Miguel A. González-Casado, Alejandro Cruzado Rey, Miroslav Pulgar Corrotea, Christopher McCarty, José Luis Molina, Angel Sánchez
4 min
This study investigates how different data collection strategies—specifically, the number of alters (social contacts) elicited from an ego—impact the structural and compositional properties of personal networks. The authors compare three experimental conditions: a fixed limit of 30 alters, a variable limit up to 45, and a variable limit up to 45 with a minimum requirement of 20. By randomly assigning 298 participants to these groups, the researchers aim to determine if constraining network size introduces systematic biases or artifacts in how we measure and interpret social network structure.
At the non-structural level, the study finds that demographic and relational attributes—such as age, gender, emotional proximity, and interaction contexts—are consistently sampled regardless of the survey design. However, the structural analysis reveals that the choice of survey constraint significantly affects the correlation structure of network metrics. Specifically, when a survey limits the number of alters, it reduces the sparsity of the correlation matrix and decreases the structural diversity of the collected networks. This suggests that while individual metrics might behave similarly across groups, the collective, global representation of network structure becomes less interpretable and more constrained when researchers impose strict limits on the number of alters.
This research challenges the assumption that different methods of collecting ego network data are interchangeable. It highlights that methodological choices regarding network size are not neutral; they shape the mathematical relationships between variables, potentially leading researchers to different conclusions about network structure based solely on how they designed their survey. The findings suggest that the field needs to be more cautious about the potential for 'mathematical artifacts' to influence the interpretation of social network data, particularly when comparing studies that use different elicitation thresholds.
This article presents an analysis of the impact of the number of alters elicited in an ego network on the structural properties of those networks. There continues to be debate about the pros and cons of eliciting a fixed number of alters for each respondent versus allowing the respondent to list as many or few alters as they would like. This article explores a random assignment of respondents to three treatment groups – (1) a fixed number of alters set at 30, (2) a variable number of alters up to 45, and (3) a variable number of alters up to 45 with a 20 alter minimum. The results indicate that, from a non-structural perspective, all levels of emotional proximity, interaction contexts, genders, and ages are consistently sampled across the three treatment groups. At the structural level, the behavior of individual metrics is also largely similar. However, the most significant differences arise in the collective behavior of structural metrics—specifically, in their correlation structure, the amount of redundant information each variable provides, and the diversity and interpretability of the observed structural variability. When a data collection strategy constrains network size, it reduces the sparsity of the correlation matrix, effectively decreasing the number of independent global variables needed to describe network structure and making these global variables less interpretable. In other words, networks constructed with a survey that limits size tend to be more similar to each other, exhibiting less structural diversity and yielding differences that are harder to interpret. However, we discuss how these differences may simply be mathematical artifacts, without necessarily implying a clear advantage in choosing one treatment over another. Finally, we argue that the field needs a targeted study to answer whether the differing numbers of alters listed is a function of network size.
Alex: So the "structure" of the network isn't just about who knows whom—it's also shaped by how we chose to ask the question in the first place?
Sam: You've captured it well. The paper suggests that when we constrain the list size, we're essentially creating what researchers call a mathematical artifact—a pattern in the data that comes from the survey design itself, not from real life. It's not that one method is simply "wrong," but the choice of how to ask the question fundamentally changes the landscape you end up mapping.
Alex: That's a sobering thought for anyone designing a survey. The tool itself is shaping the reality it's supposed to be measuring.
Sam: And that concern has real consequences. If researchers use these networks to study things like how information spreads, or how people support each other through difficult times, a distorted map could lead to genuinely misleading conclusions.
Alex: You mentioned the study used university students. Does that limit what we can take away from this?
Sam: It does. University students tend to be younger, more socially active, and more similar to each other than the general population. So while the findings point to a meaningful concern about survey design, we should be cautious about assuming the same patterns would hold across, say, older adults or people in very different social circumstances. The authors are clear that more research is needed—particularly to understand whether the number of names someone lists reflects their actual social world, or is simply a product of whatever rules the survey imposed.
Alex: So the bigger takeaway is really a question of scientific honesty—are we measuring people's lives, or are we measuring our own survey design?
Sam: That's a fair way to put it. In social science, the instrument you use to collect data is never neutral. The way you ask a question shapes the answer you're able to receive. This study is a useful reminder to look carefully at the tools themselves, not just the results they produce.
Alex: Thanks for listening to ResearchPod.