The Effect of Data Collection Technique on Estimated Landowner Personal Network Attributes

Eli S. Sagor, Dennis R. Becker

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Social network analysis, focusing on the role of interpersonal relationships on the flow of information, trust, and service delivery, is increasingly recognized as a valuable approach to understanding landowner behavior. Landowner personal networks are central to Diffusion of Innovations theory and the Theory of Planned Behavior, both of which are commonly invoked in the design of interventions to encourage sustainable private forest management. However, personal network data can be difficult to obtain for a large sample. We tested the effect of three different personal network name generators on estimates of Minnesota landowners' forestry information networks: a list of generic alter categories, an open-ended written survey, and a combination of written survey and follow-up telephone survey. Generic network data provided a relatively accurate baseline. Personal network data from a written survey provided more detailed data but underestimated network diversity and failed to account for potentially influential weak ties. A combination of written and follow-up telephone survey both doubled estimated average network size from 2.8 to 5.5 alters and increased estimated network diversity from 2.3 alter categories per respondent to 3.7. Network data from the written survey revealed a bias in favor of strong ties that was largely overcome through additional prompting during the telephone survey. A combination of written surveys and telephone or in-person interviews may be the best strategy to balance the benefit of a large sample with the cost of more intensive, yet more reliable, data collection methods.

Original languageEnglish (US)
Pages (from-to)299-317
Number of pages19
JournalSmall-scale Forestry
Volume13
Issue number3
DOIs
StatePublished - Sep 2014

Keywords

  • Egocentric networks
  • Family forest
  • Landowners
  • Social network analysis
  • United States

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