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Quadratic Assignment Procedure×Network Autocorrelation Model×
领域SociologySociology
方法族Process / pipelineRegression model
起源年份1976 (QAP); 1988 (network application)1980 (spatial/network models); 2002 (weight matrix)
提出者Lawrence Hubert & James Schultz; David KrackhardtPatrick Doreian; Roger Leenders (weight-matrix synthesis)
类型Permutation-based test of association between two matricesRegression with an autoregressive term on a network weight matrix
开创性文献Krackhardt, D. (1988). Predicting with networks: Nonparametric multiple regression analysis of dyadic data. Social Networks, 10(4), 359–381. DOI ↗Leenders, R. Th. A. J. (2002). Modeling social influence through network autocorrelation: Constructing the weight matrix. Social Networks, 24(1), 21–47. DOI ↗
别名QAP correlation, QAP permutation test, matrix permutation test, Hubert-Schultz QAPnetwork effects model, social influence model, network disturbances model, autoregressive network model
相关44
摘要The quadratic assignment procedure (QAP) is a permutation-based method for testing the association between two relational matrices measured on the same set of actors — for example, whether who advises whom is correlated with who is friends with whom. Because the dyads in a network are not independent, ordinary correlation and regression give invalid p-values; QAP fixes this by comparing the observed matrix correlation to a reference distribution generated by randomly relabeling the nodes of one matrix many times.The network autocorrelation model adapts spatial-econometric regression to social networks to estimate peer influence: it explains an actor's outcome — an attitude, behavior, or performance — as a function of their own covariates plus a weighted average of their network partners' outcomes. The autocorrelation parameter ρ captures the strength of social influence, and the network weight matrix W encodes who influences whom and how strongly.
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ScholarGate方法对比: Quadratic Assignment Procedure · Network Autocorrelation Model. 于 2026-06-24 检索自 https://scholargate.app/zh/compare