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Using statistical modeling to determine the structure of expectation and covarianceemployed during analysis is a common feature of analytical research. This paper describesthe necessary methodology for, and illustrates those techniques that are of specialimportance in, practical modeling and evaluation scenarios (likelihood ratio test,analytical criteria, residual analysis). Our approach is demonstrated upon a populationcomparison, taken on various measurement dates, that focuses on a wild population anda laboratory population of Golden Hamsters. The selected example is particularly suiteddue to the fact that – aside from the actual growth function of interest – additional fixed(e.g. effect of different mating periods, litter size) and random factors (e.g. maternalenvironment, repeated performances per animal) must be considered. The modelingshows significant efficiency regarding the improvement of the analytical criteria. Therecommended evaluation model leads to a very close match of the observed ordinaryleast square residuals and of the variance and covariance functions, respectively, thathave been derived from the estimated covariance structure

Statistical modeling for growth data in linear mixed models – Implications derived from an example of a population comparison of Golden Hamsters