Sampling to Reduce Respondent Burden in Personal Network Studies and Its Effect on Estimates of Structural Measures

Published In: Field Methods, v. 22, no. 3, Aug. 2010, p. 217-230

Posted on on January 01, 2010

by Daniela Golinelli, Gery W. Ryan, Harold D. Green, David P. Kennedy, Joan S. Tucker, Suzanne L. Wenzel

Read More

Access further information on this document at SAGE Publications

This article was published outside of RAND. The full text of the article can be found at the link above.

Recently, researchers have been increasingly interested in collecting personal network data. Collecting this type of data is particularly burdensome on the respondents, who need to elicit the names of alters, answer questions about each alter (network composition), and evaluate the strength of possible relationships among the named alters (network structure). In line with the research of McCarty et al., the authors propose reducing respondent burden by randomly sampling a smaller set of alters from those originally elicited. Via simulation, the authors assess the estimation error they incur when measures of the network structure are computed on a random sample of alters and illustrate the trade-offs between reduction in respondent burden (measured with the amount of interview time saved) and total estimation error incurred. Researchers can use the provided trade-offs figure to make an informed decision regarding the number of alters to sample when they need to reduce respondent burden.

This report is part of the RAND Corporation External publication series. Many RAND studies are published in peer-reviewed scholarly journals, as chapters in commercial books, or as documents published by other organizations.

The RAND Corporation is a nonprofit institution that helps improve policy and decisionmaking through research and analysis. RAND's publications do not necessarily reflect the opinions of its research clients and sponsors.