This comment reconsiders advice offered by Bruch and Mare regarding sampling choice sets in conditional logistic regression models of residential mobility. Contradicting Bruch and Mare's advice, past econometric research shows that no statistical correction is needed when using simple random sampling of unchosen alternatives to pare down respondents' choice sets. Using data on stated residential preferences contained in the Los Angeles portion of the Multi-City Study of Urban Inequality, it is shown that following Bruch and Mare's advice-to implement a statistical correction for simple random choice set sampling-leads to biased coefficient estimates. This bias is all but eliminated if the sampling correction is omitted.
Social networks represent two different facets of social life: (1) stable paths for diffusion, or the spread of something through a connected population, and (2) random draws from an underlying social space, which indicate the relative positions of the people in the network to one another. The dual nature of networks creates a challenge - if the observed network ties are a single random draw, is it realistic to expect that diffusion only follows the observed network ties? This study takes a first step towards integrating these two perspectives by introducing a social space diffusion model. In the model, network ties indicate positions in social space, and diffusion occurs proportionally to distance in social space. Practically, the simulation occurs in two parts. First, positions are estimated using a statistical model (in this example, a latent space model). Then, second, the predicted probabilities of a tie from that model - representing the distances in social space - or a series of networks drawn from those probabilities - representing routine churn in the network - are used as weights in a weighted averaging framework. Using longitudinal data from high school friendship networks, I explore the properties of the model. I show that the model produces smoothed diffusion results, which predict attitudes in future waves 10% better than a diffusion model using the observed network, and up to 5% better than diffusion models using alternative, non-model-based smoothing approaches.
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