Efficient analysis of mixed hierarchical and cross-classified random structures using a multilevel model
- Journal of Educational and Behavioral Statistics, 22:3, 364-375
The analysis of repeated measures data can be conducted efficiently using a two-level random coefficients model. A standard assumption is that the within-individual (level 1) residuals are uncorrelated. In some cases, especially where measurements are made close together in time, this may not be reasonable and this additional correlation structure should also be modelled. A time series model for such data is proposed which consists of a standard multilevel model for repeated measures data augmented by an autocorrelation model for the level 1 residuals. First- and second-order autoregressive models are considered in detail, together with a seasonal component. Both discrete and continuous time are considered and it is shown how the autocorrelation parameters can themselves be structured in terms of further explanatory variables. The models are fitted to a data set consisting of repeated height measurements on children.
- Number of levels
- Model data structure
- Response types
- Multivariate response model?
- Longitudinal data?
- Substantive discipline
Sets out algorithm for general use.
- Paper submitted by
- Harvey Goldstein, Graduate School of Education, University of Bristol, email@example.com