Small networks and selectivity bias in the analysis of survey network data

• Published in: Social networks 1987-12, Vol.9 (4), p.333-349
• Main Authors: Marsden, Peter V., Hurlbert, Jeanne S.
• Format: Article
• Language: English
• Subjects: Bias; Group Size; Methodology (Data Collection); Research Methodology; Social Networks
• ISSN: 0378-8733; 1879-2111
• DOI: 10.1016/0378-8733(87)90003-7

Selectivity bias is a danger whenever observations are systematically excluded from a data set on the basis of a dependent variable, whether this exclusion is explicit or implicit. If present, the problem has severe consequences for the validity of statistical estimates of effects. The problem is of importance to the analysis of survey network data, since many network measures (such as density) are available only for persons having networks of size two or larger, while others (such as percent kin) are defined only for those having networks of size one or more. Analysts can adjust for selectivity bias by estimating the risk of exclusion (in this case, of having a network of size 0 or 1), and including the modeled risk as a control in substantive equations. Such estimates are presented for the 1985 General Social Survey network data; in the course of this results of Fischer and Phillips on social isolation are replicated. Other ways of guarding against selection bias are also discussed; at a minimum, network size should be included among the set of regressors in analyses of survey network data, as a methodological control if not as a substantive variable.