  The interpretation of the experimental data collected by testing systems
across input datasets and model parameters is of strategic importance for
system design and implementation. In particular, finding relationships between
variables and detecting the latent variables affecting retrieval performance
can provide designers, engineers and experimenters with useful if not necessary
information about how a system is performing. This paper discusses the use of
Structural Equation Modelling (SEM) in providing an in-depth explanation of
evaluation results and an explanation of failures and successes of a system; in
particular, we focus on the case of Information Retrieval.
