Aggregation: Determine The Highest Level

Aggregation: Determine the lowest level at which observations are independent and then average scores of both the causal and outcome measures at that level. For instance, if children are nested in classrooms, which are nested in schools, make school the unit of analysis. Child is the lowest level and school is the highest level. If there are no school or classroom effect, then child would be the unit of analysis. If there were no school effects, but there were classroom effects, then classroom would be the unit of analysis. If there were school effect, then school would be the unit of analysis. This strategy is advisable when the causal variables are measured at the level of aggregation or when most of the variation in the causal variable is at that level. Thus, the scores, before they are aggregated on the causal variable, all have the same value.
Save the estimates from these separate analyses and then test if the mean of the estimates is different from zero. This strategy is advisable if the causal variable varies considerably within the nonindependent units. So for instance, if classrooms were not independent and gender of student was an independent variable, then one would compute the mean difference between boys and girls for each classroom. One issue with this approach is that often some of these estimates are more precise and so the analysis should weight some estimates more than others.
Combined or pooled analysis: Multilevel or hierarchical linear modeling essentially combines the two above strategies. In essence, it solves the unit of analysis question by making it a pseudo question. All the observations are analyzed, and the degree of nonindependence is empirically estimated. (This strategy is virtually required when units are