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Researchers studying personal networks often collect network data using name generators and name interpreters. We argue that when studying social support, multiple name generators ensure that researchers sample from a multidimensional definition of support. However, because administering multiple name generators is time consuming and strains respondent motivation, researchers often use single name generators. We compared network measures obtained from single generators to measures obtained from a six-item multiple-name generator. Although some single generators provided passable estimates of some measures, no single generator provided reliable estimates across a broad spectrum of network measures. We then evaluated two alternative methods of reducing respondent burden: (1) the MMG, a multiple generator using the two most robust name generators and (2) the MGRI, a six-item name generator with name interpreters administered for a random subset of alters. Both the MMG and the MGRI were more reliable than single generators when measuring size, density, and mean measures of network composition or activity, though some single name generators were more reliable for measures consisting of sums or counts.