Multiple ratings are becoming increasingly popular for the assessment of a wide range of behaviors. Teaching evaluation designs and 360-degree feedback often use multiple raters alongside self-ratings. Eid and colleagues (2008) proposed a multilevel structural equation model for the analysis of data stemming from such designs that assumes that raters stem from independent populations of raters. However, it is quite common for raters to rate multiple targets, thus implying a cross-classified data structure. A simulation study was conducted to assess the effects of this rater nonindependence on parameter and standard error estimates in multilevel structural equation models. Results show parameter estimation biases to be small, whereas standard errors pertaining to the Level 1 covariance matrices as well as the mean structure on Level 2 are underestimated.