An Omnibus Test for Social Structure Using Triads
Bibliographic Data
| ID | 2331378 |
|---|---|
| Authors | P W Holland (0009-0006-0202-8404, Educational Testing Service, corresponding author), Samuel Leinhardt (Carnegie Mellon University) |
| Year | 1978 |
| Volume | 7 |
| Issue | 2 |
| Pages | 227-256 |
| Publication date | 1978-11-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sociological Methods & Research (JOURNAL) |
| Journal identifiers | ISSN: 0049-1241 • E-ISSN: 1552-8294 |
| Publisher | SAGE Publications Inc (PUBLISHER) |
| DOI | 10.1177/004912417800700207 |
| OpenAlex | W1965712896 |
| Language | EN |
| Citations received | 28 |
| References cited | 17 |
A general or omnibus test of structure in social network data is proposed. The test exploits all of the information contained in the triad census. Analogous to the classical F-test for contrasts among means, the proposed test involves finding a weighting vector which maximizes a test statistic, τ 2 ( max), in the context ofan empirical data matrix and then determining whether this quantity is statistically significant by reference to a table of the chi-square distribution. An insignificant value of τ2 ( max) implies that the structure of the network data matrix is ran dom, and, therefore, that the search for recognizable or substantively meaning ful pattern in the data may be subject to artifactual discoveries. Empirical results are presented which indicate that, of the networks commonly studied by social researchers, some have random structure, others have nonrandom structure and exhibit strong indications of transitivity and still others, with strong indications of nonrandom structure, do not exhibit strong indications of transitivity
Chi-square test · Combinatorics · Context (archaeology · Econometrics · Geography · Statistic · Statistical hypothesis testing · Statistics · Test (biology · Test statistic · Transitive relation · Weighting · Complex Network Analysis Techniques · Computer Science · Evolutionary Game Theory and Cooperation · Mathematics · Medicine · Opinion Dynamics and Social Influence
Pseudolikelihood Estimation for Social Networks
An Exponential Family of Probability Distributions for Directed Graphs
Logit Models and Logistic Regressions for Social Networks
Unification Research Programs
Semiparametric Analysis of Network Formation
An Empirical Model of Dyadic Link Formation in a Network with Unobserved Heterogeneity
Markov chain Monte Carlo exact inference for social networks
A p* primer
The strength of weak conversational ties in the flow of information and influence
The development of structure in random networks
Tribal regimes in academia
Random and biased networks
A puzzle concerning triads in social networks
On the discovery of social roles in large scale social systems
A Survey of Statistical Methods for Graph Analysis
Social Grammar for Culture
Embedded Trade
Do we know how much people like one another
Models for network evolution
Structural models with qualitative values
Turning a profit from mathematics
Relational equilibrium in a social topology
The Network Structures of Organizations
Models of Network Structure
Comparing Networks across Space and Time, Size and Species
Structures of Mediation
Beyond Dyads and Triads
Test for triadic closure and triadic protection in temporal relational event data
The advanced theory of statistics
Transitivity in Structural Models of Small Groups
A Review of Mathematical Methods in Sociometry
Exploratory Data Analysis
A Method for Depicting Social Relationships Obtained by Sociometric Testing
A Comparison of Several Sociometric Partitioning Techniques
The structural implications of measurement error in sociometry
Random directed graph distributions and the triad census in social networks
A graph‐theoretic definition of a sociometric clique
Social Structure from Multiple Networks. I. Blockmodels of Roles and Positions
Holland and Leinhardt Reply
Clustering and Structural Balance in Graphs
Local Structure in Social Networks
Social Structure as a Network Process / Sozialstruktur als ein Netzwerk-Prozeß
A Method for Detecting Structure in Sociometric Data
| Citations per year | 0.6 |
|---|---|
| Citation span | 1979 - 2020 (42) |
| Highly cited | No |