Balancing bias and burden in personal network studies
Personal network data is increasingly used to answer research questions about the interplay between individuals (i.e., egos) and their social environment (i.e., alters). Researchers designing such data collections face a trade-off: When eliciting a high number of alters, study participation can be particularly burdensome as all data is obtained by surveying the ego. Eliciting a low number of alters, however, may incur bias in network characteristics. In the present study we use a sample of 701 Dutch women and their personal networks of 25 alters to investigate two strategies reducing respondent burden in personal network data collections: (1) eliciting fewer alters and (2) selecting a random subsample from the original set of elicited alters for full assessment. We present the amount of bias in structural and compositional network characteristics connected to applying these strategies for every possible network size (2–24 alters) as well as the potential study time savings as a proxy for respondent burden reduction. Our results can aid researchers designing a personal network study to balance respondent burden and bias in estimates for a range of compositional and structural network characteristics.
Balancing bias and burden in personal network studies
Social Networks
Marie Stadel, Gert Stulp
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Abstract
Personal network data is increasingly used to answer research questions about the interplay between individuals (i.e., egos) and their social environment (i.e., alters). Researchers designing such data collections face a trade-off: When eliciting a high number of alters, study participation can be particularly burdensome as all data is obtained by surveying the ego. Eliciting a low number of alters, however, may incur bias in network characteristics. In the present study we use a sample of 701 Dutch women and their personal networks of 25 alters to investigate two strategies reducing respondent burden in personal network data collections: (1) eliciting fewer alters and (2) selecting a random subsample from the original set of elicited alters for full assessment. We present the amount of bias in structural and compositional network characteristics connected to applying these strategies for every possible network size (2–24 alters) as well as the potential study time savings as a proxy for respondent burden reduction. Our results can aid researchers designing a personal network study to balance respondent burden and bias in estimates for a range of compositional and structural network characteristics.