The answer depends on whether you believe: (a) the respective data sets are essentially independent of one another; and (b) there is no external variable (such as year of data collection, locale of data collection, data collection method, and so on) that could account for why one data set may have different values on one or more variables than any of the other data sets.
If you can't embrace these two conditions, then your best bet is to treat the problem as mixed-levels: cases nested within data set (and data set as a coded variable). In other words, a two-level, hierarchical linear model.