Mixed-effects models are the standard technique for analyzing data with grouping structure. In defense testing, these models are useful because they allow us to account for correlations between observations, a feature common in many operational tests. In this article, we describe the advantages of modeling data from a mixed-effects perspective and discuss an R package—ciTools—that equips the user with easy methods for presenting results from this type of model.
Suggested Citation
Haman, John, Matthew Avery, and Heather Wojton. “The Purpose of Mixed-Effects Models in Test and Evaluation.” The ITEA Journal of Test and Evaluation 40, no. 4 (2019): 249–55.