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

Kessinee Chitakasempornkul (), Michael W. Sanderson (), Elva Cha (), David G. Renter (), Abigail Jager () and Nora M. Bello ()
Additional contact information
Kessinee Chitakasempornkul: Kansas State University
Michael W. Sanderson: Kansas State University
Elva Cha: Kansas State University
David G. Renter: Kansas State University
Abigail Jager: Kansas State University
Nora M. Bello: Kansas State University

Journal of Agricultural, Biological and Environmental Statistics, 2018, vol. 23, issue 4, No 6, 529-549

Abstract: 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. Supplementary materials accompanying this paper appear on-line.

Keywords: Hierarchical modeling; Multilevel correlation; Structural equation models; Beef cattle (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s13253-018-0336-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jagbes:v:23:y:2018:i:4:d:10.1007_s13253-018-0336-7

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/13253

DOI: 10.1007/s13253-018-0336-7

Access Statistics for this article

Journal of Agricultural, Biological and Environmental Statistics is currently edited by Stephen Buckland

More articles in Journal of Agricultural, Biological and Environmental Statistics from Springer, The International Biometric Society, American Statistical Association
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().