Event-history analysis of the diffusion of practices in a social system can
show how actors are influenced by each other as well as by their own chara
cteristics The presumption that complete data on the entire population are
essential to draw valid inferences about diffusion processes has been a maj
or limitation in empirical analyses and has precluded diffusion studies in
large populations. The authors examine the impacts of several forms of inco
mplete data on estimation of the heterogeneous diffusion model proposed by
Strang and Tuma. Left censoring causes bias, but right censoring leads to n
o noteworthy problems. Extensive time aggregation biases estimates of intri
nsic propensities but not cross-case influences. Importantly, random sampli
ng can yield good results on diffusion processes if there are supplementary
data on influential cases outside the sample, The capability of obtaining
good estimates from sampled diffusion histories should help to advance rese
arch on diffusion.