Estimating social influence in a social network using potential outcomes

Over recent years, latent state-trait theory (LST) and generalizability theory (GT) have been applied to a wide variety of situations in numerous disciplines to enhance understanding of the reliability and validity of assessment data. Both methodologies involve partitioning of observed score variation into systematic and measurement error components. LST theory is focused on separating state, trait, error, and sometimes method effects, whereas generalizability theory is concerned with distinguishing universe score effects from multiple sources of measurement error. Despite these fundamental differences in focus, LST and GT share much in common. In this article, we use data from a widely used personality measure to illustrate similarities and differences between these two frameworks and show how the same data can be readily interpreted from both perspectives. We also provide comprehensive instructional online supplemental materials to demonstrate how to analyze data using the R package for all LST models and GT designs discussed