Analyzing person, situation, and person × situation interaction effects: Latent state-trait models for the combination of random and fixed situations

Latent state-trait (LST) models (Steyer, Ferring, & Schmitt, 1992) allow separating person-specific (trait) effects from (1) effects of the situation and person × situation interactions, and (2) random measurement error in purely observational studies. Typical LST applications use measurement designs in which all situations are sampled randomly and do not have to be known for any individual. Limitations of conventional LST models for only random situations are that traits are implicitly assumed to generalize perfectly across situations, and that main effects of situations are inseparable from person × situation interaction effects because both are measured by the same latent variable. In this article, we show how these limitations can be overcome by using measurement designs in which two or more random situations are nested within two or more fixed situations that are known for each individual. We present extended LST models for the combination of random and fixed situations (LST-RF approach) and show that the extensions allow (1) examining the extent to which traits are situation-specific and (2) isolating person × situation interactions from situation main effects. We demonstrate that the LST-RF approach can be applied with both homogenous and heterogeneous indicators in either the single- or multilevel structural equation modeling frameworks. Advantages and limitations of the new models as well as their relation to other approaches for studying person × situation interactions are discussed