Sammenlign metoder

	Network Autocorrelation Model	MRQAP Network Regression
Fagområde	Sociology	Sociology
Familie	Regression model	Regression model
Oprindelsesår	1980 (spatial/network models); 2002 (weight matrix)	1988 (MRQAP); 2007 (double-semipartialing test)
Ophavsperson	Patrick Doreian; Roger Leenders (weight-matrix synthesis)	David Krackhardt; David Dekker, David Krackhardt & Tom Snijders
Type	Regression with an autoregressive term on a network weight matrix	Permutation-based multiple regression for dyadic (matrix) outcomes
Oprindelig kilde	Leenders, R. Th. A. J. (2002). Modeling social influence through network autocorrelation: Constructing the weight matrix. Social Networks, 24(1), 21–47.	Krackhardt, D. (1988). Predicting with networks: Nonparametric multiple regression analysis of dyadic data. Social Networks, 10(4), 359–381.
Aliasser	network effects model, social influence model, network disturbances model, autoregressive network model	MRQAP, multiple regression QAP, Dekker double-semipartialing, QAP regression
Relaterede	4	4
Resumé	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.	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.