Research Note: The consequences of different methods for handling missing network data in Stochastic Actor Based Models.
Although stochastic actor based models (e.g., as implemented in the SIENA software program) are growing in popularity as a technique for estimating longitudinal network data, a relatively understudied issue is the consequence of missing network data for longitudinal analysis. We explore this issue in our research note by utilizing data from four schools in an existing dataset (the AddHealth dataset) over three time points, assessing the substantive consequences of using four different strategies for addressing missing network data. The results indicate that whereas some measures in such models are estimated relatively robustly regardless of the strategy chosen for addressing missing network data, some of the substantive conclusions will differ based on the missing data strategy chosen. These results have important implications for this burgeoning applied research area, implying that researchers should more carefully consider how they address missing data when estimating such models.
Department of Criminology, Law and Society and Department of Sociology, University of California, Irvine.
John R
JR
Hipp
Department of Criminology, Law and Society and Department of Sociology, University of California, Irvine.
Cheng
C
Wang
Department of Criminology, Law and Society and Department of Sociology, University of California, Irvine.
Carter T
CT
Butts
Department of Criminology, Law and Society and Department of Sociology, University of California, Irvine.
Rupa
R
Jose
Department of Criminology, Law and Society and Department of Sociology, University of California, Irvine.
Cynthia M
CM
Lakon
SIENA; Smoking; peer influence substance use behavior; peer selection; stochastic actor based model