title: Latent variable models for multivariate longitudinal ordinal responses.
abstract: The paper proposes a full information maximum likelihood estimation method for modelling multivariate longitudinal ordinal variables. Two latent variable models are proposed that account for dependencies among items within time and between time. One model fits item-specific random effects which account for the between time points correlations and the second model uses a common factor. The relationships between the time-dependent latent variables are modelled with a non-stationary autoregressive model. The proposed models are fitted to a real data set.
authors.affiliation: University of Bologna, Bologna, Italy.
authors.forename: Silvia
authors.initials: S
authors.lastname: Cagnone
authors.forename: Irini
authors.initials: I
authors.lastname: Moustaki
authors.forename: Vassilis
authors.initials: V
authors.lastname: Vasdekis
mesh_terms: D015982:Bias; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D006801:Humans; D016013:Likelihood Functions; D008137:Longitudinal Studies; D015233:Models, Statistical; D015999:Multivariate Analysis; D016011:Normal Distribution; D011639:Public Opinion; D012044:Regression Analysis; D015203:Reproducibility of Results; D018401:Sample Size; D013223:Statistics as Topic / Q000379:methods; D006113:United Kingdom