Use the *Compute Weights* dialog under the *Data* menu to calculate sampling weights based on reported network size (degree). This dialog will store weights in your data set as a new variable (column) in the data set. Weights may be used for fitting a generalized linear model to the sample data. Several other dialogs in the software ask you to choose a weight type as part of their calculation, e.g. Frequency Estimates and Population Crosstabs. The methods available for calculating weights are described below. (In some dialogs, *Arithmetic Mean* is also listed as a weight type, which simply means that all members of the sample are weighted equally.)

- ''HCG' (Homophily Configuration Graph estimator): Recommended when the sample is a significant fraction of the target population and recruitment time is known. An estimate of the population size is required to use this estimator.
*Gile's SS*(Sequential Sampler): Recommended when the sample is a significant fraction of the target population. It is based on the inclusion probabilities of members of the sample, which are based on reported network sizes (how many people a respondent knows within the target population). An estimate of the population size is required to use this estimator.*RDS-I*: The Salganik-Heckathorn estimator. RDS-I weights are calculated in reference to a particular categorical variable of interest (the*Group Variable*; it cannot be continuous). RDS-I calculations are based on the number of connections between "in-group" (e.g. infected) and "out-of-group" (e.g. uninfected) members of the sample (though more than two classes are possible). A Markov process is used to model population mixing, i.e. recruitment across groups, and generate an equilibrium estimate of group prevalence (e.g. infection rate).*RDS-I (DS)*: Data-smoothed version of RDS-I. It is assumed that the Markov process is reversible.*RDS-II*: The Volz-Heckathorn estimator, a generalized Horvitz-Thomspon form. Like*Gile's SS*, it is based on inclusion probabilities for members of the sample, which are based on reported network sizes. It treats the sampling process as a random walk through the network of the target population.

For *Gile's SS* and *RDS-II*, weights will be unique for each reported network size. For *RDS-I* and *RDS-I (DS)*, weights will be unique for each class of the *Group Variable.*