B946 - Methods for modelling repeated measures in a lifecourse framework - 21/01/2010
Objective 1: To develop methods for detecting and modelling autocorrelation.
Objective 2: To examine the impact of variance assumptions on multilevel models
Objective 3: To develop methods for developing individual trajectories and spline models
Objective 4: Taking extra variance from estimating splines into account. Including comparison with SEMs/WINBUGS.
Objective 5: To examine and develop methods for using class assignment probabilities
Objective 6: To develop methods for the initial conditions of cross-lagged models
Objective 7: To develop methods for handling missing data in cross-lagged models and in GMM models
Objective 8: To develop more objective tests for assumptions of multilevel models, and identify tests for when departures from these assumptions may cause problems. Assumptions: normality of residuals (see objective 3), autocorrelation (see objective 1), identification of mixtures (see objective 3 and latent class section), variance function (see objective 2).
Objective 9: To examine the plausibility and usefulness of methods developed above.