heterogeneity via the random coefficient model;
cross‐level interactions; multilevel modeling applications to time‐series
cross‐sectional and panel data; and beyond. The workshop will focus on linear
models as well as nonlinear models for binary, ordinal, nominal, and other
noncontinuous dependent variables. For each model discussed, a special focus
will be placed on interpretation of effects, assessing goodness of fit, model
comparison, and generating postestimation quantities of interest for richer
substantive interpretation. The workshop will feature extensive use of applied
data examples and will expose workshop participants to the full suite of
commands available in Stata software for estimating multilevel models.
Comparisons to additional software packages will be explored and discussed.