Items that capture group members’ outcomes from small group processes (e.g., satisfaction, cohesion) are often nonindependent. A primary assumption of most measurement models is that the data are independent; applying such models to group-outcome data measured at the individual level of analysis is thus likely to produce inaccurate estimates. A solution to the measurement of nonindependent data involves the use of multilevel modeling to estimate variances at item, individual, and group levels of analysis. Examples from several different statistics programs are provided, and Monte Carlo simulations are used to evaluate the effects of group size and number of items on reliability estimates.