A longitudinal SEM approach to STAI data: Two comprehensive multitrait-multistate models

I investigated psychometric properties of data gathered with the State-Trait Anxiety Inventory (STAI; Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983, 1993) using structural equation modeling. Two features were integrated into the modeling: Bistability refers to the duality that is raised once the wording effect is recognized, and second-order, state-residual/trait decomposition refers to the basic bidimensionality of the data, which is revealed in a longitudinal perspective. I tested the approach on 2 samples of French adults (total N = 888) borrowed from Vander and Jmel (2003). The models provided acceptable goodness of fit and supported the empirical need for hypothesizing 2 trait factors and 4 transient sources of true variance. I assessed the multidimensional composition of the composites using reliability analyses. I discuss theoretical consequences of the approach.