Miranda Lubbers, Beate Völker, and Michal Bojanowski are guest editing a special issue for the journal Social Networks on the "Network Scale-Up Method and Aggregate Relational Data in Social Network Research", based on their joint work on the PATCHWORK project. The idea for the special issue was born at the International Advisory Board meeting in March. For this issue, we invite scholars to contribute innovative, original papers. In particular, we are looking for cutting-edge or unexpected empirical applications (particularly when they also inform us about social network structures), theoretical contributions centrally involving NSUM or ARD, and methodological work or novel methodological approaches inspired by the method
Guest editors:
Prof. dr. Miranda Lubbers, COALESCE Lab, Department of Anthropology, Universitat Autònoma de Barcelona (Spain); mirandajessica.lubbers@uab.cat
Prof. dr. Beate Völker, Scientific Director, Netherlands Institute for the Study of Crime and Law Enforcement (NSCR), Amsterdam (The Netherlands); bvolker@nscr.nl / b.volker@uu.nl
Dr Michał Bojanowski, COALESCE Lab, Department of Anthropology, Universitat Autònoma de Barcelona (Spain) and Kozminski University (Poland); Michal.Bojanowski@uab.cat
Special issue information:
More than three decades ago, a team of researchers (Bernard, Johnsen, Killworth, & Robinson, 1989, 1991; Killworth, Johnsen, Bernard, Shelley, & McCarty, 1990) invented the Network Scale-Up Method (NSUM) to estimate the size of hard-to-count populations, such as the number of people who died in an earthquake. The team’s seminal idea was that under certain assumptions, the fraction of these subpopulations could be estimated by the average fraction that these subpopulations make up in the social networks of a representative sample in a national population. To calculate this fraction in networks, they asked respondents survey questions of the form “How many people do you know who [are part of the subpopulation of interest]?” Responses to these questions are now known as “aggregate relational data” (ARD). The answers, divided by individuals’ network size, are then averaged across all respondents to estimate the population fraction. Since the denominator, network size, is unknown, the authors proposed to invert the NSUM method to estimate it, by asking individuals how many people they know in a set of subpopulations of known size. The answers were then divided by the fraction of the subpopulations in the overall population, as indicated by national statistics, to obtain an estimate of network size. This method was called the “known population method” (Bernard et al., 1991) or “back estimation” (Killworth, McCarty, Bernard, Shelley, & Johnsen, 1998).
Since its invention, researchers have further scrutinized the methodology and its assumptions, improved estimation methods, and implemented them in statistical software (e.g., Baum & Marsden, 2023; Feehan & Salganik, 2016; Fenoy, Bojanowski, & Lubbers, 2023; Habecker, Dombrowski, & Khan, 2015; Josephs, Feehan, & Crawford, 2023; Killworth, McCarty, Johnsen, Bernard, & Shelley, 2006; Kunke, Laga, Niu, & McCormick, 2023; Laga, Bao, & Niu, 2021, 2023; Maltiel, Raftery, McCormick, & Baraff, 2015; McCarty, Killworth, Bernard, & Johnsen, 2001;McCormick & Zheng, 2015; Parsons, Niu, & Bao, 2022; Verdery, Weir, Reynolds, Mulholland, & Edwards, 2019; Zheng, Salganik, & Gelman, 2006). The method has been used empirically for estimating the size of a wide range of populations worldwide. It has been demonstrated to be valuable not only for scientists but also for giving important information to policymakers, particularly in public health, human rights, and disaster management. Applications ranged from people at risk for HIV (e.g., Guo et al., 2013; Killworth, Johnsen, McCarty, Shelley, & Bernard, 1998; Salganik et al., 2011; Teo et al., 2019) to women undergoing abortions (e.g., Rossier et al., 2022; Sully, Giorgio, & Anjur-Dietrich, 2020), child trafficking in Sierra Leone (Yi et al., 2023), violence against women in Iran (Gohari et al., 2023), victims of the 9/11 terrorist attacks in the United States (Bernard, Killworth, Johnsen, Shelley, & McCarty, 2001), religious populations in the United States (Yang & Yang, 2017), and COVID incidence and mortality around the world (e.g., Ocagli et al., 2021; Ramírez et al., 2023).
Recently, social network researchers have also taken an interest in NSUM and ARD for their potential to understand the organization of individuals’ acquaintanceship networks (or “weak-tie networks”), particularly their size (Feehan, Hai Son, & Abdul-Quader, 2022; Hofstra, Corten, & van Tubergen, 2021; Ishiguro, 2016; Lubbers, Molina, & Valenzuela-García, 2019; Shati, Haghdoost, Majdzadeh, Mohammad, & Mortazavi, 2014) and segregation along categorical lines, such as by ethnicity or social class (e.g., Breza, Chandrasekhar, McCormick, & Pan, 2020; DiPrete, Gelman, McCormick, Teitler, & Zheng, 2011; McCormick et al., 2013; Otero, Völker, & Rozer, 2021; Park, 2021). Apart from describing these network features, NSUM and ARD started to be included in sociological analysis both as explanandum (e.g., what are the antecedents of personal network size or cohesion?; DiPrete et al., 2011; Hofstra et al., 2021; Ishiguro, 2016; Lubbers et al., 2019; Shati et al., 2014) and as explanans (e.g., how does personal network size affect perceived support?; Lu & Hampton, 2017). It can be expected that this research line will flourish even more in the coming years.
The journal Social Networks provided comprehensive coverage of the method’s origins (cf. Johnsen, Bernard, Killworth, Shelley, & McCarty, 1995; Killworth, Johnsen, Bernard, Shelley, & McCarty, 1990; Killworth et al., 2003; Killworth, Johnsen, McCarty, Shelley, & Bernard, 1998; Shelley, Bernard, Killworth, Johnsen, & McCarty, 1995), but as it became more accepted and widely used in the fields of health science, demography, criminology, and statistics, much of the newer literature has been published elsewhere. Particularly in the last five years, the literature on NSUM and ARD has mushroomed, with approximately 70 publications in journals including Social Forces, the Journal of the American Statistical Association, the Annals of Applied Statistics, Sociological Methodology, Sociological Methods and Research, PLoS One, Econometrica, American Economic Review, the American Journal of Public Health, Epidemiology, and Demographic Research, including two in Social Networks. As further proof of its momentum, INSNA’s 2023 Best Paper Award was conferred to Derick Baum and Peter Marsden for their innovative paper on the NSUM methodology (Baum & Marsden, 2023).
We believe that it is an opportune moment for Social Networks to reflect on this global, interdisciplinary literature originating from social networks research and move it forward with the community of network researchers working on NSUM and ARD.
We therefore invite scholars to contribute innovative, original papers to the Special Issue to advance this literature. In particular, we are looking for cutting-edge or unexpected empirical applications (particularly when they also inform us about social network structures), theoretical contributions centrally involving NSUM or ARD, and methodological work or novel methodological approaches inspired by the method.
Questions we would like to see papers address in the Special Issue can thus be theoretical, empirical, or methodological, including, for instance:
- How do NSUM or ARD relate to theoretical models of acquaintanceship networks? How can we strengthen the theoretical underpinnings of the method? What theoretical puzzles can NSUM and ARD help solve?
- What novel insights can NSUM and ARD generate about the size, composition, or structure of individuals’ broad acquaintanceship networks or their predictors or outcomes?
- What can NSUM or ARD teach us about the size of unknown subpopulations, particularly for subpopulations not traditionally studied in this literature (e.g., in specific countries or new substantive areas)?
- How do NSUM and ARD outcomes (network size, composition, subpopulation size) compare across countries, social boundaries, or over time?
- How do survey respondents engage with NSUM or ARD questions? (e.g., how visible are different subpopulations for respondents, or how well do they memorize their relationships to different subpopulations?)
- What good practices can be added to the already established ones for formulating ARD questions in light of the method’s stringent assumptions?
- How robust are estimations of network size, composition, or structure based on the known population method?
- How can transmission error, memory bias, or barrier effects be better contained? How can the robustness of NSUM or other methods for ARD be further improved, particularly for use in social network analysis?
- How do NSUM or ARD compare to other methods of measuring or estimating network size or segregation? What does this teach us about NSUM or ARD methods?
- How can aggregate relational data be combined with other types of data (e.g., big data, qualitative data) or methods (e.g., agent-based modeling and simulation) to create novel uses and applications for social network analysis or to improve estimation?
- What do key authors in the literature foresee for the future of NSUM analysis?
Sunbelt conference in Edinburgh 2024:
We plan to connect the call for abstracts with an organized session at the 2024 Annual Conference of the International Network for Social Network Analysis (INSNA; “Sunbelt” conference) in Edinburgh, and invite interested authors to present a draft of the paper in the session, to be able to give feedback to one another. Of course, participation and presentation in the Sunbelt session is optional.
Manuscript submission information:
Timeline:
We invite authors interested in participating in this special issue to submit an extended abstract to the guest editors at nsumsocialnetworks@gmail.com by February 1st, 2024. This abstract should have a maximum of 500 words (references are excluded from the word count), and identify the authors, their institutions, and their e-mail addresses.
The extended abstract needs to provide a clear outline of the full paper the authors envision submitting. Specifically, the authors should clearly define the question(s) the paper will address, the data and methods used to answer these questions, and how these questions contribute to increasing our comprehension of NSUM methods, ARD data, and social networks. We will assess the originality of the questions, data, and methods. In the case of theoretical work, the extended abstract should allow us to evaluate the novelty of the proposed work and how it will be elaborated.
Selected authors will be invited before March 1st, 2024, to submit the full paper by September 30th, 2024 at 23:59 anywhere on Earth. The papers will need to be submitted to the journal’s peer review, and editorial decisions will be made in accordance with the review outcomes.
Summary of timeline:
Abstract submission: February 1st, 2024
Decision on abstract submission: March 1st, 2024
Full paper submission: September 30th, 2024
Outcome review process: January 1st, 2025
Final submission: March 1st, 2025
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