Accounting for Data Architecture on Structural Equation Modeling of Feedlot Cattle Performance

Abstract

Structural equation models (SEM) are a type of multi-trait model increasingly being used for inferring functional relationships between multiple outcomes using operational data from livestock production systems. These data often present a hierarchical architecture given by clustering of observations at multiple levels including animals, cohorts and farms. A hierarchical data architecture introduces correlation patterns that, if ignored, can have detrimental effects on parameter estimation and inference. Here, we evaluate the inferential implications of accounting for, or conversely, misspecifying data architecture in the context of SEM. Motivated by beef cattle feedlot data, we designed simulation scenarios consisting of multiple responses in a clustered architecture. Competing fitted SEMs differed in their model specification so that data architecture was explicitly accounted for (M1; true model) or misspecified due to disregarding either the cluster-level correlation between responses (M2) or the correlation between observations of a response within a cluster (M3), or ignored all together (M4). Model fit was increasingly impaired when data architecture was misspecified or ignored. Both accuracy and precision of estimation were also negatively affected when data architecture was disregarded. Our findings are further illustrated using data from feedlot operations from the US Great Plains. Standing statistical recommendations that call for proper model specification capturing relevant hierarchical levels in data structure extend to the multivariate context of structural equation modeling.