In the study 'Balancing Bias and Burden in Personal Network Studies' 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 as well as the potential study time gain for every possible network size (2 to 24 alters) 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.

This folder contains the analysis scripts for the analyses presented in the paper. This includes: 1) data preparation (R Markdown), 2) emulations and error calculation for dropping alters completely (R-Markdown), 3) emulations and error calculations for randomly picking a subsample of all generated alters (R-Scripts), 4) summarizing errors and plotting them for randomly picking a subsample of all generated alters (R-Markdown), and 5) the accompanying shiny app (R-Script). 6) a figure on how the scripts relate to each other