Interviewer-driven Variability in Social Network Reporting
Dados Bibliográficos
| AUTOR(ES) | |
|---|---|
| AFILIAÇÃO(ÕES) | Harvard Center for Population and Development Studies, Harvard University, Cambridge, MA, USA, Department of Sociology, Harvard University, Cambridge, MA, USA, Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA, Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, MA, USA, INDEPTH Network, Accra, Ghana, Africa Health Research Institute, KwaZulu-Natal, South Africa, MRC/Wits Rural Public Health and Heath Transitions Research Unit, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa |
| ANO | 2018 |
| TIPO | Artigo |
| PERIÓDICO | Field Methods |
| ISSN | 1525-822X |
| E-ISSN | 1552-3969 |
| EDITORA | Annual Reviews (United States) |
| DOI | 10.1177/1525822x18769498 |
| CITAÇÕES | 5 |
| ADICIONADO EM | 2025-08-18 |
| MD5 |
3b988c1e8ad4e238917c380120264291
|
Resumo
Social network analysis depends on how social ties to others are elicited during interviews, a process easily affected by respondent and interviewer behaviors. We investigate how the number of self-reported important social contacts varied within a single data collection round. Our data come from Health and Aging in Africa: a Longitudinal Study of an INDEPTH community (HAALSI), a comprehensive population-based survey of individuals aged 40 years and older conducted over 13 months at the Agincourt health and demographic surveillance site in rural South Africa. As part of HAALSI, interviewers elicited detailed egocentric network data. The average number of contacts reported by the 5,059 respondents both varied significantly across interviewers and fell over time as the data collection progressed, even after adjusting for respondent, interviewer, and respondent–interviewer dyad characteristics. Contact numbers rose substantially after a targeted interviewer intervention. We conclude that checking (and adjusting) for interviewer effects, even within one data collection round, is critical to valid and reliable social network analysis.