Experimental designs that sample both subjects and stimuli from a larger population need to account for random effects of using mixed-effects models. However, much this research is analyzed analysis variance on aggregated responses because researchers are not confident specifying interpreting This Tutorial explains how simulate data with random-effects structure analyze the linear regression (w...

Recent changes in higher education, especially associated with its massification, have led to calls for more accountability and to an increasing need to evaluate universities' performance. However, comparing universities with different subject mixes poses some significant problems. In this article, a linear mixed effect model methodology allowing for random effects at university level is used t...

Mixed linear models can be used to improve the informational value of milk yield forecasts. For this purpose two different functional approaches for modelling lactation curves are as well compared as three linear mixed models with varying random effects of individual animals and lactation numbers. It can be shown that more complex random regression models fit significantly better than fixed reg...