MLwiN v2.27

A software package for fitting multilevel models

Multilevel modelling has rapidly become established as the
appropriate tool for modelling data with complex hierarchical
structures. It is important for extending our understanding of
social, biological and other sciences beyond that which can be
obtained through single level modelling. Multilevel modelling
is now being used in Education, Medical science, Demography,
Economics, Agriculture and many other areas.

The term multilevel refers to a nested membership relation
among units in a system. In an education system, for example,
students are members of classes, and classes are grouped within
schools. When 'single level' techniques such as multiple
regression are applied to data from a structure such as this,
the analysis will ignore important aspects of the data
structure and the results will often be misleading.

The basic procedures for modelling purely hierarchical data
have been extended to include cross-classifications and cases
where lower level units belong to more than one higher level
unit. Thus, models can now be fitted to data with extremely
complex structures.

Multivariate regression and multivariate analysis of variance
can be conducted in a particularly flexible manner using a
multilevel approach. The models can also be used to fit growth
curves and other repeated measures data with either continuous
or discrete responses, estimate variance and covariance
components from studies with complex designs, and analyse data
from studies employing rotation sampling. Multilevel time
series data can be modelled. Multilevel generalised linear
models can be fitted: for example, logit, log-log or probit
models for binary response data and macros are available for
multinomial ordered or unordered logistic models. Multilevel
survival or event history models can be fitted. Complex sample
survey data can be modelled flexibly and efficiently.