Comparer des méthodes

	MRQAP Network Regression	Network Autocorrelation Model
Domaine	Sociology	Sociology
Famille	Regression model	Regression model
Année d'origine	1988 (MRQAP); 2007 (double-semipartialing test)	1980 (spatial/network models); 2002 (weight matrix)
Auteur d'origine	David Krackhardt; David Dekker, David Krackhardt & Tom Snijders	Patrick Doreian; Roger Leenders (weight-matrix synthesis)
Type	Permutation-based multiple regression for dyadic (matrix) outcomes	Regression with an autoregressive term on a network weight matrix
Source fondatrice	Krackhardt, D. (1988). Predicting with networks: Nonparametric multiple regression analysis of dyadic data. Social Networks, 10(4), 359–381.	Leenders, R. Th. A. J. (2002). Modeling social influence through network autocorrelation: Constructing the weight matrix. Social Networks, 24(1), 21–47.
Alias	MRQAP, multiple regression QAP, Dekker double-semipartialing, QAP regression	network effects model, social influence model, network disturbances model, autoregressive network model
Apparentées	4	4
Résumé	Multiple regression quadratic assignment procedure (MRQAP) extends QAP to the regression setting: it predicts a dependent relational matrix from several independent relational matrices on the same actors — for example, modeling who collaborates with whom as a function of who is co-located, who shares a department, and who has prior friendship. Coefficients are estimated by ordinary least squares on the vectorized matrices, but significance is assessed by permutation, because dyadic dependence invalidates the standard regression standard errors.	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.