Multilevel Analysis for Repeated Measures Data in Lambs1

Abstract

The study was conducted to compare the individual growth curves models and to detect individual differences in the growth rate by a performing multilevel analysis. The data set used for this purpose consisted of live weight records of 52 crossbred lambs from birth to 182 days of age. There were 670 observations in level-1 units which were the repeated measurements over time, and there were 52 observations in level-2 units which were lambs. In the study, parameter estimation of timeindependent covariate factors, such as gender, birth type and birth weight, was performed by using five different models within the framework of multilevel modeling. LRT, AIC and BIC were used for the selection of the best model. The “Conditional Quadratic Growth Model-B” provided the best fit to the data set. The multilevel analysis indicated that linear and quadratic growth in lambs was significant. According to the results of the study, individual growth curves can be investigated using multilevel modeling in animal studies which is an important parameter of the individual growth rate.  

Keywords

• repeated data; multilevel models; intra-class correlation; individual growth models