ABSTRACT
In a mixed-model analysis, both the objectives and the procedure are different from those of a fixed effects study. Mixed model analyses are based on a grouping factor. The grouping factor “groups” the data in the sense that data values within the same group are presumed to be more related than data records from different groups. One crucial difference between the fixed-effects model and the mixed-effects model concerns the covariance relationship between the response variables. One crucial difference between the fixed-effects model and the mixed-effects model concerns the covariance relationship between the response variables. The individual plots generally look as though they would be appropriate for linear regression. The variable Irrig, however, is an ordinal scale variable, and as such the operations of multiplication and addition, which are used to compute the regression coefficients, are not meaningful when applied to its values.
