Módszerek összehasonlítása

	Quadratic Assignment Procedure	MRQAP Network Regression
Tudományterület	Sociology	Sociology
Módszercsalád	Process / pipeline	Regression model
Keletkezés éve	1976 (QAP); 1988 (network application)	1988 (MRQAP); 2007 (double-semipartialing test)
Megalkotó	Lawrence Hubert & James Schultz; David Krackhardt	David Krackhardt; David Dekker, David Krackhardt & Tom Snijders
Típus	Permutation-based test of association between two matrices	Permutation-based multiple regression for dyadic (matrix) outcomes
Alapmű	Krackhardt, D. (1988). Predicting with networks: Nonparametric multiple regression analysis of dyadic data. Social Networks, 10(4), 359–381.	Krackhardt, D. (1988). Predicting with networks: Nonparametric multiple regression analysis of dyadic data. Social Networks, 10(4), 359–381.
Alternatív nevek	QAP correlation, QAP permutation test, matrix permutation test, Hubert-Schultz QAP	MRQAP, multiple regression QAP, Dekker double-semipartialing, QAP regression
Kapcsolódó	4	4
Összefoglaló	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.	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.