An Introduction to the Use of Linear Models with Correlated Data
because of sampling design, study design (especially panel studies), or spatial
distribution of the population. In these situations, conventional methods for
estimating the parameters of linear models are inappropriate, and the
conventional methods for estimating the variances of these estimates may yield biased results. These two problems are different, but they are related. This paper provides an introduction to the problems caused by correlated data and to possible solutions to these problems. First, we present the two problems and try to specify the relations between the two as clearly as possible. Second, we provide a critical presentation of random effects, mixed effects and hierarchical models that would help researchers to see their relevance in other kinds of linear models, particularly the so-called measurement models.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.
Canadian Studies in Population | E-ISSN 1927-629X
Copyright © Canadian Population Society