Ego Network Analysis — Personal Network Analysis
Also known as: personal network analysis, egocentric network analysis, Ego Ağı Analizi (Personal Network Analysis)
Ego network analysis examines the personal network of a focal individual — the ego — by mapping their direct contacts (alters) and the ties those contacts share with one another. Formalised through Ronald Burt's structural holes framework (1992) and Marsden's egocentric measurement approach (2002), the method produces ego-level indicators such as network size, density, constraint, and brokerage role that reveal how each individual's social position shapes their access to information, resources, and influence.
Key highlights
- Captures structural position at the individual level without needing complete network data on the entire population.
- Burt's constraint index provides a theoretically grounded, quantitative measure of structural advantage that predicts real outcomes such as career mobility and information access.
- Flexible data collection: works with survey name generators, observed interaction logs, or archival contact records.
- Applicable across social, health, organisational, and economic research where interpersonal ties matter.
Intuition
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How it works
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When to use it
Ego network analysis is appropriate when the research question concerns how an individual's local network structure — rather than the global topology of a complete network — shapes outcomes. It fits descriptive, exploratory, and relational study designs, and can handle binary, continuous, or categorical tie attributes collected via survey or observation. The method requires an alter list and alter-alter tie data for every ego; without alter-alter information, structural hole and density metrics cannot be computed. A minimum of around 10 egos is needed for any meaningful analysis, and fewer than 20 egos constrain interpretation to descriptive summaries. No normality assumption applies to the network metrics themselves, though downstream regression or correlation analyses carry their own assumptions.
Strengths & limitations
- Captures structural position at the individual level without needing complete network data on the entire population.
- Burt's constraint index provides a theoretically grounded, quantitative measure of structural advantage that predicts real outcomes such as career mobility and information access.
- Flexible data collection: works with survey name generators, observed interaction logs, or archival contact records.
- Applicable across social, health, organisational, and economic research where interpersonal ties matter.
- Alter-alter tie data add survey burden; respondents may underreport or misremember connections among their contacts.
- Ego networks are bounded by the respondent's awareness — contacts outside the ego's cognitive map are invisible to the method.
- Network size is cognitively constrained (approximately 150 alters per the Dunbar number), so very large personal networks are rarely captured completely.
- Survey-based data introduce representation bias: socially marginal individuals may be systematically undersampled.
Common pitfalls
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Applications
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Frequently asked
What data do I need to run an ego network analysis?
You need two levels of data for every focal respondent (ego): first, a list of their contacts (alters) collected via a name-generator question; second, information on which pairs of those alters are connected to each other (alter-alter ties), typically gathered with a name-interpreter follow-up. Without the alter-alter data you can measure network size but cannot compute density, constraint, or structural holes.
What is the constraint index and why does it matter?
Burt's constraint index measures how tightly interconnected an ego's contacts are. A high constraint score means the ego's alters are all connected to each other, so the ego receives the same information from multiple redundant sources and has little brokerage power. A low constraint score indicates many structural holes — the ego bridges disconnected groups and gains informational diversity and brokerage advantage. The index is computed using Burt's formula over the ego graph adjacency matrix.
How is ego network analysis different from whole-network analysis?
Whole-network (sociocentric) analysis requires complete relational data among all members of a defined population, and computes global metrics such as betweenness centrality across the entire network. Ego network analysis requires only each respondent's local neighbourhood — alters and alter-alter ties — making it practical for large or boundary-spanning populations where complete network data are infeasible to collect. The trade-off is that ego-level metrics are locally bounded and cannot reveal global network topology.
What should I do if I have fewer than 20 egos?
With fewer than 20 egos, ego network metrics become unstable and inferential analysis is unreliable. The recommended fallback is descriptive statistics: report network size and density distributions as summaries rather than inputs to regression or comparison tests, and treat findings as exploratory pending a larger sample.
Sources
- 1.Burt, R.S. (1992). Structural Holes: The Social Structure of Competition. Harvard University Press.ISBN 9780674843714
- 2.Marsden, P.V. (2002). Egocentric and Sociocentric Measures of Network Centrality. Social Networks, 24(4), 407-422.
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Cite this page
ScholarGate. (2026, June 1). Ego Network Analysis. ScholarGate. https://scholargate.app/network-analysis/ego-network-analysis