Sampling to Reduce Respondent Burden in Personal Network Studies and Its Effect on Estimates of Structural Measures
Year: 2010
Type: article
Abstract: 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 McCarty et al.'s (2007) research, we propose reducing respondent burden by randomly sampling a smaller set of alters from those originally elicited. Via simulation, we assess the estimation error we 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 in need to reduce respondent burden.
Source: Field Methods
Institutions: RAND Corporation, University of Southern California
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W1976682181