Since this specification uses data at the network level, estimation will require a large sample of networks in order to recover precise estimates of the parameters, even in the absence of endogeneity from network formation and mismeasurement of the network.
The CUG test for transitivity[17, 18] evaluates the observed degree of transitivity (the proportion of potential transitive triads that are actually closed) against a null distribution computed on a random sample of networks without transitivity.
However, for the sample of networks used here, as noted above, the mean node degree 〈k〉i is approximately constant.
Moreover, we show a method for assigning a unique Watts-Strogatz (WS) model to any real-world network, and show analytically that the WS models associated with our sample of networks also show linearity between S and n.
To quantify this approximation, we performed a linear regression on log-transformed quantities (just as for the real networks) over the typical range of n encountered in our sample of networks, 102≤n≤107, and found a linear fit, with r2 within 10−5 of unity.
Subsequently, a Monte Carlo process generates a sample of networks of specific character.
Goals vary among the networks given the diverse sample of networks, which prevented us from using more exact effectiveness measurements.
Our inference approach is based on the mean of a random sample of networks which are consistent with our data.
The eligibility criteria aimed to generate a sample of networks that had many treatments and studies and sufficient data to explore the impact of exclusions.
As RDS obtains a random sample of networks, 16– 18 we used a combination of parametric and non-parametric tests as appropriate.
